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第1行： − 此词条暂由彩云小译翻译，未经人工整理和审校，带来阅读不便，请见谅。{{Redirect|AI|other uses|AI (disambiguation)|and|Artificial intelligence (disambiguation)}}+ − − − − {{pp-pc1}} − − − − {{short description|Intelligence demonstrated by machines}} − − − − {{Use dmy dates|date=January 2018}} − − − − {{artificial intelligence}} − − − − − − − − <!-- DEFINITIONS --> − − <!-- DEFINITIONS --> 第33行：
第7行： − 在计算机科学中，人工智能(AI) ，有时也被称为机器智能，是由机器演示的智能，与自然智能形成鲜明对比。领先的人工智能教科书将这一领域定义为“智能代理人”的研究: 任何感知其环境并采取行动以最大化其成功实现其目标的机会的设备。通俗地说，”人工智能”一词通常用来描述模仿人类与人类大脑相关的”认知”功能的机器(或计算机) ，例如”学习”和”解决问题”。+ − − − − + − + − − 随着机器的能力越来越强，被认为需要“智能”的任务往往从人工智能的定义中移除，这种现象被称为人工智能效应。特斯勒定理中的一句俏皮话说: “人工智能就是尚未完成的事情。”例如，光学字符识别经常被排除在被认为是人工智能的东西之外，已经成为一种常规技术。现代机器能力通常被归类为人工智能，包括成功地理解人类语言，在战略游戏系统(如国际象棋和围棋)中处于最高级别的竞争。 − − + + 第59行：
第28行： − 1955年，人工智能作为一门学科成立，此后几年，人们经历了几波乐观情绪，接着是失望和资金流失(被称为“人工智能的冬天”) ，接着是新的方法、成功和新的资金。在人工智能发展的大部分历史中，人工智能研究一直被划分为许多子领域，这些子领域之间往往无法进行交流。这些子领域是基于技术考虑的，例如特定的目标(例如:。“机器人”或“机器学习”) ，使用特定的工具(“逻辑”或人工神经网络) ，或深刻的哲学差异。子领域也基于社会因素(特定机构或特定研究人员的工作)。+ 第75行：
第44行： − 人工智能研究的传统问题(或目标)包括推理、知识表示、规划、学习、自然语言处理、知觉以及移动和操作物体的能力。一般智力是该领域的长期目标之一。方法包括统计方法、计算智能和传统的符号人工智能。人工智能中使用了许多工具，包括搜索和最优化，人工神经网络，以及基于统计学、概率和经济学的方法。人工智能领域利用计算机科学，信息工程，数学，心理学，语言学，哲学和许多其他领域。+ 第91行：
第60行： − 这个领域建立在这样的假设之上: 人类的智能“可以被如此精确地描述，以至于可以制造一台机器来模拟它”。这引发了关于心智本质和创造具有类人智能的人工生命的伦理学的哲学争论。自古以来，神话、小说和哲学一直在探索这些问题。其他人则认为，人工智能与以往的技术革命不同，它将带来大规模失业的风险。+ − − 第107行：
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第120行： − 具有思维能力的人造生物在古代以讲故事的方式出现，在小说中也很常见，比如玛丽 · 雪莱的《弗兰肯斯坦》或卡雷尔 · 阿佩克的《 r.u.r. 》。(Rossum's Universal Robots).<!-- PLEASE DON'T ADD MORE EXAMPLES.这就够了。见关于投机的文章末尾部分。 ——这些角色和他们的命运提出了许多现在在人工智能伦理学中讨论的同样的问题。+ 第167行：
第130行： − + 第173行：
第136行： − 对机械或“正式”推理的研究始于古代的哲学家和数学家。对数理逻辑的研究直接导致了 Alan Turing 的计算理论理论，该理论认为一台机器，通过移动像“0”和“1”这样简单的符号，可以模拟任何可以想象的数学推理行为。这种数字计算机可以模拟任何形式推理过程的见解，被称为丘奇-图灵论文。现在被公认为人工智能的第一项工作是 McCullouch 和 Pitts 在1943年为图灵完整的“人工神经元”所做的正式设计。+ − + 第189行：
第152行： − 人工智能研究领域诞生于1956年达特茅斯学院的一个研讨会上，与会者 Allen Newell (CMU) ，Herbert Simon (CMU) ，John McCarthy (MIT) ，Marvin Minsky (MIT)和 Arthur Samuel (IBM)成为了人工智能研究的创始人和领导者。(到1959年，据说玩得比普通人好) ，解决代数中的文字问题，证明逻辑定理(逻辑理论家，1956年第一次运行)和说英语。到20世纪60年代中期，美国的研究得到了国防部的大量资助，世界各地都建立了实验室。人工智能的创始人对未来很乐观: 赫伯特 · 西蒙预言，“机器将在20年内完成人类能做的任何工作。”。马文•明斯基(Marvin Minsky)对此表示同意，他写道: “在一代人的时间里... ... 创造‘人工智能’的问题将得到实质性的解决。”。+ − + 第205行：
第168行： − 他们没有认识到剩下的一些任务的困难。在1974年，为了回应 James Lighthill 爵士的批评和来自美国国会资助更多生产性项目的持续压力，美国和英国政府都切断了人工智能领域的探索性研究。接下来的几年后来被称为“人工智能的冬天” ，这个时期人工智能项目很难获得资金。+ − 第221行：
第183行： − 在20世纪80年代早期，人工智能研究因专家系统的商业成功而复兴，这是一种模拟人类专家知识和分析技能的人工智能程序。到1985年，人工智能的市场已经超过了10亿美元。与此同时，日本的第五代计算机项目促使美国和英国政府恢复对学术研究的资助。然而，随着1987年 Lisp 机器市场的崩溃，人工智能再次声名狼藉，并开始了第二次更长时间的停滞。+ − − 第231行：
第191行： − 20世纪80年代，以互补 MOS (CMOS)晶体管技术形式出现的金属氧化物半导体(MOS)超大规模集成电路(VLSI)的发展，使实用的人工神经网络(ANN)技术得以发展。这一领域里程碑式的出版物是1989年出版的《模拟 VLSI 神经系统的实现》 ，作者是卡弗 · a · 米德和穆罕默德 · 伊斯梅尔。+ − − 第247行：
第205行： − + 第263行：
第221行： − 2011年，《危险边缘》 ！展览会智力竞赛，IBM 的问答系统，沃森，击败了两个最伟大的危险边缘！冠军布拉德 · 拉特和肯 · 詹宁斯以微弱优势获胜。更快的计算机，算法的改进，以及大量数据的获取，使得机器学习和感知能力得到提高; 数据饥渴的深度学习方法在2012年左右开始主导精确度基准。Kinect 为 Xbox 360和 Xbox One 提供了3D 人体运动界面，它使用的算法来自冗长的人工智能研究，智能手机上的智能个人助理也是如此。2016年3月，AlphaGo 与围棋冠军李世石在5局围棋中赢了4局，成为第一个击败无残疾围棋职业选手的计算机围棋系统。在2017年围棋未来峰会上，阿尔法狗赢得了与柯洁的三局比赛，柯洁当时一直是世界第一。一个排名两年。这标志着人工智能发展的一个重要里程碑的完成，围棋是一个相对复杂的游戏，比国际象棋更复杂。+ − − 第273行：
第229行： − 彭博社的杰克 · 克拉克(Jack Clark)表示，2015年是人工智能领域具有里程碑意义的一年，使用谷歌人工智能的软件项目数量从2012年的“零星使用”增加到2700多个项目。克拉克还提供了事实数据，表明自2012年以来人工智能的改进得到了图像处理任务中较低错误率的支持。 0"{ cite web+ + 第351行：
第308行： − } / ref 他把这归因于负担得起的神经网络的增加，因为云计算基础设施的增加，以及研究工具和数据集的增加。2016年前后，中国大大加快了政府资助的步伐; 鉴于其大量数据供应和快速增长的研究产出，一些观察人士认为，中国可能正走上成为“人工智能超级大国”的道路。然而，人们承认，有关人工智能的报告有夸大之嫌。+ − + − 第367行：
第323行： − 计算机科学将人工智能研究定义为对“智能代理人”的研究: 任何感知周围环境并采取行动以最大化其成功实现目标的机会的设备。+ − − 第393行：
第347行： − 典型的人工智能分析其环境，并采取行动，最大限度地提高其成功的机会。+ − − − 第403行：
第354行： − 人工智能经常围绕着算法的使用。算法是机械计算机可以执行的一组明确的指令。复杂的算法通常是建立在其他更简单的算法之上的。一个算法的简单例子是下面的井字游戏配方(对于第一个玩家来说是最佳的) :+ − 第412行：
第362行： − + − 如果某人有一个“威胁”(也就是说，连续两个) ，取剩下的方块。否则, 第419行：
第368行： − 如果一招“叉子”能同时制造两种威胁，就用那招。否则,+ 第425行：
第374行： − 如果有空的话，就走中间的广场。否则,+ 第431行：
第380行： − 如果你的对手在一个角落里打球，就走另一个角落。否则,+ 第437行：
第386行： − 找个空角落，如果有的话。否则,+ 第453行：
第402行： − 许多人工智能算法能够从数据中学习; 它们可以通过学习新的启发式算法(策略或“经验法则” ，在过去运行良好)来提高自己，或者自己编写其他算法。下面描述的一些“学习者” ，包括贝叶斯网络、决策树和最近邻，在理论上，(给定的无限数据、时间和记忆)可以学习近似任何函数，包括哪些数学函数的组合可以最好地描述世界。因此，这些学习者可以通过考虑每一个可能的假设，并将它们与数据进行匹配，来获得所有可能的知识。在实践中，几乎从来不可能考虑到每一种可能性，因为“组合爆炸”现象，解决一个问题所需的时间呈指数增长。许多人工智能研究涉及到如何识别和避免考虑范围广泛的可能性，这些可能性不太可能是有益的。例如，当你查看地图并寻找从丹佛到东部纽约的最短的行车路线时，你可以在大多数情况下跳过旧金山或其他遥远的西部地区的任何路径; 因此，一个人工智能机器人可以使用像 a * 这样的寻路算法来避开组合爆炸，如果每一条可能的路径都必须被仔细考虑的话。 文献{ cite journal+ − | first = P. E. + − | first = P. E.+ − 第一次体育。+ − | last = Hart+ − | last = Hart − | last Hart − |author2= Nilsson, N. J.|author3= Raphael, B. − |author2= Nilsson, N. J.|author3= Raphael, B.+ − 作者: 尼尔森 | 作者: 拉斐尔。+ − | title = Correction to "A Formal Basis for the Heuristic Determination of Minimum Cost Paths"+ − | title = Correction to "A Formal Basis for the Heuristic Determination of Minimum Cost Paths" − 修正“启发式确定最小费用路径的形式基础” − | journal = SIGART Newsletter+ − | journal = SIGART Newsletter+ − 期刊 SIGART 时事通讯+ − | issue = 37+ − | issue = 37+ − 第37期+ − | pages = 28–29 − | pages = 28–29 − 第28-29页 − | year = 1972+ − | year = 1972+ − 1972年+ − | doi=10.1145/1056777.1056779+ − | doi=10.1145/1056777.1056779+ − 10.1145 / 1056777.1056779+ − }}</ref> − }}</ref>+ − {} / ref+ + + + + − The earliest (and easiest to understand) approach to AI was symbolism (such as formal logic): "If an otherwise healthy adult has a fever, then they may have [[influenza]]". A second, more general, approach is [[Bayesian inference]]: "If the current patient has a fever, adjust the probability they have influenza in such-and-such way". The third major approach, extremely popular in routine business AI applications, are analogizers such as [[Support vector machine|SVM]] and [[K-nearest neighbor algorithm|nearest-neighbor]]: "After examining the records of known past patients whose temperature, symptoms, age, and other factors mostly match the current patient, X% of those patients turned out to have influenza". A fourth approach is harder to intuitively understand, but is inspired by how the brain's machinery works: the [[artificial neural network]] approach uses artificial "[[neurons]]" that can learn by comparing itself to the desired output and altering the strengths of the connections between its internal neurons to "reinforce" connections that seemed to be useful. These four main approaches can overlap with each other and with evolutionary systems; for example, neural nets can learn to make inferences, to generalize, and to make analogies. Some systems implicitly or explicitly use multiple of these approaches, alongside many other AI and non-AI algorithms; the best approach is often different depending on the problem.{{sfn|Domingos|2015|loc=Chapter 2, Chapter 4, Chapter 6}}<!-- The influenza example is expanded from Domingos chapter 6; feel free to put in a better example if you have one --><ref>{{cite news|title=Can neural network computers learn from experience, and if so, could they ever become what we would call 'smart'?|url=https://www.scientificamerican.com/article/can-neural-network-comput/|accessdate=24 March 2018|work=Scientific American|date=2018|language=en}}</ref> − The earliest (and easiest to understand) approach to AI was symbolism (such as formal logic): "If an otherwise healthy adult has a fever, then they may have influenza". A second, more general, approach is Bayesian inference: "If the current patient has a fever, adjust the probability they have influenza in such-and-such way". The third major approach, extremely popular in routine business AI applications, are analogizers such as SVM and nearest-neighbor: "After examining the records of known past patients whose temperature, symptoms, age, and other factors mostly match the current patient, X% of those patients turned out to have influenza". A fourth approach is harder to intuitively understand, but is inspired by how the brain's machinery works: the artificial neural network approach uses artificial "neurons" that can learn by comparing itself to the desired output and altering the strengths of the connections between its internal neurons to "reinforce" connections that seemed to be useful. These four main approaches can overlap with each other and with evolutionary systems; for example, neural nets can learn to make inferences, to generalize, and to make analogies. Some systems implicitly or explicitly use multiple of these approaches, alongside many other AI and non-AI algorithms; the best approach is often different depending on the problem.<!-- The influenza example is expanded from Domingos chapter 6; feel free to put in a better example if you have one --> − 人工智能最早(也是最容易理解的)的研究方法是象征主义(比如形式逻辑) : “如果一个原本健康的成年人发烧了，那么他们可能患上了流感。”。第二种更普遍的方法是贝叶斯推断: “如果当前患者发烧，调整他们感染某种流感的可能性”。第三个主要的方法，在日常商业人工智能应用中非常流行，是类比方法，例如支持向量机和最近的邻居: “在检查了已知过去的病人的记录，这些病人的体温、症状、年龄和其他因素主要匹配现在的病人，x% 的病人被证明患有流感”。第四种方法更难以直观理解，但它受到大脑机制工作方式的启发: 人工神经网络方法使用人工“神经元” ，这种神经元可以通过将自身与期望的输出进行比较，并改变内部神经元之间的连接强度，以“强化”似乎有用的连接，从而进行学习。这四种主要方法可以相互重叠，也可以与进化系统重叠; 例如，神经网络可以学习做出推论、概括和进行类比。一些系统隐式或显式地使用多种这些方法，以及许多其他人工智能和非人工智能算法; 最佳方法往往根据问题的不同而不同。 ! -- 流感的例子是从多明戈斯第六章扩展而来的; 如果你有更好的例子，请随意举一个 + + + − Learning algorithms work on the basis that strategies, algorithms, and inferences that worked well in the past are likely to continue working well in the future. These inferences can be obvious, such as "since the sun rose every morning for the last 10,000 days, it will probably rise tomorrow morning as well". They can be nuanced, such as "X% of [[Family (biology)|families]] have geographically separate species with color variants, so there is a Y% chance that undiscovered [[black swan theory|black swans]] exist". Learners also work on the basis of "[[Occam's razor#Probability theory and statistics|Occam's razor]]": The simplest theory that explains the data is the likeliest. Therefore, according to Occam's razor principle, a learner must be designed such that it prefers simpler theories to complex theories, except in cases where the complex theory is proven substantially better. − Learning algorithms work on the basis that strategies, algorithms, and inferences that worked well in the past are likely to continue working well in the future. These inferences can be obvious, such as "since the sun rose every morning for the last 10,000 days, it will probably rise tomorrow morning as well". They can be nuanced, such as "X% of families have geographically separate species with color variants, so there is a Y% chance that undiscovered black swans exist". Learners also work on the basis of "Occam's razor": The simplest theory that explains the data is the likeliest. Therefore, according to Occam's razor principle, a learner must be designed such that it prefers simpler theories to complex theories, except in cases where the complex theory is proven substantially better. − 学习算法的工作基础是策略、算法和推论，这些在过去运行良好，在未来可能继续运行良好。这些推论可以是显而易见的，例如“在过去的10000天里，太阳每天早上都升起，明天早上也可能升起”。他们可能会有细微差别，比如“ x% 的科在地理上分属不同的物种，有颜色变异，所以有 y% 的可能性存在未被发现的黑天鹅”。学习者也在“ Occam 剃刀”的基础上学习: 解释数据最简单的理论是最有可能的。因此，根据 Occam 的剃刀原则，一个学习者必须被设计成更喜欢简单的理论而不是复杂的理论，除非复杂的理论被证明实质上更好。+ + + − [[File:Overfitted Data.png|thumb|The blue line could be an example of [[overfitting]] a linear function due to random noise.]]+ − The blue line could be an example of [[overfitting a linear function due to random noise.]]+ − 蓝线可能是[[由于随机噪声过拟合线性函数]的一个例子。]+ − Settling on a bad, overly complex theory gerrymandered to fit all the past training data is known as [[overfitting]]. Many systems attempt to reduce overfitting by rewarding a theory in accordance with how well it fits the data, but penalizing the theory in accordance with how complex the theory is.{{sfn|Domingos|2015|loc=Chapter 6, Chapter 7}} Besides classic overfitting, learners can also disappoint by "learning the wrong lesson". A toy example is that an image classifier trained only on pictures of brown horses and black cats might conclude that all brown patches are likely to be horses.{{sfn|Domingos|2015|p=286}} A real-world example is that, unlike humans, current image classifiers don't determine the spatial relationship between components of the picture; instead, they learn abstract patterns of pixels that humans are oblivious to, but that linearly correlate with images of certain types of real objects. Faintly superimposing such a pattern on a legitimate image results in an "adversarial" image that the system misclassifies.{{efn|Adversarial vulnerabilities can also result in nonlinear systems, or from non-pattern perturbations. Some systems are so brittle that changing a single adversarial pixel predictably induces misclassification.}}<ref>{{cite news|title=Single pixel change fools AI programs|url=https://www.bbc.com/news/technology-41845878|accessdate=12 March 2018|work=BBC News|date=3 November 2017}}</ref><ref>{{cite news|title=AI Has a Hallucination Problem That's Proving Tough to Fix|url=https://www.wired.com/story/ai-has-a-hallucination-problem-thats-proving-tough-to-fix/|accessdate=12 March 2018|work=WIRED|date=2018}}</ref><ref>{{cite arxiv|eprint=1412.6572|last1=Goodfellow|first1=Ian J.|last2=Shlens|first2=Jonathon|last3=Szegedy|first3=Christian|title=Explaining and Harnessing Adversarial Examples|class=stat.ML|year=2014}}</ref>+ − Settling on a bad, overly complex theory gerrymandered to fit all the past training data is known as overfitting. Many systems attempt to reduce overfitting by rewarding a theory in accordance with how well it fits the data, but penalizing the theory in accordance with how complex the theory is. Besides classic overfitting, learners can also disappoint by "learning the wrong lesson". A toy example is that an image classifier trained only on pictures of brown horses and black cats might conclude that all brown patches are likely to be horses. A real-world example is that, unlike humans, current image classifiers don't determine the spatial relationship between components of the picture; instead, they learn abstract patterns of pixels that humans are oblivious to, but that linearly correlate with images of certain types of real objects. Faintly superimposing such a pattern on a legitimate image results in an "adversarial" image that the system misclassifies.+ − 对一个错误的、过于复杂的理论做出错误的选择，以适应过去所有的训练数据，这被称为过度拟合。许多系统试图减少过度拟合的奖励一个理论如何适合数据，但惩罚理论如何复杂的理论是一致的。除了经典的过分修饰，学习者也会因为“学错了课程”而失望。一个玩具例子是，一个图像分类器训练只对图片的棕色马和黑猫可能得出结论，所有的棕色斑块可能是马。一个现实世界的例子是，与人类不同，当前的图像分类器并不确定图像组件之间的空间关系; 相反，它们学习人类遗忘的像素抽象模式，但与某些类型的真实物体的图像线性相关。将这种模式隐约地叠加在合法的图像上，会导致系统错误分类的“敌对”图像。+ + + + − [[File:Détection de personne - exemple 3.jpg|thumb|A self-driving car system may use a neural network to determine which parts of the picture seem to match previous training images of pedestrians, and then model those areas as slow-moving but somewhat unpredictable rectangular prisms that must be avoided.<ref>{{cite book|last1=Matti|first1=D.|last2=Ekenel|first2=H. K.|last3=Thiran|first3=J. P.|title=Combining LiDAR space clustering and convolutional neural networks for pedestrian detection|journal=2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)|date=2017|pages=1–6|doi=10.1109/AVSS.2017.8078512|isbn=978-1-5386-2939-0|arxiv=1710.06160}}</ref><ref>{{cite book|last1=Ferguson|first1=Sarah|last2=Luders|first2=Brandon|last3=Grande|first3=Robert C.|last4=How|first4=Jonathan P.|title=Real-Time Predictive Modeling and Robust Avoidance of Pedestrians with Uncertain, Changing Intentions|journal=Algorithmic Foundations of Robotics XI|volume=107|date=2015|pages=161–177|doi=10.1007/978-3-319-16595-0_10|publisher=Springer, Cham|language=en|series=Springer Tracts in Advanced Robotics|isbn=978-3-319-16594-3|arxiv=1405.5581}}</ref>]] − A self-driving car system may use a neural network to determine which parts of the picture seem to match previous training images of pedestrians, and then model those areas as slow-moving but somewhat unpredictable rectangular prisms that must be avoided.+ − 自动驾驶汽车系统可以使用神经网络来确定图像的哪些部分与先前训练的行人图像匹配，然后将这些区域建模为移动缓慢但有点不可预测的矩形棱镜，这是必须避免的。+ − Compared with humans, existing AI lacks several features of human "[[commonsense reasoning]]"; most notably, humans have powerful mechanisms for reasoning about "[[naïve physics]]" such as space, time, and physical interactions. This enables even young children to easily make inferences like "If I roll this pen off a table, it will fall on the floor". Humans also have a powerful mechanism of "[[folk psychology]]" that helps them to interpret natural-language sentences such as "The city councilmen refused the demonstrators a permit because they advocated violence". (A generic AI has difficulty discerning whether the ones alleged to be advocating violence are the councilmen or the demonstrators.)<ref>{{cite news|title=Cultivating Common Sense {{!}} DiscoverMagazine.com|url=http://discovermagazine.com/2017/april-2017/cultivating-common-sense|accessdate=24 March 2018|work=Discover Magazine|date=2017}}</ref><ref>{{cite journal|last1=Davis|first1=Ernest|last2=Marcus|first2=Gary|title=Commonsense reasoning and commonsense knowledge in artificial intelligence|journal=Communications of the ACM|date=24 August 2015|volume=58|issue=9|pages=92–103|doi=10.1145/2701413|url=https://cacm.acm.org/magazines/2015/9/191169-commonsense-reasoning-and-commonsense-knowledge-in-artificial-intelligence/}}</ref><ref>{{cite journal|last1=Winograd|first1=Terry|title=Understanding natural language|journal=Cognitive Psychology|date=January 1972|volume=3|issue=1|pages=1–191|doi=10.1016/0010-0285(72)90002-3}}</ref> This lack of "common knowledge" means that AI often makes different mistakes than humans make, in ways that can seem incomprehensible. For example, existing self-driving cars cannot reason about the location nor the intentions of pedestrians in the exact way that humans do, and instead must use non-human modes of reasoning to avoid accidents.<ref>{{cite news|title=Don't worry: Autonomous cars aren't coming tomorrow (or next year)|url=http://autoweek.com/article/technology/fully-autonomous-vehicles-are-more-decade-down-road|accessdate=24 March 2018|work=Autoweek|date=2016}}</ref><ref>{{cite news|last1=Knight|first1=Will|title=Boston may be famous for bad drivers, but it's the testing ground for a smarter self-driving car|url=https://www.technologyreview.com/s/608871/finally-a-driverless-car-with-some-common-sense/|accessdate=27 March 2018|work=MIT Technology Review|date=2017|language=en}}</ref><ref>{{cite journal|last1=Prakken|first1=Henry|title=On the problem of making autonomous vehicles conform to traffic law|journal=Artificial Intelligence and Law|date=31 August 2017|volume=25|issue=3|pages=341–363|doi=10.1007/s10506-017-9210-0|doi-access=free}}</ref>+ − Compared with humans, existing AI lacks several features of human "commonsense reasoning"; most notably, humans have powerful mechanisms for reasoning about "naïve physics" such as space, time, and physical interactions. This enables even young children to easily make inferences like "If I roll this pen off a table, it will fall on the floor". Humans also have a powerful mechanism of "folk psychology" that helps them to interpret natural-language sentences such as "The city councilmen refused the demonstrators a permit because they advocated violence". (A generic AI has difficulty discerning whether the ones alleged to be advocating violence are the councilmen or the demonstrators.) This lack of "common knowledge" means that AI often makes different mistakes than humans make, in ways that can seem incomprehensible. For example, existing self-driving cars cannot reason about the location nor the intentions of pedestrians in the exact way that humans do, and instead must use non-human modes of reasoning to avoid accidents. − 与人类相比，现有的人工智能缺少人类“常识推理”的几个特征; 最值得注意的是，人类拥有强大的推理“天真的物理学”的机制，如空间、时间和物理互动。这使得即使是小孩子也能够轻易地做出推论，比如“如果我把这支笔从桌子上滚下来，它就会掉到地板上”。人类还有一种强大的“民间心理”机制，帮助他们解释自然语言的句子，如“市议员因为示威者鼓吹暴力而拒绝给予许可”。(一般的大赦国际难以辨别被指控鼓吹暴力的人是议员还是示威者。)这种“常识”的缺乏意味着人工智能经常会犯一些与人类不同的错误，这些错误看起来是难以理解的。例如，现有的自动驾驶汽车不能像人类那样精确推理位置和行人的意图，而必须使用非人类的推理模式来避免事故。 + + + + − == Challenges ==+ − == Challenges ==+ − 挑战+ − <!--- This is linked to in the introduction to the article and to the "AI research" section -->+ − <!--- This is linked to in the introduction to the article and to the "AI research" section -->+ − ! ——这跟文章的导言和“人工智能研究”部分有关——+ 第587行：
第535行： − The cognitive capabilities of current architectures are very limited, using only a simplified version of what intelligence is really capable of. For instance, the human mind has come up with ways to reason beyond measure and logical explanations to different occurrences in life. What would have been otherwise straightforward, an equivalently difficult problem may be challenging to solve computationally as opposed to using the human mind. This gives rise to two classes of models: structuralist and functionalist. The structural models aim to loosely mimic the basic intelligence operations of the mind such as reasoning and logic. The functional model refers to the correlating data to its computed counterpart.<ref>{{Cite journal|last=Lieto|first=Antonio|date=May 2018|title=The knowledge level in cognitive architectures: Current limitations and possible developments|journal=Cognitive Systems Research|volume=48|pages=39–55|doi=10.1016/j.cogsys.2017.05.001|hdl=2318/1665207|hdl-access=free}}</ref> − The cognitive capabilities of current architectures are very limited, using only a simplified version of what intelligence is really capable of. For instance, the human mind has come up with ways to reason beyond measure and logical explanations to different occurrences in life. What would have been otherwise straightforward, an equivalently difficult problem may be challenging to solve computationally as opposed to using the human mind. This gives rise to two classes of models: structuralist and functionalist. The structural models aim to loosely mimic the basic intelligence operations of the mind such as reasoning and logic. The functional model refers to the correlating data to its computed counterpart. − 当前架构的认知能力非常有限，只使用了智能真正能够做到的简化版本。例如，人类的大脑已经想出了各种方法来推理超出测量和逻辑解释不同的事件在生活中。原本直截了当的问题，与使用人类思维相比，一个同等困难的问题，在计算上可能是具有挑战性的。这就产生了两类模型: 结构主义和功能主义。结构模型旨在松散地模拟大脑的基本智力操作，如推理和逻辑。函数模型是指与其计算的对应数据相关联的数据。+ + + − The overall research goal of artificial intelligence is to create technology that allows computers and machines to function in an intelligent manner. The general problem of simulating (or creating) intelligence has been broken down into sub-problems. These consist of particular traits or capabilities that researchers expect an intelligent system to display. The traits described below have received the most attention.<ref name="Problems of AI" /> − The overall research goal of artificial intelligence is to create technology that allows computers and machines to function in an intelligent manner. The general problem of simulating (or creating) intelligence has been broken down into sub-problems. These consist of particular traits or capabilities that researchers expect an intelligent system to display. The traits described below have received the most attention.+ − 人工智能的总体研究目标是创造能够使计算机和机器以智能方式运行的技术。模拟(或创造)智能的一般问题已分解为子问题。这些特征或能力是研究人员期望智能系统显示出来的。下面描述的特征受到了最多的关注。+ + + + + − === Reasoning, problem solving ===+ − === Reasoning, problem solving ===+ − 推理，解决问题+ − <!-- This is linked to in the introduction --><!-- SOLVED PROBLEMS -->+ − <!-- This is linked to in the introduction --><!-- SOLVED PROBLEMS -->+ − ! ——这在介绍中有关——解决问题——+ − Early researchers developed algorithms that imitated step-by-step reasoning that humans use when they solve puzzles or make logical deductions.<ref name="Reasoning"/> By the late 1980s and 1990s, AI research had developed methods for dealing with [[uncertainty|uncertain]] or incomplete information, employing concepts from [[probability]] and [[economics]].<ref name="Uncertain reasoning"/>+ − Early researchers developed algorithms that imitated step-by-step reasoning that humans use when they solve puzzles or make logical deductions. By the late 1980s and 1990s, AI research had developed methods for dealing with uncertain or incomplete information, employing concepts from probability and economics.+ − 早期的研究人员开发了一种算法，这种算法模仿了人类在解决谜题或进行逻辑推理时所使用的循序渐进的推理。到20世纪80年代末和90年代，人工智能研究已经开发出处理不确定或不完全信息的方法，使用概率和经济学的概念。 + + + + − These algorithms proved to be insufficient for solving large reasoning problems because they experienced a "combinatorial explosion": they became exponentially slower as the problems grew larger.<ref name="Intractability"/> In fact, even humans rarely use the step-by-step deduction that early AI research was able to model. They solve most of their problems using fast, intuitive judgments.<ref name="Psychological evidence of sub-symbolic reasoning"/>+ − These algorithms proved to be insufficient for solving large reasoning problems because they experienced a "combinatorial explosion": they became exponentially slower as the problems grew larger. In fact, even humans rarely use the step-by-step deduction that early AI research was able to model. They solve most of their problems using fast, intuitive judgments.+ − 这些算法被证明不足以解决大型推理问题，因为它们经历了一个“组合爆炸” : 随着问题变得越来越大，它们变得越来越慢。事实上，即使是人类也很少使用早期人工智能研究能够建模的逐步推理。他们通过快速、直觉的判断来解决大多数问题。+ + + − === Knowledge representation ===+ − === Knowledge representation === − 知识表示 − <!-- This is linked to in the introduction --> − <!-- This is linked to in the introduction --> − ! ——这个链接在介绍中—— − [[File:GFO taxonomy tree.png|right|thumb|An ontology represents knowledge as a set of concepts within a domain and the relationships between those concepts.]] − An ontology represents knowledge as a set of concepts within a domain and the relationships between those concepts.+ − 本体将知识表示为领域中的一组概念以及这些概念之间的关系。+ − {{Main|Knowledge representation|Commonsense knowledge}}+ + + + + − [[Knowledge representation]]<ref name="Knowledge representation"/> and [[knowledge engineering]]<ref name="Knowledge engineering"/> are central to classical AI research. Some "expert systems" attempt to gather together explicit knowledge possessed by experts in some narrow domain. In addition, some projects attempt to gather the "commonsense knowledge" known to the average person into a database containing extensive knowledge about the world. Among the things a comprehensive commonsense knowledge base would contain are: objects, properties, categories and relations between objects;<ref name="Representing categories and relations"/> situations, events, states and time;<ref name="Representing time"/> causes and effects;<ref name="Representing causation"/> knowledge about knowledge (what we know about what other people know);<ref name="Representing knowledge about knowledge"/> and many other, less well researched domains. A representation of "what exists" is an [[ontology (computer science)|ontology]]: the set of objects, relations, concepts, and properties formally described so that software agents can interpret them. The [[semantics]] of these are captured as [[description logic]] concepts, roles, and individuals, and typically implemented as classes, properties, and individuals in the [[Web Ontology Language]].<ref>{{cite book |last=Sikos |first=Leslie F. |date=June 2017 |title=Description Logics in Multimedia Reasoning |url=https://www.springer.com/us/book/9783319540658 |location=Cham |publisher=Springer |isbn=978-3-319-54066-5 |doi=10.1007/978-3-319-54066-5 |url-status=live |archiveurl=https://web.archive.org/web/20170829120912/https://www.springer.com/us/book/9783319540658 |archivedate=29 August 2017 |df=dmy-all }}</ref> The most general ontologies are called [[upper ontology|upper ontologies]], which attempt to provide a foundation for all other knowledge<ref name="Ontology"/> by acting as mediators between [[Domain ontology|domain ontologies]] that cover specific knowledge about a particular knowledge domain (field of interest or area of concern). Such formal knowledge representations can be used in content-based indexing and retrieval,<ref>{{cite journal|last1=Smoliar|first1=Stephen W.|last2=Zhang|first2=HongJiang|title=Content based video indexing and retrieval|journal=IEEE Multimedia|date=1994|volume=1|issue=2|pages=62–72|doi=10.1109/93.311653}}</ref> scene interpretation,<ref>{{cite journal|last1=Neumann|first1=Bernd|last2=Möller|first2=Ralf|title=On scene interpretation with description logics|journal=Image and Vision Computing|date=January 2008|volume=26|issue=1|pages=82–101|doi=10.1016/j.imavis.2007.08.013}}</ref> clinical decision support,<ref>{{cite journal|last1=Kuperman|first1=G. J.|last2=Reichley|first2=R. M.|last3=Bailey|first3=T. C.|title=Using Commercial Knowledge Bases for Clinical Decision Support: Opportunities, Hurdles, and Recommendations|journal=Journal of the American Medical Informatics Association|date=1 July 2006|volume=13|issue=4|pages=369–371|doi=10.1197/jamia.M2055|pmid=16622160|pmc=1513681}}</ref> knowledge discovery (mining "interesting" and actionable inferences from large databases),<ref>{{cite journal|last1=MCGARRY|first1=KEN|title=A survey of interestingness measures for knowledge discovery|journal=The Knowledge Engineering Review|date=1 December 2005|volume=20|issue=1|page=39|doi=10.1017/S0269888905000408|url=https://semanticscholar.org/paper/baf7f99e1b567868a6dc6238cc5906881242da01}}</ref> and other areas.<ref>{{cite conference |url= |title=Automatic annotation and semantic retrieval of video sequences using multimedia ontologies |last1=Bertini |first1=M |last2=Del Bimbo |first2=A |last3=Torniai |first3=C |date=2006 |publisher=ACM |book-title=MM '06 Proceedings of the 14th ACM international conference on Multimedia |pages=679–682 |location=Santa Barbara |conference=14th ACM international conference on Multimedia}}</ref> − Knowledge representation The most general ontologies are called upper ontologies, which attempt to provide a foundation for all other knowledge scene interpretation, clinical decision support, knowledge discovery (mining "interesting" and actionable inferences from large databases), and other areas.+ − 知识表示最一般的本体称为上本体，它试图为所有其他知识场景解释、临床决策支持、知识发现(从大型数据库中挖掘“有趣的”和可操作的推论)等领域提供基础。+ + + + + − Among the most difficult problems in knowledge representation are:+ + + + + + + − Among the most difficult problems in knowledge representation are: − 知识表示中最困难的问题是: − ;[[Default reasoning]] and the [[qualification problem]]: Many of the things people know take the form of "working assumptions". For example, if a bird comes up in conversation, people typically picture an animal that is fist-sized, sings, and flies. None of these things are true about all birds. [[John McCarthy (computer scientist)|John McCarthy]] identified this problem in 1969<ref name="Qualification problem"/> as the qualification problem: for any commonsense rule that AI researchers care to represent, there tend to be a huge number of exceptions. Almost nothing is simply true or false in the way that abstract logic requires. AI research has explored a number of solutions to this problem.<ref name="Default reasoning and non-monotonic logic"/> − Default reasoning and the qualification problem: Many of the things people know take the form of "working assumptions". For example, if a bird comes up in conversation, people typically picture an animal that is fist-sized, sings, and flies. None of these things are true about all birds. John McCarthy identified this problem in 1969 as the qualification problem: for any commonsense rule that AI researchers care to represent, there tend to be a huge number of exceptions. Almost nothing is simply true or false in the way that abstract logic requires. AI research has explored a number of solutions to this problem. − 缺省推理和限定性问题: 人们所知道的许多事情都采取“工作假设”的形式。例如，如果一只鸟出现在谈话中，人们通常会想象一只拳头大小、会唱歌、会飞的动物。这些关于所有鸟类的事情都不是真的。约翰 · 麦卡锡在1969年将这个问题定义为资格问题: 对于人工智能研究人员所关心的任何常识性规则来说，往往存在大量的例外。在抽象逻辑所要求的方式中，几乎没有什么是简单的真或假。人工智能研究已经探索了许多解决这个问题的方法。 − ;Breadth of commonsense knowledge: The number of atomic facts that the average person knows is very large. Research projects that attempt to build a complete knowledge base of [[commonsense knowledge]] (e.g., [[Cyc]]) require enormous amounts of laborious [[ontology engineering|ontological engineering]]—they must be built, by hand, one complicated concept at a time.<ref name="Breadth of commonsense knowledge"/> − Breadth of commonsense knowledge: The number of atomic facts that the average person knows is very large. Research projects that attempt to build a complete knowledge base of commonsense knowledge (e.g., Cyc) require enormous amounts of laborious ontological engineering—they must be built, by hand, one complicated concept at a time.+ − 常识知识的广度: 一般人知道的原子事实的数量是非常大的。试图建立一个完整的常识知识知识库的研究项目(例如 Cyc)需要大量艰苦的本体工程学ーー它们必须一次手工构建一个复杂的概念。+ − ;Subsymbolic form of some commonsense knowledge: Much of what people know is not represented as "facts" or "statements" that they could express verbally. For example, a chess master will avoid a particular chess position because it "feels too exposed"{{sfn|Dreyfus|Dreyfus|1986}} or an art critic can take one look at a statue and realize that it is a fake.{{sfn|Gladwell|2005}} These are non-conscious and sub-symbolic intuitions or tendencies in the human brain.<ref name="Intuition"/> Knowledge like this informs, supports and provides a context for symbolic, conscious knowledge. As with the related problem of sub-symbolic reasoning, it is hoped that [[situated artificial intelligence|situated AI]], [[computational intelligence]], or [[#Statistical|statistical AI]] will provide ways to represent this kind of knowledge.<ref name="Intuition"/>+ − Subsymbolic form of some commonsense knowledge: Much of what people know is not represented as "facts" or "statements" that they could express verbally. For example, a chess master will avoid a particular chess position because it "feels too exposed" or an art critic can take one look at a statue and realize that it is a fake. These are non-conscious and sub-symbolic intuitions or tendencies in the human brain. Knowledge like this informs, supports and provides a context for symbolic, conscious knowledge. As with the related problem of sub-symbolic reasoning, it is hoped that situated AI, computational intelligence, or statistical AI will provide ways to represent this kind of knowledge. − 某些常识知识的次象征形式: 人们所知道的许多东西并没有表现为他们可以口头表达的“事实”或“陈述”。例如，一个国际象棋大师会避免一个特定的象棋位置，因为它“感觉太暴露” ，或者一个艺术评论家可以看一眼雕像，意识到它是一个假的。这些是人类大脑中无意识和次象征性的直觉或倾向。像这样的知识为象征性的、有意识的知识提供信息、支持和提供背景。与子符号推理的相关问题一样，我们希望情境中的人工智能、计算智能或统计人工智能能够提供表示这类知识的方法。+ + + − + − + − 规划+ 第715行：
第673行： − + − + − 分层控制系统是控制系统的一种形式，在这种控制系统中，一组设备和管理软件被安排在一个层次结构中+ + − {{Main|Automated planning and scheduling}} + + + + − Intelligent agents must be able to set goals and achieve them.<ref name="Planning"/> They need a way to visualize the future—a representation of the state of the world and be able to make predictions about how their actions will change it—and be able to make choices that maximize the [[utility]] (or "value") of available choices.<ref name="Information value theory"/>+ − Intelligent agents must be able to set goals and achieve them. They need a way to visualize the future—a representation of the state of the world and be able to make predictions about how their actions will change it—and be able to make choices that maximize the utility (or "value") of available choices.+ − 智能代理必须能够设定目标并实现它们。他们需要一种可视化未来的方式——一种对世界状况的表述，并能够预测他们的行动将如何改变世界——以及能够做出选择，使可用选择的效用(或“价值”)最大化。 + − In classical planning problems, the agent can assume that it is the only system acting in the world, allowing the agent to be certain of the consequences of its actions.<ref name="Classical planning"/> However, if the agent is not the only actor, then it requires that the agent can reason under uncertainty. This calls for an agent that can not only assess its environment and make predictions but also evaluate its predictions and adapt based on its assessment.<ref name="Non-deterministic planning"/>+ − In classical planning problems, the agent can assume that it is the only system acting in the world, allowing the agent to be certain of the consequences of its actions. However, if the agent is not the only actor, then it requires that the agent can reason under uncertainty. This calls for an agent that can not only assess its environment and make predictions but also evaluate its predictions and adapt based on its assessment.+ − 在经典的规划问题中，主体可以假设它是世界上唯一的系统，允许主体确定其行为的后果。然而，如果代理人不是唯一的参与者，那么它要求代理人能够在不确定的情况下推理。这需要一个代理人，不仅能够评估其环境和作出预测，而且还评估其预测和适应基于其评估。+ + + + − [[Multi-agent planning]] uses the [[cooperation]] and competition of many agents to achieve a given goal. [[Emergent behavior]] such as this is used by [[evolutionary algorithms]] and [[swarm intelligence]].<ref name="Multi-agent planning"/> − Multi-agent planning uses the cooperation and competition of many agents to achieve a given goal. Emergent behavior such as this is used by evolutionary algorithms and swarm intelligence. − 多智能体规划是利用多个智能体之间的协作和竞争来实现给定的目标。类似这样的突发行为被进化算法和群体智能应用。 + + − === Learning ===+ − === Learning === − 学习 − <!-- This is linked to in the introduction --> − <!-- This is linked to in the introduction --> − ! ——这个链接在介绍中——+ − {{Main|Machine learning}}+ + + + − Machine learning (ML), a fundamental concept of AI research since the field's inception,<ref>[[Alan Turing]] discussed the centrality of learning as early as 1950, in his classic paper "[[Computing Machinery and Intelligence]]".{{Harv|Turing|1950}} In 1956, at the original Dartmouth AI summer conference, [[Ray Solomonoff]] wrote a report on unsupervised probabilistic machine learning: "An Inductive Inference Machine".{{Harv|Solomonoff|1956}}</ref> is the study of computer algorithms that improve automatically through experience.<ref>This is a form of [[Tom M. Mitchell|Tom Mitchell]]'s widely quoted definition of machine learning: "A computer program is set to learn from an experience ''E'' with respect to some task ''T'' and some performance measure ''P'' if its performance on ''T'' as measured by ''P'' improves with experience ''E''."</ref><ref name="Machine learning"/>+ − Machine learning (ML), a fundamental concept of AI research since the field's inception, is the study of computer algorithms that improve automatically through experience.+ + + + + + + − 机器学习(ML)是人工智能研究的一个基本概念，它是通过经验自动改进计算机算法的研究。 第793行：
第761行： − [[Unsupervised learning]] is the ability to find patterns in a stream of input, without requiring a human to label the inputs first. [[Supervised learning]] includes both [[statistical classification|classification]] and numerical [[Regression analysis|regression]], which requires a human to label the input data first. Classification is used to determine what category something belongs in, and occurs after a program sees a number of examples of things from several categories. Regression is the attempt to produce a function that describes the relationship between inputs and outputs and predicts how the outputs should change as the inputs change.<ref name="Machine learning"/> Both classifiers and regression learners can be viewed as "function approximators" trying to learn an unknown (possibly implicit) function; for example, a spam classifier can be viewed as learning a function that maps from the text of an email to one of two categories, "spam" or "not spam". [[Computational learning theory]] can assess learners by [[computational complexity]], by [[sample complexity]] (how much data is required), or by other notions of [[optimization theory|optimization]].<ref>{{cite journal|last1=Jordan|first1=M. I.|last2=Mitchell|first2=T. M.|title=Machine learning: Trends, perspectives, and prospects|journal=Science|date=16 July 2015|volume=349|issue=6245|pages=255–260|doi=10.1126/science.aaa8415|pmid=26185243|bibcode=2015Sci...349..255J}}</ref> In [[reinforcement learning]]<ref name="Reinforcement learning"/> the agent is rewarded for good responses and punished for bad ones. The agent uses this sequence of rewards and punishments to form a strategy for operating in its problem space. − Unsupervised learning is the ability to find patterns in a stream of input, without requiring a human to label the inputs first. Supervised learning includes both classification and numerical regression, which requires a human to label the input data first. Classification is used to determine what category something belongs in, and occurs after a program sees a number of examples of things from several categories. Regression is the attempt to produce a function that describes the relationship between inputs and outputs and predicts how the outputs should change as the inputs change. In reinforcement learning the agent is rewarded for good responses and punished for bad ones. The agent uses this sequence of rewards and punishments to form a strategy for operating in its problem space.+ − 非监督式学习是在输入流中发现模式的能力，而不需要人类首先标记输入。监督式学习包括分类和数值回归，这需要人类首先标记输入数据。分类用于确定某物属于哪个类别，并且在程序看到来自几个类别的事物的大量例子后发生。回归是试图产生一个函数来描述输入和输出之间的关系，并预测输出应该如何随着输入的变化而变化。在强化学习，代理人会因为好的回应而受到奖励，因为坏的回应而受到惩罚。代理使用这一系列的奖励和惩罚来形成一个在其问题空间中操作的策略。+ + + + + − + − + − 自然语言处理+ 第815行：
第786行： − [[File:ParseTree.svg|thumb| A [[parse tree]] represents the [[syntax|syntactic]] structure of a sentence according to some [[formal grammar]].]]+ − A [[parse tree represents the syntactic structure of a sentence according to some formal grammar.]] − 一个[[根据某种形式语法，解析树表示一个句子的句法结构] − + + + 第829行：
第800行： − [[Natural language processing]]<ref name="Natural language processing"/> (NLP) gives machines the ability to read and [[natural language understanding|understand]] human language. A sufficiently powerful natural language processing system would enable [[natural-language user interface]]s and the acquisition of knowledge directly from human-written sources, such as newswire texts. Some straightforward applications of natural language processing include [[information retrieval]], [[text mining]], [[question answering]]<ref>[https://www.academia.edu/2475776/Versatile_question_answering_systems_seeing_in_synthesis "Versatile question answering systems: seeing in synthesis"] {{webarchive|url=https://web.archive.org/web/20160201125047/http://www.academia.edu/2475776/Versatile_question_answering_systems_seeing_in_synthesis |date=1 February 2016 }}, Mittal et al., IJIIDS, 5(2), 119–142, 2011+ − Natural language processing (NLP) gives machines the ability to read and understand human language. A sufficiently powerful natural language processing system would enable natural-language user interfaces and the acquisition of knowledge directly from human-written sources, such as newswire texts. Some straightforward applications of natural language processing include information retrieval, text mining, question answering<ref>[https://www.academia.edu/2475776/Versatile_question_answering_systems_seeing_in_synthesis "Versatile question answering systems: seeing in synthesis"] , Mittal et al., IJIIDS, 5(2), 119–142, 2011+ − 自然语言处理(NLP)赋予机器阅读和理解人类语言的能力。一个足够强大的自然语言处理系统将使自然语言用户界面成为可能，并能够直接从人类书面来源(如新闻通讯社文本)获取知识。一些自然语言处理的直接应用包括信息检索，文本挖掘，问题回答参考[ https://www.academia.edu/2475776/versatile_question_answering_systems_seeing_in_synthesis : 通用的问题回答系统: 在综合中看到] ，Mittal et al. ，IJIIDS，5(2) ，119-142,2011+ − </ref> and [[machine translation]].<ref name="Applications of natural language processing"/> Many current approaches use word co-occurrence frequencies to construct syntactic representations of text. "Keyword spotting" strategies for search are popular and scalable but dumb; a search query for "dog" might only match documents with the literal word "dog" and miss a document with the word "poodle". "Lexical affinity" strategies use the occurrence of words such as "accident" to [[sentiment analysis|assess the sentiment]] of a document. Modern statistical NLP approaches can combine all these strategies as well as others, and often achieve acceptable accuracy at the page or paragraph level, but continue to lack the semantic understanding required to classify isolated sentences well. Besides the usual difficulties with encoding semantic commonsense knowledge, existing semantic NLP sometimes scales too poorly to be viable in business applications. Beyond semantic NLP, the ultimate goal of "narrative" NLP is to embody a full understanding of commonsense reasoning.<ref>{{cite journal|last1=Cambria|first1=Erik|last2=White|first2=Bebo|title=Jumping NLP Curves: A Review of Natural Language Processing Research [Review Article]|journal=IEEE Computational Intelligence Magazine|date=May 2014|volume=9|issue=2|pages=48–57|doi=10.1109/MCI.2014.2307227}}</ref> − + − / ref 和机器翻译。+ + − + − + − 知觉+ 第857行：
第828行： − + 第865行：
第836行： − + − Feature detection (pictured: edge detection) helps AI compose informative abstract structures out of raw data.]]+ − [图片: 边缘检测特征提取]帮助人工智能从原始数据中组成信息丰富的抽象结构+ + − [[Machine perception]]<ref name="Machine perception"/> is the ability to use input from sensors (such as cameras (visible spectrum or infrared), microphones, wireless signals, and active [[lidar]], sonar, radar, and [[tactile sensor]]s) to deduce aspects of the world. Applications include [[speech recognition]],<ref name="Speech recognition"/> [[facial recognition system|facial recognition]], and [[object recognition]].<ref name="Object recognition"/> [[Computer vision]] is the ability to analyze visual input. Such input is usually ambiguous; a giant, fifty-meter-tall pedestrian far away may produce exactly the same pixels as a nearby normal-sized pedestrian, requiring the AI to judge the relative likelihood and reasonableness of different interpretations, for example by using its "object model" to assess that fifty-meter pedestrians do not exist.<ref name="Computer vision"/>+ − Machine perception is the ability to use input from sensors (such as cameras (visible spectrum or infrared), microphones, wireless signals, and active lidar, sonar, radar, and tactile sensors) to deduce aspects of the world. Applications include speech recognition, facial recognition, and object recognition. Computer vision is the ability to analyze visual input. Such input is usually ambiguous; a giant, fifty-meter-tall pedestrian far away may produce exactly the same pixels as a nearby normal-sized pedestrian, requiring the AI to judge the relative likelihood and reasonableness of different interpretations, for example by using its "object model" to assess that fifty-meter pedestrians do not exist.+ − 机器感知是利用传感器(如相机(可见光或红外线)、麦克风、无线信号、激光雷达、声纳、雷达和触觉传感器)的输入来推断世界的方方面面的能力。应用包括语音识别、面部识别和物体识别。计算机视觉是分析视觉输入的能力。这种输入通常是模棱两可的; 一个巨大的50米高的行人可能会产生与附近正常大小的行人完全相同的像素，这就要求人工智能判断不同解释的相对可能性和合理性，例如使用其”对象模型”来评估50米高的行人不存在。 + + + − + − === Motion and manipulation ===+ − 运动和操作+ − + − <!-- This is linked to in the introduction -->+ − ! ——这个链接在介绍中——+ − {{Main|Robotics}} + + + + − AI is heavily used in [[robotics]].<ref name="Robotics"/> Advanced [[robotic arm]]s and other [[industrial robot]]s, widely used in modern factories, can learn from experience how to move efficiently despite the presence of friction and gear slippage.<ref name="Configuration space"/> A modern mobile robot, when given a small, static, and visible environment, can easily determine its location and [[robotic mapping|map]] its environment; however, dynamic environments, such as (in [[endoscopy]]) the interior of a patient's breathing body, pose a greater challenge. [[Motion planning]] is the process of breaking down a movement task into "primitives" such as individual joint movements. Such movement often involves compliant motion, a process where movement requires maintaining physical contact with an object.{{sfn|Tecuci|2012}}<ref name="Robotic mapping"/><ref>{{cite journal|last1=Cadena|first1=Cesar|last2=Carlone|first2=Luca|last3=Carrillo|first3=Henry|last4=Latif|first4=Yasir|last5=Scaramuzza|first5=Davide|last6=Neira|first6=Jose|last7=Reid|first7=Ian|last8=Leonard|first8=John J.|title=Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age|journal=IEEE Transactions on Robotics|date=December 2016|volume=32|issue=6|pages=1309–1332|doi=10.1109/TRO.2016.2624754|arxiv=1606.05830|bibcode=2016arXiv160605830C}}</ref> [[Moravec's paradox]] generalizes that low-level sensorimotor skills that humans take for granted are, counterintuitively, difficult to program into a robot; the paradox is named after [[Hans Moravec]], who stated in 1988 that "it is comparatively easy to make computers exhibit adult level performance on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one-year-old when it comes to perception and mobility".<ref>{{Cite book| first = Hans | last = Moravec | year = 1988 | title = Mind Children | publisher = Harvard University Press | author-link =Hans Moravec| p=15}}</ref><ref>{{cite news|last1=Chan|first1=Szu Ping|title=This is what will happen when robots take over the world|url=https://www.telegraph.co.uk/finance/economics/11994694/Heres-what-will-happen-when-robots-take-over-the-world.html|accessdate=23 April 2018|date=15 November 2015}}</ref> This is attributed to the fact that, unlike checkers, physical dexterity has been a direct target of [[natural selection]] for millions of years.<ref name="The Economist">{{cite news|title=IKEA furniture and the limits of AI|url=https://www.economist.com/news/leaders/21740735-humans-have-had-good-run-most-recent-breakthrough-robotics-it-clear|accessdate=24 April 2018|work=The Economist|date=2018|language=en}}</ref>+ − AI is heavily used in robotics. Moravec's paradox generalizes that low-level sensorimotor skills that humans take for granted are, counterintuitively, difficult to program into a robot; the paradox is named after Hans Moravec, who stated in 1988 that "it is comparatively easy to make computers exhibit adult level performance on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one-year-old when it comes to perception and mobility". This is attributed to the fact that, unlike checkers, physical dexterity has been a direct target of natural selection for millions of years.+ − 人工智能在机器人技术中应用广泛。莫拉维克的悖论概括了人类认为理所当然的低层次的感知运动技能很难在机器人中编程的事实，这个悖论是以汉斯 · 莫拉维克的名字命名的，他在1988年表示: “让计算机在智力测试或下跳棋中展现出成人水平的表现相对容易，但要让计算机拥有一岁小孩的感知和移动能力却很难，甚至不可能。”。这是因为，与跳棋不同，身体灵巧性一直是自然选择数百万年的直接目标。+ + + + − === Social intelligence ===+ − === Social intelligence === − 社会智力 − <!-- This is linked to in the introduction -->+ − <!-- This is linked to in the introduction -->+ − ! ——这个链接在介绍中——+ − {{Main|Affective computing}}+ + + − [[File:Kismet robot at MIT Museum.jpg|thumb|[[Kismet (robot)|Kismet]], a robot with rudimentary social skills{{sfn|''Kismet''}}]] − Kismet, a robot with rudimentary social skills]] − Kismet，一个具有基本社交技能的机器人]] + + − Moravec's paradox can be extended to many forms of social intelligence.<ref>{{cite magazine |last1=Thompson|first1=Derek|title=What Jobs Will the Robots Take?|url=https://www.theatlantic.com/business/archive/2014/01/what-jobs-will-the-robots-take/283239/|accessdate=24 April 2018|magazine=The Atlantic|date=2018}}</ref><ref>{{cite journal|last1=Scassellati|first1=Brian|title=Theory of mind for a humanoid robot|journal=Autonomous Robots|volume=12|issue=1|year=2002|pages=13–24|doi=10.1023/A:1013298507114}}</ref> Distributed multi-agent coordination of autonomous vehicles remains a difficult problem.<ref>{{cite journal|last1=Cao|first1=Yongcan|last2=Yu|first2=Wenwu|last3=Ren|first3=Wei|last4=Chen|first4=Guanrong|title=An Overview of Recent Progress in the Study of Distributed Multi-Agent Coordination|journal=IEEE Transactions on Industrial Informatics|date=February 2013|volume=9|issue=1|pages=427–438|doi=10.1109/TII.2012.2219061|arxiv=1207.3231}}</ref> [[Affective computing]] is an interdisciplinary umbrella that comprises systems which recognize, interpret, process, or simulate human [[Affect (psychology)|affects]].{{sfn|Thro|1993}}{{sfn|Edelson|1991}}{{sfn|Tao|Tan|2005}} Moderate successes related to affective computing include textual [[sentiment analysis]] and, more recently, multimodal affect analysis (see [[multimodal sentiment analysis]]), wherein AI classifies the affects displayed by a videotaped subject.<ref>{{cite journal|last1=Poria|first1=Soujanya|last2=Cambria|first2=Erik|last3=Bajpai|first3=Rajiv|last4=Hussain|first4=Amir|title=A review of affective computing: From unimodal analysis to multimodal fusion|journal=Information Fusion|date=September 2017|volume=37|pages=98–125|doi=10.1016/j.inffus.2017.02.003|hdl=1893/25490|hdl-access=free}}</ref>+ − Moravec's paradox can be extended to many forms of social intelligence. Distributed multi-agent coordination of autonomous vehicles remains a difficult problem. Affective computing is an interdisciplinary umbrella that comprises systems which recognize, interpret, process, or simulate human affects. Moderate successes related to affective computing include textual sentiment analysis and, more recently, multimodal affect analysis (see multimodal sentiment analysis), wherein AI classifies the affects displayed by a videotaped subject.+ − 莫拉维克的悖论可以扩展到许多形式的社会智力。自主车辆的分布式多智能体协调一直是一个难题。情感计算是一个跨学科的保护伞，包括系统，识别，解释，处理，或模拟人的影响。与情感计算相关的一些成功包括文本情感分析，以及最近的多模态情感分析(见多模态情感分析) ，其中人工智能通过视频主题将情感分类。+ + + + − In the long run, social skills and an understanding of human emotion and [[game theory]] would be valuable to a social agent. Being able to predict the actions of others by understanding their motives and emotional states would allow an agent to make better decisions. Some computer systems mimic human emotion and expressions to appear more sensitive to the emotional dynamics of human interaction, or to otherwise facilitate [[human–computer interaction]].<ref name="Emotion and affective computing"/> Similarly, some [[virtual assistant]]s are programmed to speak conversationally or even to banter humorously; this tends to give naïve users an unrealistic conception of how intelligent existing computer agents actually are.<ref>{{cite magazine|last1=Waddell|first1=Kaveh|title=Chatbots Have Entered the Uncanny Valley|url=https://www.theatlantic.com/technology/archive/2017/04/uncanny-valley-digital-assistants/523806/|accessdate=24 April 2018|magazine=The Atlantic|date=2018}}</ref>+ − In the long run, social skills and an understanding of human emotion and game theory would be valuable to a social agent. Being able to predict the actions of others by understanding their motives and emotional states would allow an agent to make better decisions. Some computer systems mimic human emotion and expressions to appear more sensitive to the emotional dynamics of human interaction, or to otherwise facilitate human–computer interaction.+ − 从长远来看，社交技巧以及对人类情感和博弈论的理解对于社会行为者来说是很有价值的。能够通过理解他人的动机和情绪状态来预测他人的行为，会让代理人做出更好的决策。有些计算机系统模仿人类的情感和表情，以显得对人类互动的情感动力学更敏感，或以其他方式促进人机交互。+ + + − + − === General intelligence ===+ − 一般情报+ − <!-- This is linked to in the introduction -->+ − <!-- This is linked to in the introduction -->+ − ! ——这个链接在介绍中—— − {{Main|Artificial general intelligence|AI-complete}} + + + + + + − Historically, projects such as the Cyc knowledge base (1984–) and the massive Japanese [[Fifth generation computer|Fifth Generation Computer Systems]] initiative (1982–1992) attempted to cover the breadth of human cognition. These early projects failed to escape the limitations of non-quantitative symbolic logic models and, in retrospect, greatly underestimated the difficulty of cross-domain AI. Nowadays, the vast majority of current AI researchers work instead on tractable "narrow AI" applications (such as medical diagnosis or automobile navigation).<ref name="contemporary agi">{{cite book|last1=Pennachin|first1=C.|last2=Goertzel|first2=B.|title=Contemporary Approaches to Artificial General Intelligence|journal=Artificial General Intelligence. Cognitive Technologies|date=2007|doi=10.1007/978-3-540-68677-4_1|publisher=Springer|location=Berlin, Heidelberg|series=Cognitive Technologies|isbn=978-3-540-23733-4}}</ref> Many researchers predict that such "narrow AI" work in different individual domains will eventually be incorporated into a machine with [[artificial general intelligence]] (AGI), combining most of the narrow skills mentioned in this article and at some point even exceeding human ability in most or all these areas.<ref name="General intelligence"/><ref name="Roberts">{{cite magazine|last1=Roberts|first1=Jacob|title=Thinking Machines: The Search for Artificial Intelligence|magazine=Distillations|date=2016|volume=2|issue=2|pages=14–23|url=https://www.sciencehistory.org/distillations/magazine/thinking-machines-the-search-for-artificial-intelligence|accessdate=20 March 2018|archive-url=https://web.archive.org/web/20180819152455/https://www.sciencehistory.org/distillations/magazine/thinking-machines-the-search-for-artificial-intelligence|archive-date=19 August 2018|url-status=dead}}</ref> Many advances have general, cross-domain significance. One high-profile example is that [[DeepMind]] in the 2010s developed a "generalized artificial intelligence" that could learn many diverse [[Atari 2600|Atari]] games on its own, and later developed a variant of the system which succeeds at [[Catastrophic interference#The Sequential Learning Problem: McCloskey and Cohen (1989)|sequential learning]].<ref>{{cite news|title=The superhero of artificial intelligence: can this genius keep it in check?|url=https://www.theguardian.com/technology/2016/feb/16/demis-hassabis-artificial-intelligence-deepmind-alphago|accessdate=26 April 2018|work=the Guardian|date=16 February 2016|language=en}}</ref><ref>{{cite journal|last1=Mnih|first1=Volodymyr|last2=Kavukcuoglu|first2=Koray|last3=Silver|first3=David|last4=Rusu|first4=Andrei A.|last5=Veness|first5=Joel|last6=Bellemare|first6=Marc G.|last7=Graves|first7=Alex|last8=Riedmiller|first8=Martin|last9=Fidjeland|first9=Andreas K.|last10=Ostrovski|first10=Georg|last11=Petersen|first11=Stig|last12=Beattie|first12=Charles|last13=Sadik|first13=Amir|last14=Antonoglou|first14=Ioannis|last15=King|first15=Helen|last16=Kumaran|first16=Dharshan|last17=Wierstra|first17=Daan|last18=Legg|first18=Shane|last19=Hassabis|first19=Demis|title=Human-level control through deep reinforcement learning|journal=Nature|date=26 February 2015|volume=518|issue=7540|pages=529–533|doi=10.1038/nature14236|pmid=25719670|bibcode=2015Natur.518..529M}}</ref><ref>{{cite news|last1=Sample|first1=Ian|title=Google's DeepMind makes AI program that can learn like a human|url=https://www.theguardian.com/global/2017/mar/14/googles-deepmind-makes-ai-program-that-can-learn-like-a-human|accessdate=26 April 2018|work=the Guardian|date=14 March 2017|language=en}}</ref> Besides [[transfer learning]],<ref>{{cite news|title=From not working to neural networking|url=https://www.economist.com/news/special-report/21700756-artificial-intelligence-boom-based-old-idea-modern-twist-not|accessdate=26 April 2018|work=The Economist|date=2016|language=en}}</ref> hypothetical AGI breakthroughs could include the development of reflective architectures that can engage in decision-theoretic metareasoning, and figuring out how to "slurp up" a comprehensive knowledge base from the entire unstructured [[World Wide Web|Web]].{{sfn|Russell|Norvig|2009|chapter=27. AI: The Present and Future}} Some argue that some kind of (currently-undiscovered) conceptually straightforward, but mathematically difficult, "Master Algorithm" could lead to AGI.{{sfn|Domingos|2015|chapter=9. The Pieces of the Puzzle Fall into Place}} Finally, a few "emergent" approaches look to simulating human intelligence extremely closely, and believe that [[anthropomorphism|anthropomorphic]] features like an [[artificial brain]] or simulated [[developmental robotics|child development]] may someday reach a critical point where general intelligence emerges.<ref name="Brain simulation"/><ref>{{cite journal|last1=Goertzel|first1=Ben|last2=Lian|first2=Ruiting|last3=Arel|first3=Itamar|last4=de Garis|first4=Hugo|last5=Chen|first5=Shuo|title=A world survey of artificial brain projects, Part II: Biologically inspired cognitive architectures|journal=Neurocomputing|date=December 2010|volume=74|issue=1–3|pages=30–49|doi=10.1016/j.neucom.2010.08.012}}</ref> − Historically, projects such as the Cyc knowledge base (1984–) and the massive Japanese Fifth Generation Computer Systems initiative (1982–1992) attempted to cover the breadth of human cognition. These early projects failed to escape the limitations of non-quantitative symbolic logic models and, in retrospect, greatly underestimated the difficulty of cross-domain AI. Nowadays, the vast majority of current AI researchers work instead on tractable "narrow AI" applications (such as medical diagnosis or automobile navigation). Many researchers predict that such "narrow AI" work in different individual domains will eventually be incorporated into a machine with artificial general intelligence (AGI), combining most of the narrow skills mentioned in this article and at some point even exceeding human ability in most or all these areas. Many advances have general, cross-domain significance. One high-profile example is that DeepMind in the 2010s developed a "generalized artificial intelligence" that could learn many diverse Atari games on its own, and later developed a variant of the system which succeeds at sequential learning. Besides transfer learning, hypothetical AGI breakthroughs could include the development of reflective architectures that can engage in decision-theoretic metareasoning, and figuring out how to "slurp up" a comprehensive knowledge base from the entire unstructured Web. Some argue that some kind of (currently-undiscovered) conceptually straightforward, but mathematically difficult, "Master Algorithm" could lead to AGI. Finally, a few "emergent" approaches look to simulating human intelligence extremely closely, and believe that anthropomorphic features like an artificial brain or simulated child development may someday reach a critical point where general intelligence emerges. − 历史上，诸如 Cyc 知识库(1984 -)和大规模的日本第五代计算机系统倡议(1982-1992)等项目试图涵盖人类认知的广度。这些早期的项目未能逃脱非定量符号逻辑模型的限制，回顾过去，大大低估了跨领域人工智能的难度。如今，绝大多数当前的人工智能研究人员致力于易于处理的“狭义人工智能”应用(如医疗诊断或汽车导航)。许多研究人员预测，这种在不同领域的“狭义人工智能”工作最终将被整合到一台具有人工通用智能(AGI)的机器中，结合本文中提到的大多数狭义技能，甚至在某种程度上超过人类在大多数或所有这些领域的能力。许多进展具有普遍的、跨领域的意义。一个引人注目的例子是，DeepMind 在2010年代开发了一种“通用人工智能”(generalized artificial intelligence) ，它可以自己学习许多不同的 Atari 游戏，后来又开发了一种系统的变体，在顺序学习方面取得了成功。除了迁移学习，假想的 AGI 突破可能包括开发能够进行决策理论元推理的反射架构，以及从整个非结构化网络中找出如何“吸取”一个全面的知识库。一些人认为，某种(目前尚未发现的)概念简单，但在数学上困难的“主算法”可以导致 AGI。最后，一些“涌现”的方法着眼于极其密切地模拟人类智能，并相信拟人化的特征，如人工大脑或模拟儿童发展，可能有一天达到一个临界点，一般智能出现。+ + + + + − Many of the problems in this article may also require general intelligence, if machines are to solve the problems as well as people do. For example, even specific straightforward tasks, like [[machine translation]], require that a machine read and write in both languages ([[#Natural language processing|NLP]]), follow the author's argument ([[#Deduction, reasoning, problem solving|reason]]), know what is being talked about ([[#Knowledge representation|knowledge]]), and faithfully reproduce the author's original intent ([[#Social intelligence|social intelligence]]). A problem like machine translation is considered "[[AI-complete]]", because all of these problems need to be solved simultaneously in order to reach human-level machine performance.+ − Many of the problems in this article may also require general intelligence, if machines are to solve the problems as well as people do. For example, even specific straightforward tasks, like machine translation, require that a machine read and write in both languages (NLP), follow the author's argument (reason), know what is being talked about (knowledge), and faithfully reproduce the author's original intent (social intelligence). A problem like machine translation is considered "AI-complete", because all of these problems need to be solved simultaneously in order to reach human-level machine performance. − 如果机器要像人一样解决问题，那么本文中的许多问题也可能需要一般的智能。例如，即使是特定的直接任务，如机器翻译，也要求机器用两种语言进行读写(NLP) ，遵循作者的论点(理由) ，知道谈论的内容(知识) ，并忠实地再现作者的原始意图(社会智能)。像机器翻译这样的问题被认为是“人工智能完全”的，因为所有这些问题都需要同时解决，以达到人类水平的机器性能。 + + − == Approaches ==+ − + − 方法+ − There is no established unifying theory or [[paradigm]] that guides AI research. Researchers disagree about many issues.<ref>[[Nils Nilsson (researcher)|Nils Nilsson]] writes: "Simply put, there is wide disagreement in the field about what AI is all about" {{Harv|Nilsson|1983|p=10}}.</ref> A few of the most long standing questions that have remained unanswered are these: should artificial intelligence simulate natural intelligence by studying [[psychology]] or [[Neuroscience|neurobiology]]? Or is [[human biology]] as irrelevant to AI research as bird biology is to [[aeronautical engineering]]?<ref name="Biological intelligence vs. intelligence in general"/>+ − There is no established unifying theory or paradigm that guides AI research. Researchers disagree about many issues. A few of the most long standing questions that have remained unanswered are these: should artificial intelligence simulate natural intelligence by studying psychology or neurobiology? Or is human biology as irrelevant to AI research as bird biology is to aeronautical engineering? − 目前还没有统一的理论或范式来指导人工智能的研究。研究人员在许多问题上存在分歧。一些长期悬而未决的问题是: 人工智能是否应该通过研究心理学或神经生物学来模拟自然智能？或者人类生物学和人工智能研究的关系就像鸟类生物学和航空工程学的关系一样？ − Can intelligent behavior be described using simple, elegant principles (such as [[logic]] or [[optimization (mathematics)|optimization]])? Or does it necessarily require solving a large number of completely unrelated problems?<ref name="Neats vs. scruffies"/>+ − Can intelligent behavior be described using simple, elegant principles (such as logic or optimization)? Or does it necessarily require solving a large number of completely unrelated problems?+ − 智能行为可以用简单、优雅的原则(如逻辑或优化)来描述吗？还是需要解决大量完全不相关的问题？+ + − + − === Cybernetics and brain simulation ===+ − 控制论与大脑模拟+ − {{Main|Cybernetics|Computational neuroscience}}+ + − In the 1940s and 1950s, a number of researchers explored the connection between [[neurobiology]], [[information theory]], and [[cybernetics]]. Some of them built machines that used electronic networks to exhibit rudimentary intelligence, such as [[W. Grey Walter]]'s [[turtle (robot)|turtles]] and the [[Johns Hopkins Beast]]. Many of these researchers gathered for meetings of the Teleological Society at [[Princeton University]] and the [[Ratio Club]] in England.<ref name="AI's immediate precursors"/> By 1960, this approach was largely abandoned, although elements of it would be revived in the 1980s. − In the 1940s and 1950s, a number of researchers explored the connection between neurobiology, information theory, and cybernetics. Some of them built machines that used electronic networks to exhibit rudimentary intelligence, such as W. Grey Walter's turtles and the Johns Hopkins Beast. Many of these researchers gathered for meetings of the Teleological Society at Princeton University and the Ratio Club in England. By 1960, this approach was largely abandoned, although elements of it would be revived in the 1980s.+ − 在20世纪40年代和50年代，许多研究人员探索了神经生物学、信息论和控制论之间的联系。他们中的一些人利用电子网络制造机器来展示基本的智能，比如 w · 格雷 · 沃尔特的乌龟和约翰 · 霍普金斯的野兽。这些研究人员中的许多人聚集在英格兰的普林斯顿大学和比率俱乐部参加目的论学会的会议。到了1960年，这种方法基本上被放弃了，尽管其中的一些元素在1980年代又复活了。+ + + + + − === Symbolic === − + − 象征性的+ − {{Main|Symbolic AI}}+ + + − When access to digital computers became possible in the mid-1950s, AI research began to explore the possibility that human intelligence could be reduced to symbol manipulation. The research was centered in three institutions: [[Carnegie Mellon University]], [[Stanford]] and [[MIT]], and as described below, each one developed its own style of research. [[John Haugeland]] named these symbolic approaches to AI "good old fashioned AI" or "[[GOFAI]]".<ref name="GOFAI"/> During the 1960s, symbolic approaches had achieved great success at simulating high-level "thinking" in small demonstration programs. Approaches based on [[cybernetics]] or [[artificial neural network]]s were abandoned or pushed into the background.<ref>The most dramatic case of sub-symbolic AI being pushed into the background was the devastating critique of [[perceptron]]s by [[Marvin Minsky]] and [[Seymour Papert]] in 1969. See [[History of AI]], [[AI winter]], or [[Frank Rosenblatt]].</ref>+ − When access to digital computers became possible in the mid-1950s, AI research began to explore the possibility that human intelligence could be reduced to symbol manipulation. The research was centered in three institutions: Carnegie Mellon University, Stanford and MIT, and as described below, each one developed its own style of research. John Haugeland named these symbolic approaches to AI "good old fashioned AI" or "GOFAI". − 20世纪50年代中期，当数字计算机成为可能时，人工智能研究开始探索人类智能可以降低为符号操纵的可能性。这项研究集中在3个机构: 卡内基梅隆大学，斯坦福和麻省理工学院，正如下面所描述的，每个机构都有自己的研究风格。约翰 · 豪格兰德将这些具有象征意义的人工智能方法命名为“好的老式人工智能”或“ GOFAI”。 − + − + − 20世纪60年代和70年代的研究人员相信，象征性的方法最终会成功地创造出一台具有人工通用智能的机器，并认为这是他们研究领域的目标。+ + + − + − ==== Cognitive simulation ====+ − 认知模拟+ − Economist [[Herbert A. Simon|Herbert Simon]] and [[Allen Newell]] studied human problem-solving skills and attempted to formalize them, and their work laid the foundations of the field of artificial intelligence, as well as [[cognitive science]], [[operations research]] and [[management science]]. Their research team used the results of [[psychology|psychological]] experiments to develop programs that simulated the techniques that people used to solve problems. This tradition, centered at [[Carnegie Mellon University]] would eventually culminate in the development of the [[Soar (cognitive architecture)|Soar]] architecture in the middle 1980s.<ref name="AI at CMU in the 60s"/><ref name="Soar"/>+ − Economist Herbert Simon and Allen Newell studied human problem-solving skills and attempted to formalize them, and their work laid the foundations of the field of artificial intelligence, as well as cognitive science, operations research and management science. Their research team used the results of psychological experiments to develop programs that simulated the techniques that people used to solve problems. This tradition, centered at Carnegie Mellon University would eventually culminate in the development of the Soar architecture in the middle 1980s.+ − 经济学家赫伯特 · 西蒙和艾伦 · 纽厄尔研究了人类解决问题的能力，并试图将其形式化，他们的工作为人工智能、认知科学、运筹学和管理科学奠定了基础。他们的研究团队利用心理学实验的结果来开发程序，模拟人们用来解决问题的技术。这个传统，以卡内基梅隆大学为中心，最终在20世纪80年代中期的 Soar 建筑的发展达到顶峰。 + + + − ==== Logic-based ==== − ==== Logic-based ==== − 基于逻辑的 − Unlike Simon and Newell, [[John McCarthy (computer scientist)|John McCarthy]] felt that machines did not need to simulate human thought, but should instead try to find the essence of abstract reasoning and problem-solving, regardless whether people used the same algorithms.<ref name="Biological intelligence vs. intelligence in general"/> His laboratory at [[Stanford University|Stanford]] ([[Stanford Artificial Intelligence Laboratory|SAIL]]) focused on using formal [[logic]] to solve a wide variety of problems, including [[knowledge representation]], [[automated planning and scheduling|planning]] and [[machine learning|learning]].<ref name="AI at Stanford in the 60s"/> Logic was also the focus of the work at the [[University of Edinburgh]] and elsewhere in Europe which led to the development of the programming language [[Prolog]] and the science of [[logic programming]].<ref name="AI at Edinburgh and France in the 60s"/>+ − Unlike Simon and Newell, John McCarthy felt that machines did not need to simulate human thought, but should instead try to find the essence of abstract reasoning and problem-solving, regardless whether people used the same algorithms. His laboratory at Stanford (SAIL) focused on using formal logic to solve a wide variety of problems, including knowledge representation, planning and learning. Logic was also the focus of the work at the University of Edinburgh and elsewhere in Europe which led to the development of the programming language Prolog and the science of logic programming. − 与西蒙和纽厄尔不同，约翰 · 麦卡锡认为机器不需要模拟人类的思维，而是应该尝试寻找抽象推理和解决问题的本质，不管人们是否使用相同的算法。他在斯坦福大学的实验室(SAIL)致力于使用形式逻辑来解决各种各样的问题，包括知识表示、规划和学习。逻辑也是爱丁堡大学和欧洲其他地方工作的重点，这导致了编程语言 Prolog 和逻辑编程科学的发展。 第1,101行：
第1,092行： − + − ==== Anti-logic or scruffy ====+ − 反逻辑的或邋遢的+ − Researchers at [[MIT]] (such as [[Marvin Minsky]] and [[Seymour Papert]])<ref name="AI at MIT in the 60s"/> found that solving difficult problems in [[computer vision|vision]] and [[natural language processing]] required ad-hoc solutions—they argued that there was no simple and general principle (like [[logic]]) that would capture all the aspects of intelligent behavior. [[Roger Schank]] described their "anti-logic" approaches as "[[Neats vs. scruffies|scruffy]]" (as opposed to the "[[neats vs. scruffies|neat]]" paradigms at [[Carnegie Mellon University|CMU]] and Stanford).<ref name="Neats vs. scruffies"/> [[Commonsense knowledge bases]] (such as [[Doug Lenat]]'s [[Cyc]]) are an example of "scruffy" AI, since they must be built by hand, one complicated concept at a time.<ref name="Cyc"/> − Researchers at MIT (such as Marvin Minsky and Seymour Papert) found that solving difficult problems in vision and natural language processing required ad-hoc solutions—they argued that there was no simple and general principle (like logic) that would capture all the aspects of intelligent behavior. Roger Schank described their "anti-logic" approaches as "scruffy" (as opposed to the "neat" paradigms at CMU and Stanford). Commonsense knowledge bases (such as Doug Lenat's Cyc) are an example of "scruffy" AI, since they must be built by hand, one complicated concept at a time. − 麻省理工学院(MIT)的研究人员(如马文•明斯基(Marvin Minsky)和西摩•派珀特(Seymour Papert))发现，解决视觉和自然语言处理中的难题需要特定的解决方案——他们认为，没有简单而普遍的原则(如逻辑)可以涵盖智能行为的。罗杰•尚克(Roger Schank)将他们的“反逻辑”方法形容为“邋遢”(相对于卡内基梅隆大学(CMU)和斯坦福大学(Stanford)的“整洁”范式)。常识性知识库(如 Doug Lenat 的 Cyc)是“邋遢”人工智能的一个例子，因为它们必须手工构建，一次构建一个复杂的概念。+ + + + − ====Knowledge-based====+ − + − 以知识为本 − When computers with large memories became available around 1970, researchers from all three traditions began to build [[knowledge representation|knowledge]] into AI applications.<ref name="Knowledge revolution"/> This "knowledge revolution" led to the development and deployment of [[expert system]]s (introduced by [[Edward Feigenbaum]]), the first truly successful form of AI software.<ref name="Expert systems"/> A key component of the system architecture for all expert systems is the knowledge base, which stores facts and rules that illustrate AI.<ref>{{Cite journal |last=Frederick |first=Hayes-Roth |last2=William |first2=Murray |last3=Leonard |first3=Adelman |title=Expert systems|journal=AccessScience |language=en |doi=10.1036/1097-8542.248550}}</ref> The knowledge revolution was also driven by the realization that enormous amounts of knowledge would be required by many simple AI applications. − When computers with large memories became available around 1970, researchers from all three traditions began to build knowledge into AI applications. The knowledge revolution was also driven by the realization that enormous amounts of knowledge would be required by many simple AI applications.+ − 1970年左右，当拥有大容量存储器的计算机出现时，来自这三个传统的研究人员开始将知识应用于人工智能领域。推动知识革命的另一个原因是人们认识到，许多简单的人工智能应用程序需要大量的知识。+ + + + + − === Sub-symbolic === − === Sub-symbolic === − 子符号 − By the 1980s, progress in symbolic AI seemed to stall and many believed that symbolic systems would never be able to imitate all the processes of human cognition, especially [[machine perception|perception]], [[robotics]], [[machine learning|learning]] and [[pattern recognition]]. A number of researchers began to look into "sub-symbolic" approaches to specific AI problems.<ref name="Symbolic vs. sub-symbolic"/> Sub-symbolic methods manage to approach intelligence without specific representations of knowledge.+ − By the 1980s, progress in symbolic AI seemed to stall and many believed that symbolic systems would never be able to imitate all the processes of human cognition, especially perception, robotics, learning and pattern recognition. A number of researchers began to look into "sub-symbolic" approaches to specific AI problems. Sub-symbolic methods manage to approach intelligence without specific representations of knowledge.+ − 到了20世纪80年代，符号人工智能的进步似乎停滞不前，许多人认为符号系统永远无法模仿人类认知的所有过程，尤其是感知、机器人、学习和模式识别。许多研究人员开始研究针对特定人工智能问题的“次象征性”方法。子符号方法在没有特定知识表示的情况下，设法接近智能。+ 第1,149行：
第1,140行： − ==== Embodied intelligence ====+ − ==== Embodied intelligence ==== − 具身智慧 − This includes [[embodied agent|embodied]], [[situated]], [[behavior-based AI|behavior-based]], and [[nouvelle AI]]. Researchers from the related field of [[robotics]], such as [[Rodney Brooks]], rejected symbolic AI and focused on the basic engineering problems that would allow robots to move and survive.<ref name="Embodied AI"/> Their work revived the non-symbolic point of view of the early [[cybernetic]]s researchers of the 1950s and reintroduced the use of [[control theory]] in AI. This coincided with the development of the [[embodied mind thesis]] in the related field of [[cognitive science]]: the idea that aspects of the body (such as movement, perception and visualization) are required for higher intelligence. − This includes embodied, situated, behavior-based, and nouvelle AI. Researchers from the related field of robotics, such as Rodney Brooks, rejected symbolic AI and focused on the basic engineering problems that would allow robots to move and survive. Their work revived the non-symbolic point of view of the early cybernetics researchers of the 1950s and reintroduced the use of control theory in AI. This coincided with the development of the embodied mind thesis in the related field of cognitive science: the idea that aspects of the body (such as movement, perception and visualization) are required for higher intelligence. − 这包括具体化的、情境化的、基于行为的和 nouvelle AI。来自机器人相关领域的研究人员，如罗德尼 · 布鲁克斯，拒绝接受符号化人工智能，而专注于使机器人能够移动和生存的基本工程问题。他们的工作复活了20世纪50年代早期控制论研究者的非符号观点，并重新引入了控制理论在人工智能中的应用。这与认知科学相关领域的具身心理论的发展相吻合: 认为身体的各个方面(如运动、感知和可视化)是高智力所必需的。 + + + − Within [[developmental robotics]], developmental learning approaches are elaborated upon to allow robots to accumulate repertoires of novel skills through autonomous self-exploration, social interaction with human teachers, and the use of guidance mechanisms (active learning, maturation, motor synergies, etc.).{{sfn|Weng|McClelland|Pentland|Sporns|2001}}{{sfn|Lungarella|Metta|Pfeifer|Sandini|2003}}{{sfn|Asada|Hosoda|Kuniyoshi|Ishiguro|2009}}{{sfn|Oudeyer|2010}} − Within developmental robotics, developmental learning approaches are elaborated upon to allow robots to accumulate repertoires of novel skills through autonomous self-exploration, social interaction with human teachers, and the use of guidance mechanisms (active learning, maturation, motor synergies, etc.). − 在发展型机器人中，发展型学习方法被详细阐述，通过自主的自我探索、与人类教师的社会互动，以及使用指导机制(主动学习、成熟、协同运动等) ，使机器人积累新技能的能力。). + + + − + − ====Computational intelligence and soft computing====+ − 计算智能与软计算+ + − Interest in [[Artificial neural network|neural networks]] and "[[connectionism]]" was revived by [[David Rumelhart]] and others in the middle of the 1980s.<ref name="Revival of connectionism"/> [[Artificial neural network]]s are an example of [[soft computing]]—they are solutions to problems which cannot be solved with complete logical certainty, and where an approximate solution is often sufficient. Other [[soft computing]] approaches to AI include [[fuzzy system]]s, [[Grey system theory]], [[evolutionary computation]] and many statistical tools. The application of soft computing to AI is studied collectively by the emerging discipline of [[computational intelligence]].<ref name="Computational intelligence"/> − Interest in neural networks and "connectionism" was revived by David Rumelhart and others in the middle of the 1980s. Artificial neural networks are an example of soft computing—they are solutions to problems which cannot be solved with complete logical certainty, and where an approximate solution is often sufficient. Other soft computing approaches to AI include fuzzy systems, Grey system theory, evolutionary computation and many statistical tools. The application of soft computing to AI is studied collectively by the emerging discipline of computational intelligence. − 上世纪80年代中期，大卫•鲁梅尔哈特(David Rumelhart)等人重新激发了人们对神经网络和“连接主义”的兴趣。人工神经网络是软计算的一个例子ーー它们是不能完全用逻辑确定性解决的问题的解决方案，而且近似解常常是充分的。人工智能的其他软计算方法包括模糊系统、灰色系统理论、进化计算和许多统计工具。软计算在人工智能中的应用是计算智能这一新兴学科的集体研究领域。 + + + − === Statistical learning === − === Statistical learning === − 统计学习 − Much of traditional [[Symbolic artificial intelligence|GOFAI]] got bogged down on ''ad hoc'' patches to [[symbolic computation]] that worked on their own toy models but failed to generalize to real-world results. However, around the 1990s, AI researchers adopted sophisticated mathematical tools, such as [[hidden Markov model]]s (HMM), [[information theory]], and normative Bayesian [[decision theory]] to compare or to unify competing architectures. The shared mathematical language permitted a high level of collaboration with more established fields (like [[mathematics]], economics or [[operations research]]).{{efn|While such a "victory of the neats" may be a consequence of the field becoming more mature, [[Artificial Intelligence: A Modern Approach|AIMA]] states that in practice both [[neats and scruffies|neat and scruffy]] approaches continue to be necessary in AI research.}} Compared with GOFAI, new "statistical learning" techniques such as HMM and neural networks were gaining higher levels of accuracy in many practical domains such as [[data mining]], without necessarily acquiring a semantic understanding of the datasets. The increased successes with real-world data led to increasing emphasis on comparing different approaches against shared test data to see which approach performed best in a broader context than that provided by idiosyncratic toy models; AI research was becoming more [[scientific method|scientific]]. Nowadays results of experiments are often rigorously measurable, and are sometimes (with difficulty) reproducible.<ref name="Formal methods in AI"/><ref>{{cite news|last1=Hutson|first1=Matthew|title=Artificial intelligence faces reproducibility crisis|url=http://science.sciencemag.org/content/359/6377/725|accessdate=28 April 2018|work=[[Science Magazine|Science]]|date=16 February 2018|pages=725–726|language=en|doi=10.1126/science.359.6377.725|bibcode=2018Sci...359..725H}}</ref> Different statistical learning techniques have different limitations; for example, basic HMM cannot model the infinite possible combinations of natural language.{{sfn|Norvig|2012}} Critics note that the shift from GOFAI to statistical learning is often also a shift away from [[explainable AI]]. In AGI research, some scholars caution against over-reliance on statistical learning, and argue that continuing research into GOFAI will still be necessary to attain general intelligence.{{sfn|Langley|2011}}{{sfn|Katz|2012}}+ − Much of traditional GOFAI got bogged down on ad hoc patches to symbolic computation that worked on their own toy models but failed to generalize to real-world results. However, around the 1990s, AI researchers adopted sophisticated mathematical tools, such as hidden Markov models (HMM), information theory, and normative Bayesian decision theory to compare or to unify competing architectures. The shared mathematical language permitted a high level of collaboration with more established fields (like mathematics, economics or operations research). Compared with GOFAI, new "statistical learning" techniques such as HMM and neural networks were gaining higher levels of accuracy in many practical domains such as data mining, without necessarily acquiring a semantic understanding of the datasets. The increased successes with real-world data led to increasing emphasis on comparing different approaches against shared test data to see which approach performed best in a broader context than that provided by idiosyncratic toy models; AI research was becoming more scientific. Nowadays results of experiments are often rigorously measurable, and are sometimes (with difficulty) reproducible. Different statistical learning techniques have different limitations; for example, basic HMM cannot model the infinite possible combinations of natural language. Critics note that the shift from GOFAI to statistical learning is often also a shift away from explainable AI. In AGI research, some scholars caution against over-reliance on statistical learning, and argue that continuing research into GOFAI will still be necessary to attain general intelligence. − 许多传统的 GOFAI 陷入了特别补丁的符号计算，工作在自己的玩具模型，但未能推广到现实世界的结果。然而，在20世纪90年代前后，人工智能研究人员采用了复杂的数学工具，如隐马尔可夫模型(HMM)、信息理论和规范贝叶斯决策理论来比较或统一竞争架构。共享的数学语言允许与更成熟的领域(如数学、经济学或运筹学)进行高层次的合作。与 GOFAI 相比，隐马尔可夫模型(HMM)和神经网络(neural networks)等新的“统计学习”技术在数据挖掘等许多实际领域中获得了更高的精度，而不必获得对数据集的语义理解。随着现实世界数据的日益成功，人们越来越重视将不同的方法与共享的测试数据进行比较，以查明哪种方法在更广泛的背景下比特殊玩具模型提供的方法表现得更好; 人工智能研究正变得更加科。如今，实验结果经常是严格可测的，有时(很难)重现。不同的统计学习技术有不同的局限性，例如，基本的 HMM 不能为自然语言的无限可能组合建模。批评家们指出，从 GOFAI 到统计学习的转变也经常是从可解释的人工智能的转变。在 AGI 的研究中，一些学者警告不要过度依赖统计学习，并认为继续研究 GOFAI 仍然是获得一般智力的必要条件。 + + + − === Integrating the approaches === − === Integrating the approaches === − 整合各种方法 − ;Intelligent agent paradigm: An [[intelligent agent]] is a system that perceives its environment and takes actions that maximize its chances of success. The simplest intelligent agents are programs that solve specific problems. More complicated agents include human beings and organizations of human beings (such as [[firm]]s). The paradigm allows researchers to directly compare or even combine different approaches to isolated problems, by asking which agent is best at maximizing a given "goal function". An agent that solves a specific problem can use any approach that works—some agents are symbolic and logical, some are sub-symbolic [[artificial neural network]]s and others may use new approaches. The paradigm also gives researchers a common language to communicate with other fields—such as [[decision theory]] and economics—that also use concepts of abstract agents. Building a complete agent requires researchers to address realistic problems of integration; for example, because sensory systems give uncertain information about the environment, planning systems must be able to function in the presence of uncertainty. The intelligent agent paradigm became widely accepted during the 1990s.<ref name="Intelligent agents"/>+ − Intelligent agent paradigm: An intelligent agent is a system that perceives its environment and takes actions that maximize its chances of success. The simplest intelligent agents are programs that solve specific problems. More complicated agents include human beings and organizations of human beings (such as firms). The paradigm allows researchers to directly compare or even combine different approaches to isolated problems, by asking which agent is best at maximizing a given "goal function". An agent that solves a specific problem can use any approach that works—some agents are symbolic and logical, some are sub-symbolic artificial neural networks and others may use new approaches. The paradigm also gives researchers a common language to communicate with other fields—such as decision theory and economics—that also use concepts of abstract agents. Building a complete agent requires researchers to address realistic problems of integration; for example, because sensory systems give uncertain information about the environment, planning systems must be able to function in the presence of uncertainty. The intelligent agent paradigm became widely accepted during the 1990s.+ − 智能代理范式: 智能代理是一个系统，感知其环境，并采取行动，最大限度地提高其成功的机会。最简单的智能代理是解决特定问题的程序。更复杂的行为者包括人类和人类组织(如公司)。这种范式允许研究人员直接比较甚至结合不同的方法来解决孤立的问题，通过询问哪一个主体最适合最大化给定的“目标函数”。解决特定问题的代理可以使用任何有效的方法ーー有些代理是符号化和逻辑化的，有些是次符号化的人工神经网络，还有一些可能使用新的方法。这种范式还为研究人员提供了一种与其他领域(如决策理论和经济学)进行交流的共同语言，这些领域也使用了抽象代理的概念。建立一个完整的主体需要研究人员解决现实的集成问题; 例如，由于感官系统提供关于环境的不确定信息，计划系统必须能够在不确定性的存在下运作。智能主体范式在20世纪90年代被广泛接受。+ 第1,223行：
第1,208行： − ;[[Agent architecture]]s and [[cognitive architecture]]s:Researchers have designed systems to build intelligent systems out of interacting [[intelligent agent]]s in a [[multi-agent system]].<ref name="Agent architectures"/> A [[hierarchical control system]] provides a bridge between sub-symbolic AI at its lowest, reactive levels and traditional symbolic AI at its highest levels, where relaxed time constraints permit planning and world modeling.<ref name="Hierarchical control system"/> Some cognitive architectures are custom-built to solve a narrow problem; others, such as [[Soar (cognitive architecture)|Soar]], are designed to mimic human cognition and to provide insight into general intelligence. Modern extensions of Soar are [[hybrid intelligent system]]s that include both symbolic and sub-symbolic components.<ref>{{cite journal|last1=Laird|first1=John|title=Extending the Soar cognitive architecture|journal=Frontiers in Artificial Intelligence and Applications|date=2008|volume=171|page=224|citeseerx=10.1.1.77.2473}}</ref><ref>{{cite journal|last1=Lieto|first1=Antonio|last2=Lebiere|first2=Christian|last3=Oltramari|first3=Alessandro|title=The knowledge level in cognitive architectures: Current limitations and possibile developments|journal=Cognitive Systems Research|date=May 2018|volume=48|pages=39–55|doi=10.1016/j.cogsys.2017.05.001|hdl=2318/1665207|hdl-access=free}}</ref><ref>{{cite journal|last1=Lieto|first1=Antonio|last2=Bhatt|first2=Mehul|last3=Oltramari|first3=Alessandro|last4=Vernon|first4=David|title=The role of cognitive architectures in general artificial intelligence|journal=Cognitive Systems Research|date=May 2018|volume=48|pages=1–3|doi=10.1016/j.cogsys.2017.08.003|hdl=2318/1665249|hdl-access=free}}</ref> − Agent architectures and cognitive architectures:Researchers have designed systems to build intelligent systems out of interacting intelligent agents in a multi-agent system.+ − 代理体系结构和认知体系结构: 研究人员已经设计了一些系统，以便在多智能体系统中利用相互作用的智能代理构建智能系统。+ + + − == Tools ==+ − == Tools ==+ − 工具 − AI has developed many tools to solve the most difficult problems in [[computer science]]. A few of the most general of these methods are discussed below.+ − AI has developed many tools to solve the most difficult problems in computer science. A few of the most general of these methods are discussed below.+ − 人工智能已经开发出许多工具来解决计算机科学中最困难的问题。下面将讨论其中一些最常用的方法。+ 第1,249行：
第1,234行： − === Search and optimization ===+ − === Search and optimization === − 搜索和优化 第1,259行：
第1,242行： − {{Main|Search algorithm|Mathematical optimization|Evolutionary computation}}+ + + + + + + − Many problems in AI can be solved in theory by intelligently searching through many possible solutions:<ref name="Search"/> [[#Deduction, reasoning, problem solving|Reasoning]] can be reduced to performing a search. For example, logical proof can be viewed as searching for a path that leads from [[premise]]s to [[Logical consequence|conclusions]], where each step is the application of an [[inference rule]].<ref name="Logic as search"/> [[Automated planning and scheduling|Planning]] algorithms search through trees of goals and subgoals, attempting to find a path to a target goal, a process called [[means-ends analysis]].<ref name="Planning as search"/> [[Robotics]] algorithms for moving limbs and grasping objects use [[local search (optimization)|local searches]] in [[Configuration space (physics)|configuration space]].<ref name="Configuration space"/> Many [[machine learning|learning]] algorithms use search algorithms based on [[optimization (mathematics)|optimization]]. − Many problems in AI can be solved in theory by intelligently searching through many possible solutions: Reasoning can be reduced to performing a search. For example, logical proof can be viewed as searching for a path that leads from premises to conclusions, where each step is the application of an inference rule. Planning algorithms search through trees of goals and subgoals, attempting to find a path to a target goal, a process called means-ends analysis. Robotics algorithms for moving limbs and grasping objects use local searches in configuration space. Many learning algorithms use search algorithms based on optimization.+ − 人工智能中的许多问题可以通过智能地搜索许多可能的解决方案而在理论上得到解决: 推理可以简化为执行一次搜索。例如，逻辑证明可以看作是寻找从前提到结论的路径，其中每一步都是推理规则的应用。规划算法通过目标和子目标的树搜索，试图找到一条通往目标的路径，这个过程称为目的手段分析。机器人学中的移动肢体和抓取物体的算法使用的是位形空间的局部搜索。许多学习算法使用基于优化的搜索算法。+ + + + + − Simple exhaustive searches<ref name="Uninformed search"/> are rarely sufficient for most real-world problems: the [[search algorithm|search space]] (the number of places to search) quickly grows to [[Astronomically large|astronomical numbers]]. The result is a search that is [[Computation time|too slow]] or never completes. The solution, for many problems, is to use "[[heuristics]]" or "rules of thumb" that prioritize choices in favor of those that are more likely to reach a goal and to do so in a shorter number of steps. In some search methodologies heuristics can also serve to entirely eliminate some choices that are unlikely to lead to a goal (called "[[pruning (algorithm)|pruning]] the [[search tree]]"). [[Heuristics]] supply the program with a "best guess" for the path on which the solution lies.<ref name="Informed search"/> Heuristics limit the search for solutions into a smaller sample size.{{sfn|Tecuci|2012}}+ − Simple exhaustive searches are rarely sufficient for most real-world problems: the search space (the number of places to search) quickly grows to astronomical numbers. The result is a search that is too slow or never completes. The solution, for many problems, is to use "heuristics" or "rules of thumb" that prioritize choices in favor of those that are more likely to reach a goal and to do so in a shorter number of steps. In some search methodologies heuristics can also serve to entirely eliminate some choices that are unlikely to lead to a goal (called "pruning the search tree"). Heuristics supply the program with a "best guess" for the path on which the solution lies. Heuristics limit the search for solutions into a smaller sample size.+ − 对于大多数真实世界的问题，简单的穷举搜索很难满足要求: 搜索空间(要搜索的位置数)很快就会增加到天文数字。结果就是搜索速度太慢或者永远不能完成。对于许多问题，解决方法是使用“启发式”或“经验法则” ，优先考虑那些更有可能达到目标的选择，并且在较短的步骤内完成。在一些搜索方法中，启发式方法还可以完全消除一些不可能导致目标的选择(称为“剪枝搜索树”)。启发式为程序提供了解决方案所在路径的“最佳猜测”。启发式限制解的搜索到一个更小的样本大小。+ + + − A very different kind of search came to prominence in the 1990s, based on the mathematical theory of [[optimization (mathematics)|optimization]]. For many problems, it is possible to begin the search with some form of a guess and then refine the guess incrementally until no more refinements can be made. These algorithms can be visualized as blind [[hill climbing]]: we begin the search at a random point on the landscape, and then, by jumps or steps, we keep moving our guess uphill, until we reach the top. Other optimization algorithms are [[simulated annealing]], [[beam search]] and [[random optimization]].<ref name="Optimization search"/>+ − A very different kind of search came to prominence in the 1990s, based on the mathematical theory of optimization. For many problems, it is possible to begin the search with some form of a guess and then refine the guess incrementally until no more refinements can be made. These algorithms can be visualized as blind hill climbing: we begin the search at a random point on the landscape, and then, by jumps or steps, we keep moving our guess uphill, until we reach the top. Other optimization algorithms are simulated annealing, beam search and random optimization. − 在20世纪90年代，一种基于数学最优化理论的非常不同的搜索引起了人们的注意。对于许多问题，可以从某种形式的猜测开始搜索，然后逐步完善猜测，直到无法进行更多的细化。这些算法可以被视为盲目的爬山: 我们从地形上的一个随机点开始搜索，然后，通过跳跃或步骤，我们继续向山上移动我们的猜测，直到我们到达山顶。其他的优化算法有模拟退火搜索、波束搜索和随机优化。 + − [[File:ParticleSwarmArrowsAnimation.gif|thumb|A [[particle swarm optimization|particle swarm]] seeking the [[global minimum]]]] − particle swarm seeking the global minimum]]+ − + − [[Evolutionary computation]] uses a form of optimization search. For example, they may begin with a population of organisms (the guesses) and then allow them to mutate and recombine, [[artificial selection|selecting]] only the fittest to survive each generation (refining the guesses). Classic [[evolutionary algorithms]] include [[genetic algorithms]], [[gene expression programming]], and [[genetic programming]].<ref name="Genetic programming"/> Alternatively, distributed search processes can coordinate via [[swarm intelligence]] algorithms. Two popular swarm algorithms used in search are [[particle swarm optimization]] (inspired by bird [[flocking (behavior)|flocking]]) and [[ant colony optimization]] (inspired by [[ant trail]]s).<ref name="Society based learning"/><ref>{{cite book|author1=Daniel Merkle|author2=Martin Middendorf|editor1-last=Burke|editor1-first=Edmund K.|editor2-last=Kendall|editor2-first=Graham|title=Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques|date=2013|publisher=Springer Science & Business Media|isbn=978-1-4614-6940-7|language=en|chapter=Swarm Intelligence}}</ref>+ − Evolutionary computation uses a form of optimization search. For example, they may begin with a population of organisms (the guesses) and then allow them to mutate and recombine, selecting only the fittest to survive each generation (refining the guesses). Classic evolutionary algorithms include genetic algorithms, gene expression programming, and genetic programming. − 进化计算使用了一种优化搜索的形式。例如，他们可能从一群有机体(猜测)开始，然后允许它们变异和重组，选择适者生存每一代(改进猜测)。经典的进化算法包括遗传算法、基因表达式编程和遗传编程。 + + − === Logic ===+ − === Logic === − 逻辑+ + + − {{Main|Logic programming|Automated reasoning}}+ + + + + − [[Logic]]<ref name="Logic"/> is used for knowledge representation and problem solving, but it can be applied to other problems as well. For example, the [[satplan]] algorithm uses logic for [[automated planning and scheduling|planning]]<ref name="Satplan"/> and [[inductive logic programming]] is a method for [[machine learning|learning]].<ref name="Symbolic learning techniques"/>+ − Logic is used for knowledge representation and problem solving, but it can be applied to other problems as well. For example, the satplan algorithm uses logic for planning and inductive logic programming is a method for learning. − 逻辑用于知识表示和问题解决，但它也可以应用于其他问题。例如，satplan 算法使用逻辑进行规划，归纳逻辑规划是一种学习方法。 + + + − Several different forms of logic are used in AI research. [[Propositional logic]]<ref name="Propositional logic"/> involves [[truth function]]s such as "or" and "not". [[First-order logic]]<ref name="First-order logic"/> adds [[quantifier (logic)|quantifiers]] and [[predicate (mathematical logic)|predicates]], and can express facts about objects, their properties, and their relations with each other. [[Fuzzy set theory]] assigns a "degree of truth" (between 0 and 1) to vague statements such as "Alice is old" (or rich, or tall, or hungry) that are too linguistically imprecise to be completely true or false. [[Fuzzy logic]] is successfully used in [[control system]]s to allow experts to contribute vague rules such as "if you are close to the destination station and moving fast, increase the train's brake pressure"; these vague rules can then be numerically refined within the system. Fuzzy logic fails to scale well in knowledge bases; many AI researchers question the validity of chaining fuzzy-logic inferences.{{efn|"There exist many different types of uncertainty, vagueness, and ignorance... [We] independently confirm the inadequacy of systems for reasoning about uncertainty that propagates numerical factors according to only to which connectives appear in assertions."<ref>{{cite journal|last1=Elkan|first1=Charles|title=The paradoxical success of fuzzy logic|journal=IEEE Expert|date=1994|volume=9|issue=4|pages=3–49|doi=10.1109/64.336150|citeseerx=10.1.1.100.8402}}</ref>}}<ref name="Fuzzy logic"/><ref>{{cite news|title=What is 'fuzzy logic'? Are there computers that are inherently fuzzy and do not apply the usual binary logic?|url=https://www.scientificamerican.com/article/what-is-fuzzy-logic-are-t/|accessdate=5 May 2018|work=Scientific American|language=en}}</ref> − Several different forms of logic are used in AI research. Propositional logic}} − 人工智能研究中使用了几种不同形式的逻辑。命题逻辑 + + + − [[Default logic]]s, [[non-monotonic logic]]s and [[circumscription (logic)|circumscription]]<ref name="Default reasoning and non-monotonic logic"/> are forms of logic designed to help with default reasoning and the [[qualification problem]]. Several extensions of logic have been designed to handle specific domains of [[knowledge representation|knowledge]], such as: [[description logic]]s;<ref name="Representing categories and relations"/> [[situation calculus]], [[event calculus]] and [[fluent calculus]] (for representing events and time);<ref name="Representing time"/> [[Causality#Causal calculus|causal calculus]];<ref name="Representing causation"/> [[Belief revision|belief calculus (belief revision)]];<ref>"The Belief Calculus and Uncertain Reasoning", Yen-Teh Hsia</ref> and [[modal logic]]s.<ref name="Representing knowledge about knowledge"/> Logics to model contradictory or inconsistent statements arising in multi-agent systems have also been designed, such as [[paraconsistent logic]]s. − Default logics, non-monotonic logics and circumscription and modal logics. Logics to model contradictory or inconsistent statements arising in multi-agent systems have also been designed, such as paraconsistent logics.+ − 默认逻辑、非单调逻辑、限制逻辑和模态逻辑。对多智能体系统中出现的矛盾或不一致的陈述进行建模的逻辑也已经被设计出来，例如次协调逻辑。+ + − === Probabilistic methods for uncertain reasoning === − === Probabilistic methods for uncertain reasoning ===+ − 不确定推理的概率方法 第1,371行：
第1,367行： − {{Main|Bayesian network|Hidden Markov model|Kalman filter|Particle filter|Decision theory|Utility theory}} + + − [[File:EM Clustering of Old Faithful data.gif|right|frame|[[Expectation-maximization]] clustering of [[Old Faithful]] eruption data starts from a random guess but then successfully converges on an accurate clustering of the two physically distinct modes of eruption.]]+ − [[Expectation-maximization clustering of Old Faithful eruption data starts from a random guess but then successfully converges on an accurate clustering of the two physically distinct modes of eruption.]] − + + + + − Many problems in AI (in reasoning, planning, learning, perception, and robotics) require the agent to operate with incomplete or uncertain information. AI researchers have devised a number of powerful tools to solve these problems using methods from [[probability]] theory and economics.<ref name="Stochastic methods for uncertain reasoning"/>+ − Many problems in AI (in reasoning, planning, learning, perception, and robotics) require the agent to operate with incomplete or uncertain information. AI researchers have devised a number of powerful tools to solve these problems using methods from probability theory and economics.+ − 人工智能中的许多问题(在推理、规划、学习、感知和机器人技术方面)要求智能体在信息不完整或不确定的情况下进行操作。人工智能研究人员从概率论和经济学的角度设计了许多强大的工具来解决这些问题。+ + − [[Bayesian network]]s<ref name="Bayesian networks"/> are a very general tool that can be used for various problems: reasoning (using the [[Bayesian inference]] algorithm),<ref name="Bayesian inference"/> [[Machine learning|learning]] (using the [[expectation-maximization algorithm]]),{{efn|Expectation-maximization, one of the most popular algorithms in machine learning, allows clustering in the presence of unknown [[latent variables]]{{sfn|Domingos|2015|p=210}}}}<ref name="Bayesian learning"/> [[Automated planning and scheduling|planning]] (using [[decision network]]s)<ref name="Bayesian decision networks"/> and [[machine perception|perception]] (using [[dynamic Bayesian network]]s).<ref name="Stochastic temporal models"/> Probabilistic algorithms can also be used for filtering, prediction, smoothing and finding explanations for streams of data, helping [[machine perception|perception]] systems to analyze processes that occur over time (e.g., [[hidden Markov model]]s or [[Kalman filter]]s).<ref name="Stochastic temporal models"/> Compared with symbolic logic, formal Bayesian inference is computationally expensive. For inference to be tractable, most observations must be [[conditionally independent]] of one another. Complicated graphs with diamonds or other "loops" (undirected [[cycle (graph theory)|cycles]]) can require a sophisticated method such as [[Markov chain Monte Carlo]], which spreads an ensemble of [[random walk]]ers throughout the Bayesian network and attempts to converge to an assessment of the conditional probabilities. Bayesian networks are used on [[Xbox Live]] to rate and match players; wins and losses are "evidence" of how good a player is{{citation needed|date=July 2019}}. [[Google AdSense|AdSense]] uses a Bayesian network with over 300 million edges to learn which ads to serve.{{sfn|Domingos|2015|loc=chapter 6}}+ − Bayesian networks are a very general tool that can be used for various problems: reasoning (using the Bayesian inference algorithm), learning (using the expectation-maximization algorithm),}} planning (using decision networks) and perception (using dynamic Bayesian networks). Probabilistic algorithms can also be used for filtering, prediction, smoothing and finding explanations for streams of data, helping perception systems to analyze processes that occur over time (e.g., hidden Markov models or Kalman filters). Compared with symbolic logic, formal Bayesian inference is computationally expensive. For inference to be tractable, most observations must be conditionally independent of one another. Complicated graphs with diamonds or other "loops" (undirected cycles) can require a sophisticated method such as Markov chain Monte Carlo, which spreads an ensemble of random walkers throughout the Bayesian network and attempts to converge to an assessment of the conditional probabilities. Bayesian networks are used on Xbox Live to rate and match players; wins and losses are "evidence" of how good a player is. AdSense uses a Bayesian network with over 300 million edges to learn which ads to serve.+ + − 贝叶斯网络是一个非常通用的工具，可用于各种问题: 推理(使用贝叶斯推断算法) ，学习(使用期望最大化算法) ，}规划(使用决策网络)和知觉(使用动态贝叶斯网络)。概率算法也可以用于滤波、预测、平滑和为数据流寻找解释，帮助感知系统分析随时间发生的过程(例如，隐马尔可夫模型或卡尔曼滤波器)。与符号逻辑相比，正式的贝叶斯推断逻辑运算量很大。为了使推论易于处理，大多数观察值必须彼此有条件地独立。带有方块或其他“循环”(无向循环)的复杂图形可能需要一种复杂的方法，比如马尔科夫蒙特卡洛图，这种方法将一组随机行走遍布整个贝氏网路，并试图收敛到对条件概率的评估。贝叶斯网络在 Xbox Live 上被用来评估和匹配玩家; 胜负是一个玩家有多优秀的“证据”。使用一个有超过3亿个边缘的贝氏网路来了解哪些广告可以提供服务。+ + + + − A key concept from the science of economics is "[[utility]]": a measure of how valuable something is to an intelligent agent. Precise mathematical tools have been developed that analyze how an agent can make choices and plan, using [[decision theory]], [[decision analysis]],<ref name="Decisions theory and analysis"/> and [[applied information economics|information value theory]].<ref name="Information value theory"/> These tools include models such as [[Markov decision process]]es,<ref name="Markov decision process"/> dynamic [[decision network]]s,<ref name="Stochastic temporal models"/> [[game theory]] and [[mechanism design]].<ref name="Game theory and mechanism design"/> − A key concept from the science of economics is "utility": a measure of how valuable something is to an intelligent agent. Precise mathematical tools have been developed that analyze how an agent can make choices and plan, using decision theory, decision analysis, and information value theory. These tools include models such as Markov decision processes, dynamic decision networks, game theory and mechanism design. − 经济学中的一个关键概念是“效用” : 一种衡量某物对于一个聪明的代理人的价值的方法。运用决策理论、决策分析和信息价值理论，已经开发出精确的数学工具来分析代理人如何做出选择和计划。这些工具包括马尔可夫决策过程、动态决策网络、博弈论和机制设计等模型。 + + − === Classifiers and statistical learning methods ===+ − === Classifiers and statistical learning methods === − 分类器与统计学习方法 + + − {{Main|Classifier (mathematics)|Statistical classification|Machine learning}}+ 第1,431行：
第1,434行： + − The simplest AI applications can be divided into two types: classifiers ("if shiny then diamond") and controllers ("if shiny then pick up"). Controllers do, however, also classify conditions before inferring actions, and therefore classification forms a central part of many AI systems. [[Classifier (mathematics)|Classifiers]] are functions that use [[pattern matching]] to determine a closest match. They can be tuned according to examples, making them very attractive for use in AI. These examples are known as observations or patterns. In supervised learning, each pattern belongs to a certain predefined class. A class can be seen as a decision that has to be made. All the observations combined with their class labels are known as a data set. When a new observation is received, that observation is classified based on previous experience.<ref name="Classifiers"/> − The simplest AI applications can be divided into two types: classifiers ("if shiny then diamond") and controllers ("if shiny then pick up"). Controllers do, however, also classify conditions before inferring actions, and therefore classification forms a central part of many AI systems. Classifiers are functions that use pattern matching to determine a closest match. They can be tuned according to examples, making them very attractive for use in AI. These examples are known as observations or patterns. In supervised learning, each pattern belongs to a certain predefined class. A class can be seen as a decision that has to be made. All the observations combined with their class labels are known as a data set. When a new observation is received, that observation is classified based on previous experience. − 最简单的人工智能应用程序可以分为两类: 分类器(“ if shiny then diamond”)和控制器(“ if shiny then pick up”)。然而，控制器在推断动作之前也对条件进行分类，因此分类构成了许多人工智能系统的核心部分。分类器是使用模式匹配来确定最接近的匹配的函数。它们可以根据例子进行调整，使它们在人工智能中非常有吸引力。这些例子被称为观察或模式。在监督式学习中，每个模式都属于某个预定义的类别。一个类可以被看作是一个必须做出的决定。所有的观测结合它们的类标签被称为数据集。当接收到一个新的观察结果时，这个观察结果将根据以前的经验进行分类。 + + − A classifier can be trained in various ways; there are many statistical and [[machine learning]] approaches. The [[decision tree learning|decision tree]]<ref name="Decision tree"/> is perhaps the most widely used machine learning algorithm.{{sfn|Domingos|2015|p=88}} Other widely used classifiers are the [[Artificial neural network|neural network]],<ref name="Neural networks"/>+ − A classifier can be trained in various ways; there are many statistical and machine learning approaches. The decision tree is perhaps the most widely used machine learning algorithm. Other widely used classifiers are the neural network, − 分类器可以通过多种方式进行训练; 有许多统计学和机器学习方法。决策树可能是应用最广泛的机器学习算法。其他广泛使用的分类器是神经网络, − + − k-nearest neighbor algorithm,}}+ − 最近邻居法，开始+ − [[kernel methods]] such as the [[support vector machine]] (SVM),{{efn|SVM displaced k-nearest neighbor in the 1990s{{sfn|Domingos|2015|p=188}}}}<ref name="Kernel methods"/> − kernel methods such as the support vector machine (SVM),}}+ − 内核方法，例如支持向量机+ − [[Gaussian mixture model]],<ref name="Gaussian mixture model"/> and the extremely popular [[naive Bayes classifier]].{{efn|Naive Bayes is reportedly the "most widely used learner" at Google, due in part to its scalability.{{sfn|Domingos|2015|p=152}}}}<ref name="Naive Bayes classifier"/> Classifier performance depends greatly on the characteristics of the data to be classified, such as the dataset size, distribution of samples across classes, the dimensionality, and the level of noise. Model-based classifiers perform well if the assumed model is an extremely good fit for the actual data. Otherwise, if no matching model is available, and if accuracy (rather than speed or scalability) is the sole concern, conventional wisdom is that discriminative classifiers (especially SVM) tend to be more accurate than model-based classifiers such as "naive Bayes" on most practical data sets.<ref name="Classifier performance"/>{{sfn|Russell|Norvig|2009|loc=18.12: Learning from Examples: Summary}}+ − Gaussian mixture model, and the extremely popular naive Bayes classifier.}} Classifier performance depends greatly on the characteristics of the data to be classified, such as the dataset size, distribution of samples across classes, the dimensionality, and the level of noise. Model-based classifiers perform well if the assumed model is an extremely good fit for the actual data. Otherwise, if no matching model is available, and if accuracy (rather than speed or scalability) is the sole concern, conventional wisdom is that discriminative classifiers (especially SVM) tend to be more accurate than model-based classifiers such as "naive Bayes" on most practical data sets.+ − 高斯混合模型，以及非常流行的朴素贝叶斯分类器分类器的性能在很大程度上取决于待分类数据的特征，如数据集的大小、样本跨类别的分布、维数和噪声水平。如果假设的模型非常适合实际数据，那么基于模型的分类器表现良好。否则，如果没有匹配模型可用，而且只关心准确性(而不是速度或可伸缩性) ，传统观点认为，在大多数实际数据集上，鉴别分类器(尤其是支持向量机)往往比基于模型的分类器(如“朴素贝叶斯”)更准确。 + + + − === Artificial neural networks === − === Artificial neural networks === − 人工神经网络 + + + − {{Main|Artificial neural network|Connectionism}} + − [[File:Artificial neural network.svg|thumb|A neural network is an interconnected group of nodes, akin to the vast network of [[neuron]]s in the [[human brain]].]]+ − A neural network is an interconnected group of nodes, akin to the vast network of [[neurons in the human brain.]]+ − 神经网络是一组相互连接的节点，类似于人脑中庞大的神经元网络 + + + − Neural networks were inspired by the architecture of neurons in the human brain. A simple "neuron" ''N'' accepts input from other neurons, each of which, when activated (or "fired"), cast a weighted "vote" for or against whether neuron ''N'' should itself activate. Learning requires an algorithm to adjust these weights based on the training data; one simple algorithm (dubbed "[[Hebbian learning|fire together, wire together]]") is to increase the weight between two connected neurons when the activation of one triggers the successful activation of another. The neural network forms "concepts" that are distributed among a subnetwork of shared{{efn|Each individual neuron is likely to participate in more than one concept.}} neurons that tend to fire together; a concept meaning "leg" might be coupled with a subnetwork meaning "foot" that includes the sound for "foot". Neurons have a continuous spectrum of activation; in addition, neurons can process inputs in a nonlinear way rather than weighing straightforward votes. Modern neural networks can learn both continuous functions and, surprisingly, digital logical operations. Neural networks' early successes included predicting the stock market and (in 1995) a mostly self-driving car.{{efn|Steering for the 1995 "[[History of autonomous cars#1990s|No Hands Across America]]" required "only a few human assists".}}{{sfn|Domingos|2015|loc=Chapter 4}} In the 2010s, advances in neural networks using deep learning thrust AI into widespread public consciousness and contributed to an enormous upshift in corporate AI spending; for example, AI-related [[mergers and acquisitions|M&A]] in 2017 was over 25 times as large as in 2015.<ref>{{cite news|title=Why Deep Learning Is Suddenly Changing Your Life|url=http://fortune.com/ai-artificial-intelligence-deep-machine-learning/|accessdate=12 March 2018|work=Fortune|date=2016}}</ref><ref>{{cite news|title=Google leads in the race to dominate artificial intelligence|url=https://www.economist.com/news/business/21732125-tech-giants-are-investing-billions-transformative-technology-google-leads-race|accessdate=12 March 2018|work=The Economist|date=2017|language=en}}</ref> − Neural networks were inspired by the architecture of neurons in the human brain. A simple "neuron" N accepts input from other neurons, each of which, when activated (or "fired"), cast a weighted "vote" for or against whether neuron N should itself activate. Learning requires an algorithm to adjust these weights based on the training data; one simple algorithm (dubbed "fire together, wire together") is to increase the weight between two connected neurons when the activation of one triggers the successful activation of another. The neural network forms "concepts" that are distributed among a subnetwork of shared neurons that tend to fire together; a concept meaning "leg" might be coupled with a subnetwork meaning "foot" that includes the sound for "foot". Neurons have a continuous spectrum of activation; in addition, neurons can process inputs in a nonlinear way rather than weighing straightforward votes. Modern neural networks can learn both continuous functions and, surprisingly, digital logical operations. Neural networks' early successes included predicting the stock market and (in 1995) a mostly self-driving car. In the 2010s, advances in neural networks using deep learning thrust AI into widespread public consciousness and contributed to an enormous upshift in corporate AI spending; for example, AI-related M&A in 2017 was over 25 times as large as in 2015. − 神经网络的灵感来自于人脑中神经元的结构。一个简单的“神经元” n 接受来自其他神经元的输入，每个神经元在被激活(或“被激活”)时，对神经元 n 本身是否应该被激活投下加权的“选票”。学习需要一个根据训练数据调整这些权重的算法; 一个简单的算法(称为“一起发射，一起连线”)是在一个神经元的激活触发另一个神经元的成功激活时，增加两个连接神经元之间的权重。神经网络形成”概念” ，这些概念分布在一个共享的神经元子网络中，这些神经元往往一起发射信号; 一个意为”腿”的概念可能与一个意为”脚”的子网络相结合，后者包括”脚”的发音。神经元有一个连续的激活光谱; 此外，神经元可以以非线性的方式处理输入，而不是权衡简单的投票。现代神经网络可以学习连续函数和令人惊讶的数字逻辑操作。神经网络的早期成功包括预测股票市场和(1995年)自动驾驶汽车。2010年代，使用深度学习的神经网络的进步将人工智能推向了广泛的公众意识，并促成了企业人工智能支出的巨大上升; 例如，2017年与人工智能相关的并购交易规模是2015年的25倍多。+ + + + + − The study of non-learning [[artificial neural network]]s<ref name="Neural networks"/> began in the decade before the field of AI research was founded, in the work of [[Walter Pitts]] and [[Warren McCullouch]]. [[Frank Rosenblatt]] invented the [[perceptron]], a learning network with a single layer, similar to the old concept of [[linear regression]]. Early pioneers also include [[Alexey Grigorevich Ivakhnenko]], [[Teuvo Kohonen]], [[Stephen Grossberg]], [[Kunihiko Fukushima]], [[Christoph von der Malsburg]], David Willshaw, [[Shun-Ichi Amari]], [[Bernard Widrow]], [[John Hopfield]], [[Eduardo R. Caianiello]], and others{{citation needed|date=July 2019}}.+ − The study of non-learning artificial neural networks began in the decade before the field of AI research was founded, in the work of Walter Pitts and Warren McCullouch. Frank Rosenblatt invented the perceptron, a learning network with a single layer, similar to the old concept of linear regression. Early pioneers also include Alexey Grigorevich Ivakhnenko, Teuvo Kohonen, Stephen Grossberg, Kunihiko Fukushima, Christoph von der Malsburg, David Willshaw, Shun-Ichi Amari, Bernard Widrow, John Hopfield, Eduardo R. Caianiello, and others.+ − 非学习型人工神经网络的研究开始于人工智能研究领域成立之前的十年，由沃尔特 · 皮茨和沃伦 · 麦克卢奇共同完成。发明了感知器，一个单层的学习网络，类似于线性回归的旧概念。早期的先驱者还包括 Alexey Grigorevich Ivakhnenko，Teuvo Kohonen，Stephen Grossberg，Kunihiko Fukushima，Christoph von der Malsburg，David Willshaw，Shun-Ichi Amari，Bernard Widrow，John Hopfield，Eduardo r. Caianiello 等。 第1,515行：
第1,520行： − The main categories of networks are acyclic or [[feedforward neural network]]s (where the signal passes in only one direction) and [[recurrent neural network]]s (which allow feedback and short-term memories of previous input events). Among the most popular feedforward networks are [[perceptron]]s, [[multi-layer perceptron]]s and [[radial basis network]]s.<ref name="Feedforward neural networks"/> Neural networks can be applied to the problem of [[intelligent control]] (for robotics) or [[machine learning|learning]], using such techniques as [[Hebbian learning]] ("fire together, wire together"), [[GMDH]] or [[competitive learning]].<ref name="Learning in neural networks"/> − The main categories of networks are acyclic or feedforward neural networks (where the signal passes in only one direction) and recurrent neural networks (which allow feedback and short-term memories of previous input events). Among the most popular feedforward networks are perceptrons, multi-layer perceptrons and radial basis networks. Neural networks can be applied to the problem of intelligent control (for robotics) or learning, using such techniques as Hebbian learning ("fire together, wire together"), GMDH or competitive learning.+ − 网络的主要类别是非循环或前馈神经网络(信号只向一个方向传递)和循环神经网络(允许对以前的输入事件进行反馈和短期记忆)。其中最常用的前馈网络有感知器、多层感知器和径向基网络。神经网络可以应用于智能控制(机器人)或学习的问题，使用赫布学习(“火在一起，线在一起”) ，GMDH 或竞争学习等技术。+ + + + − Today, neural networks are often trained by the [[backpropagation]] algorithm, which had been around since 1970 as the reverse mode of [[automatic differentiation]] published by [[Seppo Linnainmaa]],<ref name="lin1970">[[Seppo Linnainmaa]] (1970). The representation of the cumulative rounding error of an algorithm as a Taylor expansion of the local rounding errors. Master's Thesis (in Finnish), Univ. Helsinki, 6–7.</ref><ref name="grie2012">Griewank, Andreas (2012). Who Invented the Reverse Mode of Differentiation?. Optimization Stories, Documenta Matematica, Extra Volume ISMP (2012), 389–400.</ref> and was introduced to neural networks by [[Paul Werbos]].<ref name="WERBOS1974">[[Paul Werbos]], "Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences", ''PhD thesis, Harvard University'', 1974.</ref><ref name="werbos1982">[[Paul Werbos]] (1982). Applications of advances in nonlinear sensitivity analysis. In System modeling and optimization (pp. 762–770). Springer Berlin Heidelberg. [http://werbos.com/Neural/SensitivityIFIPSeptember1981.pdf Online] {{webarchive|url=https://web.archive.org/web/20160414055503/http://werbos.com/Neural/SensitivityIFIPSeptember1981.pdf |date=14 April 2016 }}</ref><ref name="Backpropagation"/>+ − Today, neural networks are often trained by the backpropagation algorithm, which had been around since 1970 as the reverse mode of automatic differentiation published by Seppo Linnainmaa, and was introduced to neural networks by Paul Werbos. − 今天，神经网络经常用反向传播算法来训练，这种算法从1970年开始就作为 Seppo Linnainmaa 发表的自动微分的反向模式出现，Paul Werbos 将其引入神经网络。+ + + − + − Hierarchical temporal memory is an approach that models some of the structural and algorithmic properties of the neocortex.+ − 分级暂存记忆是一种模拟大脑新皮层结构和算法特性的方法。+ + − To summarize, most neural networks use some form of [[gradient descent]] on a hand-created neural topology. However, some research groups, such as [[Uber]], argue that simple [[neuroevolution]] to mutate new neural network topologies and weights may be competitive with sophisticated gradient descent approaches{{citation needed|date=July 2019}}. One advantage of neuroevolution is that it may be less prone to get caught in "dead ends".<ref>{{cite news|title=Artificial intelligence can 'evolve' to solve problems|url=http://www.sciencemag.org/news/2018/01/artificial-intelligence-can-evolve-solve-problems|accessdate=7 February 2018|work=Science {{!}} AAAS|date=10 January 2018|language=en}}</ref>+ − To summarize, most neural networks use some form of gradient descent on a hand-created neural topology. However, some research groups, such as Uber, argue that simple neuroevolution to mutate new neural network topologies and weights may be competitive with sophisticated gradient descent approaches. One advantage of neuroevolution is that it may be less prone to get caught in "dead ends".+ − 总之，大多数神经网络在手工创建的神经拓扑结构上使用某种形式的梯度下降法。然而，一些研究小组，比如 Uber，认为通过简单的神经进化来改变新的神经网络拓扑结构和重量可能比复杂的梯度下降法 / 神经网络方法更有竞争力。神经进化的一个优势是，它可能不太容易陷入“死胡同”。+ + + − ==== Deep feedforward neural networks ====+ − ==== Deep feedforward neural networks ==== − 深层前馈神经网络 + + + − {{Main|Deep learning}}+ + + + − + − Deep learning is any artificial neural network that can learn a long chain of causal links. For example, a feedforward network with six hidden layers can learn a seven-link causal chain (six hidden layers + output layer) and has a "credit assignment path" (CAP) depth of seven. Many deep learning systems need to be able to learn chains ten or more causal links in length.+ − 深度学习是任何人工神经网络，可以学习一个长链的因果关系。例如，一个具有六个隐藏层的前馈网络可以学习七个链接的因果链(六个隐藏层 + 输出层) ，并且具有七个“信用分配路径”(CAP)深度。许多深度学习系统需要能够学习链的长度十个或更多的因果关系。+ + − According to one overview,<ref name="scholarpedia">{{cite journal | last1 = Schmidhuber | first1 = Jürgen | authorlink = Jürgen Schmidhuber | year = 2015 | title = Deep Learning | journal = Scholarpedia | volume = 10 | issue = 11 | page = 32832 | doi = 10.4249/scholarpedia.32832 | df = dmy-all | bibcode = 2015SchpJ..1032832S | doi-access = free }}</ref> the expression "Deep Learning" was introduced to the [[machine learning]] community by [[Rina Dechter]] in 1986<ref name="dechter1986">[[Rina Dechter]] (1986). Learning while searching in constraint-satisfaction problems. University of California, Computer Science Department, Cognitive Systems Laboratory.[https://www.researchgate.net/publication/221605378_Learning_While_Searching_in_Constraint-Satisfaction-Problems Online] {{webarchive|url=https://web.archive.org/web/20160419054654/https://www.researchgate.net/publication/221605378_Learning_While_Searching_in_Constraint-Satisfaction-Problems |date=19 April 2016 }}</ref> and gained traction after+ − According to one overview, the expression "Deep Learning" was introduced to the machine learning community by Rina Dechter in 1986 and gained traction after+ − 根据一篇综述，“深度学习”这个表达在1986年被 Rina Dechter 引入到机器学习社区，并在之后获得了关注+ − Igor Aizenberg and colleagues introduced it to [[artificial neural network]]s in 2000.<ref name="aizenberg2000">Igor Aizenberg, Naum N. Aizenberg, Joos P.L. Vandewalle (2000). Multi-Valued and Universal Binary Neurons: Theory, Learning and Applications. Springer Science & Business Media.</ref> The first functional Deep Learning networks were published by [[Alexey Grigorevich Ivakhnenko]] and V. G. Lapa in 1965.<ref>{{Cite book|title=Cybernetic Predicting Devices|last=Ivakhnenko|first=Alexey|publisher=Naukova Dumka|year=1965|isbn=|location=Kiev|pages=}}</ref>{{page needed|date=December 2016}} These networks are trained one layer at a time. Ivakhnenko's 1971 paper<ref name="ivak1971">{{Cite journal |doi = 10.1109/TSMC.1971.4308320|title = Polynomial Theory of Complex Systems|journal = IEEE Transactions on Systems, Man, and Cybernetics|issue = 4|pages = 364–378|year = 1971|last1 = Ivakhnenko|first1 = A. G.|url = https://semanticscholar.org/paper/b7efb6b6f7e9ffa017e970a098665f76d4dfeca2}}</ref> describes the learning of a deep feedforward multilayer perceptron with eight layers, already much deeper than many later networks. In 2006, a publication by [[Geoffrey Hinton]] and Ruslan Salakhutdinov introduced another way of pre-training many-layered [[feedforward neural network]]s (FNNs) one layer at a time, treating each layer in turn as an [[unsupervised learning|unsupervised]] [[restricted Boltzmann machine]], then using [[supervised learning|supervised]] [[backpropagation]] for fine-tuning.{{sfn|Hinton|2007}} Similar to shallow artificial neural networks, deep neural networks can model complex non-linear relationships. Over the last few years, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks that contain many layers of non-linear hidden units and a very large output layer.<ref>{{cite web|last1=Research|first1=AI|title=Deep Neural Networks for Acoustic Modeling in Speech Recognition|url=http://airesearch.com/ai-research-papers/deep-neural-networks-for-acoustic-modeling-in-speech-recognition/|website=airesearch.com|accessdate=23 October 2015|date=23 October 2015}}</ref> − Igor Aizenberg and colleagues introduced it to artificial neural networks in 2000. The first functional Deep Learning networks were published by Alexey Grigorevich Ivakhnenko and V. G. Lapa in 1965. These networks are trained one layer at a time. Ivakhnenko's 1971 paper describes the learning of a deep feedforward multilayer perceptron with eight layers, already much deeper than many later networks. In 2006, a publication by Geoffrey Hinton and Ruslan Salakhutdinov introduced another way of pre-training many-layered feedforward neural networks (FNNs) one layer at a time, treating each layer in turn as an unsupervised restricted Boltzmann machine, then using supervised backpropagation for fine-tuning. Similar to shallow artificial neural networks, deep neural networks can model complex non-linear relationships. Over the last few years, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks that contain many layers of non-linear hidden units and a very large output layer. − 2000年，Igor Aizenberg 和他的同事将其引入人工神经网络。第一个功能性的深度学习网络是由 Alexey Grigorevich Ivakhnenko 和 v. g. Lapa 在1965年发表的。这些网络每次只训练一层。在1971年的论文中描述了一个8层的深度前馈多层感知机网络的学习过程，这个网络已经比许多后来的网络要深得多了。2006年，Geoffrey Hinton 和 Ruslan Salakhutdinov 的出版物介绍了另一种预训练多层前向神经网络(FNNs)的方法，一次训练一层，将每一层依次视为无监督的受限玻尔兹曼机，然后使用有监督的反向传播进行微调。与浅层人工神经网络类似，深层神经网络可以模拟复杂的非线性关系。在过去的几年里，机器学习算法和计算机硬件的进步已经导致了更有效的方法训练深层神经网络，其中包含许多层非线性隐藏单元和一个非常大的输出层。+ + + + − Deep learning often uses [[convolutional neural network]]s (CNNs), whose origins can be traced back to the [[Neocognitron]] introduced by [[Kunihiko Fukushima]] in 1980.<ref name="FUKU1980">{{cite journal | last1 = Fukushima | first1 = K. | year = 1980 | title = Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position | url = | journal = Biological Cybernetics | volume = 36 | issue = 4| pages = 193–202 | doi=10.1007/bf00344251 | pmid=7370364}}</ref> In 1989, [[Yann LeCun]] and colleagues applied [[backpropagation]] to such an architecture. In the early 2000s, in an industrial application, CNNs already processed an estimated 10% to 20% of all the checks written in the US.<ref name="lecun2016slides">[[Yann LeCun]] (2016). Slides on Deep Learning [https://indico.cern.ch/event/510372/ Online] {{webarchive|url=https://web.archive.org/web/20160423021403/https://indico.cern.ch/event/510372/ |date=23 April 2016 }}</ref>+ − Deep learning often uses convolutional neural networks (CNNs), whose origins can be traced back to the Neocognitron introduced by Kunihiko Fukushima in 1980. In 1989, Yann LeCun and colleagues applied backpropagation to such an architecture. In the early 2000s, in an industrial application, CNNs already processed an estimated 10% to 20% of all the checks written in the US.+ − 深度学习通常使用卷积神经网络(CNNs) ，其起源可以追溯到1980年由福岛邦彦(Kunihiko Fukushima)引进的神经网络。1989年，Yann LeCun 和他的同事将反向传播应用于这样的架构。在21世纪初，在一项工业应用中，cnn 已经处理了美国所有签发支票的10% 到20% 。 − Since 2011, fast implementations of CNNs on GPUs have − Since 2011, fast implementations of CNNs on GPUs have+ − 自2011年以来，在 gpu 上快速实现的 cnn+ − won many visual pattern recognition competitions.<ref name="schmidhuber2015"/>+ − won many visual pattern recognition competitions. − 赢得了许多视觉模式识别比赛。 + + + − CNNs with 12 convolutional layers were used in conjunction with [[reinforcement learning]] by Deepmind's "[[AlphaGo]] Lee", the program that beat a top [[Go (game)|Go]] champion in 2016.<ref name="Nature2017">{{cite journal |first1=David |last1=Silver|author-link1=David Silver (programmer)|first2= Julian|last2= Schrittwieser|first3= Karen|last3= Simonyan|first4= Ioannis|last4= Antonoglou|first5= Aja|last5= Huang|author-link5=Aja Huang|first6=Arthur|last6= Guez|first7= Thomas|last7= Hubert|first8= Lucas|last8= Baker|first9= Matthew|last9= Lai|first10= Adrian|last10= Bolton|first11= Yutian|last11= Chen|author-link11=Chen Yutian|first12= Timothy|last12= Lillicrap|first13=Hui|last13= Fan|author-link13=Fan Hui|first14= Laurent|last14= Sifre|first15= George van den|last15= Driessche|first16= Thore|last16= Graepel|first17= Demis|last17= Hassabis |author-link17=Demis Hassabis|title=Mastering the game of Go without human knowledge|journal=[[Nature (journal)|Nature]]|issn= 0028-0836|pages=354–359|volume =550|issue =7676|doi =10.1038/nature24270|pmid=29052630|date=19 October 2017|quote=AlphaGo Lee... 12 convolutional layers|bibcode=2017Natur.550..354S|url=http://discovery.ucl.ac.uk/10045895/1/agz_unformatted_nature.pdf}}{{closed access}}</ref> − CNNs with 12 convolutional layers were used in conjunction with reinforcement learning by Deepmind's "AlphaGo Lee", the program that beat a top Go champion in 2016. − 带有12个卷积层的 CNNs 被 Deepmind 的“阿尔法狗李”与强化学习一起使用，这个程序在2016年击败了一个围棋冠军。 + − ==== Deep recurrent neural networks ====+ − ==== Deep recurrent neural networks ====+ − 深层递归神经网络 + + + − + + + + + + − Early on, deep learning was also applied to sequence learning with [[recurrent neural network]]s (RNNs)<ref name="Recurrent neural networks"/> which are in theory Turing complete<ref>{{cite journal|last1=Hyötyniemi|first1=Heikki|title=Turing machines are recurrent neural networks|journal=Proceedings of STeP '96/Publications of the Finnish Artificial Intelligence Society|pages=13–24|date=1996}}</ref> and can run arbitrary programs to process arbitrary sequences of inputs. The depth of an RNN is unlimited and depends on the length of its input sequence; thus, an RNN is an example of deep learning.<ref name="schmidhuber2015"/> RNNs can be trained by [[gradient descent]]<ref>P. J. Werbos. Generalization of backpropagation with application to a recurrent gas market model" ''Neural Networks'' 1, 1988.</ref><ref>A. J. Robinson and F. Fallside. The utility driven dynamic error propagation network. Technical Report CUED/F-INFENG/TR.1, Cambridge University Engineering Department, 1987.</ref><ref>R. J. Williams and D. Zipser. Gradient-based learning algorithms for recurrent networks and their computational complexity. In Back-propagation: Theory, Architectures and Applications. Hillsdale, NJ: Erlbaum, 1994.</ref> but suffer from the [[vanishing gradient problem]].<ref name="goodfellow2016"/><ref name="hochreiter1991">[[Sepp Hochreiter]] (1991), [http://people.idsia.ch/~juergen/SeppHochreiter1991ThesisAdvisorSchmidhuber.pdf Untersuchungen zu dynamischen neuronalen Netzen] {{webarchive|url=https://web.archive.org/web/20150306075401/http://people.idsia.ch/~juergen/SeppHochreiter1991ThesisAdvisorSchmidhuber.pdf |date=6 March 2015 }}, Diploma thesis. Institut f. Informatik, Technische Univ. Munich. Advisor: J. Schmidhuber.</ref> In 1992, it was shown that unsupervised pre-training of a stack of [[recurrent neural network]]s can speed up subsequent supervised learning of deep sequential problems.<ref name="SCHMID1992">{{cite journal | last1 = Schmidhuber | first1 = J. | year = 1992 | title = Learning complex, extended sequences using the principle of history compression | url = | journal = Neural Computation | volume = 4 | issue = 2| pages = 234–242 | doi=10.1162/neco.1992.4.2.234| citeseerx = 10.1.1.49.3934}}</ref> − Early on, deep learning was also applied to sequence learning with recurrent neural networks (RNNs) and can run arbitrary programs to process arbitrary sequences of inputs. The depth of an RNN is unlimited and depends on the length of its input sequence; thus, an RNN is an example of deep learning. but suffer from the vanishing gradient problem. In 1992, it was shown that unsupervised pre-training of a stack of recurrent neural networks can speed up subsequent supervised learning of deep sequential problems.+ − 早期，深度学习也被用于循环神经网络(RNNs)的序列学习，可以运行任意程序来处理任意的输入序列。一个神经网络的深度是无限的，并取决于其输入序列的长度; 因此，一个神经网络是一个深度学习的例子。但却要忍受梯度消失的问题。1992年，研究表明，无监督的预训练一堆循环神经网络可以加速后续的深度连续问题的监督式学习。+ + + − Numerous researchers now use variants of a deep learning recurrent NN called the [[long short-term memory]] (LSTM) network published by Hochreiter & Schmidhuber in 1997.<ref name=lstm>[[Sepp Hochreiter|Hochreiter, Sepp]]; and [[Jürgen Schmidhuber|Schmidhuber, Jürgen]]; ''Long Short-Term Memory'', Neural Computation, 9(8):1735–1780, 1997</ref> LSTM is often trained by [[Connectionist temporal classification|Connectionist Temporal Classification]] (CTC).<ref name="graves2006">Alex Graves, Santiago Fernandez, Faustino Gomez, and [[Jürgen Schmidhuber]] (2006). Connectionist temporal classification: Labelling unsegmented sequence data with recurrent neural nets. Proceedings of ICML'06, pp. 369–376.</ref> At Google, Microsoft and Baidu this approach has revolutionized [[speech recognition]].<ref name="hannun2014">{{cite arXiv+ − Numerous researchers now use variants of a deep learning recurrent NN called the long short-term memory (LSTM) network published by Hochreiter & Schmidhuber in 1997. LSTM is often trained by Connectionist Temporal Classification (CTC). At Google, Microsoft and Baidu this approach has revolutionized speech recognition.<ref name="hannun2014">{{cite arXiv+ − 许多研究人员现在使用一种名为长期短期记忆(LSTM)网络的深度学习循环神经网络的变种，该网络由 Hochreiter 和 Schmidhuber 于1997年发表。Lstm 常用连接主义时态分类(CTC)进行训练。在谷歌，微软和百度这种方法已经彻底改变了语音识别 − |last1=Hannun |first1=Awni − |last1=Hannun |first1=Awni+ − 1 Awni+ − |last2=Case |first2=Carl+ − |last2=Case |first2=Carl+ − 2 Case | first2 Carl − |last3=Casper |first3=Jared − + − 3 / last 3 / Casper | first3 / Jared+ − |last4=Catanzaro |first4=Bryan+ − |last4=Catanzaro |first4=Bryan − 4 Catanzaro | first4 Bryan+ − |last5=Diamos |first5=Greg+ − |last5=Diamos |first5=Greg+ − 5 Diamos | first5 Greg+ − |last6=Elsen |first6=Erich − |last6=Elsen |first6=Erich − 6 Elsen | first6 Erich+ − |last7=Prenger |first7=Ryan+ − |last7=Prenger |first7=Ryan+ − 7 Prenger | first7 Ryan − |last8=Satheesh |first8=Sanjeev − |last8=Satheesh |first8=Sanjeev − |last8=Satheesh |first8=Sanjeev − |last9=Sengupta |first9=Shubho+ − |last9=Sengupta |first9=Shubho+ − 9 Sengupta | first9 Shubho+ − |last10=Coates |first10=Adam − |last10=Coates |first10=Adam − 2010年10月10日+ − |last11=Ng |first11=Andrew Y. |author11-link=Andrew Ng+ − |last11=Ng |first11=Andrew Y. |author11-link=Andrew Ng+ − 11 Ng | first11 Andrew y.| author11-link+ − |year=2014 − |year=2014 − 2014年+ − |title=Deep Speech: Scaling up end-to-end speech recognition+ − |title=Deep Speech: Scaling up end-to-end speech recognition+ − | 标题深度语音: 扩展端到端语音识别 − |eprint=1412.5567+ − |eprint=1412.5567+ − 1412.5567+ − |class=cs.CL − |class=cs.CL − | cs.CL 类 − }}</ref><ref name="sak2014">Hasim Sak and Andrew Senior and Francoise Beaufays (2014). Long Short-Term Memory recurrent neural network architectures for large scale acoustic modeling. Proceedings of Interspeech 2014.</ref><ref name="liwu2015">{{cite arXiv+ − }}</ref><ref name="liwu2015">{{cite arXiv+ − }}</ref><ref name="liwu2015">{{cite arXiv+ − |last1=Li |first1=Xiangang+ − |last1=Li |first1=Xiangang − 1 Li | first1 Xiangang − |last2=Wu |first2=Xihong+ − |last2=Wu |first2=Xihong+ − |last2=Wu |first2=Xihong+ − + − |year=2015+ − 2015年+ − |title=Constructing Long Short-Term Memory based Deep Recurrent Neural Networks for Large Vocabulary Speech Recognition+ − |title=Constructing Long Short-Term Memory based Deep Recurrent Neural Networks for Large Vocabulary Speech Recognition+ − 基于长短期记忆的大词汇量语音识别深层递归神经网络的构建 − |eprint=1410.4281 − |eprint=1410.4281+ − 1410.4281+ − |class=cs.CL+ − |class=cs.CL − + − }}</ref> For example, in 2015, Google's speech recognition experienced a dramatic performance jump of 49% through CTC-trained LSTM, which is now available through [[Google Voice]] to billions of smartphone users.<ref name="sak2015">Haşim Sak, Andrew Senior, Kanishka Rao, Françoise Beaufays and Johan Schalkwyk (September 2015): [http://googleresearch.blogspot.ch/2015/09/google-voice-search-faster-and-more.html Google voice search: faster and more accurate.] {{webarchive|url=https://web.archive.org/web/20160309191532/http://googleresearch.blogspot.ch/2015/09/google-voice-search-faster-and-more.html |date=9 March 2016 }}</ref> Google also used LSTM to improve machine translation,<ref name="sutskever2014">{{cite arXiv+ − }}</ref> For example, in 2015, Google's speech recognition experienced a dramatic performance jump of 49% through CTC-trained LSTM, which is now available through Google Voice to billions of smartphone users. Google also used LSTM to improve machine translation,<ref name="sutskever2014">{{cite arXiv+ − } / ref 例如，在2015年，Google 的语音识别通过 ctc 训练的 LSTM 经历了49% 的戏剧性增长，现在数十亿的智能手机用户可以通过 Google Voice 使用该技术。谷歌还利用 LSTM 改进机器翻译，ref name"sutskever2014"{ cite arXiv − |last1=Sutskever |first1=Ilya − |last1=Sutskever |first1=Ilya+ − 1 / 01 / Ilya+ − |last2=Vinyals |first2=Oriol+ − |last2=Vinyals |first2=Oriol+ − 2 Vinyals | first2 Oriol+ − |last3=Le |first3=Quoc V.+ − |last3=Le |first3=Quoc V. − 最后3天 | 最初3天。 − |year=2014 − |year=2014 − 2014年+ − |title=Sequence to Sequence Learning with Neural Networks+ − |title=Sequence to Sequence Learning with Neural Networks+ − + − |eprint=1409.3215+ − |eprint=1409.3215+ − 1409.3215 − |class=cs.CL − |class=cs.CL+ − | cs.CL 类+ − }}</ref> Language Modeling<ref name="vinyals2016">{{cite arXiv+ − }}</ref> Language Modeling<ref name="vinyals2016">{{cite arXiv+ − 2016"{ cite arXiv+ − |last1=Jozefowicz |first1=Rafal+ − |last1=Jozefowicz |first1=Rafal − 1 Rafal | last 1 Jozefowicz | first1 Rafal+ − |last2=Vinyals |first2=Oriol+ − |last2=Vinyals |first2=Oriol+ − 2 Vinyals | first2 Oriol − |last3=Schuster |first3=Mike+ − |last3=Schuster |first3=Mike+ − 最后3舒斯特最初3迈克+ − |last4=Shazeer |first4=Noam+ − |last4=Shazeer |first4=Noam − | last 4 Shazeer | first4 Noam − |last5=Wu |first5=Yonghui+ − |last5=Wu |first5=Yonghui+ − 5永辉+ − |year=2016 − |year=2016+ − 2016年+ − |title=Exploring the Limits of Language Modeling+ − |title=Exploring the Limits of Language Modeling+ − 探索语言建模的极限 − |eprint=1602.02410 − |eprint=1602.02410+ − 1602.02410+ − |class=cs.CL+ − |class=cs.CL+ − | cs.CL 类+ − }}</ref> and Multilingual Language Processing.<ref name="gillick2015">{{cite arXiv+ − }}</ref> and Multilingual Language Processing.<ref name="gillick2015">{{cite arXiv+ − 2015"{ cite arXiv+ − |last1=Gillick |first1=Dan+ − |last1=Gillick |first1=Dan+ − 最后1吉利克最初1丹+ − |last2=Brunk |first2=Cliff+ − |last2=Brunk |first2=Cliff − 2 | last 2 Brunk | first2 Cliff − |last3=Vinyals |first3=Oriol+ − |last3=Vinyals |first3=Oriol+ − 3 Vinyals | first3 Oriol+ − |last4=Subramanya |first4=Amarnag+ − |last4=Subramanya |first4=Amarnag − 4 Subramanya | first4 Amarnag − |year=2015 − |year=2015 − 2015年 − |title=Multilingual Language Processing From Bytes − |title=Multilingual Language Processing From Bytes+ − 字节多语言处理+ − |eprint=1512.00103+ − |eprint=1512.00103 − 1512.00103 − |class=cs.CL − |class=cs.CL − + − }}</ref> LSTM combined with CNNs also improved automatic image captioning<ref name="vinyals2015">{{cite arXiv+ − }}</ref> LSTM combined with CNNs also improved automatic image captioning<ref name="vinyals2015">{{cite arXiv+ − {{{ cite arXiv，} / ref LSTM 结合 CNNs 也改进了自动图像字幕 ref name"vinyals2015"+ − |last1=Vinyals |first1=Oriol+ − |last1=Vinyals |first1=Oriol+ − 1欧力欧+ − |last2=Toshev |first2=Alexander+ − |last2=Toshev |first2=Alexander+ − 2 Toshev | first2 Alexander+ − |last3=Bengio |first3=Samy − |last3=Bengio |first3=Samy − 3 Bengio | first3 Samy+ − |last4=Erhan |first4=Dumitru+ − |last4=Erhan |first4=Dumitru+ − |last4=Erhan |first4=Dumitru − |year=2015 − |year=2015 − 2015年 − |title=Show and Tell: A Neural Image Caption Generator+ − |title=Show and Tell: A Neural Image Caption Generator+ − 显示与讲述: 一个神经图像标题生成器+ − |eprint=1411.4555+ − |eprint=1411.4555 − 1411.4555 − |class=cs.CV+ − |class=cs.CV+ − | cs.CV+ − }}</ref> and a plethora of other applications. − }}</ref> and a plethora of other applications.+ − } / ref 和大量其他应用程序。+ + + + − + + + − === Evaluating progress ===+ − 评估进度+ − {{Further|Progress in artificial intelligence|Competitions and prizes in artificial intelligence}} + + − AI, like electricity or the steam engine, is a general purpose technology. There is no consensus on how to characterize which tasks AI tends to excel at.<ref>{{cite news|last1=Brynjolfsson|first1=Erik|last2=Mitchell|first2=Tom|title=What can machine learning do? Workforce implications|url=http://science.sciencemag.org/content/358/6370/1530|accessdate=7 May 2018|work=Science|date=22 December 2017|pages=1530–1534|language=en|doi=10.1126/science.aap8062|bibcode=2017Sci...358.1530B}}</ref> While projects such as [[AlphaZero]] have succeeded in generating their own knowledge from scratch, many other machine learning projects require large training datasets.<ref>{{cite news|last1=Sample|first1=Ian|title='It's able to create knowledge itself': Google unveils AI that learns on its own|url=https://www.theguardian.com/science/2017/oct/18/its-able-to-create-knowledge-itself-google-unveils-ai-learns-all-on-its-own|accessdate=7 May 2018|work=the Guardian|date=18 October 2017|language=en}}</ref><ref>{{cite news|title=The AI revolution in science|url=http://www.sciencemag.org/news/2017/07/ai-revolution-science|accessdate=7 May 2018|work=Science {{!}} AAAS|date=5 July 2017|language=en}}</ref> Researcher [[Andrew Ng]] has suggested, as a "highly imperfect rule of thumb", that "almost anything a typical human can do with less than one second of mental thought, we can probably now or in the near future automate using AI."<ref>{{cite news|title=Will your job still exist in 10 years when the robots arrive?|url=http://www.scmp.com/tech/innovation/article/2098164/robots-are-coming-here-are-some-jobs-wont-exist-10-years|accessdate=7 May 2018|work=[[South China Morning Post]]|date=2017|language=en}}</ref> [[Moravec's paradox]] suggests that AI lags humans at many tasks that the human brain has specifically evolved to perform well.<ref name="The Economist"/>+ + − AI, like electricity or the steam engine, is a general purpose technology. There is no consensus on how to characterize which tasks AI tends to excel at. While projects such as AlphaZero have succeeded in generating their own knowledge from scratch, many other machine learning projects require large training datasets. Researcher Andrew Ng has suggested, as a "highly imperfect rule of thumb", that "almost anything a typical human can do with less than one second of mental thought, we can probably now or in the near future automate using AI." Moravec's paradox suggests that AI lags humans at many tasks that the human brain has specifically evolved to perform well.+ − 人工智能就像电或蒸汽机一样，是一种通用技术。关于如何描述 AI 倾向于擅长的任务，目前还没有共识。虽然像 AlphaZero 这样的项目已经成功地从零开始生成了自己的知识，但是许多其他的机器学习项目需要大量的训练数据集。研究人员 Andrew Ng 认为，作为一个“极不完美的经验法则” ，“几乎任何一个典型的人类只需要不到一秒钟的思维就能做到的事情，我们现在或者在不久的将来都可以使用人工智能自动化。”莫拉维克的悖论表明，人工智能在许多人类大脑专门进化出来的、能够很好完成的任务上落后于人类。+ + + − Games provide a well-publicized benchmark for assessing rates of progress. [[AlphaGo]] around 2016 brought the era of classical board-game benchmarks to a close. Games of imperfect knowledge provide new challenges to AI in the area of [[game theory]].<ref>{{cite news|last1=Borowiec|first1=Tracey Lien, Steven|title=AlphaGo beats human Go champ in milestone for artificial intelligence|url=https://www.latimes.com/world/asia/la-fg-korea-alphago-20160312-story.html|accessdate=7 May 2018|work=latimes.com|date=2016}}</ref><ref>{{cite news|last1=Brown|first1=Noam|last2=Sandholm|first2=Tuomas|title=Superhuman AI for heads-up no-limit poker: Libratus beats top professionals|url=http://science.sciencemag.org/content/359/6374/418|accessdate=7 May 2018|work=Science|date=26 January 2018|pages=418–424|language=en|doi=10.1126/science.aao1733}}</ref> [[Esports|E-sports]] such as [[StarCraft]] continue to provide additional public benchmarks.<ref>{{cite journal|last1=Ontanon|first1=Santiago|last2=Synnaeve|first2=Gabriel|last3=Uriarte|first3=Alberto|last4=Richoux|first4=Florian|last5=Churchill|first5=David|last6=Preuss|first6=Mike|title=A Survey of Real-Time Strategy Game AI Research and Competition in StarCraft|journal=IEEE Transactions on Computational Intelligence and AI in Games|date=December 2013|volume=5|issue=4|pages=293–311|doi=10.1109/TCIAIG.2013.2286295|citeseerx=10.1.1.406.2524}}</ref><ref>{{cite news|title=Facebook Quietly Enters StarCraft War for AI Bots, and Loses|url=https://www.wired.com/story/facebook-quietly-enters-starcraft-war-for-ai-bots-and-loses/|accessdate=7 May 2018|work=WIRED|date=2017}}</ref> There are many competitions and prizes, such as the [[ImageNet|Imagenet Challenge]], to promote research in artificial intelligence. The most common areas of competition include general machine intelligence, conversational behavior, data-mining, [[autonomous car|robotic cars]], and robot soccer as well as conventional games.<ref>{{Cite web|url=http://image-net.org/challenges/LSVRC/2017/|title=ILSVRC2017|website=image-net.org|language=en|access-date=2018-11-06}}</ref> − Games provide a well-publicized benchmark for assessing rates of progress. AlphaGo around 2016 brought the era of classical board-game benchmarks to a close. Games of imperfect knowledge provide new challenges to AI in the area of game theory. E-sports such as StarCraft continue to provide additional public benchmarks. There are many competitions and prizes, such as the Imagenet Challenge, to promote research in artificial intelligence. The most common areas of competition include general machine intelligence, conversational behavior, data-mining, robotic cars, and robot soccer as well as conventional games. − 奥运会为评估进步率提供了一个广为宣传的基准。2016年左右，AlphaGo 结束了传统棋类基准的时代。不完全知识的博弈为人工智能在博弈论领域提供了新的挑战。星际争霸等电子竞技继续提供额外的公众基准。有许多比赛和奖项，如 Imagenet 挑战赛，以促进人工智能的研究。最常见的竞争领域包括一般机器智能、会话行为、数据挖掘、机器人汽车、机器人足球以及传统游戏。 + + + − + − The "imitation game" (an interpretation of the 1950 Turing test that assesses whether a computer can imitate a human) is nowadays considered too exploitable to be a meaningful benchmark. A derivative of the Turing test is the Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA). As the name implies, this helps to determine that a user is an actual person and not a computer posing as a human. In contrast to the standard Turing test, CAPTCHA is administered by a machine and targeted to a human as opposed to being administered by a human and targeted to a machine. A computer asks a user to complete a simple test then generates a grade for that test. Computers are unable to solve the problem, so correct solutions are deemed to be the result of a person taking the test. A common type of CAPTCHA is the test that requires the typing of distorted letters, numbers or symbols that appear in an image undecipherable by a computer. − “模仿游戏”(对1950年图灵测试的一种解释，用来评估计算机是否可以模仿人类)如今被认为是一个过于可利用的有意义的基准。图灵测试的一个衍生物是完全自动的公共图灵测试，用于区分计算机和人类(CAPTCHA)。顾名思义，这有助于确定用户是一个真实的人，而不是一台伪装成人的计算机。与标准的图灵测试不同，CAPTCHA 是由机器实施的，针对的是人，而不是由人实施的，针对的是机器。计算机要求用户完成一个简单的测试，然后为该测试生成一个等级。计算机无法解决这个问题，所以正确的解决方案被认为是一个人参加考试的结果。验证码的一个常见类型是测试，要求输入扭曲的字母，数字或符号出现在一个图像无法破译的计算机。 第2,063行：
第2,032行： − Proposed "universal intelligence" tests aim to compare how well machines, humans, and even non-human animals perform on problem sets that are generic as possible. At an extreme, the test suite can contain every possible problem, weighted by [[Kolmogorov complexity]]; unfortunately, these problem sets tend to be dominated by impoverished pattern-matching exercises where a tuned AI can easily exceed human performance levels.<ref name="Mathematical definitions of intelligence"/><ref>{{cite journal|last1=Hernández-Orallo|first1=José|last2=Dowe|first2=David L.|last3=Hernández-Lloreda|first3=M.Victoria|title=Universal psychometrics: Measuring cognitive abilities in the machine kingdom|journal=Cognitive Systems Research|date=March 2014|volume=27|pages=50–74|doi=10.1016/j.cogsys.2013.06.001|hdl=10251/50244|hdl-access=free}}</ref>+ − Proposed "universal intelligence" tests aim to compare how well machines, humans, and even non-human animals perform on problem sets that are generic as possible. At an extreme, the test suite can contain every possible problem, weighted by Kolmogorov complexity; unfortunately, these problem sets tend to be dominated by impoverished pattern-matching exercises where a tuned AI can easily exceed human performance levels.+ − 提出的“通用智能”测试旨在比较机器、人类甚至非人类动物在尽可能通用的问题集上的表现。在极端情况下，测试套件可以包含所有可能出现的问题，这些问题的权重是柯氏复杂性; 不幸的是，这些问题集往往被贫乏的模式匹配练习所主导，在这些练习中，调优的 AI 可以轻易地超过人类。+ + + − == Applications{{anchor|Goals}} ==+ − + − 申请+ − [[File:Automated online assistant.png|thumb|An [[automated online assistant]] providing customer service on a web page – one of many very primitive applications of artificial intelligence]]+ − An [[automated online assistant providing customer service on a web page – one of many very primitive applications of artificial intelligence]] − 一个[在网页上提供客户服务的自动在线助理——人工智能的许多原始应用之一]+ − {{Main|Applications of artificial intelligence}}+ + + + + − AI is relevant to any intellectual task.{{sfn|Russell|Norvig|2009|p=1}} Modern artificial intelligence techniques are pervasive<ref name=":1">{{Cite book|last=|first=|url=https://ec.europa.eu/info/sites/info/files/commission-white-paper-artificial-intelligence-feb2020_en.pdf|title=White Paper: On Artificial Intelligence - A European approach to excellence and trust|publisher=European Commission|year=2020|isbn=|location=Brussels|pages=1}}</ref> and are too numerous to list here. Frequently, when a technique reaches mainstream use, it is no longer considered artificial intelligence; this phenomenon is described as the [[AI effect]].{{sfn|''CNN''|2006}}+ − AI is relevant to any intellectual task. Modern artificial intelligence techniques are pervasive and are too numerous to list here. Frequently, when a technique reaches mainstream use, it is no longer considered artificial intelligence; this phenomenon is described as the AI effect.+ − 人工智能与任何智力任务都息息相关。现代人工智能技术无处不在，数量众多，无法在此列举。通常，当一种技术达到主流应用时，它就不再被认为是人工智能; 这种现象被称为人工智能效应。 + + + − High-profile examples of AI include autonomous vehicles (such as [[Unmanned aerial vehicle|drones]] and [[self-driving cars]]), medical diagnosis, creating art (such as poetry), proving mathematical theorems, playing games (such as Chess or Go), search engines (such as [[Google search]]), online assistants (such as [[Siri]]), image recognition in photographs, spam filtering, predicting flight delays,<ref>[https://ishti.org/2018/11/19/using-artificial-intelligence-to-predict-flight-delays/ Using AI to predict flight delays], Ishti.org.</ref> prediction of judicial decisions,<ref name="ecthr2016">{{cite journal |author1=N. Aletras |author2=D. Tsarapatsanis |author3=D. Preotiuc-Pietro |author4=V. Lampos |title=Predicting judicial decisions of the European Court of Human Rights: a Natural Language Processing perspective |journal=PeerJ Computer Science |volume=2 |pages=e93 |year=2016 |df=dmy-all |doi=10.7717/peerj-cs.93 |doi-access=free }}</ref> targeting online advertisements, {{sfn|Russell|Norvig|2009|p=1}}<ref>{{cite news|title=The Economist Explains: Why firms are piling into artificial intelligence|url=https://www.economist.com/blogs/economist-explains/2016/04/economist-explains|accessdate=19 May 2016|work=[[The Economist]]|date=31 March 2016|url-status=live|archiveurl=https://web.archive.org/web/20160508010311/http://www.economist.com/blogs/economist-explains/2016/04/economist-explains|archivedate=8 May 2016|df=dmy-all}}</ref><ref>{{cite news|url=https://www.nytimes.com/2016/02/29/technology/the-promise-of-artificial-intelligence-unfolds-in-small-steps.html|title=The Promise of Artificial Intelligence Unfolds in Small Steps|last=Lohr|first=Steve|work=[[The New York Times]]|date=28 February 2016|accessdate=29 February 2016|url-status=live|archiveurl=https://web.archive.org/web/20160229171843/http://www.nytimes.com/2016/02/29/technology/the-promise-of-artificial-intelligence-unfolds-in-small-steps.html|archivedate=29 February 2016|df=dmy-all}}</ref> and [[energy storage]]<ref>{{Cite web|url=https://www.cnbc.com/2019/06/14/the-business-using-ai-to-change-how-we-think-about-energy-storage.html|title=A Californian business is using A.I. to change the way we think about energy storage|last=Frangoul|first=Anmar|date=2019-06-14|website=CNBC|language=en|access-date=2019-11-05}}</ref> − High-profile examples of AI include autonomous vehicles (such as drones and self-driving cars), medical diagnosis, creating art (such as poetry), proving mathematical theorems, playing games (such as Chess or Go), search engines (such as Google search), online assistants (such as Siri), image recognition in photographs, spam filtering, predicting flight delays, prediction of judicial decisions, targeting online advertisements, and energy storage − 引人注目的人工智能例子包括自动驾驶汽车(如无人机和自动驾驶汽车)、医疗诊断、创造艺术(如诗歌)、证明数学定理、玩游戏(如国际象棋或围棋)、搜索引擎(如谷歌搜索)、在线助手(如 Siri)、照片图像识别、垃圾邮件过滤、航班延误预测、司法判决预测、针对在线广告和能源储存+ + + + − With social media sites overtaking TV as a source for news for young people and news organizations increasingly reliant on social media platforms for generating distribution,<ref>{{cite web|url=https://www.bbc.co.uk/news/uk-36528256|title=Social media 'outstrips TV' as news source for young people|date=15 June 2016|author=Wakefield, Jane|work=BBC News|url-status=live|archiveurl=https://web.archive.org/web/20160624000744/http://www.bbc.co.uk/news/uk-36528256|archivedate=24 June 2016|df=dmy-all}}</ref> major publishers now use artificial intelligence (AI) technology to post stories more effectively and generate higher volumes of traffic.<ref>{{cite web|url=https://www.bbc.co.uk/news/business-36837824|title=So you think you chose to read this article?|date=22 July 2016|author=Smith, Mark|work=BBC News|url-status=live|archiveurl=https://web.archive.org/web/20160725205007/http://www.bbc.co.uk/news/business-36837824|archivedate=25 July 2016|df=dmy-all}}</ref> − With social media sites overtaking TV as a source for news for young people and news organizations increasingly reliant on social media platforms for generating distribution, major publishers now use artificial intelligence (AI) technology to post stories more effectively and generate higher volumes of traffic. − 随着社交媒体网站取代电视成为年轻人的新闻来源，以及新闻机构越来越依赖社交媒体平台来发布新闻，大型出版商现在使用人工智能技术来更有效地发布新闻，并产生更高的流量。 + + − AI can also produce [[Deepfake]]s, a content-altering technology. ZDNet reports, "It presents something that did not actually occur," Though 88% of Americans believe Deepfakes can cause more harm than good, only 47% of them believe they can be targeted. The boom of election year also opens public discourse to threats of videos of falsified politician media.<ref>{{Cite web|url=https://www.zdnet.com/article/half-of-americans-do-not-believe-deepfake-news-could-target-them-online/|title=Half of Americans do not believe deepfake news could target them online|last=Brown|first=Eileen|website=ZDNet|language=en|access-date=2019-12-03}}</ref>+ − AI can also produce Deepfakes, a content-altering technology. ZDNet reports, "It presents something that did not actually occur," Though 88% of Americans believe Deepfakes can cause more harm than good, only 47% of them believe they can be targeted. The boom of election year also opens public discourse to threats of videos of falsified politician media. − 人工智能还可以生产“深度假货” ，这是一种改变内容的技术。Zdnet 报道说，“它展示了一些并没有真正发生的东西。”尽管88% 的美国人认为伪造的东西弊大于利，但只有47% 的人认为他们可以成为目标。选举年的繁荣也开启了公共话语，政治家虚假媒体视频的威胁。 + + + − + − + − 医疗+ − + − + − A patient-side surgical arm of [[Da Vinci Surgical System]]AI in healthcare is often used for classification, whether to automate initial evaluation of a CT scan or EKG or to identify high-risk patients for population health. The breadth of applications is rapidly increasing.+ − 在医疗保健中，[[达芬奇外科手术系统]人工智能的患者侧手术臂通常用于分类，无论是自动进行 CT 扫描或心电图的初步评估，还是为群体健康识别高风险患者。应用范围正在迅速扩大。+ + + + − As an example, AI is being applied to the high-cost problem of dosage issues—where findings suggested that AI could save $16 billion. In 2016, a groundbreaking study in California found that a mathematical formula developed with the help of AI correctly determined the accurate dose of immunosuppressant drugs to give to organ patients.<ref>{{Cite news|url=https://hbr.org/2018/05/10-promising-ai-applications-in-health-care|title=10 Promising AI Applications in Health Care|date=2018-05-10|work=Harvard Business Review|access-date=2018-08-28|archive-url=https://web.archive.org/web/20181215015645/https://hbr.org/2018/05/10-promising-ai-applications-in-health-care|archive-date=15 December 2018|url-status=dead}}</ref> [[File:X-ray of a hand with automatic bone age calculation.jpg|thumb|[[Projectional radiography|X-ray]] of a hand, with automatic calculation of [[bone age]] by computer software]]+ − As an example, AI is being applied to the high-cost problem of dosage issues—where findings suggested that AI could save $16 billion. In 2016, a groundbreaking study in California found that a mathematical formula developed with the help of AI correctly determined the accurate dose of immunosuppressant drugs to give to organ patients. X-ray of a hand, with automatic calculation of bone age by computer software]] − 例如，人工智能正被用于解决高成本的剂量问题ーー研究结果表明，人工智能可以节省160亿美元。2016年，加利福尼亚州的一项开创性研究发现，在人工智能的帮助下开发的一个数学公式正确地确定了免疫抑制药给予器官患者的准确剂量。用计算机软件自动计算骨龄] − Artificial intelligence is assisting doctors. According to Bloomberg Technology, Microsoft has developed AI to help doctors find the right treatments for cancer.<ref>{{cite news | author=Dina Bass | title=Microsoft Develops AI to Help Cancer Doctors Find the Right Treatments | url=https://www.bloomberg.com/news/articles/2016-09-20/microsoft-develops-ai-to-help-cancer-doctors-find-the-right-treatments | date=20 September 2016 | publisher=Bloomberg | url-status=live | archiveurl=https://web.archive.org/web/20170511103625/https://www.bloomberg.com/news/articles/2016-09-20/microsoft-develops-ai-to-help-cancer-doctors-find-the-right-treatments | archivedate=11 May 2017 | df=dmy-all | newspaper=Bloomberg.com }}</ref> There is a great amount of research and drugs developed relating to cancer. In detail, there are more than 800 medicines and vaccines to treat cancer. This negatively affects the doctors, because there are too many options to choose from, making it more difficult to choose the right drugs for the patients. Microsoft is working on a project to develop a machine called "Hanover"{{citation needed|date=July 2019}}. Its goal is to memorize all the papers necessary to cancer and help predict which combinations of drugs will be most effective for each patient. One project that is being worked on at the moment is fighting [[acute myeloid leukemia|myeloid leukemia]], a fatal cancer where the treatment has not improved in decades. Another study was reported to have found that artificial intelligence was as good as trained doctors in identifying skin cancers.<ref>{{Cite news|url=https://www.bbc.co.uk/news/health-38717928|title=Artificial intelligence 'as good as cancer doctors'|last=Gallagher|first=James|date=26 January 2017|work=BBC News|language=en-GB|access-date=26 January 2017|url-status=live|archiveurl=https://web.archive.org/web/20170126133849/http://www.bbc.co.uk/news/health-38717928|archivedate=26 January 2017|df=dmy-all}}</ref> Another study is using artificial intelligence to try to monitor multiple high-risk patients, and this is done by asking each patient numerous questions based on data acquired from live doctor to patient interactions.<ref>{{Citation|title=Remote monitoring of high-risk patients using artificial intelligence|date=18 Oct 1994|url=https://www.google.com/patents/US5357427|editor-last=Langen|editor2-last=Katz|editor3-last=Dempsey|editor-first=Pauline A.|editor2-first=Jeffrey S.|editor3-first=Gayle|issue=US5357427 A|accessdate=27 February 2017|url-status=live|archiveurl=https://web.archive.org/web/20170228090520/https://www.google.com/patents/US5357427|archivedate=28 February 2017|df=dmy-all}}</ref> One study was done with transfer learning, the machine performed a diagnosis similarly to a well-trained ophthalmologist, and could generate a decision within 30 seconds on whether or not the patient should be referred for treatment, with more than 95% accuracy.<ref>{{Cite journal|url=https://www.cell.com/action/captchaChallenge?redirectUri=%2Fcell%2Fpdf%2FS0092-8674%2818%2930154-5.pdf|title=Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning|last=Kermany|first=D|last2=Goldbaum|first2=M|journal=Cell|access-date=2018-12-18|last3=Zhang|first3=Kang|volume=172|issue=5|pages=1122–1131.e9|pmid=29474911|year=2018|doi=10.1016/j.cell.2018.02.010}}</ref>+ − Artificial intelligence is assisting doctors. According to Bloomberg Technology, Microsoft has developed AI to help doctors find the right treatments for cancer. There is a great amount of research and drugs developed relating to cancer. In detail, there are more than 800 medicines and vaccines to treat cancer. This negatively affects the doctors, because there are too many options to choose from, making it more difficult to choose the right drugs for the patients. Microsoft is working on a project to develop a machine called "Hanover". Its goal is to memorize all the papers necessary to cancer and help predict which combinations of drugs will be most effective for each patient. One project that is being worked on at the moment is fighting myeloid leukemia, a fatal cancer where the treatment has not improved in decades. Another study was reported to have found that artificial intelligence was as good as trained doctors in identifying skin cancers. Another study is using artificial intelligence to try to monitor multiple high-risk patients, and this is done by asking each patient numerous questions based on data acquired from live doctor to patient interactions. One study was done with transfer learning, the machine performed a diagnosis similarly to a well-trained ophthalmologist, and could generate a decision within 30 seconds on whether or not the patient should be referred for treatment, with more than 95% accuracy.+ − 人工智能正在协助医生。据彭博科技报道，微软已经开发出人工智能来帮助医生找到正确的癌症治疗方法。有大量的研究和药物开发与癌症有关。具体来说，有800多种药物和疫苗可以治疗癌症。这对医生造成了负面影响，因为有太多的选择可供选择，使得更难为病人选择合适的药物。微软正在进行一个项目，开发一种名为“汉诺威”的机器。它的目标是记住所有与癌症有关的论文，并帮助预测哪些药物组合对每个病人最有效。目前正在进行的一个项目是抗击髓系白血病，这是一种致命的癌症，几十年来治疗一直没有改善。据报道，另一项研究发现，在识别皮肤癌方面，人工智能与训练有素的医生一样优秀。另一项研究是使用人工智能来监测多个高风险患者，这是通过询问每个患者许多问题来完成的，这些问题是基于从现场医生与患者互动中获得的数据。其中一项研究是通过转移学习完成的，机器进行的诊断类似于训练有素的眼科医生，可以在30秒内做出是否应该转诊治疗的决定，准确率超过95% 。+ + + − According to [[CNN]], a recent study by surgeons at the Children's National Medical Center in Washington successfully demonstrated surgery with an autonomous robot. The team supervised the robot while it performed soft-tissue surgery, stitching together a pig's bowel during open surgery, and doing so better than a human surgeon, the team claimed.<ref>{{cite news|author=Senthilingam, Meera|title=Are Autonomous Robots Your next Surgeons?|work=CNN|publisher=Cable News Network|date=12 May 2016|accessdate=4 December 2016|url=http://www.cnn.com/2016/05/12/health/robot-surgeon-bowel-operation/|url-status=live|archiveurl=https://web.archive.org/web/20161203154119/http://www.cnn.com/2016/05/12/health/robot-surgeon-bowel-operation|archivedate=3 December 2016|df=dmy-all}}</ref> IBM has created its own artificial intelligence computer, the [[IBM Watson]], which has beaten human intelligence (at some levels). Watson has struggled to achieve success and adoption in healthcare.<ref>{{Cite web|url=https://spectrum.ieee.org/biomedical/diagnostics/how-ibm-watson-overpromised-and-underdelivered-on-ai-health-care|title=Full Page Reload|website=IEEE Spectrum: Technology, Engineering, and Science News|language=en|access-date=2019-09-03}}</ref>+ − According to CNN, a recent study by surgeons at the Children's National Medical Center in Washington successfully demonstrated surgery with an autonomous robot. The team supervised the robot while it performed soft-tissue surgery, stitching together a pig's bowel during open surgery, and doing so better than a human surgeon, the team claimed. IBM has created its own artificial intelligence computer, the IBM Watson, which has beaten human intelligence (at some levels). Watson has struggled to achieve success and adoption in healthcare.+ − 据 CNN 报道，华盛顿国家儿童医疗中心的外科医生最近的一项研究成功地展示了一台自主机器人手术。研究小组声称，当机器人进行软组织手术、在开放手术中缝合猪肠时，他们负责监督机器人，而且比人类外科医生做得更好。Ibm 已经创造了自己的人工智能计算机，IBM 沃森，它在某些层面上已经超越了人类智能。沃森一直在努力实现医疗保健领域的成功和采用。+ + + − + − === Automotive ===+ − 汽车+ − {{Main|driverless cars}}+ + − Advancements in AI have contributed to the growth of the automotive industry through the creation and evolution of self-driving vehicles. {{as of|2016}}, there are over 30 companies utilizing AI into the creation of [[self-driving car]]s. A few companies involved with AI include [[Tesla Motors|Tesla]], [[Google]], and [[Apple Inc.|Apple]].<ref>"33 Corporations Working On Autonomous Vehicles". CB Insights. N.p., 11 August 2016. 12 November 2016.</ref> − Advancements in AI have contributed to the growth of the automotive industry through the creation and evolution of self-driving vehicles. , there are over 30 companies utilizing AI into the creation of self-driving cars. A few companies involved with AI include Tesla, Google, and Apple. − 人工智能技术的进步通过自动驾驶汽车的创造和发展促进了汽车工业的发展。目前，有超过30家公司利用人工智能开发自动驾驶汽车。少数涉及人工智能的公司包括特斯拉、谷歌和苹果。+ + + + − Many components contribute to the functioning of self-driving cars. These vehicles incorporate systems such as braking, lane changing, collision prevention, navigation and mapping. Together, these systems, as well as high-performance computers, are integrated into one complex vehicle.<ref>West, Darrell M. "Moving forward: Self-driving vehicles in China, Europe, Japan, Korea, and the United States". Center for Technology Innovation at Brookings. N.p., September 2016. 12 November 2016.</ref> − Many components contribute to the functioning of self-driving cars. These vehicles incorporate systems such as braking, lane changing, collision prevention, navigation and mapping. Together, these systems, as well as high-performance computers, are integrated into one complex vehicle.+ − 许多组件有助于自动驾驶汽车的功能。这些车辆集成了诸如刹车、换车道、防撞、导航和测绘等系统。这些系统以及高性能计算机一起集成到一个复杂的车辆中。+ + − Recent developments in autonomous automobiles have made the innovation of self-driving trucks possible, though they are still in the testing phase. The UK government has passed legislation to begin testing of self-driving truck platoons in 2018.<ref>{{cite journal|last1=Burgess|first1=Matt|title=The UK is about to Start Testing Self-Driving Truck Platoons|url=https://www.wired.co.uk/article/uk-trial-self-driving-trucks-platoons-roads|journal=Wired UK|accessdate=20 September 2017|url-status=live|archiveurl=https://web.archive.org/web/20170922055917/http://www.wired.co.uk/article/uk-trial-self-driving-trucks-platoons-roads|archivedate=22 September 2017|df=dmy-all|date=2017-08-24}}</ref> Self-driving truck platoons are a fleet of self-driving trucks following the lead of one non-self-driving truck, so the truck platoons aren't entirely autonomous yet. Meanwhile, the Daimler, a German automobile corporation, is testing the Freightliner Inspiration which is a semi-autonomous truck that will only be used on the highway.<ref>{{cite journal|last1=Davies|first1=Alex|title=World's First Self-Driving Semi-Truck Hits the Road|url=https://www.wired.com/2015/05/worlds-first-self-driving-semi-truck-hits-road/|journal=WIRED|accessdate=20 September 2017|url-status=live|archiveurl=https://web.archive.org/web/20171028222802/https://www.wired.com/2015/05/worlds-first-self-driving-semi-truck-hits-road/|archivedate=28 October 2017|df=dmy-all|date=2015-05-05}}</ref>+ − Recent developments in autonomous automobiles have made the innovation of self-driving trucks possible, though they are still in the testing phase. The UK government has passed legislation to begin testing of self-driving truck platoons in 2018. Self-driving truck platoons are a fleet of self-driving trucks following the lead of one non-self-driving truck, so the truck platoons aren't entirely autonomous yet. Meanwhile, the Daimler, a German automobile corporation, is testing the Freightliner Inspiration which is a semi-autonomous truck that will only be used on the highway.+ − 自动驾驶汽车的最新发展使自动驾驶卡车的创新成为可能，尽管它们仍处于测试阶段。英国政府已通过立法，将于2018年开始测试自动驾驶卡车排。自动驾驶卡车排是一队自动驾驶卡车跟随一辆非自动驾驶卡车的领导，所以卡车排还不是完全自动的。与此同时，德国汽车公司戴姆勒正在测试福莱纳灵感，这是一种只在高速公路上使用的半自动卡车。+ + + − One main factor that influences the ability for a driver-less automobile to function is mapping. In general, the vehicle would be pre-programmed with a map of the area being driven. This map would include data on the approximations of street light and curb heights in order for the vehicle to be aware of its surroundings. However, Google has been working on an algorithm with the purpose of eliminating the need for pre-programmed maps and instead, creating a device that would be able to adjust to a variety of new surroundings.<ref>McFarland, Matt. "Google's artificial intelligence breakthrough may have a huge impact on self-driving cars and much more". ''The Washington Post'' 25 February 2015. Infotrac Newsstand. 24 October 2016</ref> Some self-driving cars are not equipped with steering wheels or brake pedals, so there has also been research focused on creating an algorithm that is capable of maintaining a safe environment for the passengers in the vehicle through awareness of speed and driving conditions.<ref>"Programming safety into self-driving cars". National Science Foundation. N.p., 2 February 2015. 24 October 2016.</ref>+ − One main factor that influences the ability for a driver-less automobile to function is mapping. In general, the vehicle would be pre-programmed with a map of the area being driven. This map would include data on the approximations of street light and curb heights in order for the vehicle to be aware of its surroundings. However, Google has been working on an algorithm with the purpose of eliminating the need for pre-programmed maps and instead, creating a device that would be able to adjust to a variety of new surroundings. Some self-driving cars are not equipped with steering wheels or brake pedals, so there has also been research focused on creating an algorithm that is capable of maintaining a safe environment for the passengers in the vehicle through awareness of speed and driving conditions.+ − 影响无人驾驶汽车性能的一个主要因素是地图。一般来说，车辆将预先编程与地图的区域正在驾驶。这张地图将包括近似的街灯和路缘高度的数据，以便车辆能够感知周围环境。然而，谷歌一直在研究一种算法，其目的是消除对预编程地图的需求，而是创造一种能够适应各种新环境的设备。一些自动驾驶汽车没有配备方向盘或刹车踏板，因此也有研究集中于创建一种算法，能够通过对速度和驾驶条件的了解，为车内乘客维持一个安全的环境。 + + + − Another factor that is influencing the ability of a driver-less automobile is the safety of the passenger. To make a driver-less automobile, engineers must program it to handle high-risk situations. These situations could include a head-on collision with pedestrians. The car's main goal should be to make a decision that would avoid hitting the pedestrians and saving the passengers in the car. But there is a possibility the car would need to make a decision that would put someone in danger. In other words, the car would need to decide to save the pedestrians or the passengers.<ref>ArXiv, E. T. (26 October 2015). Why Self-Driving Cars Must Be Programmed to Kill. Retrieved 17 November 2017, from https://www.technologyreview.com/s/542626/why-self-driving-cars-must-be-programmed-to-kill/{{Dead link|date=October 2019 |bot=InternetArchiveBot |fix-attempted=yes }}</ref> The programming of the car in these situations is crucial to a successful driver-less automobile.+ − Another factor that is influencing the ability of a driver-less automobile is the safety of the passenger. To make a driver-less automobile, engineers must program it to handle high-risk situations. These situations could include a head-on collision with pedestrians. The car's main goal should be to make a decision that would avoid hitting the pedestrians and saving the passengers in the car. But there is a possibility the car would need to make a decision that would put someone in danger. In other words, the car would need to decide to save the pedestrians or the passengers. The programming of the car in these situations is crucial to a successful driver-less automobile.+ − 另一个影响无人驾驶汽车能力的因素是乘客的安全。为了制造一辆无人驾驶的汽车，工程师们必须对其进行编程，使其能够处理高风险的情况。这些情况可能包括与行人迎面相撞。这辆车的主要目标应该是做出一个决定，避免撞到行人，救出车内的乘客。但是汽车有可能需要做出一个将某人置于危险之中的决定。换句话说，汽车需要决定是拯救行人还是乘客。汽车在这些情况下的编程对于一辆成功的无人驾驶汽车是至关重要的。+ + + + + − === Finance and economics ===+ − === Finance and economics === − 金融和经济 − + − Financial institutions have long used artificial neural network systems to detect charges or claims outside of the norm, flagging these for human investigation. The use of AI in banking can be traced back to 1987 when Security Pacific National Bank in US set-up a Fraud Prevention Task force to counter the unauthorized use of debit cards. Programs like Kasisto and Moneystream are using AI in financial services.+ − 长期以来，金融机构一直使用人工神经网络系统来检测超出常规的费用或索赔，并将其标记为人工调查。人工智能在银行业的应用可以追溯到1987年，当时美国国家安全太平洋银行成立了一个防止欺诈工作队，以打击未经授权使用借记卡的行为。像 Kasisto 和 Moneystream 这样的程序正在金融服务中使用人工智能。+ 第2,255行：
第2,245行： − Banks use artificial intelligence systems today to organize operations, maintain book-keeping, invest in stocks, and manage properties. AI can react to changes overnight or when business is not taking place.<ref name="Eleanor">{{cite web|url=https://www.icas.com/ca-today-news/how-accountancy-and-finance-are-using-artificial-intelligence|title=Accounting, automation and AI|first=Eleanor|last=O'Neill|website=icas.com|language=English|date=31 July 2016|access-date=18 November 2016|url-status=live|archiveurl=https://web.archive.org/web/20161118165901/https://www.icas.com/ca-today-news/how-accountancy-and-finance-are-using-artificial-intelligence|archivedate=18 November 2016|df=dmy-all}}</ref> In August 2001, robots beat humans in a simulated [[stock trader|financial trading]] competition.<ref>[http://news.bbc.co.uk/2/hi/business/1481339.stm Robots Beat Humans in Trading Battle.] {{webarchive|url=https://web.archive.org/web/20090909001249/http://news.bbc.co.uk/2/hi/business/1481339.stm |date=9 September 2009 }} BBC.com (8 August 2001)</ref> AI has also reduced fraud and financial crimes by [[Statistical software|monitoring]] [[behavioral pattern]]s of users for any abnormal changes or anomalies.<ref name="fsroundtable.org">{{Cite news|url=http://fsroundtable.org/cto-corner-artificial-intelligence-use-in-financial-services/|title=CTO Corner: Artificial Intelligence Use in Financial Services – Financial Services Roundtable|date=2 April 2015|work=Financial Services Roundtable|language=en-US|access-date=18 November 2016|url-status=dead|archiveurl=https://web.archive.org/web/20161118165842/http://fsroundtable.org/cto-corner-artificial-intelligence-use-in-financial-services/|archivedate=18 November 2016|df=dmy-all}}</ref><ref>{{Cite web|url=https://www.sas.com/en_ae/solutions/ai.html|title=Artificial Intelligence Solutions, AI Solutions|website=www.sas.com}}</ref><ref>{{Cite web|url=https://www.latimes.com/business/la-fi-palantir-sales-ipo-20190107-story.html|title=Palantir once mocked the idea of salespeople. Now it's hiring them|last=Chapman|first=Lizette|website=latimes.com|access-date=2019-02-28|date=7 January 2019}}</ref> − Banks use artificial intelligence systems today to organize operations, maintain book-keeping, invest in stocks, and manage properties. AI can react to changes overnight or when business is not taking place. In August 2001, robots beat humans in a simulated financial trading competition. AI has also reduced fraud and financial crimes by monitoring behavioral patterns of users for any abnormal changes or anomalies.+ − 如今，银行使用人工智能系统来组织业务、记账、投资股票和管理房地产。人工智能可以对一夜之间的变化做出反应，或者当业务没有发生的时候。2001年8月，机器人在一场模拟金融交易竞赛中击败了人类。人工智能还通过监测用户的行为模式以发现任何异常变化或异常现象，减少了欺诈和金融犯罪。+ + + − AI is increasingly being used by [[Corporate finance|corporations]]. [[Jack Ma]] has controversially predicted that AI [[CEO]]'s are 30 years away.<ref>{{Cite web|url=https://money.cnn.com/2017/04/24/technology/alibaba-jack-ma-30-years-pain-robot-ceo/index.html|title=Jack Ma: In 30 years, the best CEO could be a robot|first=Sherisse|last=Pham|date=24 April 2017|website=CNNMoney}}</ref><ref>{{Cite web|url=https://venturebeat.com/2016/10/22/cant-find-a-perfect-ceo-create-an-ai-one-yourself/|title=Can't find a perfect CEO? Create an AI one yourself|date=22 October 2016}}</ref>+ − AI is increasingly being used by corporations. Jack Ma has controversially predicted that AI CEO's are 30 years away.+ − 人工智能正越来越多地被企业所使用。马曾有争议地预测，人工智能 CEO 离苹果还有30年的时间。+ + + + − The use of AI machines in the market in applications such as online trading and decision making has changed major economic theories.<ref>{{cite book |last1=Marwala |first1= Tshilidzi| last2=Hurwitz |first2= Evan |title=Artificial Intelligence and Economic Theory: Skynet in the Market |year=2017 |publisher=[[Springer Science+Business Media|Springer]] |location=London |isbn=978-3-319-66104-9}}</ref> For example, AI-based buying and selling platforms have changed the law of [[supply and demand]] in that it is now possible to easily estimate individualized demand and supply curves and thus individualized pricing. Furthermore, AI machines reduce [[information asymmetry]] in the market and thus making markets more efficient while reducing the volume of trades{{citation needed|date=July 2019}}. Furthermore, AI in the markets limits the consequences of behavior in the markets again making markets more efficient{{citation needed|date=July 2019}}. Other theories where AI has had impact include in [[rational choice]], [[rational expectations]], [[game theory]], [[Lewis turning point]], [[portfolio optimization]] and [[counterfactual thinking]]{{citation needed|date=July 2019}}.. In August 2019, the [[American Institute of Certified Public Accountants|AICPA]] introduced AI training course for accounting professionals.<ref>{{Cite web|url=https://www.mileseducation.com/finance/artificial_intelligence|title=Miles Education {{!}} Future Of Finance {{!}} Blockchain Fundamentals for F&A Professionals Certificate|website=www.mileseducation.com|access-date=2019-09-26|archive-url=https://web.archive.org/web/20190926102133/https://www.mileseducation.com/finance/artificial_intelligence|archive-date=26 September 2019|url-status=dead}}</ref> − The use of AI machines in the market in applications such as online trading and decision making has changed major economic theories. For example, AI-based buying and selling platforms have changed the law of supply and demand in that it is now possible to easily estimate individualized demand and supply curves and thus individualized pricing. Furthermore, AI machines reduce information asymmetry in the market and thus making markets more efficient while reducing the volume of trades. Furthermore, AI in the markets limits the consequences of behavior in the markets again making markets more efficient. Other theories where AI has had impact include in rational choice, rational expectations, game theory, Lewis turning point, portfolio optimization and counterfactual thinking.. In August 2019, the AICPA introduced AI training course for accounting professionals. − 人工智能机器在市场上的应用，如在线交易和决策，改变了主要的经济理论。例如，基于人工智能的买卖平台改变了供求规律，现在可以很容易地估计个性化的需求和供给曲线，从而实现个性化的定价。此外，人工智能机器减少了市场的信息不对称，从而使市场更有效率，同时减少了交易量。此外，市场中的人工智能限制了市场行为的后果，再次提高了市场效率。人工智能影响的其他理论包括理性选择、理性预期、博弈论、刘易斯转折点、投资组合优化和反事实思维。 .2019年8月，AICPA 为会计专业人员开设了 AI 培训课程。+ + + − + − + − 网络安全+ − + − The [[cybersecurity]] arena faces significant challenges in the form of large-scale hacking attacks of different types that harm organizations of all kinds and create billions of dollars in business damage. Artificial intelligence and Natural Language Processing (NLP) has begun to be used by security companies - for example, SIEM (Security Information and Event Management) solutions. The more advanced of these solutions use AI and NLP to automatically sort the data in networks into high risk and low-risk information. This enables security teams to focus on the attacks that have the potential to do real harm to the organization, and not become victims of attacks such as [[Denial-of-service attack|Denial of Service (DoS)]], [[Malware]] and others.+ − The cybersecurity arena faces significant challenges in the form of large-scale hacking attacks of different types that harm organizations of all kinds and create billions of dollars in business damage. Artificial intelligence and Natural Language Processing (NLP) has begun to be used by security companies - for example, SIEM (Security Information and Event Management) solutions. The more advanced of these solutions use AI and NLP to automatically sort the data in networks into high risk and low-risk information. This enables security teams to focus on the attacks that have the potential to do real harm to the organization, and not become victims of attacks such as Denial of Service (DoS), Malware and others.+ − 网络安全领域面临着各种形式的大规模黑客攻击的重大挑战，这些攻击伤害了各种组织，造成了数十亿美元的商业损失。安全公司已经开始使用人工智能和自然语言处理(NLP) ，例如，SIEM (安全信息和事件管理)解决方案。这些更先进的解决方案使用人工智能和自然语言处理自动排序的数据网络中的高风险和低风险的信息。这使得安全团队能够专注于那些有可能对组织造成真正伤害的攻击，而不是成为分布式拒绝服务攻击攻击、恶意软件和其他攻击的受害者。+ + − + − === Government ===+ − 政府+ − {{Main|Artificial intelligence in government}} + − Artificial intelligence in government consists of applications and regulation. Artificial intelligence paired with [[facial recognition system]]s may be used for [[mass surveillance]]. This is already the case in some parts of China.<ref>{{Cite news|url=https://www.nytimes.com/2019/05/22/world/asia/china-surveillance-xinjiang.html|title=How China Uses High-Tech Surveillance to Subdue Minorities|first1=Chris|last1=Buckley|first2=Paul|last2=Mozur|date=22 May 2019|work=The New York Times}}</ref><ref>{{Cite web|url=http://social.techcrunch.com/2019/05/03/china-smart-city-exposed/|title=Security lapse exposed a Chinese smart city surveillance system}}</ref> An artificial intelligence has also competed in the Tama City [[AI mayor|mayoral elections]] in 2018.+ − Artificial intelligence in government consists of applications and regulation. Artificial intelligence paired with facial recognition systems may be used for mass surveillance. This is already the case in some parts of China. An artificial intelligence has also competed in the Tama City mayoral elections in 2018.+ − 政府中的人工智能包括应用和管理。人工智能与人脸识别系统相结合可用于大规模监控。在中国的一些地区已经出现了这种情况。人工智能还参与了2018年 Tama City 市长选举的角逐。 + + + − In 2019, the tech city of Bengaluru in India is set to deploy AI managed traffic signal systems across the 387 traffic signals in the city. This system will involve use of cameras to ascertain traffic density and accordingly calculate the time needed to clear the traffic volume which will determine the signal duration for vehicular traffic across streets.<ref>{{Cite web|url=https://nextbigwhat.com/ai-traffic-signals-to-be-installed-in-bengaluru-soon/|title=AI traffic signals to be installed in Bengaluru soon|date=2019-09-24|website=NextBigWhat|language=en-US|access-date=2019-10-01}}</ref>+ − In 2019, the tech city of Bengaluru in India is set to deploy AI managed traffic signal systems across the 387 traffic signals in the city. This system will involve use of cameras to ascertain traffic density and accordingly calculate the time needed to clear the traffic volume which will determine the signal duration for vehicular traffic across streets. − 2019年，印度科技城市 Bengaluru 将在该市的387个交通信号灯上部署人工智能管理的交通信号系统。这个系统将使用摄影机来确定交通密度，并据此计算清除交通量所需的时间，这将决定横过街道的车辆交通灯持续时间。 + + + − + − + − 与法律有关的专业+ − + − Artificial intelligence (AI) is becoming a mainstay component of law-related professions. In some circumstances, this analytics-crunching technology is using algorithms and machine learning to do work that was previously done by entry-level lawyers.{{Citation needed|date=December 2019}}+ − Artificial intelligence (AI) is becoming a mainstay component of law-related professions. In some circumstances, this analytics-crunching technology is using algorithms and machine learning to do work that was previously done by entry-level lawyers.+ − 人工智能(AI)正在成为法律相关专业的主要组成部分。在某些情况下，这种分析处理技术正在使用算法和机器学习来完成以前由初级律师完成的工作。+ + + + − In [[Electronic discovery|Electronic Discovery (eDiscovery)]], the industry has been focused on machine learning (predictive coding/technology assisted review), which is a subset of AI. To add to the soup of applications, Natural Language Processing (NLP) and Automated Speech Recognition (ASR) are also in vogue in the industry.<ref>{{Cite web|url=https://www.ft.com/content/fef40df0-4a6a-11e9-bde6-79eaea5acb64|title=AI learns to read Korean, so you don't have to|last=Croft|first=Jane|date=2019-05-02|website=Financial Times|language=en-GB|access-date=2019-12-19}}</ref>+ − In Electronic Discovery (eDiscovery), the industry has been focused on machine learning (predictive coding/technology assisted review), which is a subset of AI. To add to the soup of applications, Natural Language Processing (NLP) and Automated Speech Recognition (ASR) are also in vogue in the industry. − 在电子发现(eDiscovery)中，工业界一直关注于机器学习(预测编码 / 技术辅助评审) ，这是人工智能的一个子集。自然语言处理(NLP)和自动语音识别(ASR)也正在业界流行起来。 + + + − === Video games === − === Video games === − 电子游戏+ − {{Main|Artificial intelligence (video games)}}+ + − In video games, artificial intelligence is routinely used to generate dynamic purposeful behavior in [[non-player character]]s (NPCs). In addition, well-understood AI techniques are routinely used for [[pathfinding]]. Some researchers consider NPC AI in games to be a "solved problem" for most production tasks. Games with more atypical AI include the AI director of ''[[Left 4 Dead]]'' (2008) and the neuroevolutionary training of platoons in ''[[Supreme Commander 2]]'' (2010).<ref>{{cite news|url=https://www.economist.com/news/science-and-technology/21721890-games-help-them-understand-reality-why-ai-researchers-video-games|title=Why AI researchers like video games|website=The Economist|url-status=live|archiveurl=https://web.archive.org/web/20171005051028/https://www.economist.com/news/science-and-technology/21721890-games-help-them-understand-reality-why-ai-researchers-video-games|archivedate=5 October 2017|df=dmy-all}}</ref><ref>Yannakakis, G. N. (2012, May). Game AI revisited. In Proceedings of the 9th conference on Computing Frontiers (pp. 285–292). ACM.</ref>+ − In video games, artificial intelligence is routinely used to generate dynamic purposeful behavior in non-player characters (NPCs). In addition, well-understood AI techniques are routinely used for pathfinding. Some researchers consider NPC AI in games to be a "solved problem" for most production tasks. Games with more atypical AI include the AI director of Left 4 Dead (2008) and the neuroevolutionary training of platoons in Supreme Commander 2 (2010).+ − 在视频游戏中，人工智能通常被用来在非玩家角色(npc)中产生动态的有目的的行为。此外，众所周知的人工智能技术常用于寻路。一些研究人员认为，对于大多数生产任务来说，游戏中的 NPC AI 是一个“解决了的问题”。具有更多非典型 AI 的游戏包括《左4死》(2008)的 AI 导演和《最高指挥官2》(2010)中的排神经进化训练。+ + − === Military === − === Military === − 军事 − {{Further|Artificial intelligence arms race|Lethal autonomous weapon|Unmanned combat aerial vehicle}}+ + + − The United States and other nations are developing AI applications for a range of military functions.<ref name=":2">{{Cite book|last=Congressional Research Service|first=|url=https://fas.org/sgp/crs/natsec/R45178.pdf|title=Artificial Intelligence and National Security|publisher=Congressional Research Service|year=2019|isbn=|location=Washington, DC|pages=}}[[Template:PD-notice|PD-notice]]</ref> The main military applications of Artificial Intelligence and Machine Learning are to enhance C2, Communications, Sensors, Integration and Interoperability.<ref name="AI">{{cite web|title=Artificial intelligence as the basis of future control networks.|url=https://www.researchgate.net/publication/334573170|last=Slyusar|first=Vadym|date=2019|work=Preprint}}</ref> AI research is underway in the fields of intelligence collection and analysis, logistics, cyber operations, information operations, command and control, and in a variety of semiautonomous and autonomous vehicles.<ref name=":2" /> Artificial Intelligence technologies enable coordination of sensors and effectors, threat detection and identification, marking of enemy positions, target acquisition, coordination and deconfliction of distributed Join Fires between networked combat vehicles and tanks also inside Manned and Unmanned Teams (MUM-T).<ref name=AI /> AI has been incorporated into military operations in Iraq and Syria.<ref name=":2" /> − The United States and other nations are developing AI applications for a range of military functions. The main military applications of Artificial Intelligence and Machine Learning are to enhance C2, Communications, Sensors, Integration and Interoperability. AI research is underway in the fields of intelligence collection and analysis, logistics, cyber operations, information operations, command and control, and in a variety of semiautonomous and autonomous vehicles. Artificial Intelligence technologies enable coordination of sensors and effectors, threat detection and identification, marking of enemy positions, target acquisition, coordination and deconfliction of distributed Join Fires between networked combat vehicles and tanks also inside Manned and Unmanned Teams (MUM-T). AI has been incorporated into military operations in Iraq and Syria.+ − 美国和其他国家正在为一系列军事功能开发人工智能应用程序。人工智能和机器学习的主要军事应用是增强 C2、通信、传感器、集成和互操作性。人工智能研究正在情报收集和分析、后勤、网络操作、信息操作、指挥和控制以及各种半自动和自动车辆等领域进行。人工智能技术能够协调传感器和效应器、威胁探测和识别、标记敌人阵地、目标获取、协调和消除分布式联合火力，在有人和无人小组(MUM-T)内部，联网作战车辆和坦克之间也是如此。大赦国际已被纳入伊拉克和叙利亚的军事行动。+ + − Worldwide annual military spending on robotics rose from US$5.1 billion in 2010 to US$7.5 billion in 2015.<ref>{{cite news|title=Getting to grips with military robotics|url=https://www.economist.com/news/special-report/21735478-autonomous-robots-and-swarms-will-change-nature-warfare-getting-grips|accessdate=7 February 2018|work=The Economist|date=25 January 2018|language=en}}</ref><ref>{{cite web|title=Autonomous Systems: Infographic|url=https://www.siemens.com/innovation/en/home/pictures-of-the-future/digitalization-and-software/autonomous-systems-infographic.html|website=siemens.com|accessdate=7 February 2018|language=en}}</ref> Military drones capable of autonomous action are widely considered a useful asset.<ref>{{Cite web|url=https://www.cnas.org/publications/reports/understanding-chinas-ai-strategy|title=Understanding China's AI Strategy|last=Allen|first=Gregory|date=February 6, 2019|website=www.cnas.org/publications/reports/understanding-chinas-ai-strategy|publisher=Center for a New American Security|archive-url=https://web.archive.org/web/20190317004017/https://www.cnas.org/publications/reports/understanding-chinas-ai-strategy|archive-date=March 17, 2019|url-status=|access-date=March 17, 2019}}</ref> Many artificial intelligence researchers seek to distance themselves from military applications of AI.<ref>{{cite news|last1=Metz|first1=Cade|title=Pentagon Wants Silicon Valley's Help on A.I.|url=https://www.nytimes.com/2018/03/15/technology/military-artificial-intelligence.html|accessdate=19 March 2018|work=The New York Times|date=15 March 2018}}</ref>+ − Worldwide annual military spending on robotics rose from US$5.1 billion in 2010 to US$7.5 billion in 2015. Military drones capable of autonomous action are widely considered a useful asset. Many artificial intelligence researchers seek to distance themselves from military applications of AI.+ − 全球每年在机器人方面的军费开支从2010年的51亿美元增加到2015年的75亿美元。具有自主行动能力的军用无人机被广泛认为是一种有用的资产。许多人工智能研究人员试图与人工智能的军事应用保持距离。+ + + + − === Hospitality === − === Hospitality === − 好客+ − In the hospitality industry, Artificial Intelligence based solutions are used to reduce staff load and increase efficiency<ref>{{cite web|title=Role of AI in travel and Hospitality Industry|url=https://www.infosys.com/industries/travel-hospitality/documents/ai-travel-hospitality.pdf|accessdate=14 January 2020|work=Infosys|date=2018}}</ref> by cutting repetitive tasks frequency, trends analysis, guest interaction, and customer needs prediction.<ref>{{cite web|title=Advanced analytics in hospitality|url=https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/advanced-analytics-in-hospitality|accessdate=14 January 2020|work=McKinsey & Company|date=2017}}</ref> Hotel services backed by Artificial Intelligence are represented in the form of a chatbot,<ref>{{cite web|title=Current applications of Artificial Intelligence in tourism and hospitality|url=https://www.researchgate.net/publication/333242550|accessdate=14 January 2020|work=Sinteza|date=2019}}</ref> application, virtual voice assistant and service robots.+ − In the hospitality industry, Artificial Intelligence based solutions are used to reduce staff load and increase efficiency by cutting repetitive tasks frequency, trends analysis, guest interaction, and customer needs prediction. Hotel services backed by Artificial Intelligence are represented in the form of a chatbot, application, virtual voice assistant and service robots.+ − 在款待业，基于人工智能的解决方案通过减少重复性任务的频率、趋势分析、客户互动和客户需求预测来减少员工负担和提高效率。人工智能支持的酒店服务以聊天机器人、应用程序、虚拟语音助手和服务机器人的形式表现出来。+ + + − === Audit ===+ − === Audit === − 审计署 − For financial statements audit, AI makes continuous audit possible. AI tools could analyze many sets of different information immediately. The potential benefit would be the overall audit risk will be reduced, the level of assurance will be increased and the time duration of audit will be reduced.<ref>{{cite journal|last1=Chang|first1=Hsihui|last2=Kao|first2=Yi-Ching|last3=Mashruwala|first3=Raj|last4=Sorensen|first4=Susan M.|title=Technical Inefficiency, Allocative Inefficiency, and Audit Pricing|journal=Journal of Accounting, Auditing & Finance|volume=33|issue=4|date=10 April 2017|pages=580–600|doi=10.1177/0148558X17696760}}</ref>+ − For financial statements audit, AI makes continuous audit possible. AI tools could analyze many sets of different information immediately. The potential benefit would be the overall audit risk will be reduced, the level of assurance will be increased and the time duration of audit will be reduced.+ − 对于财务报表审计，AI 使持续审计成为可能。人工智能工具可以立即分析多组不同的信息。潜在的好处是总体审计风险将减少，保证水平将提高，审计时间将缩短。+ + + − === Advertising ===+ − === Advertising ===+ − 广告 − It is possible to use AI to predict or generalize the behavior of customers from their [[digital footprints]] in order to target them with personalized promotions or build customer personas automatically.<ref name="Matz et al 2017">Matz, S. C., et al. "Psychological targeting as an effective approach to digital mass persuasion." Proceedings of the National Academy of Sciences (2017): 201710966.</ref> A documented case reports that online gambling companies were using AI to improve customer targeting.<ref>{{cite web |last1=Busby |first1=Mattha |title=Revealed: how bookies use AI to keep gamblers hooked |url=https://www.theguardian.com/technology/2018/apr/30/bookies-using-ai-to-keep-gamblers-hooked-insiders-say |website=the Guardian |language=en |date=30 April 2018}}</ref>+ − It is possible to use AI to predict or generalize the behavior of customers from their digital footprints in order to target them with personalized promotions or build customer personas automatically. A documented case reports that online gambling companies were using AI to improve customer targeting.+ − 这是可能的使用人工智能预测或归纳客户的行为从他们的数字足迹，以个性化的促销目标他们或建立客户角色自动。一个记录在案的案例报告说，在线赌博公司正在使用人工智能来改善客户定位。+ + + + − Moreover, the application of [[Personality computing]] AI models can help reducing the cost of advertising campaigns by adding psychological targeting to more traditional sociodemographic or behavioral targeting.<ref name="Celli et al. 2017">Celli, Fabio, Pietro Zani Massani, and Bruno Lepri. "Profilio: Psychometric Profiling to Boost Social Media Advertising." Proceedings of the 2017 ACM on Multimedia Conference. ACM, 2017 [https://www.researchgate.net/publication/320542489_Profilio_Psychometric_Profiling_to_Boost_Social_Media_Advertising]</ref> − Moreover, the application of Personality computing AI models can help reducing the cost of advertising campaigns by adding psychological targeting to more traditional sociodemographic or behavioral targeting. − 此外，个性计算人工智能模型的应用可以帮助降低广告活动的成本，通过增加心理定位到更传统的社会人口学或行为定位。 + + + − === Art === − + − 艺术+ − − {{Further|Computer art}} − − − − Artificial Intelligence has inspired numerous creative applications including its usage to produce visual art. The exhibition "Thinking Machines: Art and Design in the Computer Age, 1959–1989" at MoMA<ref name="moma">{{Cite web|url=https://www.moma.org/calendar/exhibitions/3863|title=Thinking Machines: Art and Design in the Computer Age, 1959–1989|website=The Museum of Modern Art|language=en|access-date=2019-07-23}}</ref> provides a good overview of the historical applications of AI for art, architecture, and design. Recent exhibitions showcasing the usage of AI to produce art include the Google-sponsored benefit and auction at the Gray Area Foundation in San Francisco, where artists experimented with the [[DeepDream]] algorithm<ref name = wp1>[https://www.washingtonpost.com/news/innovations/wp/2016/03/10/googles-psychedelic-paint-brush-raises-the-oldest-question-in-art/ Retrieved July 29]</ref> and the exhibition "Unhuman: Art in the Age of AI," which took place in Los Angeles and Frankfurt in the fall of 2017.<ref name = sf>{{cite web|url=https://www.statefestival.org/program/2017/unhuman-art-in-the-age-of-ai |title=Unhuman: Art in the Age of AI – State Festival |publisher=Statefestival.org |date= |accessdate=2018-09-13}}</ref><ref name="artsy">{{Cite web|url=https://www.artsy.net/article/artsy-editorial-hard-painting-made-computer-human|title=It's Getting Hard to Tell If a Painting Was Made by a Computer or a Human|last=Chun|first=Rene|date=2017-09-21|website=Artsy|language=en|access-date=2019-07-23}}</ref> In the spring of 2018, the Association of Computing Machinery dedicated a special magazine issue to the subject of computers and art highlighting the role of machine learning in the arts.<ref name = acm>[https://dl.acm.org/citation.cfm?id=3204480.3186697 Retrieved July 29]</ref> The Austrian [[Ars Electronica]] and [[Museum of Applied Arts, Vienna]] opened exhibitions on AI in 2019.<ref name="Ars Electronica Exhibition ''Understanding AI''">{{Cite web|url=https://ars.electronica.art/center/en/exhibitions/ai/ |access-date=September 2019}}</ref><ref name="Museum of Applied Arts Exhibition ''Uncanny Values''">{{Cite web|url=https://www.mak.at/en/program/exhibitions/uncanny_values |access-date=October 2019|title=MAK Wien - MAK Museum Wien}}</ref> The Ars Electronica's 2019 festival "Out of the box" extensively thematized the role of arts for a sustainable societal transformation with AI.<ref name="European Platform for Digital Humanism">{{Cite web|url=https://ars.electronica.art/outofthebox/en/digital-humanism-conf/ |access-date=September 2019}}</ref> − − Artificial Intelligence has inspired numerous creative applications including its usage to produce visual art. The exhibition "Thinking Machines: Art and Design in the Computer Age, 1959–1989" at MoMA provides a good overview of the historical applications of AI for art, architecture, and design. Recent exhibitions showcasing the usage of AI to produce art include the Google-sponsored benefit and auction at the Gray Area Foundation in San Francisco, where artists experimented with the DeepDream algorithm and the exhibition "Unhuman: Art in the Age of AI," which took place in Los Angeles and Frankfurt in the fall of 2017. In the spring of 2018, the Association of Computing Machinery dedicated a special magazine issue to the subject of computers and art highlighting the role of machine learning in the arts. The Austrian Ars Electronica and Museum of Applied Arts, Vienna opened exhibitions on AI in 2019. The Ars Electronica's 2019 festival "Out of the box" extensively thematized the role of arts for a sustainable societal transformation with AI. − − 人工智能激发了许多创造性的应用，包括它在视觉艺术中的应用。在纽约现代艺术博物馆举办的“思考机器: 计算机时代的艺术与设计，1959-1989”展览为艺术、建筑和设计中人工智能的历史应用提供了一个很好的概述。最近的展览展示了人工智能在艺术创作中的应用，包括谷歌赞助的旧金山灰色地带基金会(Gray Area Foundation)的慈善拍卖会，艺术家们在那里尝试了 DeepDream 算法，以及2017年秋天在洛杉矶和法兰克福举办的“非人类: 人工智能时代的艺术”展览。2018年春天，计算机协会专门发行了一期特刊，主题是计算机和艺术，突出了机器学习在艺术中的作用。奥地利电子艺术博物馆和维也纳应用艺术博物馆于2019年开设了人工智能展览。电子艺术节2019年的“跳出框框”广泛地主题化了艺术在可持续社会转型中的作用与人工智能。 − − − − − − == Philosophy and ethics == − − == Philosophy and ethics == − − 哲学和伦理学 − − {{Main|Philosophy of artificial intelligence|Ethics of artificial intelligence}} − − − − There are three philosophical questions related to AI: − − There are three philosophical questions related to AI: − − 有三个与人工智能相关的哲学问题: − − # Is [[artificial general intelligence]] possible? Can a machine solve any problem that a human being can solve using intelligence? Or are there hard limits to what a machine can accomplish? − − Is artificial general intelligence possible? Can a machine solve any problem that a human being can solve using intelligence? Or are there hard limits to what a machine can accomplish? − − 人工智能是可能的吗？机器能解决任何人类能用智能解决的问题吗？或者一台机器所能完成的事情是否有严格的限制？ − − # Are intelligent machines dangerous? How can we ensure that machines behave ethically and that they are used ethically? − − Are intelligent machines dangerous? How can we ensure that machines behave ethically and that they are used ethically? − − 智能机器危险吗？我们怎样才能确保机器的行为符合道德规范，并且它们的使用符合道德规范？ − − # Can a machine have a [[mind]], [[consciousness]] and [[philosophy of mind|mental states]] in exactly the same sense that human beings do? Can a machine be [[Sentience|sentient]], and thus deserve certain rights? Can a machine [[intention]]ally cause harm? − − Can a machine have a mind, consciousness and mental states in exactly the same sense that human beings do? Can a machine be sentient, and thus deserve certain rights? Can a machine intentionally cause harm? − − 机器能否拥有与人类完全相同的思维、意识和精神状态？一台机器是否具有感知能力，因此值得拥有某些权利？机器会故意造成伤害吗？ − − − − − − === The limits of artificial general intelligence === − − === The limits of artificial general intelligence === − − 人工智能的局限性 − − {{Main|Philosophy of AI|Turing test|Physical symbol systems hypothesis|Dreyfus' critique of AI|The Emperor's New Mind|AI effect}} − − − − − − − − Can a machine be intelligent? Can it "think"? − − Can a machine be intelligent? Can it "think"? − − 机器是智能的吗？它能“思考”吗？ − − − − − − ;''[[Computing Machinery and Intelligence|Alan Turing's "polite convention"]]'': We need not decide if a machine can "think"; we need only decide if a machine can act as intelligently as a human being. This approach to the philosophical problems associated with artificial intelligence forms the basis of the [[Turing test]].<ref name="Turing test"/> − − Alan Turing's "polite convention": We need not decide if a machine can "think"; we need only decide if a machine can act as intelligently as a human being. This approach to the philosophical problems associated with artificial intelligence forms the basis of the Turing test. − − 阿兰 · 图灵的“礼貌惯例” : 我们不需要决定一台机器是否可以“思考” ; 我们只需要决定一台机器是否可以像人一样聪明地行动。这种解决与人工智能相关的哲学问题的方法构成了图灵测试的基础。 − − − − − − ;''The [[Dartmouth Workshop|Dartmouth proposal]]'': "Every aspect of learning or any other feature of intelligence can be so precisely described that a machine can be made to simulate it." This conjecture was printed in the proposal for the Dartmouth Conference of 1956.<ref name="Dartmouth proposal"/> − − The Dartmouth proposal: "Every aspect of learning or any other feature of intelligence can be so precisely described that a machine can be made to simulate it." This conjecture was printed in the proposal for the Dartmouth Conference of 1956. − − 达特茅斯学院的建议是: “学习的每一个方面或智能的任何其他特征都可以被精确地描述，以至于一台机器可以被用来模拟它。”这个猜想被印在1956年达特茅斯学院会议的提案中。 − − − − − − ;''[[Physical symbol system|Newell and Simon's physical symbol system hypothesis]]'': "A physical symbol system has the necessary and sufficient means of general intelligent action." Newell and Simon argue that intelligence consists of formal operations on symbols.<ref name="Physical symbol system hypothesis"/> [[Hubert Dreyfus]] argued that, on the contrary, human expertise depends on unconscious instinct rather than conscious symbol manipulation and on having a "feel" for the situation rather than explicit symbolic knowledge. (See [[Dreyfus' critique of AI]].)<ref> − − Newell and Simon's physical symbol system hypothesis: "A physical symbol system has the necessary and sufficient means of general intelligent action." Newell and Simon argue that intelligence consists of formal operations on symbols. Hubert Dreyfus argued that, on the contrary, human expertise depends on unconscious instinct rather than conscious symbol manipulation and on having a "feel" for the situation rather than explicit symbolic knowledge. (See Dreyfus' critique of AI.)<ref> − − 纽威尔和西蒙的物理符号系统假说: “物理符号系统具有一般智能行为的必要和充分的手段。”纽厄尔和西蒙认为，智力是由符号的形式运算组成的。休伯特 · 德雷福斯认为，恰恰相反，人类的专业知识依赖于无意识的本能，而不是有意识的符号操纵，并且依赖于对情况的“感觉” ，而不是明确的符号知识。(见德雷福斯对人工智能的批评。) − − Dreyfus criticized the [[necessary and sufficient|necessary]] condition of the [[physical symbol system]] hypothesis, which he called the "psychological assumption": "The mind can be viewed as a device operating on bits of information according to formal rules." {{Harv|Dreyfus|1992|p=156}}</ref><ref name="Dreyfus' critique"/> − − Dreyfus criticized the necessary condition of the physical symbol system hypothesis, which he called the "psychological assumption": "The mind can be viewed as a device operating on bits of information according to formal rules." </ref> − − 德莱弗斯批评了物理符号系统假设的必要条件，他称之为“心理假设” : “头脑可以被看作是一种按照形式规则操作信息位的装置。”/ 参考 − − − − − − ;''Gödelian arguments'': [[Gödel]] himself,<ref name="Gödel himself"/> [[John Lucas (philosopher)|John Lucas]] (in 1961) and [[Roger Penrose]] (in a more detailed argument from 1989 onwards) made highly technical arguments that human mathematicians can consistently see the truth of their own "Gödel statements" and therefore have computational abilities beyond that of mechanical Turing machines.<ref name="The mathematical objection"/> However, some people do not agree with the "Gödelian arguments".<ref>{{cite web|author1=Graham Oppy|title=Gödel's Incompleteness Theorems|url=http://plato.stanford.edu/entries/goedel-incompleteness/#GdeArgAgaMec|website=[[Stanford Encyclopedia of Philosophy]]|accessdate=27 April 2016|date=20 January 2015|quote=These Gödelian anti-mechanist arguments are, however, problematic, and there is wide consensus that they fail.|author1-link=Graham Oppy}}</ref><ref>{{cite book|author1=Stuart J. Russell|author2-link=Peter Norvig|author2=Peter Norvig|title=Artificial Intelligence: A Modern Approach|date=2010|publisher=[[Prentice Hall]]|location=Upper Saddle River, NJ|isbn=978-0-13-604259-4|edition=3rd|chapter=26.1.2: Philosophical Foundations/Weak AI: Can Machines Act Intelligently?/The mathematical objection|quote=even if we grant that computers have limitations on what they can prove, there is no evidence that humans are immune from those limitations.|title-link=Artificial Intelligence: A Modern Approach|author1-link=Stuart J. Russell}}</ref><ref>Mark Colyvan. An introduction to the philosophy of mathematics. [[Cambridge University Press]], 2012. From 2.2.2, 'Philosophical significance of Gödel's incompleteness results': "The accepted wisdom (with which I concur) is that the Lucas-Penrose arguments fail."</ref> − − Gödelian arguments: Gödel himself, − − 德利安的论点: 德尔本人, − − − − − − ;''The [[artificial brain]] argument'': The brain can be simulated by machines and because brains are intelligent, simulated brains must also be intelligent; thus machines can be intelligent. [[Hans Moravec]], [[Ray Kurzweil]] and others have argued that it is technologically feasible to copy the brain directly into hardware and software and that such a simulation will be essentially identical to the original.<ref name="Brain simulation"/> − − The artificial brain argument: The brain can be simulated by machines and because brains are intelligent, simulated brains must also be intelligent; thus machines can be intelligent. Hans Moravec, Ray Kurzweil and others have argued that it is technologically feasible to copy the brain directly into hardware and software and that such a simulation will be essentially identical to the original. − − 人工大脑的论点: 大脑可以被机器模拟，因为大脑是智能的，模拟的大脑也必须是智能的; 因此机器可以是智能的。汉斯 · 莫拉维克、雷 · 库兹韦尔和其他人认为，在技术上直接将大脑复制到硬件和软件是可行的，而且这种模拟将基本上与原始模拟相同。 − − − − − − ;''The [[AI effect]]'': Machines are ''already'' intelligent, but observers have failed to recognize it. When [[Deep Blue (chess computer)|Deep Blue]] beat [[Garry Kasparov]] in chess, the machine was acting intelligently. However, onlookers commonly discount the behavior of an artificial intelligence program by arguing that it is not "real" intelligence after all; thus "real" intelligence is whatever intelligent behavior people can do that machines still cannot. This is known as the AI Effect: "AI is whatever hasn't been done yet."<!--<ref name="AI Effect"/>--> − − The AI effect: Machines are already intelligent, but observers have failed to recognize it. When Deep Blue beat Garry Kasparov in chess, the machine was acting intelligently. However, onlookers commonly discount the behavior of an artificial intelligence program by arguing that it is not "real" intelligence after all; thus "real" intelligence is whatever intelligent behavior people can do that machines still cannot. This is known as the AI Effect: "AI is whatever hasn't been done yet."<!----> − − 人工智能效应: 机器本来就是智能的，但是观察者却没有意识到这一点。当深蓝在国际象棋比赛中击败加里 · 卡斯帕罗夫时，机器正在聪明地行动。然而，旁观者通常对人工智能程序的行为不屑一顾，认为它根本不是“真正的”智能; 因此，“真正的”智能就是人类能够做到的任何智能行为，而机器仍然做不到。这就是众所周知的人工智能效应: “人工智能就是一切尚未完成的事情。"<!----> − − − − − − === Potential harm{{anchor|Potential_risks_and_moral_reasoning}} === − − === Potential harm === − − === Potential harm === − − Widespread use of artificial intelligence could have [[unintended consequences]] that are dangerous or undesirable. Scientists from the [[Future of Life Institute]], among others, described some short-term research goals to see how AI influences the economy, the laws and ethics that are involved with AI and how to minimize AI security risks. In the long-term, the scientists have proposed to continue optimizing function while minimizing possible security risks that come along with new technologies.<ref>Russel, Stuart., Daniel Dewey, and Max Tegmark. Research Priorities for Robust and Beneficial Artificial Intelligence. AI Magazine 36:4 (2015). 8 December 2016.</ref> − − Widespread use of artificial intelligence could have unintended consequences that are dangerous or undesirable. Scientists from the Future of Life Institute, among others, described some short-term research goals to see how AI influences the economy, the laws and ethics that are involved with AI and how to minimize AI security risks. In the long-term, the scientists have proposed to continue optimizing function while minimizing possible security risks that come along with new technologies. − − 人工智能的广泛使用可能会产生危险或不受欢迎的意外后果。生命未来研究所(Future of Life Institute)等机构的科学家介绍了一些短期研究目标，以了解人工智能如何影响经济、涉及人工智能的法律和道德规范，以及如何将人工智能的安全风险降到最低。从长远来看，科学家们建议继续优化功能，同时最小化伴随新技术而来的可能的安全风险。 − − − − − − The potential negative effects of AI and automation were a major issue for [[Andrew Yang]]'s [[Andrew Yang 2020 presidential campaign|2020 presidential campaign]] in the United States.<ref>{{Cite journal|url=https://www.wired.com/story/andrew-yangs-presidential-bid-is-so-very-21st-century/|title=Andrew Yang's Presidential Bid Is So Very 21st Century|journal=Wired|first=Matt|last=Simon|date=1 April 2019|via=www.wired.com}}</ref> Irakli Beridze, Head of the Centre for Artificial Intelligence and Robotics at UNICRI, United Nations, has expressed that "I think the dangerous applications for AI, from my point of view, would be criminals or large terrorist organizations using it to disrupt large processes or simply do pure harm. [Terrorists could cause harm] via digital warfare, or it could be a combination of robotics, drones, with AI and other things as well that could be really dangerous. And, of course, other risks come from things like job losses. If we have massive numbers of people losing jobs and don't find a solution, it will be extremely dangerous. Things like lethal autonomous weapons systems should be properly governed — otherwise there's massive potential of misuse."<ref>{{Cite web | url=https://futurism.com/artificial-intelligence-experts-fear/amp |title = Five experts share what scares them the most about AI|date = 5 September 2018}}</ref> − − The potential negative effects of AI and automation were a major issue for Andrew Yang's 2020 presidential campaign in the United States. Irakli Beridze, Head of the Centre for Artificial Intelligence and Robotics at UNICRI, United Nations, has expressed that "I think the dangerous applications for AI, from my point of view, would be criminals or large terrorist organizations using it to disrupt large processes or simply do pure harm. [Terrorists could cause harm] via digital warfare, or it could be a combination of robotics, drones, with AI and other things as well that could be really dangerous. And, of course, other risks come from things like job losses. If we have massive numbers of people losing jobs and don't find a solution, it will be extremely dangerous. Things like lethal autonomous weapons systems should be properly governed — otherwise there's massive potential of misuse." − − 人工智能和自动化的潜在负面影响是杨2020年美国总统竞选的一个主要问题。联合国 UNICRI 人工智能和机器人中心主任 Irakli Beridze 表示: ”我认为，从我的观点来看，人工智能的危险应用是犯罪分子或大型恐怖组织利用人工智能破坏大型流程或只是造成纯粹的伤害。(恐怖分子可能通过数字战争造成伤害) ，或者可能是机器人、无人机、人工智能以及其他可能非常危险的东西的结合。当然，其他风险也来自失业这样的事情。如果我们有大量的人失去工作，而且没有找到解决方案，这将是极其危险的。像致命的自主武器系统这样的东西应该得到适当的管理，否则就会有大量的滥用的可能。” − − − − − − ==== Existential risk ==== − − ==== Existential risk ==== − − 世界末日 − − {{Main|Existential risk from artificial general intelligence}} − − − − − − − − Physicist [[Stephen Hawking]], [[Microsoft]] founder [[Bill Gates]], and [[SpaceX]] founder [[Elon Musk]] have expressed concerns about the possibility that AI could evolve to the point that humans could not control it, with Hawking theorizing that this could "[[Global catastrophic risk|spell the end of the human race]]".<ref>{{cite news|last1=Rawlinson|first1=Kevin|title=Microsoft's Bill Gates insists AI is a threat|url=https://www.bbc.co.uk/news/31047780|work=BBC News|accessdate=30 January 2015|url-status=live|archiveurl=https://web.archive.org/web/20150129183607/http://www.bbc.co.uk/news/31047780|archivedate=29 January 2015|df=dmy-all|date=2015-01-29}}</ref><ref name="Holley">{{Cite news|title = Bill Gates on dangers of artificial intelligence: 'I don't understand why some people are not concerned'|url = https://www.washingtonpost.com/news/the-switch/wp/2015/01/28/bill-gates-on-dangers-of-artificial-intelligence-dont-understand-why-some-people-are-not-concerned/|work= The Washington Post|date = 28 January 2015|access-date = 30 October 2015|issn = 0190-8286|first = Peter|last = Holley|url-status=live|archiveurl = https://web.archive.org/web/20151030054330/https://www.washingtonpost.com/news/the-switch/wp/2015/01/28/bill-gates-on-dangers-of-artificial-intelligence-dont-understand-why-some-people-are-not-concerned/|archivedate = 30 October 2015|df = dmy-all}}</ref><ref>{{Cite news|title = Elon Musk: artificial intelligence is our biggest existential threat|url = https://www.theguardian.com/technology/2014/oct/27/elon-musk-artificial-intelligence-ai-biggest-existential-threat|work= The Guardian|accessdate = 30 October 2015|first = Samuel|last = Gibbs|url-status=live|archiveurl = https://web.archive.org/web/20151030054330/http://www.theguardian.com/technology/2014/oct/27/elon-musk-artificial-intelligence-ai-biggest-existential-threat|archivedate = 30 October 2015|df = dmy-all|date = 2014-10-27}}</ref> − − Physicist Stephen Hawking, Microsoft founder Bill Gates, and SpaceX founder Elon Musk have expressed concerns about the possibility that AI could evolve to the point that humans could not control it, with Hawking theorizing that this could "spell the end of the human race". − − 物理学家斯蒂芬 · 霍金、微软创始人比尔 · 盖茨和 SpaceX 公司创始人埃隆 · 马斯克对人工智能进化到人类无法控制的程度表示担忧，霍金认为这可能“意味着人类的终结”。 − − − − − − {{quote|text=The development of full artificial intelligence could spell the end of the human race. Once humans develop artificial intelligence, it will take off on its own and redesign itself at an ever-increasing rate. Humans, who are limited by slow biological evolution, couldn't compete and would be superseded.|author=[[Stephen Hawking]]<ref>{{Cite news|title = Stephen Hawking warns artificial intelligence could end mankind|url = https://www.bbc.com/news/technology-30290540|accessdate = 30 October 2015|url-status=live|archiveurl = https://web.archive.org/web/20151030054329/http://www.bbc.com/news/technology-30290540|archivedate = 30 October 2015|df = dmy-all|work = [[BBC News]]|date = 2014-12-02|last1 = Cellan-Jones|first1 = Rory}}</ref>}} − − − − − − − − In his book ''[[Superintelligence: Paths, Dangers, Strategies|Superintelligence]]'', philosopher [[Nick Bostrom]] provides an argument that artificial intelligence will pose a threat to humankind. He argues that sufficiently intelligent AI, if it chooses actions based on achieving some goal, will exhibit [[Instrumental convergence|convergent]] behavior such as acquiring resources or protecting itself from being shut down. If this AI's goals do not fully reflect humanity's—one example is an AI told to compute as many digits of pi as possible—it might harm humanity in order to acquire more resources or prevent itself from being shut down, ultimately to better achieve its goal. Bostrom also emphasizes the difficulty of fully conveying humanity's values to an advanced AI. He uses the hypothetical example of giving an AI the goal to make humans smile to illustrate a misguided attempt. If the AI in that scenario were to become superintelligent, Bostrom argues, it may resort to methods that most humans would find horrifying, such as inserting "electrodes into the facial muscles of humans to cause constant, beaming grins" because that would be an efficient way to achieve its goal of making humans smile.<ref>{{cite web|url=https://www.ted.com/talks/nick_bostrom_what_happens_when_our_computers_get_smarter_than_we_are/transcript|title=What happens when our computers get smarter than we are?|first=Nick|last=Bostrom|publisher=[[TED (conference)]]|date=2015}}</ref> In his book ''[[Human Compatible]]'', AI researcher [[Stuart J. Russell]] echoes some of Bostrom's concerns while also proposing [[Human Compatible#Russell's three principles|an approach]] to developing provably beneficial machines focused on uncertainty and deference to humans,<ref name="HC">{{cite book |last=Russell |first=Stuart |date=October 8, 2019 |title=Human Compatible: Artificial Intelligence and the Problem of Control |url= |location=United States |publisher=Viking |page= |isbn=978-0-525-55861-3 |author-link=Stuart J. Russell |oclc=1083694322|title-link=Human Compatible }}</ref>{{rp|173}} possibly involving [[Reinforcement learning#Inverse reinforcement learning|inverse reinforcement learning]].<ref name="HC"/>{{rp|191–193}} − − In his book Superintelligence, philosopher Nick Bostrom provides an argument that artificial intelligence will pose a threat to humankind. He argues that sufficiently intelligent AI, if it chooses actions based on achieving some goal, will exhibit convergent behavior such as acquiring resources or protecting itself from being shut down. If this AI's goals do not fully reflect humanity's—one example is an AI told to compute as many digits of pi as possible—it might harm humanity in order to acquire more resources or prevent itself from being shut down, ultimately to better achieve its goal. Bostrom also emphasizes the difficulty of fully conveying humanity's values to an advanced AI. He uses the hypothetical example of giving an AI the goal to make humans smile to illustrate a misguided attempt. If the AI in that scenario were to become superintelligent, Bostrom argues, it may resort to methods that most humans would find horrifying, such as inserting "electrodes into the facial muscles of humans to cause constant, beaming grins" because that would be an efficient way to achieve its goal of making humans smile. In his book Human Compatible, AI researcher Stuart J. Russell echoes some of Bostrom's concerns while also proposing an approach to developing provably beneficial machines focused on uncertainty and deference to humans, possibly involving inverse reinforcement learning. − − 在《超级智能》一书中，哲学家尼克 · 博斯特罗姆提出了一个论点，即人工智能将对人类构成威胁。他认为，足够智能的人工智能，如果它选择行动的基础上实现一些目标，将表现出收敛的行为，如获取资源或保护自己不被关闭。如果这个人工智能的目标不能完全反映人类的情况，比如一个人工智能被告知要尽可能多地计算圆周率的位数，那么它可能会伤害人类，以便获得更多的资源，或者防止自身被关闭，最终更好地实现目标。博斯特罗姆还强调了向高级人工智能充分传达人类价值观的困难。他用一个假设的例子来说明一个误入歧途的尝试: 给人工智能一个目标，让人类微笑。博斯特罗姆认为，如果这种情况下的人工智能变得超级聪明，它可能会采用大多数人类都会感到恐怖的方法，比如“在人类面部肌肉中插入电极，使其产生持续的笑容” ，因为这将是实现让人类微笑的目标的有效方法。人工智能研究人员 Stuart j. Russell 在他的《人类相容》一书中回应了 Bostrom 的一些担忧，同时也提出了一种开发可证明有益的机器的方法，这种机器着眼于不确定性和对人类的尊重，可能涉及逆强化学习。 − − − − − − Concern over risk from artificial intelligence has led to some high-profile donations and investments. A group of prominent tech titans including [[Peter Thiel]], Amazon Web Services and Musk have committed $1 billion to [[OpenAI]], a nonprofit company aimed at championing responsible AI development.<ref>{{cite web|url=https://www.chicagotribune.com/bluesky/technology/ct-tech-titans-against-terminators-20151214-story.html|title=Tech titans like Elon Musk are spending $1 billion to save you from terminators|first=Washington|last=Post|url-status=live|archiveurl=https://web.archive.org/web/20160607121118/http://www.chicagotribune.com/bluesky/technology/ct-tech-titans-against-terminators-20151214-story.html|archivedate=7 June 2016|df=dmy-all}}</ref> The opinion of experts within the field of artificial intelligence is mixed, with sizable fractions both concerned and unconcerned by risk from eventual superhumanly-capable AI.<ref>{{cite journal − − Concern over risk from artificial intelligence has led to some high-profile donations and investments. A group of prominent tech titans including Peter Thiel, Amazon Web Services and Musk have committed $1 billion to OpenAI, a nonprofit company aimed at championing responsible AI development. The opinion of experts within the field of artificial intelligence is mixed, with sizable fractions both concerned and unconcerned by risk from eventual superhumanly-capable AI.<ref>{{cite journal − − 对人工智能风险的担忧导致了一些备受瞩目的捐赠和投资。包括彼得 · 蒂尔、亚马逊网络服务和马斯克在内的一些知名科技巨头已经向 OpenAI 投入了10亿美元，这是一家旨在支持负责任的人工智能开发的非盈利公司。人工智能领域的专家们的意见不一，有相当一部分人既关心也不关心最终具有超人能力的人工智能带来的风险。 文献{ cite journal − − |last1 = Müller − − |last1 = Müller − − |last1 = Müller − − |first1 = Vincent C. − − |first1 = Vincent C. − − 第一名: Vincent c。 − − |last2 = Bostrom − − |last2 = Bostrom − − 2 Bostrom − − |first2 = Nick − − |first2 = Nick − − | first2 Nick − − |year = 2014 − − |year = 2014 − − 2014年 − − |title = Future Progress in Artificial Intelligence: A Poll Among Experts − − |title = Future Progress in Artificial Intelligence: A Poll Among Experts − − 人工智能的未来发展: 专家调查 − − |journal = AI Matters − − |journal = AI Matters − − 人工智能的重要性 − − |volume = 1 − − |volume = 1 − − 第一卷 − − |issue = 1 − − |issue = 1 − − 第一期 − − |pages = 9–11 − − |pages = 9–11 − − 第9-11页 − − |doi = 10.1145/2639475.2639478 − − |doi = 10.1145/2639475.2639478 − − 10.1145 / 2639475.2639478 − − |url = http://www.sophia.de/pdf/2014_PT-AI_polls.pdf − − |url = http://www.sophia.de/pdf/2014_PT-AI_polls.pdf − − Http://www.sophia.de/pdf/2014_pt-ai_polls.pdf − − |url-status = live − − |url-status = live − − 状态直播 − − |archiveurl = https://web.archive.org/web/20160115114604/http://www.sophia.de/pdf/2014_PT-AI_polls.pdf − − |archiveurl = https://web.archive.org/web/20160115114604/http://www.sophia.de/pdf/2014_PT-AI_polls.pdf − − | archiveurl https://web.archive.org/web/20160115114604/http://www.sophia.de/pdf/2014_pt-ai_polls.pdf − − |archivedate = 15 January 2016 − − |archivedate = 15 January 2016 − − 2016年1月15日 − − |df = dmy-all − − |df = dmy-all − − 我不会放过你的 − − }}</ref> Other technology industry leaders believe that artificial intelligence is helpful in its current form and will continue to assist humans. Oracle CEO [[Mark Hurd]] has stated that AI "will actually create more jobs, not less jobs" as humans will be needed to manage AI systems.<ref>{{Cite web|url=https://searcherp.techtarget.com/news/252460208/Oracle-CEO-Mark-Hurd-sees-no-reason-to-fear-ERP-AI|title=Oracle CEO Mark Hurd sees no reason to fear ERP AI|website=SearchERP|language=en|access-date=2019-05-06}}</ref> Facebook CEO [[Mark Zuckerberg]] believes AI will "unlock a huge amount of positive things," such as curing disease and increasing the safety of autonomous cars.<ref>{{Cite web|url=https://www.businessinsider.com/mark-zuckerberg-shares-thoughts-elon-musks-ai-2018-5|title=Mark Zuckerberg responds to Elon Musk's paranoia about AI: 'AI is going to... help keep our communities safe.'|last=|first=|date=25 May 2018|website=Business Insider|access-date=2019-05-06}}</ref> In January 2015, Musk donated $10 million to the [[Future of Life Institute]] to fund research on understanding AI decision making. The goal of the institute is to "grow wisdom with which we manage" the growing power of technology. Musk also funds companies developing artificial intelligence such as [[DeepMind]] and [[Vicarious (company)|Vicarious]] to "just keep an eye on what's going on with artificial intelligence.<ref>{{cite web|title = The mysterious artificial intelligence company Elon Musk invested in is developing game-changing smart computers|url = http://www.techinsider.io/mysterious-artificial-intelligence-company-elon-musk-investment-2015-10|website = Tech Insider|accessdate = 30 October 2015|url-status=live|archiveurl = https://web.archive.org/web/20151030165333/http://www.techinsider.io/mysterious-artificial-intelligence-company-elon-musk-investment-2015-10|archivedate = 30 October 2015|df = dmy-all}}</ref> I think there is potentially a dangerous outcome there."<ref>{{cite web|title = Musk-Backed Group Probes Risks Behind Artificial Intelligence|url = https://www.bloomberg.com/news/articles/2015-07-01/musk-backed-group-probes-risks-behind-artificial-intelligence|website = Bloomberg.com|accessdate = 30 October 2015|first = Jack|last = Clark|url-status=live|archiveurl = https://web.archive.org/web/20151030202356/http://www.bloomberg.com/news/articles/2015-07-01/musk-backed-group-probes-risks-behind-artificial-intelligence|archivedate = 30 October 2015|df = dmy-all}}</ref><ref>{{cite web|title = Elon Musk Is Donating $10M Of His Own Money To Artificial Intelligence Research|url = http://www.fastcompany.com/3041007/fast-feed/elon-musk-is-donating-10m-of-his-own-money-to-artificial-intelligence-research|website = Fast Company|accessdate = 30 October 2015|url-status=live|archiveurl = https://web.archive.org/web/20151030202356/http://www.fastcompany.com/3041007/fast-feed/elon-musk-is-donating-10m-of-his-own-money-to-artificial-intelligence-research|archivedate = 30 October 2015|df = dmy-all|date = 2015-01-15}}</ref> − − }}</ref> Other technology industry leaders believe that artificial intelligence is helpful in its current form and will continue to assist humans. Oracle CEO Mark Hurd has stated that AI "will actually create more jobs, not less jobs" as humans will be needed to manage AI systems. Facebook CEO Mark Zuckerberg believes AI will "unlock a huge amount of positive things," such as curing disease and increasing the safety of autonomous cars. In January 2015, Musk donated $10 million to the Future of Life Institute to fund research on understanding AI decision making. The goal of the institute is to "grow wisdom with which we manage" the growing power of technology. Musk also funds companies developing artificial intelligence such as DeepMind and Vicarious to "just keep an eye on what's going on with artificial intelligence. I think there is potentially a dangerous outcome there." − − 其他技术行业的领导者相信人工智能在目前的形式下是有帮助的，并将继续帮助人类。甲骨文首席执行官马克 · 赫德表示，人工智能“实际上将创造更多的就业机会，而不是更少的就业机会” ，因为管理人工智能系统需要人力。Facebook 首席执行官马克 · 扎克伯格相信人工智能将“解锁大量积极的东西” ，比如治愈疾病和提高自动驾驶汽车的安全性。2015年1月，马斯克向未来生命研究所捐赠了1000万美元，用于研究人工智能决策。该研究所的目标是“用智慧来管理”日益增长的技术力量。马斯克还为 DeepMind 和 Vicarious 等开发人工智能的公司提供资金，以“关注人工智能的发展情况”。我认为这可能会产生危险的后果。” − − − − − − For the danger of uncontrolled advanced AI to be realized, the hypothetical AI would have to overpower or out-think all of humanity, which a minority of experts argue is a possibility far enough in the future to not be worth researching.<ref>{{cite web|title = Is artificial intelligence really an existential threat to humanity?|url = http://thebulletin.org/artificial-intelligence-really-existential-threat-humanity8577|website = Bulletin of the Atomic Scientists|accessdate = 30 October 2015|url-status=live|archiveurl = https://web.archive.org/web/20151030054330/http://thebulletin.org/artificial-intelligence-really-existential-threat-humanity8577|archivedate = 30 October 2015|df = dmy-all|date = 2015-08-09}}</ref><ref>{{cite web|title = The case against killer robots, from a guy actually working on artificial intelligence|url = http://fusion.net/story/54583/the-case-against-killer-robots-from-a-guy-actually-building-ai/|website = Fusion.net|accessdate = 31 January 2016|url-status=live|archiveurl = https://web.archive.org/web/20160204175716/http://fusion.net/story/54583/the-case-against-killer-robots-from-a-guy-actually-building-ai/|archivedate = 4 February 2016|df = dmy-all}}</ref> Other counterarguments revolve around humans being either intrinsically or convergently valuable from the perspective of an artificial intelligence.<ref>{{cite web|title = Will artificial intelligence destroy humanity? Here are 5 reasons not to worry.|url = https://www.vox.com/2014/8/22/6043635/5-reasons-we-shouldnt-worry-about-super-intelligent-computers-taking|website = Vox|accessdate = 30 October 2015|url-status=live|archiveurl = https://web.archive.org/web/20151030092203/http://www.vox.com/2014/8/22/6043635/5-reasons-we-shouldnt-worry-about-super-intelligent-computers-taking|archivedate = 30 October 2015|df = dmy-all|date = 2014-08-22}}</ref> − − For the danger of uncontrolled advanced AI to be realized, the hypothetical AI would have to overpower or out-think all of humanity, which a minority of experts argue is a possibility far enough in the future to not be worth researching. Other counterarguments revolve around humans being either intrinsically or convergently valuable from the perspective of an artificial intelligence. − − 为了实现不受控制的先进人工智能的危险，假设的人工智能必须超越或超越整个人类，一小部分专家认为这种可能性在未来足够遥远，不值得研究。其他反对意见则围绕着从人工智能的角度来看，人类要么具有内在价值，要么具有可交流的价值。 − − − − − − ==== Devaluation of humanity ==== − − ==== Devaluation of humanity ==== − − 人性的贬值 − − {{Main|Computer Power and Human Reason}} − − − − [[Joseph Weizenbaum]] wrote that AI applications cannot, by definition, successfully simulate genuine human empathy and that the use of AI technology in fields such as [[customer service]] or [[psychotherapy]]<ref>In the early 1970s, [[Kenneth Colby]] presented a version of Weizenbaum's [[ELIZA]] known as DOCTOR which he promoted as a serious therapeutic tool. {{Harv|Crevier|1993|pp=132–144}}</ref> was deeply misguided. Weizenbaum was also bothered that AI researchers (and some philosophers) were willing to view the human mind as nothing more than a computer program (a position now known as [[computationalism]]). To Weizenbaum these points suggest that AI research devalues human life.<ref name="Weizenbaum's critique"/> − − Joseph Weizenbaum wrote that AI applications cannot, by definition, successfully simulate genuine human empathy and that the use of AI technology in fields such as customer service or psychotherapy was deeply misguided. Weizenbaum was also bothered that AI researchers (and some philosophers) were willing to view the human mind as nothing more than a computer program (a position now known as computationalism). To Weizenbaum these points suggest that AI research devalues human life. − − 写道，根据定义，人工智能应用程序不能成功地模拟真正的人类移情，并且在诸如客户服务或心理治疗等领域使用人工智能技术是被严重误导的约瑟夫·维森鲍姆。韦岑鲍姆还对人工智能研究人员(以及一些哲学家)愿意将人类思维视为一个计算机程序(现在称为计算主义)而感到困扰。对魏岑鲍姆来说，这些观点表明人工智能研究贬低了人类的生命价值。 − − − − − − ====Social justice==== − − ====Social justice==== − − 社会正义 − − {{further|Algorithmic bias}} − − − − − − − − One concern is that AI programs may be programmed to be biased against certain groups, such as women and minorities, because most of the developers are wealthy Caucasian men.<ref>{{Cite web|url=https://www.channelnewsasia.com/news/commentary/artificial-intelligence-big-data-bias-hiring-loans-key-challenge-11097374|title=Commentary: Bad news. Artificial intelligence is biased|website=CNA}}</ref> Support for artificial intelligence is higher among men (with 47% approving) than women (35% approving). − − One concern is that AI programs may be programmed to be biased against certain groups, such as women and minorities, because most of the developers are wealthy Caucasian men. Support for artificial intelligence is higher among men (with 47% approving) than women (35% approving). − − 人们担心的一个问题是，人工智能程序可能会对某些群体存在偏见，比如女性和少数民族，因为大多数开发者都是富有的白人男性。男性对人工智能的支持率(47%)高于女性(35%)。 − − − − − − Algorithms have a host of applications in today's legal system already, assisting officials ranging from judges to parole officers and public defenders in gauging the predicted likelihood of recidivism of defendants.<ref name="propublica.org">{{Cite web|url=https://www.propublica.org/article/how-we-analyzed-the-compas-recidivism-algorithm|title=How We Analyzed the COMPAS Recidivism Algorithm|last=Jeff Larson|first=Julia Angwin|date=2016-05-23|website=ProPublica|language=en|access-date=2019-07-23}}</ref> COMPAS (an acronym for Correctional Offender Management Profiling for Alternative Sanctions) counts among the most widely utilized commercially available solutions.<ref name="propublica.org"/> It has been suggested that COMPAS assigns an exceptionally elevated risk of recidivism to black defendants while, conversely, ascribing low risk estimate to white defendants significantly more often than statistically expected.<ref name="propublica.org"/> − − Algorithms have a host of applications in today's legal system already, assisting officials ranging from judges to parole officers and public defenders in gauging the predicted likelihood of recidivism of defendants. COMPAS (an acronym for Correctional Offender Management Profiling for Alternative Sanctions) counts among the most widely utilized commercially available solutions. It has been suggested that COMPAS assigns an exceptionally elevated risk of recidivism to black defendants while, conversely, ascribing low risk estimate to white defendants significantly more often than statistically expected. − − 算法在今天的法律体系中已经有了大量的应用，它们可以帮助从法官到假释官员和公设辩护人的官员们评估被告再次犯罪的可能性。Compas (替代制裁惩教罪犯管理特征分析的首字母缩写)是商业上使用最广泛的解决办法之一。有人建议，COMPAS 将非常高的累犯风险分配给黑人被告，而相反，将低风险估计分配给白人被告的频率明显高于统计预期。 − − − − − − ==== Decrease in demand for human labor ==== − − ==== Decrease in demand for human labor ==== − − 减少对人力劳动的需求 − − {{Further|Technological unemployment#21st century}} − − − − The relationship between automation and employment is complicated. While automation eliminates old jobs, it also creates new jobs through micro-economic and macro-economic effects.<ref>E McGaughey, 'Will Robots Automate Your Job Away? Full Employment, Basic Income, and Economic Democracy' (2018) [https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3044448 SSRN, part 2(3)]</ref> Unlike previous waves of automation, many middle-class jobs may be eliminated by artificial intelligence; ''[[The Economist]]'' states that "the worry that AI could do to white-collar jobs what steam power did to blue-collar ones during the Industrial Revolution" is "worth taking seriously".<ref>{{cite news|title=Automation and anxiety|url=https://www.economist.com/news/special-report/21700758-will-smarter-machines-cause-mass-unemployment-automation-and-anxiety|accessdate=13 January 2018|work=The Economist|date=9 May 2015}}</ref> Subjective estimates of the risk vary widely; for example, Michael Osborne and [[Carl Benedikt Frey]] estimate 47% of U.S. jobs are at "high risk" of potential automation, while an OECD report classifies only 9% of U.S.<!-- see report p. 33 table 4; 9% is both the OECD average and the US average --> jobs as "high risk".<ref>{{cite news|last1=Lohr|first1=Steve|title=Robots Will Take Jobs, but Not as Fast as Some Fear, New Report Says|url=https://www.nytimes.com/2017/01/12/technology/robots-will-take-jobs-but-not-as-fast-as-some-fear-new-report-says.html|accessdate=13 January 2018|work=The New York Times|date=2017}}</ref><ref>{{Cite journal|date=1 January 2017|title=The future of employment: How susceptible are jobs to computerisation?|journal=Technological Forecasting and Social Change|volume=114|pages=254–280|doi=10.1016/j.techfore.2016.08.019|issn=0040-1625|last1=Frey|first1=Carl Benedikt|last2=Osborne|first2=Michael A|citeseerx=10.1.1.395.416}}</ref><ref>Arntz, Melanie, Terry Gregory, and Ulrich Zierahn. "The risk of automation for jobs in OECD countries: A comparative analysis." OECD Social, Employment, and Migration Working Papers 189 (2016). p. 33.</ref> Jobs at extreme risk range from paralegals to fast food cooks, while job demand is likely to increase for care-related professions ranging from personal healthcare to the clergy.<ref>{{cite news|last1=Mahdawi|first1=Arwa|title=What jobs will still be around in 20 years? Read this to prepare your future|url=https://www.theguardian.com/us-news/2017/jun/26/jobs-future-automation-robots-skills-creative-health|accessdate=13 January 2018|work=The Guardian|date=26 June 2017}}</ref> Author [[Martin Ford (author)|Martin Ford]] and others go further and argue that many jobs are routine, repetitive and (to an AI) predictable; Ford warns that these jobs may be automated in the next couple of decades, and that many of the new jobs may not be "accessible to people with average capability", even with retraining. Economists point out that in the past technology has tended to increase rather than reduce total employment, but acknowledge that "we're in uncharted territory" with AI.<ref name="guardian jobs debate">{{cite news|last1=Ford|first1=Martin|last2=Colvin|first2=Geoff|title=Will robots create more jobs than they destroy?|url=https://www.theguardian.com/technology/2015/sep/06/will-robots-create-destroy-jobs|accessdate=13 January 2018|work=The Guardian|date=6 September 2015}}</ref> − − The relationship between automation and employment is complicated. While automation eliminates old jobs, it also creates new jobs through micro-economic and macro-economic effects. Unlike previous waves of automation, many middle-class jobs may be eliminated by artificial intelligence; The Economist states that "the worry that AI could do to white-collar jobs what steam power did to blue-collar ones during the Industrial Revolution" is "worth taking seriously". Subjective estimates of the risk vary widely; for example, Michael Osborne and Carl Benedikt Frey estimate 47% of U.S. jobs are at "high risk" of potential automation, while an OECD report classifies only 9% of U.S.<!-- see report p. 33 table 4; 9% is both the OECD average and the US average --> jobs as "high risk". Jobs at extreme risk range from paralegals to fast food cooks, while job demand is likely to increase for care-related professions ranging from personal healthcare to the clergy. Author Martin Ford and others go further and argue that many jobs are routine, repetitive and (to an AI) predictable; Ford warns that these jobs may be automated in the next couple of decades, and that many of the new jobs may not be "accessible to people with average capability", even with retraining. Economists point out that in the past technology has tended to increase rather than reduce total employment, but acknowledge that "we're in uncharted territory" with AI. − − 自动化与就业的关系是复杂的。自动化在消除旧工作的同时，也通过微观经济和宏观经济效应创造了新的就业机会。与以往的自动化浪潮不同，许多中产阶级的工作可能会被人工智能淘汰; 《经济学人》指出，“人工智能对白领工作的影响，就像工业革命时期蒸汽动力对蓝领工作的影响一样，值得认真对待”。对风险的主观估计差别很大，例如，迈克尔 · 奥斯本和卡尔 · 贝内迪克特 · 弗雷估计，美国47% 的工作是潜在自动化的“高风险” ，而经合组织的报告仅将美国9% 的工作分类为“高风险”——见报告第33页表4; 9% 是经合组织的平均水平和美国的平均水平——工作是“高风险”。从律师助理到快餐厨师等职业都面临着极大的风险，而从个人医疗保健到神职人员等护理相关职业的就业需求可能会增加。作家马丁•福特(Martin Ford)和其他人进一步指出，许多工作都是常规的、重复的，(对人工智能而言)是可以预测的。福特警告称，这些工作可能在未来几十年内实现自动化，而且即便进行再培训，许多新工作也可能“无法让能力一般的人获得”。经济学家指出，在过去，技术往往会增加而不是减少总就业人数，但他们承认，人工智能“正处于未知领域”。 − − − − − − ==== Autonomous weapons ==== − − ==== Autonomous weapons ==== − − 自动化武器 − − {{See also|Lethal autonomous weapon}} − − − − Currently, 50+ countries are researching battlefield robots, including the United States, China, Russia, and the United Kingdom. Many people concerned about risk from superintelligent AI also want to limit the use of artificial soldiers and drones.<ref>{{cite web|title = Stephen Hawking, Elon Musk, and Bill Gates Warn About Artificial Intelligence|url = http://observer.com/2015/08/stephen-hawking-elon-musk-and-bill-gates-warn-about-artificial-intelligence/|website = Observer|accessdate = 30 October 2015|url-status=live|archiveurl = https://web.archive.org/web/20151030053323/http://observer.com/2015/08/stephen-hawking-elon-musk-and-bill-gates-warn-about-artificial-intelligence/|archivedate = 30 October 2015|df = dmy-all|date = 2015-08-19}}</ref> − − Currently, 50+ countries are researching battlefield robots, including the United States, China, Russia, and the United Kingdom. Many people concerned about risk from superintelligent AI also want to limit the use of artificial soldiers and drones. − − 目前，包括美国、中国、俄罗斯和英国在内的50多个国家正在研究战场机器人。许多人担心来自超级智能人工智能的风险，也希望限制人造士兵和无人机的使用。 − − − − − − === Ethical machines === − − === Ethical machines === − − 道德的机器 − − Machines with intelligence have the potential to use their intelligence to prevent harm and minimize the risks; they may have the ability to use [[ethics|ethical reasoning]] to better choose their actions in the world. As such, there is a need for policy making to devise policies for and regulate artificial intelligence and robotics.<ref>{{Cite journal|last=Iphofen|first=Ron|last2=Kritikos|first2=Mihalis|date=2019-01-03|title=Regulating artificial intelligence and robotics: ethics by design in a digital society|journal=Contemporary Social Science|pages=1–15|doi=10.1080/21582041.2018.1563803|issn=2158-2041}}</ref> Research in this area includes [[machine ethics]], [[artificial moral agents]], [[friendly AI]] and discussion towards building a [[human rights]] framework is also in talks.<ref>{{cite_web|url=https://www.voanews.com/episode/ethical-ai-learns-human-rights-framework-4087171|title=Ethical AI Learns Human Rights Framework|accessdate=10 November 2019|website=Voice of America}}</ref> − − Machines with intelligence have the potential to use their intelligence to prevent harm and minimize the risks; they may have the ability to use ethical reasoning to better choose their actions in the world. As such, there is a need for policy making to devise policies for and regulate artificial intelligence and robotics. Research in this area includes machine ethics, artificial moral agents, friendly AI and discussion towards building a human rights framework is also in talks. − − 具有智能的机器有潜力利用它们的智能来防止伤害和减少风险; 它们可能有能力利用伦理推理来更好地选择它们在世界上的行动。因此，有必要制定政策，为人工智能和机器人制定和规范政策。这一领域的研究包括机器伦理学、人工道德代理、友好的人工智能以及关于建立人权框架的讨论也正在进行。 − − − − − − ==== Artificial moral agents ==== − − ==== Artificial moral agents ==== − − 人为的道德行为者 − − Wendell Wallach introduced the concept of [[artificial moral agents]] (AMA) in his book ''Moral Machines''<ref>Wendell Wallach (2010). ''Moral Machines'', Oxford University Press.</ref> For Wallach, AMAs have become a part of the research landscape of artificial intelligence as guided by its two central questions which he identifies as "Does Humanity Want Computers Making Moral Decisions"<ref>Wallach, pp 37–54.</ref> and "Can (Ro)bots Really Be Moral".<ref>Wallach, pp 55–73.</ref> For Wallach, the question is not centered on the issue of ''whether'' machines can demonstrate the equivalent of moral behavior in contrast to the ''constraints'' which society may place on the development of AMAs.<ref>Wallach, Introduction chapter.</ref> − − Wendell Wallach introduced the concept of artificial moral agents (AMA) in his book Moral Machines For Wallach, AMAs have become a part of the research landscape of artificial intelligence as guided by its two central questions which he identifies as "Does Humanity Want Computers Making Moral Decisions" and "Can (Ro)bots Really Be Moral". For Wallach, the question is not centered on the issue of whether machines can demonstrate the equivalent of moral behavior in contrast to the constraints which society may place on the development of AMAs. − − 温德尔•沃勒克(Wendell Wallach)在他的著作《沃勒克的道德机器》(Moral Machines For Wallach)中提出了人工道德代理人(AMA)的概念，在这两个核心问题的指导下，AMA 已经成为人工智能研究领域的一部分。他将这两个核心问题定义为“人类是否希望计算机做出道德决策”和“。对于 Wallach 来说，这个问题并不集中在机器是否能够证明道德行为的等价性，与社会可能对研究性行为的发展施加的限制形成对比。 − − − − − − ==== Machine ethics ==== − − ==== Machine ethics ==== − − 机器伦理学 − − {{Main|Machine ethics}} − − − − The field of machine ethics is concerned with giving machines ethical principles, or a procedure for discovering a way to resolve the ethical dilemmas they might encounter, enabling them to function in an ethically responsible manner through their own ethical decision making.<ref name="autogenerated1">Michael Anderson and Susan Leigh Anderson (2011), Machine Ethics, Cambridge University Press.</ref> The field was delineated in the AAAI Fall 2005 Symposium on Machine Ethics: "Past research concerning the relationship between technology and ethics has largely focused on responsible and irresponsible use of technology by human beings, with a few people being interested in how human beings ought to treat machines. In all cases, only human beings have engaged in ethical reasoning. The time has come for adding an ethical dimension to at least some machines. Recognition of the ethical ramifications of behavior involving machines, as well as recent and potential developments in machine autonomy, necessitate this. In contrast to computer hacking, software property issues, privacy issues and other topics normally ascribed to computer ethics, machine ethics is concerned with the behavior of machines towards human users and other machines. Research in machine ethics is key to alleviating concerns with autonomous systems—it could be argued that the notion of autonomous machines without such a dimension is at the root of all fear concerning machine intelligence. Further, investigation of machine ethics could enable the discovery of problems with current ethical theories, advancing our thinking about Ethics."<ref name="autogenerated2">{{cite web|url=http://www.aaai.org/Library/Symposia/Fall/fs05-06 |title=Machine Ethics |work=aaai.org |url-status=dead |archiveurl=https://web.archive.org/web/20141129044821/http://www.aaai.org/Library/Symposia/Fall/fs05-06 |archivedate=29 November 2014 }}</ref> Machine ethics is sometimes referred to as machine morality, computational ethics or computational morality. A variety of perspectives of this nascent field can be found in the collected edition "Machine Ethics"<ref name="autogenerated1"/> that stems from the AAAI Fall 2005 Symposium on Machine Ethics.<ref name="autogenerated2"/> − − The field of machine ethics is concerned with giving machines ethical principles, or a procedure for discovering a way to resolve the ethical dilemmas they might encounter, enabling them to function in an ethically responsible manner through their own ethical decision making. The field was delineated in the AAAI Fall 2005 Symposium on Machine Ethics: "Past research concerning the relationship between technology and ethics has largely focused on responsible and irresponsible use of technology by human beings, with a few people being interested in how human beings ought to treat machines. In all cases, only human beings have engaged in ethical reasoning. The time has come for adding an ethical dimension to at least some machines. Recognition of the ethical ramifications of behavior involving machines, as well as recent and potential developments in machine autonomy, necessitate this. In contrast to computer hacking, software property issues, privacy issues and other topics normally ascribed to computer ethics, machine ethics is concerned with the behavior of machines towards human users and other machines. Research in machine ethics is key to alleviating concerns with autonomous systems—it could be argued that the notion of autonomous machines without such a dimension is at the root of all fear concerning machine intelligence. Further, investigation of machine ethics could enable the discovery of problems with current ethical theories, advancing our thinking about Ethics." Machine ethics is sometimes referred to as machine morality, computational ethics or computational morality. A variety of perspectives of this nascent field can be found in the collected edition "Machine Ethics" that stems from the AAAI Fall 2005 Symposium on Machine Ethics. − − 机器伦理学领域关注的是给予机器伦理原则，或者一种程序，用于发现一种解决它们可能遇到的伦理困境的方法，使它们能够通过自己的伦理决策以一种伦理上负责任的方式运作。2005年美国科学促进会秋季机器伦理学专题讨论会阐述了这一领域: ”过去关于技术与伦理学之间关系的研究主要侧重于人类负责任和不负责任地使用技术，少数人对人类应当如何对待机器感兴趣。在所有情况下，只有人类参与了伦理推理。现在是时候给至少一些机器增加一个道德层面了。认识到涉及机器的行为的道德后果，以及机器自主性的最新和潜在发展，使这成为必要。与计算机黑客行为、软件产权问题、隐私问题和其他通常归因于计算机道德的主题不同，机器道德关注的是机器对人类用户和其他机器的行为。机器伦理学的研究是减轻人们对自主系统担忧的关键ーー可以说，没有这种维度的自主机器概念是人们对机器智能担忧的根源。此外，对机器伦理学的研究可以发现当前伦理学理论的问题，推进我们对伦理学的思考。”机器伦理学有时被称为机器道德、计算伦理学或计算伦理学。这个新兴领域的各种观点可以在 AAAI 秋季2005年机器伦理学研讨会上收集的“机器伦理学”版本中找到。 − − − − − − ==== Malevolent and friendly AI ==== − − ==== Malevolent and friendly AI ==== − − 邪恶而友好的人工智能 − − {{Main|Friendly AI}} − − − − Political scientist [[Charles T. Rubin]] believes that AI can be neither designed nor guaranteed to be benevolent.<ref>{{cite journal|last=Rubin |first=Charles |authorlink=Charles T. Rubin |date=Spring 2003 |title=Artificial Intelligence and Human Nature|journal=The New Atlantis |volume=1 |pages=88–100 |url=http://www.thenewatlantis.com/publications/artificial-intelligence-and-human-nature |url-status=dead |archiveurl=https://web.archive.org/web/20120611115223/http://www.thenewatlantis.com/publications/artificial-intelligence-and-human-nature |archivedate=11 June 2012 |df=dmy}}</ref> He argues that "any sufficiently advanced benevolence may be indistinguishable from malevolence." Humans should not assume machines or robots would treat us favorably because there is no ''a priori'' reason to believe that they would be sympathetic to our system of morality, which has evolved along with our particular biology (which AIs would not share). Hyper-intelligent software may not necessarily decide to support the continued existence of humanity and would be extremely difficult to stop. This topic has also recently begun to be discussed in academic publications as a real source of risks to civilization, humans, and planet Earth. − − Political scientist Charles T. Rubin believes that AI can be neither designed nor guaranteed to be benevolent. He argues that "any sufficiently advanced benevolence may be indistinguishable from malevolence." Humans should not assume machines or robots would treat us favorably because there is no a priori reason to believe that they would be sympathetic to our system of morality, which has evolved along with our particular biology (which AIs would not share). Hyper-intelligent software may not necessarily decide to support the continued existence of humanity and would be extremely difficult to stop. This topic has also recently begun to be discussed in academic publications as a real source of risks to civilization, humans, and planet Earth. − − 政治科学家查尔斯 · 鲁宾认为，人工智能既不能被设计，也不能保证是仁慈的。他认为“任何足够先进的善行可能与恶意难以区分。”人类不应该假设机器或机器人会对我们好，因为没有先验的理由相信他们会同情我们的道德体系，这个体系是随着我们特定的生物进化而来的(人工智能不会同意这一点)。超智能软件可能不一定决定支持人类的继续存在，并且将极难停止。最近学术出版物也开始讨论这个话题，认为它是对文明、人类和地球造成风险的真正来源。 − − − − − − One proposal to deal with this is to ensure that the first generally intelligent AI is '[[Friendly AI]]' and will be able to control subsequently developed AIs. Some question whether this kind of check could actually remain in place. − − One proposal to deal with this is to ensure that the first generally intelligent AI is 'Friendly AI' and will be able to control subsequently developed AIs. Some question whether this kind of check could actually remain in place. − − 解决这个问题的一个建议是确保第一个普遍具有智能的人工智能是“友好的人工智能” ，并能够随后控制已发展的人工智能。一些人质疑这种检查是否真的能够保持不变。 − − − − − − Leading AI researcher [[Rodney Brooks]] writes, "I think it is a mistake to be worrying about us developing malevolent AI anytime in the next few hundred years. I think the worry stems from a fundamental error in not distinguishing the difference between the very real recent advances in a particular aspect of AI and the enormity and complexity of building sentient volitional intelligence."<ref>{{cite web|last=Brooks|first=Rodney|title=artificial intelligence is a tool, not a threat|date=10 November 2014|url=http://www.rethinkrobotics.com/artificial-intelligence-tool-threat/|url-status=dead|archiveurl=https://web.archive.org/web/20141112130954/http://www.rethinkrobotics.com/artificial-intelligence-tool-threat/|archivedate=12 November 2014|df=dmy-all}}</ref> − − Leading AI researcher Rodney Brooks writes, "I think it is a mistake to be worrying about us developing malevolent AI anytime in the next few hundred years. I think the worry stems from a fundamental error in not distinguishing the difference between the very real recent advances in a particular aspect of AI and the enormity and complexity of building sentient volitional intelligence." − − 首席人工智能研究员罗德尼 · 布鲁克斯写道: “我认为担心我们在未来几百年的任何时候发展出恶毒的人工智能都是错误的。我认为，这种担忧源于一个根本性的错误，即没有区分人工智能某个特定方面非常现实的最新进展与构建有意识的意志智能的艰巨性和复杂性之间的区别。” − − − − − − === Machine consciousness, sentience and mind === − − === Machine consciousness, sentience and mind === − − 机器意识、知觉和思维 − − {{Main|Artificial consciousness}} − − − − If an AI system replicates all key aspects of human intelligence, will that system also be [[Sentience|sentient]]—will it have a [[mind]] which has [[consciousness|conscious experiences]]? This question is closely related to the philosophical problem as to the nature of human consciousness, generally referred to as the [[hard problem of consciousness]]. − − If an AI system replicates all key aspects of human intelligence, will that system also be sentient—will it have a mind which has conscious experiences? This question is closely related to the philosophical problem as to the nature of human consciousness, generally referred to as the hard problem of consciousness. − − 如果一个人工智能系统复制了人类智能的所有关键方面，那么这个系统是否也具有感知能力ーー它是否有一个拥有有意识经验的头脑？这个问题与人类意识本质的哲学问题密切相关，一般称之为意识的难题。 − − − − − − ==== Consciousness ==== − − ==== Consciousness ==== − − 意识 − − {{Main|Hard problem of consciousness|Theory of mind}} − − − − [[David Chalmers]] identified two problems in understanding the mind, which he named the "hard" and "easy" problems of consciousness.<ref name=Chalmers>{{cite journal |url=http://www.imprint.co.uk/chalmers.html |title=Facing up to the problem of consciousness |last=Chalmers |first=David |authorlink=David Chalmers |journal=[[Journal of Consciousness Studies]] |volume= 2 |issue=3 |year=1995 |pages=200–219}} See also [http://consc.net/papers/facing.html this link] − − David Chalmers identified two problems in understanding the mind, which he named the "hard" and "easy" problems of consciousness.<ref name=Chalmers> See also [http://consc.net/papers/facing.html this link] − − 大卫 · 查尔默斯在理解心智方面提出了两个问题，他称之为意识的“困难”和“容易”问题。 参考文献名称 chalmers 查看 http://consc.net/papers/facing.html 链接 − − </ref> The easy problem is understanding how the brain processes signals, makes plans and controls behavior. The hard problem is explaining how this ''feels'' or why it should feel like anything at all. Human [[information processing]] is easy to explain, however human [[subjective experience]] is difficult to explain. − − </ref> The easy problem is understanding how the brain processes signals, makes plans and controls behavior. The hard problem is explaining how this feels or why it should feel like anything at all. Human information processing is easy to explain, however human subjective experience is difficult to explain. − − 简单的问题是理解大脑如何处理信号，制定计划和控制行为。困难的问题是如何解释这种感觉或者为什么它应该感觉像任何东西。人类的信息处理过程很容易解释，然而人类的感质却很难解释。 − − − − − − For example, consider what happens when a person is shown a color swatch and identifies it, saying "it's red". The easy problem only requires understanding the machinery in the brain that makes it possible for a person to know that the color swatch is red. The hard problem is that people also know something else—they also know ''what red looks like''. (Consider that a person born blind can know that something is red without knowing what red looks like.){{efn|This is based on [[Mary's Room]], a thought experiment first proposed by [[Frank Cameron Jackson|Frank Jackson]] in 1982}} Everyone knows subjective experience exists, because they do it every day (e.g., all sighted people know what red looks like). The hard problem is explaining how the brain creates it, why it exists, and how it is different from knowledge and other aspects of the brain. − − For example, consider what happens when a person is shown a color swatch and identifies it, saying "it's red". The easy problem only requires understanding the machinery in the brain that makes it possible for a person to know that the color swatch is red. The hard problem is that people also know something else—they also know what red looks like. (Consider that a person born blind can know that something is red without knowing what red looks like.) Everyone knows subjective experience exists, because they do it every day (e.g., all sighted people know what red looks like). The hard problem is explaining how the brain creates it, why it exists, and how it is different from knowledge and other aspects of the brain. − − 例如，考虑当一个人看到一个颜色样本并识别它，说“它是红色的”时会发生什么。这个简单的问题只需要理解大脑中的机制，使一个人有可能知道色块是红色的。困难的问题是，人们还知道其他一些东西ーー他们也知道红色是什么样子。(想象一下，一个天生失明的人，即使不知道红色是什么样子，也能知道什么是红色。)每个人都知道感质的存在，因为他们每天都这样做(例如，所有视力正常的人都知道红色是什么样子)。困难的问题是解释大脑如何创造它，为什么它存在，以及它如何不同于知识和大脑的其他方面。 − − − − − − ==== Computationalism and functionalism ==== − − ==== Computationalism and functionalism ==== − − 计算主义和功能主义 − − {{Main|Computationalism|Functionalism (philosophy of mind)}} − − − − Computationalism is the position in the [[philosophy of mind]] that the human mind or the human brain (or both) is an information processing system and that thinking is a form of computing.<ref>[[Steven Horst|Horst, Steven]], (2005) [http://plato.stanford.edu/entries/computational-mind/ "The Computational Theory of Mind"] in ''The Stanford Encyclopedia of Philosophy''</ref> Computationalism argues that the relationship between mind and body is similar or identical to the relationship between software and hardware and thus may be a solution to the [[mind-body problem]]. This philosophical position was inspired by the work of AI researchers and cognitive scientists in the 1960s and was originally proposed by philosophers [[Jerry Fodor]] and [[Hilary Putnam]]. − − Computationalism is the position in the philosophy of mind that the human mind or the human brain (or both) is an information processing system and that thinking is a form of computing. Computationalism argues that the relationship between mind and body is similar or identical to the relationship between software and hardware and thus may be a solution to the mind-body problem. This philosophical position was inspired by the work of AI researchers and cognitive scientists in the 1960s and was originally proposed by philosophers Jerry Fodor and Hilary Putnam. − − 计算主义是心智哲学的立场，认为人类心智或人类大脑(或两者)是一个信息处理系统，思维是一种计算形式。计算主义认为，思想和身体之间的关系与软件和硬件之间的关系是相似或相同的，因此可能是一个解决方案的心身二分法。这一哲学立场的灵感来自于20世纪60年代人工智能研究人员和认知科学家的工作，最初由哲学家杰里 · 福多和希拉里 · 普特南提出。 − − − − − − ==== Strong AI hypothesis ==== − − ==== Strong AI hypothesis ==== − − 强人工智能假说 − − {{Main|Chinese room}} − − − − The philosophical position that [[John Searle]] has named [[strong AI hypothesis|"strong AI"]] states: "The appropriately programmed computer with the right inputs and outputs would thereby have a mind in exactly the same sense human beings have minds."<ref name="Searle's strong AI"/> Searle counters this assertion with his [[Chinese room]] argument, which asks us to look ''inside'' the computer and try to find where the "mind" might be.<ref name="Chinese room"/> − − The philosophical position that John Searle has named "strong AI" states: "The appropriately programmed computer with the right inputs and outputs would thereby have a mind in exactly the same sense human beings have minds." Searle counters this assertion with his Chinese room argument, which asks us to look inside the computer and try to find where the "mind" might be. − − 约翰 · 塞尔称之为“强人工智能”的哲学立场指出: “具有正确输入和输出的适当程序计算机，将因此拥有与人类拥有头脑完全相同的意义上的头脑。”塞尔用他的中文房间论点反驳了这种说法，他要求我们看看电脑内部，并试图找出“思维”可能在哪里。 − − − − − − ==== Robot rights ==== − − ==== Robot rights ==== − − 机器人的权利 − − {{Main|Robot rights}} − − − − If a machine can be created that has intelligence, could it also ''[[sentience|feel]]''? If it can feel, does it have the same rights as a human? This issue, now known as "[[robot rights]]", is currently being considered by, for example, California's [[Institute for the Future]], although many critics believe that the discussion is premature.<ref name="Robot rights"/> Some critics of [[transhumanism]] argue that any hypothetical robot rights would lie on a spectrum with [[animal rights]] and human rights. <ref Name="Evans 2015">{{cite journal | last = Evans | first = Woody | authorlink = Woody Evans | title = Posthuman Rights: Dimensions of Transhuman Worlds | journal = Teknokultura | volume = 12 | issue = 2 | date = 2015 | df = dmy-all | doi = 10.5209/rev_TK.2015.v12.n2.49072 | doi-access = free }}</ref> The subject is profoundly discussed in the 2010 documentary film ''[[Plug & Pray]]'',<ref>{{cite web|url=http://www.plugandpray-film.de/en/content.html|title=Content: Plug & Pray Film – Artificial Intelligence – Robots -|author=maschafilm|work=plugandpray-film.de|url-status=live|archiveurl=https://web.archive.org/web/20160212040134/http://www.plugandpray-film.de/en/content.html|archivedate=12 February 2016|df=dmy-all}}</ref> and many sci fi media such as [[Star Trek]] Next Generation, with the character of [[Commander Data]], who fought being disassembled for research, and wanted to "become human", and the robotic holograms in Voyager. − − If a machine can be created that has intelligence, could it also feel? If it can feel, does it have the same rights as a human? This issue, now known as "robot rights", is currently being considered by, for example, California's Institute for the Future, although many critics believe that the discussion is premature. The subject is profoundly discussed in the 2010 documentary film Plug & Pray, and many sci fi media such as Star Trek Next Generation, with the character of Commander Data, who fought being disassembled for research, and wanted to "become human", and the robotic holograms in Voyager. − − 如果可以创造出一台有智能的机器，那么它是否也有感觉呢？如果它有感觉，它是否拥有与人类同样的权利？这个问题，现在被称为“机器人权利” ，目前正在考虑，例如，加利福尼亚的未来研究所，尽管许多批评家认为这种讨论为时过早。2010年的纪录片《即插即祈》(Plug & Pray)以及《星际迷航: 下一代》(Star Trek Next Generation)等许多科幻媒体都对这个主题进行了深入讨论。这些媒体的角色是指挥官戴塔(Data) ，他为了研究而反抗被拆解，并希望“变成人类” ，还有旅行者号上的机器人全息图。 − − − − − − === Superintelligence === − − === Superintelligence === − − 超级智能 − − {{Main|Superintelligence}} − − − − Are there limits to how intelligent machines—or human-machine hybrids—can be? A superintelligence, hyperintelligence, or superhuman intelligence is a hypothetical agent that would possess intelligence far surpassing that of the brightest and most gifted human mind. ''Superintelligence'' may also refer to the form or degree of intelligence possessed by such an agent.<ref name="Roberts"/> − − Are there limits to how intelligent machines—or human-machine hybrids—can be? A superintelligence, hyperintelligence, or superhuman intelligence is a hypothetical agent that would possess intelligence far surpassing that of the brightest and most gifted human mind. Superintelligence may also refer to the form or degree of intelligence possessed by such an agent. − − 智能机器——或者说人机混合体——能达到的程度有限吗？超级智能、超级智能或者超人智能是一种假想的智能体，它拥有的智能远远超过最聪明、最有天赋的人类智慧。超级智能也可以指这种智能体所拥有的智能的形式或程度。 − − − − − − ==== Technological singularity ==== − − ==== Technological singularity ==== − − 技术奇异点 − − {{Main|Technological singularity|Moore's law}} − − − − If research into [[artificial general intelligence|Strong AI]] produced sufficiently intelligent software, it might be able to reprogram and improve itself. The improved software would be even better at improving itself, leading to [[Intelligence explosion|recursive self-improvement]].<ref name="recurse"/> The new intelligence could thus increase exponentially and dramatically surpass humans. Science fiction writer [[Vernor Vinge]] named this scenario "[[technological singularity|singularity]]".<ref name=Singularity/> Technological singularity is when accelerating progress in technologies will cause a runaway effect wherein artificial intelligence will exceed human intellectual capacity and control, thus radically changing or even ending civilization. Because the capabilities of such an intelligence may be impossible to comprehend, the technological singularity is an occurrence beyond which events are unpredictable or even unfathomable.<ref name=Singularity/><ref name="Roberts"/> − − If research into Strong AI produced sufficiently intelligent software, it might be able to reprogram and improve itself. The improved software would be even better at improving itself, leading to recursive self-improvement. The new intelligence could thus increase exponentially and dramatically surpass humans. Science fiction writer Vernor Vinge named this scenario "singularity". Technological singularity is when accelerating progress in technologies will cause a runaway effect wherein artificial intelligence will exceed human intellectual capacity and control, thus radically changing or even ending civilization. Because the capabilities of such an intelligence may be impossible to comprehend, the technological singularity is an occurrence beyond which events are unpredictable or even unfathomable. − − 如果对强大人工智能的研究产生了足够智能的软件，那么它也许能够重新编程并改进自己。改进后的软件甚至可以更好地改进自己，从而实现递归的自我改进。这种新的智能因此可以呈指数增长，并大大超过人类。科幻作家 Vernor Vinge 将这种情况命名为“奇点”。本世纪技术奇异点，技术的加速发展将导致一种失控的后果，即人工智能将超越人类智力和控制能力，从而彻底改变甚至终结文明。因为这样的情报的能力可能是不可能理解的，技术奇异点是一个发生的事件是不可预测的，甚至是深不可测的。 − − − − − − [[Ray Kurzweil]] has used [[Moore's law]] (which describes the relentless exponential improvement in digital technology) to calculate that [[desktop computer]]s will have the same processing power as human brains by the year 2029, and predicts that the singularity will occur in 2045.<ref name=Singularity/> − − Ray Kurzweil has used Moore's law (which describes the relentless exponential improvement in digital technology) to calculate that desktop computers will have the same processing power as human brains by the year 2029, and predicts that the singularity will occur in 2045. − − 雷 · 库兹韦尔利用摩尔定律(描述了数字技术无情的指数增长)计算出，到2029年，台式电脑的处理能力将与人类大脑相当，并预测奇点将出现在2045年。 − − − − − − ==== Transhumanism ==== − − ==== Transhumanism ==== − − 超人主义 − − {{Main|Transhumanism}} − − − − − − − − Robot designer [[Hans Moravec]], cyberneticist [[Kevin Warwick]] and inventor [[Ray Kurzweil]] have predicted that humans and machines will merge in the future into [[cyborg]]s that are more capable and powerful than either.<ref name="Transhumanism"/> This idea, called [[transhumanism]], has roots in [[Aldous Huxley]] and [[Robert Ettinger]]. − − Robot designer Hans Moravec, cyberneticist Kevin Warwick and inventor Ray Kurzweil have predicted that humans and machines will merge in the future into cyborgs that are more capable and powerful than either. This idea, called transhumanism, has roots in Aldous Huxley and Robert Ettinger. − − 机器人设计师汉斯 · 莫拉维克、控制论专家凯文 · 沃里克和发明家雷 · 库兹韦尔预言，人类和机器将在未来合并成为比两者都更有能力和力量的半机器人。这种观点被称为“超人主义”(transhumanism) ，起源于 Aldous Huxley 和罗伯特•艾廷格(Robert Ettinger)。 − − − − − − [[Edward Fredkin]] argues that "artificial intelligence is the next stage in evolution", an idea first proposed by [[Samuel Butler (novelist)|Samuel Butler]]'s "[[Darwin among the Machines]]" as far back as 1863, and expanded upon by [[George Dyson (science historian)|George Dyson]] in his book of the same name in 1998.<ref name="AI as evolution"/> − − Edward Fredkin argues that "artificial intelligence is the next stage in evolution", an idea first proposed by Samuel Butler's "Darwin among the Machines" as far back as 1863, and expanded upon by George Dyson in his book of the same name in 1998. − − 爱德华•弗雷德金(Edward Fredkin)认为，“人工智能是进化的下一个阶段”。早在1863年，塞缪尔•巴特勒(Samuel Butler)的《机器中的达尔文》(Darwin among the Machines)就首次提出了这一观点，乔治•戴森(George Dyson)在1998年的同名著作中对其进。 − − − − − − == Economics == − − == Economics == − − 经济学 − − The long-term economic effects of AI are uncertain. A survey of economists showed disagreement about whether the increasing use of robots and AI will cause a substantial increase in long-term [[unemployment]], but they generally agree that it could be a net benefit, if [[productivity]] gains are [[Redistribution of income and wealth|redistributed]].<ref>{{Cite web|url=http://www.igmchicago.org/surveys/robots-and-artificial-intelligence|title=Robots and Artificial Intelligence|last=|first=|date=|website=www.igmchicago.org|access-date=2019-07-03}}</ref> A February 2020 European Union white paper on artificial intelligence advocated for artificial intelligence for economic benefits, including "improving healthcare (e.g. making diagnosis more precise, enabling better prevention of diseases), increasing the efficiency of farming, contributing to climate change mitigation and adaptation, [and] improving the efficiency of production systems through predictive maintenance", while acknowledging potential risks.<ref name=":1" /> − − The long-term economic effects of AI are uncertain. A survey of economists showed disagreement about whether the increasing use of robots and AI will cause a substantial increase in long-term unemployment, but they generally agree that it could be a net benefit, if productivity gains are redistributed. A February 2020 European Union white paper on artificial intelligence advocated for artificial intelligence for economic benefits, including "improving healthcare (e.g. making diagnosis more precise, enabling better prevention of diseases), increasing the efficiency of farming, contributing to climate change mitigation and adaptation, [and] improving the efficiency of production systems through predictive maintenance", while acknowledging potential risks. − − 人工智能的长期经济效应是不确定的。一项针对经济学家的调查显示，对于机器人和人工智能的日益使用是否会导致长期失业率大幅上升，人们的意见存在分歧。但他们普遍认为，如果生产率提高的成果得到重新分配，这可能是一。2020年2月，欧盟发表了一份关于人工智能的白皮书，主张为了经济利益而使用人工智能，其中包括“改善医疗保健(例如:。使诊断更加精确，能够更好地预防疾病) ，提高耕作效率，有助于减缓和适应气候变化，(以及)通过预测性维护提高生产系统的效率” ，同时承认潜在风险。 − − − − − − == Regulation == − − == Regulation == − − 规例 − − {{Main|Regulation of artificial intelligence|Regulation of algorithms}} − − − − The development of public sector policies for promoting and regulating artificial intelligence (AI) is considered necessary to both encourage AI and manage associated risks, but challenging.<ref>{{Cite journal|last=Wirtz|first=Bernd W.|last2=Weyerer|first2=Jan C.|last3=Geyer|first3=Carolin|date=2018-07-24|title=Artificial Intelligence and the Public Sector—Applications and Challenges|journal=International Journal of Public Administration|volume=42|issue=7|pages=596–615|doi=10.1080/01900692.2018.1498103|issn=0190-0692}}</ref> In 2017 [[Elon Musk]] called for regulation of AI development.<ref>{{cite news|url=https://www.npr.org/sections/thetwo-way/2017/07/17/537686649/elon-musk-warns-governors-artificial-intelligence-poses-existential-risk|title=Elon Musk Warns Governors: Artificial Intelligence Poses 'Existential Risk'|work=NPR.org|accessdate=27 November 2017|language=en}}</ref> Multiple states now have national policies under development or in place,<ref>{{Cite book|last=Campbell|first=Thomas A.|url=http://www.unicri.it/in_focus/files/Report_AI-An_Overview_of_State_Initiatives_FutureGrasp_7-23-19.pdf|title=Artificial Intelligence: An Overview of State Initiatives|publisher=FutureGrasp, LLC|year=2019|isbn=|location=Evergreen, CO|pages=}}</ref> and in February 2020, the European Union published its draft strategy paper for promoting and regulating AI.<ref name=":12">{{Cite book|last=|first=|url=https://ec.europa.eu/info/sites/info/files/commission-white-paper-artificial-intelligence-feb2020_en.pdf|title=White Paper: On Artificial Intelligence - A European approach to excellence and trust|publisher=European Commission|year=2020|isbn=|location=Brussels|pages=1}}</ref> − − The development of public sector policies for promoting and regulating artificial intelligence (AI) is considered necessary to both encourage AI and manage associated risks, but challenging. In 2017 Elon Musk called for regulation of AI development. Multiple states now have national policies under development or in place, and in February 2020, the European Union published its draft strategy paper for promoting and regulating AI. − − 为鼓励人工智能和管理相关风险，制定促进和规范人工智能的公共部门政策被认为是必要的，但具有挑战性。2017年，埃隆 · 马斯克呼吁监管人工智能的发展。多个国家现在正在制定或实施国家政策，2020年2月，欧洲联盟公布了促进和管理人工智能的战略文件草案。 − − − − − − == In fiction == − − == In fiction == − − 在小说里 − − {{Main|Artificial intelligence in fiction}} − − − − [[File:Capek play.jpg|thumb|The word "robot" itself was coined by [[Karel Čapek]] in his 1921 play ''[[R.U.R.]]'', the title standing for "[[Rossum's Universal Robots]]"]] − − The word "robot" itself was coined by [[Karel Čapek in his 1921 play R.U.R., the title standing for "Rossum's Universal Robots"]] − − “机器人”这个词本身是由[[ Karel apek 在他1921年的戏剧《 r.u.r. 》中创造的，这部戏剧的名字代表“ Rossum 的万能机器人”] − − − − − − Thought-capable artificial beings appeared as storytelling devices since antiquity,<ref name="AI in myth"/> − − Thought-capable artificial beings appeared as storytelling devices since antiquity, − − 有思想能力的人造生物自古以来就作为讲故事的工具出现, − − and have been a persistent theme in [[science fiction]]. − − and have been a persistent theme in science fiction. − − 一直是科幻小说中的一个永恒主题。 − − − − − − A common [[Trope (literature)|trope]] in these works began with [[Mary Shelley]]'s ''[[Frankenstein]]'', where a human creation becomes a threat to its masters. This includes such works as [[2001: A Space Odyssey (novel)|Arthur C. Clarke's]] and [[2001: A Space Odyssey (film)|Stanley Kubrick's]] ''[[2001: A Space Odyssey]]'' (both 1968), with [[HAL 9000]], the murderous computer in charge of the ''[[Discovery One]]'' spaceship, as well as ''[[The Terminator]]'' (1984) and ''[[The Matrix]]'' (1999). In contrast, the rare loyal robots such as Gort from ''[[The Day the Earth Stood Still]]'' (1951) and Bishop from ''[[Aliens (film)|Aliens]]'' (1986) are less prominent in popular culture.<ref>{{cite journal|last1=Buttazzo|first1=G.|title=Artificial consciousness: Utopia or real possibility?|journal=[[Computer (magazine)|Computer]]|date=July 2001|volume=34|issue=7|pages=24–30|doi=10.1109/2.933500|df=dmy-all}}</ref> − A common trope in these works began with Mary Shelley's Frankenstein, where a human creation becomes a threat to its masters. This includes such works as Arthur C. Clarke's and Stanley Kubrick's 2001: A Space Odyssey (both 1968), with HAL 9000, the murderous computer in charge of the Discovery One spaceship, as well as The Terminator (1984) and The Matrix (1999). In contrast, the rare loyal robots such as Gort from The Day the Earth Stood Still (1951) and Bishop from Aliens (1986) are less prominent in popular culture.+ − 在这些作品中，一个常见的比喻开始于玛丽 · 雪莱的《弗兰肯斯坦》 ，在这部作品中，人类的创造物成为了对其主人的威胁。这些作品包括《亚瑟·查理斯·克拉克斯坦利 · 库布里克的《2001: 太空漫游》(2001: a Space Odyssey，都是1968年出品) ，包括哈尔9000(HAL 9000) ，负责发现一号飞船的凶残计算机，以及《终结者》(The Terminator，1984)和《黑客帝国》(The Matrix，1999)。相比之下，像《地球停止转动的日子》(1951)中的格特和《异形》(1986)中的主教这样罕见的忠诚机器人在流行文化中就不那么突出了。 − − − − − − [[Isaac Asimov]] introduced the [[Three Laws of Robotics]] in many books and stories, most notably the "Multivac" series about a super-intelligent computer of the same name. Asimov's laws are often brought up during lay discussions of machine ethics;<ref>Anderson, Susan Leigh. "Asimov's "three laws of robotics" and machine metaethics." AI & Society 22.4 (2008): 477–493.</ref> while almost all artificial intelligence researchers are familiar with Asimov's laws through popular culture, they generally consider the laws useless for many reasons, one of which is their ambiguity.<ref>{{cite journal | last1 = McCauley | first1 = Lee | year = 2007 | title = AI armageddon and the three laws of robotics | url = | journal = Ethics and Information Technology | volume = 9 | issue = 2| pages = 153–164 | doi=10.1007/s10676-007-9138-2| citeseerx = 10.1.1.85.8904}}</ref> − − Isaac Asimov introduced the Three Laws of Robotics in many books and stories, most notably the "Multivac" series about a super-intelligent computer of the same name. Asimov's laws are often brought up during lay discussions of machine ethics; while almost all artificial intelligence researchers are familiar with Asimov's laws through popular culture, they generally consider the laws useless for many reasons, one of which is their ambiguity. − − 艾萨克 · 阿西莫夫在许多书籍和故事中介绍了机器人三定律，最著名的是关于同名的超级智能计算机的“ multitvac”系列。几乎所有的人工智能研究人员都通过流行文化熟悉阿西莫夫的法律，他们通常认为这些法律因为许多原因而无用，其中一个原因就是它们的模糊性。 第3,293行：
第2,495行： − 漫画《攻壳机动队和科幻小说《沙丘》探讨了超人类主义(人类和机器的结合)。20世纪80年代，艺术家 Hajime Sorayama 的性感机器人系列在日本绘制并出版，描绘了真实的有机人类形体，拥有栩栩如生的金属肌肉皮肤，后来又出版了《雌蕊》一书，该书被乔治 · 卢卡斯等电影制作人使用或影响。Sorayama 从来没有认为这些有机机器人是真实的自然的一部分，但总是非自然的产品的人类心灵，一个幻想存在于头脑中，甚至当实际形式实现。+ − − 第3,303行：
第2,503行： − 一些作品使用人工智能迫使我们面对是什么让我们成为人类这一根本问题，向我们展示了人工智能，它们有感知的能力，因此也有受苦的能力。这出现在卡雷尔 · 阿佩克的电影《人工智能》中。人工智能和机器人，以及菲利普 · k · 迪克的小说《机器人会梦见电子羊吗? 》。迪克认为，我们对人类主观性的理解被人工智能创造的技术所改变。+ − 第3,326行：
第2,525行： − + − + − − − − * ''[[A.I. Rising]]'' − + − + − + − + − + − + − + − + − + − + − + − + − + − + − + − + − + − +
无编辑摘要
此词条已由Thingamabob初步翻译。
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In computer science, artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence<!--boldface per WP:R#PLA--> displayed by humans and animals. Leading AI textbooks define the field as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Colloquially, the term "artificial intelligence" is often used to describe machines (or computers) that mimic "cognitive" functions that humans associate with the human mind, such as "learning" and "problem solving".
In computer science, artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence<!--boldface per WP:R#PLA--> displayed by humans and animals. Leading AI textbooks define the field as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Colloquially, the term "artificial intelligence" is often used to describe machines (or computers) that mimic "cognitive" functions that humans associate with the human mind, such as "learning" and "problem solving".
'''<font color=#ff8000>人工智能 Artificial Intelligence,AI</font>'''，在计算机科学中亦称'''<font color=#ff8000>机器智能 Machine Intelligence</font>'''。与人和其他动物表现出的'''<font color=#ff8000>自然智能 Nature Intelligence</font>'''相反，AI指由人制造出来的机器所表现出来的智能。前沿AI的教科书把AI定义为对“智能体”的研究：智能体指任何感知周围环境并采取行动以最大化其成功实现目标的机会的机器。通俗来说，“AI”就是机器模仿人类与人类大脑相关的“认知”功能：例如“学习”和“解决问题”
As machines become increasingly capable, tasks considered to require "intelligence" are often removed from the definition of AI, a phenomenon known as the [[AI effect]].<ref>{{Harvnb|McCorduck|2004|p=204}}</ref> A quip in Tesler's Theorem says "AI is whatever hasn't been done yet."<ref>{{Cite web|url=http://people.cs.georgetown.edu/~maloof/cosc270.f17/cosc270-intro-handout.pdf|title=Artificial Intelligence: An Introduction, p. 37|last=Maloof|first=Mark|date=|website=georgetown.edu|access-date=}}</ref> For instance, [[optical character recognition]] is frequently excluded from things considered to be AI,<ref>{{cite web|url=https://hackernoon.com/how-ai-is-getting-groundbreaking-changes-in-talent-management-and-hr-tech-d24ty3zzd|title= How AI Is Getting Groundbreaking Changes In Talent Management And HR Tech|publisher=Hackernoon}}</ref> having become a routine technology.<ref>{{cite magazine |last=Schank |first=Roger C. |title=Where's the AI |magazine=AI magazine |volume=12 |issue=4 |year=1991|p=38}}</ref> Modern machine capabilities generally classified as AI include successfully [[natural language understanding|understanding human speech]],{{sfn|Russell|Norvig|2009}} competing at the highest level in [[strategic game]] systems (such as [[chess]] and [[Go (game)|Go]]),<ref name="bbc-alphago"/> [[autonomous car|autonomously operating cars]], intelligent routing in [[content delivery network]]s, and [[military simulations]]<ref>{{Cite web|url=https://www.ai.mil/docs/Understanding%20AI%20Technology.pdf|title=Department of Defense Joint AI Center - Understanding AI Technology|last=Allen|first=Gregory|date=April 2020|website=AI.mil - The official site of the Department of Defense Joint Artificial Intelligence Center|url-status=live|archive-url=|archive-date=|access-date=25 April 2020}}</ref>.
As machines become increasingly capable, tasks considered to require "intelligence" are often removed from the definition of AI, a phenomenon known as the [[AI effect]].<ref>{{Harvnb|McCorduck|2004|p=204}}</ref> A quip in Tesler's Theorem says "AI is whatever hasn't been done yet."<ref>{{Cite web|url=http://people.cs.georgetown.edu/~maloof/cosc270.f17/cosc270-intro-handout.pdf|title=Artificial Intelligence: An Introduction, p. 37|last=Maloof|first=Mark|date=|website=georgetown.edu|access-date=}}</ref> For instance, [[optical character recognition]] is frequently excluded from things considered to be AI,<ref>{{cite web|url=https://hackernoon.com/how-ai-is-getting-groundbreaking-changes-in-talent-management-and-hr-tech-d24ty3zzd|title= How AI Is Getting Groundbreaking Changes In Talent Management And HR Tech|publisher=Hackernoon}}</ref> having become a routine technology.<ref>{{cite magazine |last=Schank |first=Roger C. |title=Where's the AI |magazine=AI magazine |volume=12 |issue=4 |year=1991|p=38}}</ref> Modern machine capabilities generally classified as AI include successfully [[natural language understanding|understanding human speech]],{{sfn|Russell|Norvig|2009}} competing at the highest level in [[strategic game]] systems (such as [[chess]] and [[Go (game)|Go]]),<ref name="bbc-alphago"/> [[autonomous car|autonomously operating cars]], intelligent routing in [[content delivery network]]s, and [[military simulations]]<ref>{{Cite web|url=https://www.ai.mil/docs/Understanding%20AI%20Technology.pdf|title=Department of Defense Joint AI Center - Understanding AI Technology|last=Allen|first=Gregory|date=April 2020|website=AI.mil - The official site of the Department of Defense Joint Artificial Intelligence Center|url-status=live|archive-url=|archive-date=|access-date=25 April 2020}}</ref>.
As machines become increasingly capable, tasks considered to require "intelligence" are often removed from the definition of AI, a phenomenon known as the AI effect. A quip in Tesler's Theorem says "AI is whatever hasn't been done yet." For instance, optical character recognition is frequently excluded from things considered to be AI, having become a routine technology. Modern machine capabilities generally classified as AI include successfully understanding human speech, competing at the highest level in strategic game systems (such as chess and Go),.
As machines become increasingly capable, tasks considered to require "intelligence" are often removed from the definition of AI, a phenomenon known as the AI effect. A quip in Tesler's Theorem says "AI is whatever hasn't been done yet." For instance, optical character recognition is frequently excluded from things considered to be AI, having become a routine technology. Modern machine capabilities generally classified as AI include successfully understanding human speech, competing at the highest level in strategic game systems (such as chess and Go),
AI的范围也有争议：随着机器的能力越来越大，很多被认为需要“智能”的任务不再被认为是AI。这就是所谓的AI效应。'''<font color=#f32cd32>特斯勒定理</font>'''巧妙地把AI描述为“AI是任何还没有实现的东西。”所以比如光学字符识别就不再被认为属于AI行列，而已经成为了一种常规技术。被认为是AI的现代机器功能包括自然语言理解，在策略型游戏中完成高水平的竞赛（例如国际象棋和围棋）,自动驾驶汽车，内容分发网络和兵棋推演的智能规划。
--[[用户:Thingamabob|Thingamabob]]（[[用户讨论:Thingamabob|讨论]]）1. tasks considered to require "intelligence" are often removed from the definition of AI,很多被认为需要“智能”的任务不再被认为是AI 一句为意译 ；2.Tesler's Theorem（暂译为特斯勒定理）未找到确切翻译；
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Artificial intelligence was founded as an academic discipline in 1955, and in the years since has experienced several waves of optimism, followed by disappointment and the loss of funding (known as an "AI winter"), followed by new approaches, success and renewed funding. For most of its history, AI research has been divided into sub-fields that often fail to communicate with each other. These sub-fields are based on technical considerations, such as particular goals (e.g. "robotics" or "machine learning"), the use of particular tools ("logic" or artificial neural networks), or deep philosophical differences. Sub-fields have also been based on social factors (particular institutions or the work of particular researchers).
Artificial intelligence was founded as an academic discipline in 1955, and in the years since has experienced several waves of optimism, followed by disappointment and the loss of funding (known as an "AI winter"), followed by new approaches, success and renewed funding. For most of its history, AI research has been divided into sub-fields that often fail to communicate with each other. These sub-fields are based on technical considerations, such as particular goals (e.g. "robotics" or "machine learning"), the use of particular tools ("logic" or artificial neural networks), or deep philosophical differences. Sub-fields have also been based on social factors (particular institutions or the work of particular researchers).
1955年AI作为一门学科被建立起来，后来经历过几段乐观时期，但不久就陷入了亏损以及缺乏资金的困境（也就是“AI寒冬”），后来又找到了新的出路，取得了新的成果和新的投资。对于大多数描述AI的历史，AI研究被划分为互不关联的子领域。通常把技术作为划分依据，比如特殊对象（例如“机器人”或者“机器学习”），特殊工具的使用（“逻辑”或者人工神经网络），或者在哲学层面深层次的区别。子领域的划分也与社会因素有关（比如特殊机构或者特殊研究者所做的工作）。
The traditional problems (or goals) of AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception and the ability to move and manipulate objects. General intelligence is among the field's long-term goals. Approaches include statistical methods, computational intelligence, and traditional symbolic AI. Many tools are used in AI, including versions of search and mathematical optimization, artificial neural networks, and methods based on statistics, probability and economics. The AI field draws upon computer science, information engineering, mathematics, psychology, linguistics, philosophy, and many other fields.
The traditional problems (or goals) of AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception and the ability to move and manipulate objects. General intelligence is among the field's long-term goals. Approaches include statistical methods, computational intelligence, and traditional symbolic AI. Many tools are used in AI, including versions of search and mathematical optimization, artificial neural networks, and methods based on statistics, probability and economics. The AI field draws upon computer science, information engineering, mathematics, psychology, linguistics, philosophy, and many other fields.
AI研究的传统问题或者说目标包括'''<font color=#ff8000>自动推理 Automated Reasoning</font> '''，'''<font color=#ff8000>知识表示 Knowledge Representation</font>'''，'''<font color=#ff8000>自动规划 Automated Planning and Scheduling</font>'''，学习，'''<font color=#ff8000>自然语言处理 Natural Language Processing</font>'''，感知以及移动和熟练操控物体的能力。实现通用AI目前仍然是该领域的长远目标。比较流行的研究方法包括统计方法，计算智能和传统AI所用的符号计算。目前有大量的工具应用于AI，其中包括搜索和数学优化、人工神经网络以及基于概率论和经济学的算法。AI领域涉及计算机科学，数学，心理学，语言学，哲学及其他学科。
The field was founded on the assumption that human intelligence "can be so precisely described that a machine can be made to simulate it". This raises philosophical arguments about the nature of the mind and the ethics of creating artificial beings endowed with human-like intelligence. These issues have been explored by myth, fiction and philosophy since antiquity. Others believe that AI, unlike previous technological revolutions, will create a risk of mass unemployment.
The field was founded on the assumption that human intelligence "can be so precisely described that a machine can be made to simulate it". This raises philosophical arguments about the nature of the mind and the ethics of creating artificial beings endowed with human-like intelligence. These issues have been explored by myth, fiction and philosophy since antiquity. Others believe that AI, unlike previous technological revolutions, will create a risk of mass unemployment.
这一领域是建立在人类智能“可以被精确描述从而使机器可以模拟”的观点上的。从古代起就有一些神话、小说以及哲学探讨了关于思维的本质和创造AI体伦理方面的哲学争论。一些人也认为AI如果发展太快会对人类造成威胁。另一些人认为AI与以前的技术革命不同，它将带来大规模失业的风险。
In the twenty-first century, AI techniques have experienced a resurgence following concurrent advances in computer power, large amounts of data, and theoretical understanding; and AI techniques have become an essential part of the technology industry, helping to solve many challenging problems in computer science, software engineering and operations research.
In the twenty-first century, AI techniques have experienced a resurgence following concurrent advances in computer power, large amounts of data, and theoretical understanding; and AI techniques have become an essential part of the technology industry, helping to solve many challenging problems in computer science, software engineering and operations research.
在二十一世纪，随着计算机能力、大量数据和理论认识的同步发展，人工智能技术经历了一次复兴; 人工智能技术已成为技术工业的重要组成部分，帮助解决了计算机科学、软件工程和运筹学中的许多具有挑战性的问题。
在二十一世纪，随着计算机能力、大量数据和理论认识的同步发展，人工智能技术经历了一次复兴; 人工智能技术已成为技术工业的重要组成部分，帮助解决了计算机科学、软件工程和运筹学中的许多具有在21世纪，AI技术经历了一次复兴，同时计算机性能，大数据以及理论理解等方面有了进步；AI技术已经变成技术产业的不可或缺的部分，在计算机科学、软件工程、运筹学等领域解决了许多有挑战性的问题。
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Thought-capable artificial beings appeared as storytelling devices in antiquity, and have been common in fiction, as in Mary Shelley's Frankenstein or Karel Čapek's R.U.R. (Rossum's Universal Robots).<!-- PLEASE DON'T ADD MORE EXAMPLES. THIS IS ENOUGH. SEE SECTION AT BOTTOM OF ARTICLE ON SPECULATION.--> These characters and their fates raised many of the same issues now discussed in the ethics of artificial intelligence.
Thought-capable artificial beings appeared as storytelling devices in antiquity, and have been common in fiction, as in Mary Shelley's Frankenstein or Karel Čapek's R.U.R. (Rossum's Universal Robots).<!-- PLEASE DON'T ADD MORE EXAMPLES. THIS IS ENOUGH. SEE SECTION AT BOTTOM OF ARTICLE ON SPECULATION.--> These characters and their fates raised many of the same issues now discussed in the ethics of artificial intelligence.
具有思维能力的人造生物在古代以故事的方式出现，在小说中也很常见。比如玛丽 · 雪莱的《弗兰肯斯坦》和卡雷尔 · 阿佩克的《 r.u.r. 》(Rossum's Universal Robots) ——小说中的角色和他们的命运向人们提出了许多现在在人工智能伦理学中讨论的同样的问题。
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! ——主要智力先驱: 逻辑学、计算理论、控制论、信息论、早期神经网络
! ——主要智能前体: 逻辑学、计算理论、控制论、信息论、早期神经网络
The study of mechanical or [[formal reasoning|"formal" reasoning]] began with [[philosopher]]s and mathematicians in antiquity. The study of mathematical logic led directly to [[Alan Turing]]'s [[theory of computation]], which suggested that a machine, by shuffling symbols as simple as "0" and "1", could simulate any conceivable act of mathematical deduction. This insight, that digital computers can simulate any process of formal reasoning, is known as the [[Church–Turing thesis]].<ref name="Formal reasoning"/> Along with concurrent discoveries in [[Neuroscience|neurobiology]], [[information theory]] and [[cybernetic]]s, this led researchers to consider the possibility of building an electronic brain. Turing proposed changing the question from whether a machine was intelligent, to "whether or not it is possible for machinery to show intelligent behaviour".<ref>{{Citation | last = Turing | first = Alan | authorlink=Alan Turing | year=1948 | chapter=Machine Intelligence | title = The Essential Turing: The ideas that gave birth to the computer age | editor=Copeland, B. Jack | isbn = 978-0-19-825080-7 | publisher = Oxford University Press | location = Oxford | page = 412 }}</ref> The first work that is now generally recognized as AI was [[Warren McCullouch|McCullouch]] and [[Walter Pitts|Pitts]]' 1943 formal design for [[Turing-complete]] "artificial neurons".{{sfn|Russell|Norvig|2009|p=16}}
The study of mechanical or [[formal reasoning|"formal" reasoning]] began with [[philosopher]]s and mathematicians in antiquity. The study of mathematical logic led directly to [[Alan Turing]]'s [[theory of computation]], which suggested that a machine, by shuffling symbols as simple as "0" and "1", could simulate any conceivable act of mathematical deduction. This insight, that digital computers can simulate any process of formal reasoning, is known as the [[Church–Turing thesis]].<ref name="Formal reasoning"/> Along with concurrent discoveries in [[Neuroscience|neurobiology]], [[information theory]] and [[cybernetic]]s, this led researchers to consider the possibility of building an electronic brain. Turing proposed changing the question from whether a machine was intelligent, to "whether or not it is possible for machinery to show intelligent behaviour".<ref>{{Citation | last = Turing | first = Alan | authorlink=Alan Turing | year=1948 | chapter=Machine Intelligence | title = The Essential Turing: The ideas that gave birth to the computer age | editor=Copeland, B. Jack | isbn = 978-0-19-825080-7 | publisher = Oxford University Press | location = Oxford | page = 412 }}</ref> The first work that is now generally recognized as AI was [[Warren McCullouch|McCullouch]] and [[Walter Pitts|Pitts]]' 1943 formal design for [[Turing-complete]] "artificial neurons".{{sfn|Russell|Norvig|2009|p=16}}
The study of mechanical or "formal" reasoning began with philosophers and mathematicians in antiquity. The study of mathematical logic led directly to Alan Turing's theory of computation, which suggested that a machine, by shuffling symbols as simple as "0" and "1", could simulate any conceivable act of mathematical deduction. This insight, that digital computers can simulate any process of formal reasoning, is known as the Church–Turing thesis. The first work that is now generally recognized as AI was McCullouch and Pitts' 1943 formal design for Turing-complete "artificial neurons".
The study of mechanical or "formal" reasoning began with philosophers and mathematicians in antiquity. The study of mathematical logic led directly to Alan Turing's theory of computation, which suggested that a machine, by shuffling symbols as simple as "0" and "1", could simulate any conceivable act of mathematical deduction. This insight, that digital computers can simulate any process of formal reasoning, is known as the Church–Turing thesis. The first work that is now generally recognized as AI was McCullouch and Pitts' 1943 formal design for Turing-complete "artificial neurons".
机械化或者说“形式化”推理的研究始于古代的哲学家和数学家。这些数理逻辑的研究直接催生了图灵的计算理论，即机器可以通过移动如“0”和“1”的简单的符号，就能模拟任何数学推论可以想到的过程，这一观点被称为'''<font color=#ff8000>邱奇-图灵论题 Church–Turing Thesis</font>'''。图灵提出“如果人类无法区分机器和人类的回应，那么机器可以被认为是“智能的”。目前人们公认的最早的AI工作是由麦卡洛和皮茨在1943年正式设计的图灵完备“人工神经”。
--[[用户:Thingamabob|Thingamabob]]（[[用户讨论:Thingamabob|讨论]]）图灵提出“如果人类无法区分机器和人类的回应，那么机器可以被认为是“智能的” 一句为从原版wiki上补充的
The field of AI research was born at a workshop at Dartmouth College in 1956, Attendees Allen Newell (CMU), Herbert Simon (CMU), John McCarthy (MIT), Marvin Minsky (MIT) and Arthur Samuel (IBM) became the founders and leaders of AI research. (and by 1959 were reportedly playing better than the average human), solving word problems in algebra, proving logical theorems (Logic Theorist, first run c. 1956) and speaking English. By the middle of the 1960s, research in the U.S. was heavily funded by the Department of Defense and laboratories had been established around the world. AI's founders were optimistic about the future: Herbert Simon predicted, "machines will be capable, within twenty years, of doing any work a man can do". Marvin Minsky agreed, writing, "within a generation ... the problem of creating 'artificial intelligence' will substantially be solved".
The field of AI research was born at a workshop at Dartmouth College in 1956, Attendees Allen Newell (CMU), Herbert Simon (CMU), John McCarthy (MIT), Marvin Minsky (MIT) and Arthur Samuel (IBM) became the founders and leaders of AI research. (and by 1959 were reportedly playing better than the average human), solving word problems in algebra, proving logical theorems (Logic Theorist, first run c. 1956) and speaking English. By the middle of the 1960s, research in the U.S. was heavily funded by the Department of Defense and laboratories had been established around the world. AI's founders were optimistic about the future: Herbert Simon predicted, "machines will be capable, within twenty years, of doing any work a man can do". Marvin Minsky agreed, writing, "within a generation ... the problem of creating 'artificial intelligence' will substantially be solved".
AI研究于1956年起源于在达特茅斯学院举办的一个研讨会，与会者艾伦纽·厄尔(CMU)，赫伯特·西蒙(CMU)，约翰·麦卡锡(MIT)，马文•明斯基(MIT)和阿瑟·塞缪尔(IBM)成为了AI研究的创始人和领导者。他们让他们的学生做了一个被新闻表述为“叹为观止”的计算机学习策略(以及在1959年就被报道达到人类的平均水平之上) ，用代数解决应用题，证明逻辑理论'''<font color=#32cd32>（逻辑理论家）</font>'''以及说英语。到20世纪60年代中期，美国国防高级研究计划局斥重资支持研究，世界各地纷纷建立研究室。AI的创始人对未来充满乐观: 赫伯特 · 西蒙预言，“二十年内，机器将能完成人能做到的一切工作。”。马文•明斯基对此表示同意，他写道: “在一代人的时间里... ... 创造‘AI’的问题将得到实质性的解决。”
--[[用户:Thingamabob|Thingamabob]]（[[用户讨论:Thingamabob|讨论]]）不太明白first run c. 1956的含义
They failed to recognize the difficulty of some of the remaining tasks. Progress slowed and in 1974, in response to the criticism of Sir James Lighthill and ongoing pressure from the US Congress to fund more productive projects, both the U.S. and British governments cut off exploratory research in AI. The next few years would later be called an "AI winter", a period when obtaining funding for AI projects was difficult.
They failed to recognize the difficulty of some of the remaining tasks. Progress slowed and in 1974, in response to the criticism of Sir James Lighthill and ongoing pressure from the US Congress to fund more productive projects, both the U.S. and British governments cut off exploratory research in AI. The next few years would later be called an "AI winter", a period when obtaining funding for AI projects was difficult.
他们没有意识到现存任务的一些困难。研究进程放缓，在1974年，由于詹姆斯·莱特希尔的指责以及美国国会需要分拨基金给其他有成效的项目，美国和英国政府都削减了AI研究经费。接下来的几年被称为“AI寒冬”，在这一时期AI研究很难得到经费。
In the early 1980s, AI research was revived by the commercial success of expert systems, a form of AI program that simulated the knowledge and analytical skills of human experts. By 1985, the market for AI had reached over a billion dollars. At the same time, Japan's fifth generation computer project inspired the U.S and British governments to restore funding for academic research. However, beginning with the collapse of the Lisp Machine market in 1987, AI once again fell into disrepute, and a second, longer-lasting hiatus began.
In the early 1980s, AI research was revived by the commercial success of expert systems, a form of AI program that simulated the knowledge and analytical skills of human experts. By 1985, the market for AI had reached over a billion dollars. At the same time, Japan's fifth generation computer project inspired the U.S and British governments to restore funding for academic research. However, beginning with the collapse of the Lisp Machine market in 1987, AI once again fell into disrepute, and a second, longer-lasting hiatus began.
在20世纪80年代初期，由于专家系统在商业上取得的成功，AI研究迎来了复兴，专家系统是一种能够模拟人类专家的知识和分析能力的程序。到1985年，AI市场超过了10亿美元。与此同时，日本的第五代计算机项目促使了美国和英国政府恢复对学术研究的资助。然而，随着1987年 Lisp 机器市场的崩溃，AI再一次遭遇低谷，并陷入了第二次持续更长时间的停滞。
The development of metal–oxide–semiconductor (MOS) very-large-scale integration (VLSI), in the form of complementary MOS (CMOS) transistor technology, enabled the development of practical artificial neural network (ANN) technology in the 1980s. A landmark publication in the field was the 1989 book Analog VLSI Implementation of Neural Systems by Carver A. Mead and Mohammed Ismail.
The development of metal–oxide–semiconductor (MOS) very-large-scale integration (VLSI), in the form of complementary MOS (CMOS) transistor technology, enabled the development of practical artificial neural network (ANN) technology in the 1980s. A landmark publication in the field was the 1989 book Analog VLSI Implementation of Neural Systems by Carver A. Mead and Mohammed Ismail.
20世纪80年代，CMOS晶体管技术的出现带来了金属氧化物半导体(MOS)超大规模集成电路(VLSI)的发展，使实用的人工神经网络'''<font color=#ff8000>Artificial Neural Network,ANN</font>''' 技术得以发展。这一领域里程碑式的出版物是1989年出版的《模拟 VLSI 神经系统的实现》， 作者是卡弗 · a · 米德和穆罕默德 · 伊斯梅尔。
In the late 1990s and early 21st century, AI began to be used for logistics, data mining, medical diagnosis and other areas. The success was due to increasing computational power (see Moore's law and transistor count), greater emphasis on solving specific problems, new ties between AI and other fields (such as statistics, economics and mathematics), and a commitment by researchers to mathematical methods and scientific standards. Deep Blue became the first computer chess-playing system to beat a reigning world chess champion, Garry Kasparov, on 11 May 1997.
In the late 1990s and early 21st century, AI began to be used for logistics, data mining, medical diagnosis and other areas. The success was due to increasing computational power (see Moore's law and transistor count), greater emphasis on solving specific problems, new ties between AI and other fields (such as statistics, economics and mathematics), and a commitment by researchers to mathematical methods and scientific standards. Deep Blue became the first computer chess-playing system to beat a reigning world chess champion, Garry Kasparov, on 11 May 1997.
在20世纪90年代末和21世纪初，人工智能开始被用于物流、数据挖掘、医疗诊断和其他领域。这种成功归功于计算能力的提高(见摩尔定律和晶体管数量)、对解决特定问题的更大重视、人工智能与其它领域(如统计学、经济学和数学)之间的新联系，以及研究人员对数学方法和科学标准的承诺。1997年5月11日，深蓝成为第一个击败国际象棋卫冕冠军加里 · 卡斯帕罗夫的计算机国际象棋系统。
在20世纪90年代末和21世纪初，人工智能开始被用于物流、数据挖掘、医疗诊断和其他领域。这种成功归功于计算能力的提高(见摩尔定律和晶体管数量)、对解决特定问题的更大重视、人工智能与其它领域(如统计学、经济学和数学)之间的新联系，以及研究人员对数学方法和科学标准的承诺。1997年5月11日，深蓝成为第一个击败国际象棋卫冕冠军加里·卡斯帕罗夫的计算机国际象棋系统。
In 2011, a Jeopardy! quiz show exhibition match, IBM's question answering system, Watson, defeated the two greatest Jeopardy! champions, Brad Rutter and Ken Jennings, by a significant margin. Faster computers, algorithmic improvements, and access to large amounts of data enabled advances in machine learning and perception; data-hungry deep learning methods started to dominate accuracy benchmarks around 2012. The Kinect, which provides a 3D body–motion interface for the Xbox 360 and the Xbox One, uses algorithms that emerged from lengthy AI research as do intelligent personal assistants in smartphones. In March 2016, AlphaGo won 4 out of 5 games of Go in a match with Go champion Lee Sedol, becoming the first computer Go-playing system to beat a professional Go player without handicaps. In the 2017 Future of Go Summit, AlphaGo won a three-game match with Ke Jie, who at the time continuously held the world No. 1 ranking for two years. This marked the completion of a significant milestone in the development of Artificial Intelligence as Go is a relatively complex game, more so than Chess.
In 2011, a Jeopardy! quiz show exhibition match, IBM's question answering system, Watson, defeated the two greatest Jeopardy! champions, Brad Rutter and Ken Jennings, by a significant margin. Faster computers, algorithmic improvements, and access to large amounts of data enabled advances in machine learning and perception; data-hungry deep learning methods started to dominate accuracy benchmarks around 2012. The Kinect, which provides a 3D body–motion interface for the Xbox 360 and the Xbox One, uses algorithms that emerged from lengthy AI research as do intelligent personal assistants in smartphones. In March 2016, AlphaGo won 4 out of 5 games of Go in a match with Go champion Lee Sedol, becoming the first computer Go-playing system to beat a professional Go player without handicaps. In the 2017 Future of Go Summit, AlphaGo won a three-game match with Ke Jie, who at the time continuously held the world No. 1 ranking for two years. This marked the completion of a significant milestone in the development of Artificial Intelligence as Go is a relatively complex game, more so than Chess.
2011年，IBM的问答系统沃森参加《危险边缘》节目，以明显的优势打败了两名最强的人类冠军布拉德·拉特和肯·詹宁斯。更快的计算，算法的改进，以及大量数据的获取，使得机器学习和感知能力得到提高; 2012年前后，'''<font color=#ff8000>数据饥渴</font>'''深度学习方法实现的精确度已经成为基准。Xbox 360和 Xbox One 的外设Kinect提供了3D人体运动交互功能，同智能手机上的智能助手一样，它使用的算法归功于漫长的AI研究， 2016年3月，AlphaGo与围棋冠军李世石的比赛中五局四胜，成为第一个击败无残疾围棋职业选手的计算机围棋系统。在2017年围棋未来峰会上，AlphaGo赢得了与蝉联两届世界冠军的柯洁的三局比赛。一个排名两年。这标志着AI发展的一个重要里程碑的完成，因为围棋是一比国际象棋更复杂的游戏。
According to Bloomberg's Jack Clark, 2015 was a landmark year for artificial intelligence, with the number of software projects that use AI Google increased from a "sporadic usage" in 2012 to more than 2,700 projects. Clark also presents factual data indicating the improvements of AI since 2012 supported by lower error rates in image processing tasks.<ref name=":0">{{cite web
According to Bloomberg's Jack Clark, 2015 was a landmark year for artificial intelligence, with the number of software projects that use AI Google increased from a "sporadic usage" in 2012 to more than 2,700 projects. Clark also presents factual data indicating the improvements of AI since 2012 supported by lower error rates in image processing tasks.<ref name=":0">{{cite web
彭博社的杰克·克拉克认为2015年是AI的里程碑年，使用谷歌AI的软件项目从几个到2015年超过了2700个。克拉克还给出了说明2012年以来AI在进步的真实数据，这些数据显示了AI在图像处理任务中的错误率越来越低。
|url = https://www.bloomberg.com/news/articles/2015-12-08/why-2015-was-a-breakthrough-year-in-artificial-intelligence
|url = https://www.bloomberg.com/news/articles/2015-12-08/why-2015-was-a-breakthrough-year-in-artificial-intelligence
}}</ref> He attributes this to an increase in affordable neural networks, due to a rise in cloud computing infrastructure and to an increase in research tools and datasets. Around 2016, China greatly accelerated its government funding; given its large supply of data and its rapidly increasing research output, some observers believe it may be on track to becoming an "AI superpower". However, it has been acknowledged that reports regarding artificial intelligence have tended to be exaggerated.
}}</ref> He attributes this to an increase in affordable neural networks, due to a rise in cloud computing infrastructure and to an increase in research tools and datasets. Around 2016, China greatly accelerated its government funding; given its large supply of data and its rapidly increasing research output, some observers believe it may be on track to becoming an "AI superpower". However, it has been acknowledged that reports regarding artificial intelligence have tended to be exaggerated.
他把这归因于可用神经网络的增加，而神经网络的发展又是因为云计算基础设施以及研究工具和数据集的增加。还有微软的Skype系统可以将一门语言自动翻译成另一门，脸书系统可以把图片描述给盲人听。2017年的一个调查中，五分之一的公司报道“他们在一些项目中用到了AI”。
2016年前后，中国加大了政府资助。在此之后的大量数据供应和研究产出的快速增长让一些观察者认为，中国可能正走上成为“AI超级大国”之路。然而，有关AI的报告被承认了有夸大之嫌。
Computer science defines AI research as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.
Computer science defines AI research as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.
计算机科学将对AI的研究定义为对“智能体”的研究。智能体是任何感知周围环境并采取行动以最大化其成功实现目标的机会的设备。对AI更精确的定义是“一个可以正确理解并学习输入数据，并用学习结果通过灵活调整，实现具体目标或任务的系统”。
A typical AI analyzes its environment and takes actions that maximize its chance of success.
A typical AI analyzes its environment and takes actions that maximize its chance of success.
一般的AI会分析其环境，并采取行动最大限度地提高其成功的机会。AI的预期效用函数（或者说目标）可以很简单（比如如果赢了围棋就是1，否则为0），也可以很复杂（做一些从数学层面上与过去的成功案例相似的行为）。目标可以被明确定义或诱导。如果AI被设定为“'''<font color=#ff8000>强化学习 Reinforcement Learning </font>'''”，那么目标就可以通过奖励某些行为或惩罚其他行为来间接诱导出来。再比如进化系统可以通过“适应功能”产生突变或者优先发展得分高的AI系统来导出目标，这与动物进化出寻找食物的本能类似。一些诸如最近邻插值的AI系统，不是通过类比来推理的。这些系统通常没有给定目标，除非目标隐含在它们的训练数据中。如果将非目标系统框定为一个以成功完成其小范围分类任务为目标的系统，那么这些系统仍然可以作为基准。
AI often revolves around the use of algorithms. An algorithm is a set of unambiguous instructions that a mechanical computer can execute. A complex algorithm is often built on top of other, simpler, algorithms. A simple example of an algorithm is the following (optimal for first player) recipe for play at tic-tac-toe:
AI often revolves around the use of algorithms. An algorithm is a set of unambiguous instructions that a mechanical computer can execute. A complex algorithm is often built on top of other, simpler, algorithms. A simple example of an algorithm is the following (optimal for first player) recipe for play at tic-tac-toe:
AI离不开算法的使用。算法是机械计算机可以执行的一组明确的指令。复杂的算法通常是建立在其他更简单的算法之上的。一个算法的简单例子是下面的井字游戏指令(对于第一个玩家来说是最有利的) :
If someone has a "threat" (that is, two in a row), take the remaining square. Otherwise,
If someone has a "threat" (that is, two in a row), take the remaining square. Otherwise,
如果某人产生了一个“威胁”(也就是说有两个棋子连续了) ，把下一步棋下在两个棋外剩下的方上块。否则,
# if a move "forks" to create two threats at once, play that move. Otherwise,
# if a move "forks" to create two threats at once, play that move. Otherwise,
if a move "forks" to create two threats at once, play that move. Otherwise,
if a move "forks" to create two threats at once, play that move. Otherwise,
如果某一步棋可以制造“叉子”棋阵同时制造两个威胁，那就下那一步。否则,
# take the center square if it is free. Otherwise,
# take the center square if it is free. Otherwise,
take the center square if it is free. Otherwise,
take the center square if it is free. Otherwise,
如果中心的格子还是空的话，就走中间的格子。否则,
# if your opponent has played in a corner, take the opposite corner. Otherwise,
# if your opponent has played in a corner, take the opposite corner. Otherwise,
if your opponent has played in a corner, take the opposite corner. Otherwise,
if your opponent has played in a corner, take the opposite corner. Otherwise,
如果你的对手在一个角落里摆布，那就走另一个角落。否则,
# take an empty corner if one exists. Otherwise,
# take an empty corner if one exists. Otherwise,
take an empty corner if one exists. Otherwise,
take an empty corner if one exists. Otherwise,
如果有空角落，就下在空角落上。否则,
# take any empty square.
# take any empty square.
Many AI algorithms are capable of learning from data; they can enhance themselves by learning new heuristics (strategies, or "rules of thumb", that have worked well in the past), or can themselves write other algorithms. Some of the "learners" described below, including Bayesian networks, decision trees, and nearest-neighbor, could theoretically, (given infinite data, time, and memory) learn to approximate any function, including which combination of mathematical functions would best describe the world. These learners could therefore, derive all possible knowledge, by considering every possible hypothesis and matching them against the data. In practice, it is almost never possible to consider every possibility, because of the phenomenon of "combinatorial explosion", where the amount of time needed to solve a problem grows exponentially. Much of AI research involves figuring out how to identify and avoid considering broad range of possibilities that are unlikely to be beneficial. For example, when viewing a map and looking for the shortest driving route from Denver to New York in the East, one can in most cases skip looking at any path through San Francisco or other areas far to the West; thus, an AI wielding a pathfinding algorithm like A* can avoid the combinatorial explosion that would ensue if every possible route had to be ponderously considered in turn.<ref>{{cite journal
Many AI algorithms are capable of learning from data; they can enhance themselves by learning new heuristics (strategies, or "rules of thumb", that have worked well in the past), or can themselves write other algorithms. Some of the "learners" described below, including Bayesian networks, decision trees, and nearest-neighbor, could theoretically, (given infinite data, time, and memory) learn to approximate any function, including which combination of mathematical functions would best describe the world. These learners could therefore, derive all possible knowledge, by considering every possible hypothesis and matching them against the data. In practice, it is almost never possible to consider every possibility, because of the phenomenon of "combinatorial explosion", where the amount of time needed to solve a problem grows exponentially. Much of AI research involves figuring out how to identify and avoid considering broad range of possibilities that are unlikely to be beneficial. For example, when viewing a map and looking for the shortest driving route from Denver to New York in the East, one can in most cases skip looking at any path through San Francisco or other areas far to the West; thus, an AI wielding a pathfinding algorithm like A* can avoid the combinatorial explosion that would ensue if every possible route had to be ponderously considered in turn.<ref>{{cite journal
许多AI算法可以从数据中学习；他们可以通过学习新的启发式(过去起作用的策略，或“经验法则”) 或者自己编写其他算法来强化自己。下面的一些“学习者”，包括'''<font color=#ff8000>贝叶斯网络 Bayesian Networks</font>'''、'''<font color=#ff8000>决策树 Decision Trees</font>'''和'''<font color=#ff8000>最近邻插值 Nearest-neighbor</font>'''，在理论上(给定无限的数据、时间和记忆)可以学习近似任何函数，包括数学函数如何组合可以最好地描述世界。因此，这些学习者可以通过考虑每一种可能的假设，并将它们与数据进行匹配，从而获得所有可能的知识。实际上考虑所有的可能性几乎是不可能，因为很可能会导致“'''<font color=#ff8000>组合爆炸 Combinatorial Explosion</font>'''”，即解决一个问题所需的时间呈指数级增长。很多AI研究都在探索如何识别和避免考虑广泛且无益的可能性。例如,当看地图寻找从丹佛到东边纽约的最短行驶路线时,大部分人都不会去看通过西边的旧金山或其他领域的路径;因此，一个使用像A*这样的寻路算法的AI可以避免每条可能的路径都必须依次考虑的的情况。
The earliest (and easiest to understand) approach to AI was symbolism (such as formal logic): "If an otherwise healthy adult has a fever, then they may have [[influenza]]". A second, more general, approach is [[Bayesian inference]]: "If the current patient has a fever, adjust the probability they have influenza in such-and-such way". The third major approach, extremely popular in routine business AI applications, are analogizers such as [[Support vector machine|SVM]] and [[K-nearest neighbor algorithm|nearest-neighbor]]: "After examining the records of known past patients whose temperature, symptoms, age, and other factors mostly match the current patient, X% of those patients turned out to have influenza". A fourth approach is harder to intuitively understand, but is inspired by how the brain's machinery works: the [[artificial neural network]] approach uses artificial "[[neurons]]" that can learn by comparing itself to the desired output and altering the strengths of the connections between its internal neurons to "reinforce" connections that seemed to be useful. These four main approaches can overlap with each other and with evolutionary systems; for example, neural nets can learn to make inferences, to generalize, and to make analogies. Some systems implicitly or explicitly use multiple of these approaches, alongside many other AI and non-AI algorithms; the best approach is often different depending on the problem.{{sfn|Domingos|2015|loc=Chapter 2, Chapter 4, Chapter 6}}<!-- The influenza example is expanded from Domingos chapter 6; feel free to put in a better example if you have one --><ref>{{cite news|title=Can neural network computers learn from experience, and if so, could they ever become what we would call 'smart'?|url=https://www.scientificamerican.com/article/can-neural-network-comput/|accessdate=24 March 2018|work=Scientific American|date=2018|language=en}}</ref>
The earliest (and easiest to understand) approach to AI was symbolism (such as formal logic): "If an otherwise healthy adult has a fever, then they may have influenza". A second, more general, approach is Bayesian inference: "If the current patient has a fever, adjust the probability they have influenza in such-and-such way". The third major approach, extremely popular in routine business AI applications, are analogizers such as SVM and nearest-neighbor: "After examining the records of known past patients whose temperature, symptoms, age, and other factors mostly match the current patient, X% of those patients turned out to have influenza". A fourth approach is harder to intuitively understand, but is inspired by how the brain's machinery works: the artificial neural network approach uses artificial "neurons" that can learn by comparing itself to the desired output and altering the strengths of the connections between its internal neurons to "reinforce" connections that seemed to be useful. These four main approaches can overlap with each other and with evolutionary systems; for example, neural nets can learn to make inferences, to generalize, and to make analogies. Some systems implicitly or explicitly use multiple of these approaches, alongside many other AI and non-AI algorithms; the best approach is often different depending on the problem.<!-- The influenza example is expanded from Domingos chapter 6; feel free to put in a better example if you have one -->
AI最早的(也是最容易理解的)研究方法是'''<font color=#ff8000>符号主义 Symbolism </font>'''(比如形式逻辑):“如果一个原本健康的成年人发烧了，那么他们可能患上了流感。”第二种更普遍的方法是贝叶斯推断: “如果这个患者发烧了，会考虑多个方面判断他们感染流感的可能性”。第三个主要的方法：类比，在日常商业AI应用中非常常见，例如'''<font color=#ff8000>支持向量机 Support Vector Machine, SVM</font>'''和'''<font color=#ff8000>最近邻 Nearest-neighbor </font>''': “在考量了过去体温、症状、年龄和其他因素与现在的病人匹配的病人的记录，这些病人中x%患有流感”。第四种方法相较来说更难以直观理解，它受到大脑工作机制的启发: 人工神经网络方法使用人工“神经元” ，这种神经元可以通过将自身与期望的输出进行比较，并改变内部神经元之间的连接强度，以“强化”似乎有用的连接，从而进行学习。这四种主要的方法可以有交叉，也可以与进化系统交叉; 例如，神经网络可以学习做推论、概括和进行类比。一些系统隐式或显式地使用这些方法中的多个，以及许多其他AI和非AI算法; 根据问题不同往往最佳解决方案也不同.
Learning algorithms work on the basis that strategies, algorithms, and inferences that worked well in the past are likely to continue working well in the future. These inferences can be obvious, such as "since the sun rose every morning for the last 10,000 days, it will probably rise tomorrow morning as well". They can be nuanced, such as "X% of [[Family (biology)|families]] have geographically separate species with color variants, so there is a Y% chance that undiscovered [[black swan theory|black swans]] exist". Learners also work on the basis of "[[Occam's razor#Probability theory and statistics|Occam's razor]]": The simplest theory that explains the data is the likeliest. Therefore, according to Occam's razor principle, a learner must be designed such that it prefers simpler theories to complex theories, except in cases where the complex theory is proven substantially better.
Learning algorithms work on the basis that strategies, algorithms, and inferences that worked well in the past are likely to continue working well in the future. These inferences can be obvious, such as "since the sun rose every morning for the last 10,000 days, it will probably rise tomorrow morning as well". They can be nuanced, such as "X% of families have geographically separate species with color variants, so there is a Y% chance that undiscovered black swans exist". Learners also work on the basis of "Occam's razor": The simplest theory that explains the data is the likeliest. Therefore, according to Occam's razor principle, a learner must be designed such that it prefers simpler theories to complex theories, except in cases where the complex theory is proven substantially better.
学习算法的工作基础是策略、算法和推论，如果这些基础在之前运行良好，那么在未来可能能继续很好地运作。这些推论是显而易见的，例如“在过去的10000天里，太阳每天早上都升起，明天早上也可能升起”。他们可能会有细微差别，比如“ x% 的科有颜色变异且地理隔离的物种，所以有 y% 的可能性未被发现的黑天鹅是存在的”。学习者也在“'''<font color=#ff8000>奥卡姆剃刀 Occam's Razor</font>'''”的基础上学习: 最简单的可以解释数据的理论是最有可能的。因此，根据奥卡姆剃刀原则，一个学习者必须被设计成更倾向于简单的理论而不是复杂的理论，除非复杂的理论被证明实质上更好。
[[File:Overfitted Data.png|thumb|The blue line could be an example of [[overfitting]] a linear function due to random noise.]]
The blue line could be an example of [[overfitting a linear function due to random noise.]]
蓝线是[[由于随机噪声过拟合线性函数]的一个例子。]
Settling on a bad, overly complex theory gerrymandered to fit all the past training data is known as [[overfitting]]. Many systems attempt to reduce overfitting by rewarding a theory in accordance with how well it fits the data, but penalizing the theory in accordance with how complex the theory is.{{sfn|Domingos|2015|loc=Chapter 6, Chapter 7}} Besides classic overfitting, learners can also disappoint by "learning the wrong lesson". A toy example is that an image classifier trained only on pictures of brown horses and black cats might conclude that all brown patches are likely to be horses.{{sfn|Domingos|2015|p=286}} A real-world example is that, unlike humans, current image classifiers don't determine the spatial relationship between components of the picture; instead, they learn abstract patterns of pixels that humans are oblivious to, but that linearly correlate with images of certain types of real objects. Faintly superimposing such a pattern on a legitimate image results in an "adversarial" image that the system misclassifies.{{efn|Adversarial vulnerabilities can also result in nonlinear systems, or from non-pattern perturbations. Some systems are so brittle that changing a single adversarial pixel predictably induces misclassification.}}<ref>{{cite news|title=Single pixel change fools AI programs|url=https://www.bbc.com/news/technology-41845878|accessdate=12 March 2018|work=BBC News|date=3 November 2017}}</ref><ref>{{cite news|title=AI Has a Hallucination Problem That's Proving Tough to Fix|url=https://www.wired.com/story/ai-has-a-hallucination-problem-thats-proving-tough-to-fix/|accessdate=12 March 2018|work=WIRED|date=2018}}</ref><ref>{{cite arxiv|eprint=1412.6572|last1=Goodfellow|first1=Ian J.|last2=Shlens|first2=Jonathon|last3=Szegedy|first3=Christian|title=Explaining and Harnessing Adversarial Examples|class=stat.ML|year=2014}}</ref>
Settling on a bad, overly complex theory gerrymandered to fit all the past training data is known as overfitting. Many systems attempt to reduce overfitting by rewarding a theory in accordance with how well it fits the data, but penalizing the theory in accordance with how complex the theory is. Besides classic overfitting, learners can also disappoint by "learning the wrong lesson". A toy example is that an image classifier trained only on pictures of brown horses and black cats might conclude that all brown patches are likely to be horses. A real-world example is that, unlike humans, current image classifiers don't determine the spatial relationship between components of the picture; instead, they learn abstract patterns of pixels that humans are oblivious to, but that linearly correlate with images of certain types of real objects. Faintly superimposing such a pattern on a legitimate image results in an "adversarial" image that the system misclassifies.
用一个错误且过于复杂的理论拟合过去所有的训练数据，这被称为'''<font color=#ff8000>过拟合 Overfitting</font>'''。许多系统试图通过依据理论拟合数据的程度奖励和复杂程度惩罚来减少过拟合。除了常见的过拟合外，学习者也会因为“走错方向”而失望。一个简单的例子是，一个图像分类器训练时只用棕马和黑猫的图片，那么它就很可能得出所有的棕色斑块都是马的结论。一个现实世界的例子是，目前的图像分类器并与人类不同，他们不厘清图像各部分间的空间关系; 相反，它们学习人类察觉不到的抽象，但与某类真实物体图像线性相关的像素图案。将这种图案稍微叠加在原本正确的图像上，就会导致系统将图像错误分类。
[[File:Détection de personne - exemple 3.jpg|thumb|A self-driving car system may use a neural network to determine which parts of the picture seem to match previous training images of pedestrians, and then model those areas as slow-moving but somewhat unpredictable rectangular prisms that must be avoided.<ref>{{cite book|last1=Matti|first1=D.|last2=Ekenel|first2=H. K.|last3=Thiran|first3=J. P.|title=Combining LiDAR space clustering and convolutional neural networks for pedestrian detection|journal=2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)|date=2017|pages=1–6|doi=10.1109/AVSS.2017.8078512|isbn=978-1-5386-2939-0|arxiv=1710.06160}}</ref><ref>{{cite book|last1=Ferguson|first1=Sarah|last2=Luders|first2=Brandon|last3=Grande|first3=Robert C.|last4=How|first4=Jonathan P.|title=Real-Time Predictive Modeling and Robust Avoidance of Pedestrians with Uncertain, Changing Intentions|journal=Algorithmic Foundations of Robotics XI|volume=107|date=2015|pages=161–177|doi=10.1007/978-3-319-16595-0_10|publisher=Springer, Cham|language=en|series=Springer Tracts in Advanced Robotics|isbn=978-3-319-16594-3|arxiv=1405.5581}}</ref>]]
A self-driving car system may use a neural network to determine which parts of the picture seem to match previous training images of pedestrians, and then model those areas as slow-moving but somewhat unpredictable rectangular prisms that must be avoided.
自动驾驶汽车系统可以使用神经网络来确定图像的哪些部分与先前训练数据里的行人图像匹配，然后将这些区域建模为移动缓慢但有点不可预测，且必须避让的矩形棱柱。
Compared with humans, existing AI lacks several features of human "[[commonsense reasoning]]"; most notably, humans have powerful mechanisms for reasoning about "[[naïve physics]]" such as space, time, and physical interactions. This enables even young children to easily make inferences like "If I roll this pen off a table, it will fall on the floor". Humans also have a powerful mechanism of "[[folk psychology]]" that helps them to interpret natural-language sentences such as "The city councilmen refused the demonstrators a permit because they advocated violence". (A generic AI has difficulty discerning whether the ones alleged to be advocating violence are the councilmen or the demonstrators.)<ref>{{cite news|title=Cultivating Common Sense {{!}} DiscoverMagazine.com|url=http://discovermagazine.com/2017/april-2017/cultivating-common-sense|accessdate=24 March 2018|work=Discover Magazine|date=2017}}</ref><ref>{{cite journal|last1=Davis|first1=Ernest|last2=Marcus|first2=Gary|title=Commonsense reasoning and commonsense knowledge in artificial intelligence|journal=Communications of the ACM|date=24 August 2015|volume=58|issue=9|pages=92–103|doi=10.1145/2701413|url=https://cacm.acm.org/magazines/2015/9/191169-commonsense-reasoning-and-commonsense-knowledge-in-artificial-intelligence/}}</ref><ref>{{cite journal|last1=Winograd|first1=Terry|title=Understanding natural language|journal=Cognitive Psychology|date=January 1972|volume=3|issue=1|pages=1–191|doi=10.1016/0010-0285(72)90002-3}}</ref> This lack of "common knowledge" means that AI often makes different mistakes than humans make, in ways that can seem incomprehensible. For example, existing self-driving cars cannot reason about the location nor the intentions of pedestrians in the exact way that humans do, and instead must use non-human modes of reasoning to avoid accidents.<ref>{{cite news|title=Don't worry: Autonomous cars aren't coming tomorrow (or next year)|url=http://autoweek.com/article/technology/fully-autonomous-vehicles-are-more-decade-down-road|accessdate=24 March 2018|work=Autoweek|date=2016}}</ref><ref>{{cite news|last1=Knight|first1=Will|title=Boston may be famous for bad drivers, but it's the testing ground for a smarter self-driving car|url=https://www.technologyreview.com/s/608871/finally-a-driverless-car-with-some-common-sense/|accessdate=27 March 2018|work=MIT Technology Review|date=2017|language=en}}</ref><ref>{{cite journal|last1=Prakken|first1=Henry|title=On the problem of making autonomous vehicles conform to traffic law|journal=Artificial Intelligence and Law|date=31 August 2017|volume=25|issue=3|pages=341–363|doi=10.1007/s10506-017-9210-0|doi-access=free}}</ref>
Compared with humans, existing AI lacks several features of human "commonsense reasoning"; most notably, humans have powerful mechanisms for reasoning about "naïve physics" such as space, time, and physical interactions. This enables even young children to easily make inferences like "If I roll this pen off a table, it will fall on the floor". Humans also have a powerful mechanism of "folk psychology" that helps them to interpret natural-language sentences such as "The city councilmen refused the demonstrators a permit because they advocated violence". (A generic AI has difficulty discerning whether the ones alleged to be advocating violence are the councilmen or the demonstrators.) This lack of "common knowledge" means that AI often makes different mistakes than humans make, in ways that can seem incomprehensible. For example, existing self-driving cars cannot reason about the location nor the intentions of pedestrians in the exact way that humans do, and instead must use non-human modes of reasoning to avoid accidents.
与人类相比，现有的AI缺少人类“常识推理”的几个特征; 最值得注意的是，人类拥有强大的对如空间、时间和物理交互等“自然物理”推理机制。这使得即使是小孩子也能够轻易地做出推论，比如“如果我把这支笔从桌子上滚下来，它就会掉到地板上”。人类还有一种强大的“人群心理”机制，帮助他们理解诸如“市议员因为示威者鼓吹暴力而拒绝给予许可”的自然语言，但一般的AI难以辨别被指控鼓吹暴力的人是议员还是示威者。这种“常识”的缺乏意味着AI经常会犯一些与人类不同的错误，这些错误看起来是难以理解的。例如，现在的自动驾驶汽车不能像人类那样准确推理方位和行人的意图，而只能使用非人类的推理模式来避免事故。
== Challenges ==
== Challenges ==
挑战
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The cognitive capabilities of current architectures are very limited, using only a simplified version of what intelligence is really capable of. For instance, the human mind has come up with ways to reason beyond measure and logical explanations to different occurrences in life. What would have been otherwise straightforward, an equivalently difficult problem may be challenging to solve computationally as opposed to using the human mind. This gives rise to two classes of models: structuralist and functionalist. The structural models aim to loosely mimic the basic intelligence operations of the mind such as reasoning and logic. The functional model refers to the correlating data to its computed counterpart.<ref>{{Cite journal|last=Lieto|first=Antonio|date=May 2018|title=The knowledge level in cognitive architectures: Current limitations and possible developments|journal=Cognitive Systems Research|volume=48|pages=39–55|doi=10.1016/j.cogsys.2017.05.001|hdl=2318/1665207|hdl-access=free}}</ref>
The cognitive capabilities of current architectures are very limited, using only a simplified version of what intelligence is really capable of. For instance, the human mind has come up with ways to reason beyond measure and logical explanations to different occurrences in life. What would have been otherwise straightforward, an equivalently difficult problem may be challenging to solve computationally as opposed to using the human mind. This gives rise to two classes of models: structuralist and functionalist. The structural models aim to loosely mimic the basic intelligence operations of the mind such as reasoning and logic. The functional model refers to the correlating data to its computed counterpart.
当前架构的认知能力非常有限，只做到了智能真正能够做到的事情的冰山一角。例如，人类的大脑已经想出了各种方法来推理生活中难以度量且不太合逻辑的事件。原本直截了当且困难程度相差不大的问题，与使用人类思维相比，对于计算机可能是具有挑战性的。这就产生了两类模型: '''<font color=#ff8000>结构主义 Structuralist</font>'''和'''<font color=#ff8000>功能主义 Functionalist</font>'''。结构模型旨在大致模拟大脑的基本认知功能，如推理和逻辑。函数模型是指与其计算的数据相关联的数据。
The overall research goal of artificial intelligence is to create technology that allows computers and machines to function in an intelligent manner. The general problem of simulating (or creating) intelligence has been broken down into sub-problems. These consist of particular traits or capabilities that researchers expect an intelligent system to display. The traits described below have received the most attention.<ref name="Problems of AI" />
The overall research goal of artificial intelligence is to create technology that allows computers and machines to function in an intelligent manner. The general problem of simulating (or creating) intelligence has been broken down into sub-problems. These consist of particular traits or capabilities that researchers expect an intelligent system to display. The traits described below have received the most attention.
AI的总体研究目标是创造能够使计算机和机器以智能方式运行的技术。模拟(或创造)智能的一般问题已被分解为若干子问题。这些问题中涉及到的特征或能力是研究人员期望智能系统展示的。受到了最多的关注的是下面描述的几个特征。
=== Reasoning, problem solving ===
=== Reasoning, problem solving ===
推理，解决问题
<!-- This is linked to in the introduction --><!-- SOLVED PROBLEMS -->
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! ——这在介绍中有关——解决问题——
Early researchers developed algorithms that imitated step-by-step reasoning that humans use when they solve puzzles or make logical deductions.<ref name="Reasoning"/> By the late 1980s and 1990s, AI research had developed methods for dealing with [[uncertainty|uncertain]] or incomplete information, employing concepts from [[probability]] and [[economics]].<ref name="Uncertain reasoning"/>
Early researchers developed algorithms that imitated step-by-step reasoning that humans use when they solve puzzles or make logical deductions. By the late 1980s and 1990s, AI research had developed methods for dealing with uncertain or incomplete information, employing concepts from probability and economics.
早期的研究人员开发了一种算法，这种算法模仿了人类在解决谜题或进行逻辑推理时所使用的循序渐进的推理。到20世纪80年代末和90年代，AI研究使用概率论和经济学的理论开发出了处理不确定或不完全信息的方法。
These algorithms proved to be insufficient for solving large reasoning problems because they experienced a "combinatorial explosion": they became exponentially slower as the problems grew larger.<ref name="Intractability"/> In fact, even humans rarely use the step-by-step deduction that early AI research was able to model. They solve most of their problems using fast, intuitive judgments.<ref name="Psychological evidence of sub-symbolic reasoning"/>
These algorithms proved to be insufficient for solving large reasoning problems because they experienced a "combinatorial explosion": they became exponentially slower as the problems grew larger. In fact, even humans rarely use the step-by-step deduction that early AI research was able to model. They solve most of their problems using fast, intuitive judgments.
这些算法被证明不足以解决大型推理问题，因为它们经历了一个“组合爆炸” : 随着问题规模变得越来越大，它们的处理效率呈指数级下降。事实上，即使是人类也很少使用早期AI研究建模的逐步推理。人们通过快速、直觉的判断来解决大多数问题。
=== Knowledge representation ===
=== Knowledge representation ===
知识表示
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! ——这个链接在介绍中——
[[File:GFO taxonomy tree.png|right|thumb|An ontology represents knowledge as a set of concepts within a domain and the relationships between those concepts.]]
An ontology represents knowledge as a set of concepts within a domain and the relationships between those concepts.
本体将知识表示为领域中的一组概念以及这些概念之间的关系。
{{Main|Knowledge representation|Commonsense knowledge}}
[[Knowledge representation]]<ref name="Knowledge representation"/> and [[knowledge engineering]]<ref name="Knowledge engineering"/> are central to classical AI research. Some "expert systems" attempt to gather together explicit knowledge possessed by experts in some narrow domain. In addition, some projects attempt to gather the "commonsense knowledge" known to the average person into a database containing extensive knowledge about the world. Among the things a comprehensive commonsense knowledge base would contain are: objects, properties, categories and relations between objects;<ref name="Representing categories and relations"/> situations, events, states and time;<ref name="Representing time"/> causes and effects;<ref name="Representing causation"/> knowledge about knowledge (what we know about what other people know);<ref name="Representing knowledge about knowledge"/> and many other, less well researched domains. A representation of "what exists" is an [[ontology (computer science)|ontology]]: the set of objects, relations, concepts, and properties formally described so that software agents can interpret them. The [[semantics]] of these are captured as [[description logic]] concepts, roles, and individuals, and typically implemented as classes, properties, and individuals in the [[Web Ontology Language]].<ref>{{cite book |last=Sikos |first=Leslie F. |date=June 2017 |title=Description Logics in Multimedia Reasoning |url=https://www.springer.com/us/book/9783319540658 |location=Cham |publisher=Springer |isbn=978-3-319-54066-5 |doi=10.1007/978-3-319-54066-5 |url-status=live |archiveurl=https://web.archive.org/web/20170829120912/https://www.springer.com/us/book/9783319540658 |archivedate=29 August 2017 |df=dmy-all }}</ref> The most general ontologies are called [[upper ontology|upper ontologies]], which attempt to provide a foundation for all other knowledge<ref name="Ontology"/> by acting as mediators between [[Domain ontology|domain ontologies]] that cover specific knowledge about a particular knowledge domain (field of interest or area of concern). Such formal knowledge representations can be used in content-based indexing and retrieval,<ref>{{cite journal|last1=Smoliar|first1=Stephen W.|last2=Zhang|first2=HongJiang|title=Content based video indexing and retrieval|journal=IEEE Multimedia|date=1994|volume=1|issue=2|pages=62–72|doi=10.1109/93.311653}}</ref> scene interpretation,<ref>{{cite journal|last1=Neumann|first1=Bernd|last2=Möller|first2=Ralf|title=On scene interpretation with description logics|journal=Image and Vision Computing|date=January 2008|volume=26|issue=1|pages=82–101|doi=10.1016/j.imavis.2007.08.013}}</ref> clinical decision support,<ref>{{cite journal|last1=Kuperman|first1=G. J.|last2=Reichley|first2=R. M.|last3=Bailey|first3=T. C.|title=Using Commercial Knowledge Bases for Clinical Decision Support: Opportunities, Hurdles, and Recommendations|journal=Journal of the American Medical Informatics Association|date=1 July 2006|volume=13|issue=4|pages=369–371|doi=10.1197/jamia.M2055|pmid=16622160|pmc=1513681}}</ref> knowledge discovery (mining "interesting" and actionable inferences from large databases),<ref>{{cite journal|last1=MCGARRY|first1=KEN|title=A survey of interestingness measures for knowledge discovery|journal=The Knowledge Engineering Review|date=1 December 2005|volume=20|issue=1|page=39|doi=10.1017/S0269888905000408|url=https://semanticscholar.org/paper/baf7f99e1b567868a6dc6238cc5906881242da01}}</ref> and other areas.<ref>{{cite conference |url= |title=Automatic annotation and semantic retrieval of video sequences using multimedia ontologies |last1=Bertini |first1=M |last2=Del Bimbo |first2=A |last3=Torniai |first3=C |date=2006 |publisher=ACM |book-title=MM '06 Proceedings of the 14th ACM international conference on Multimedia |pages=679–682 |location=Santa Barbara |conference=14th ACM international conference on Multimedia}}</ref>
Knowledge representation The most general ontologies are called upper ontologies, which attempt to provide a foundation for all other knowledge scene interpretation, clinical decision support, knowledge discovery (mining "interesting" and actionable inferences from large databases), and other areas.
传统的AI研究的重点是'''<font color=#ff8000>知识表示 Knowledge Representation</font>'''和'''<font color=#ff8000>知识工程 Knowledge Engineering</font>'''。有些“专家系统”试图将某一小领域的专家所拥有的知识收集起来。此外，一些项目试图将普通人的“常识”收集到一个包含对世界的认知的知识的大数据库中。这些常识包括：情景、事件、状态和时间;原因和结果;关于知识的知识(我们知道别人知道什么);和许多其他研究较少的领域。“存在的东西”的表示是本体，本体是被正式描述的对象、关系、概念和属性的集合，这样的形式可以让软件智能体能够理解它。本体的语义描述了逻辑概念、角色和个体，通常在Web本体语言中以类、属性和个体的形式实现。最常见的本体称为'''<font color=#ff8000>上本体 Upper Ontology</font>''',它试图为所有其他知识提供一个基础，它充当涵盖有关特定知识领域(兴趣领域或关注领域)的特定知识的领域本体之间的中介。这种形式化的知识表示可以用于基于内容的索引和检索，场景解释，临床决策，知识发现(从大型数据库中挖掘“有趣的”和可操作的推论)等领域。
Among the most difficult problems in knowledge representation are:
Among the most difficult problems in knowledge representation are:
知识表示中最困难的问题是:
;[[Default reasoning]] and the [[qualification problem]]: Many of the things people know take the form of "working assumptions". For example, if a bird comes up in conversation, people typically picture an animal that is fist-sized, sings, and flies. None of these things are true about all birds. [[John McCarthy (computer scientist)|John McCarthy]] identified this problem in 1969<ref name="Qualification problem"/> as the qualification problem: for any commonsense rule that AI researchers care to represent, there tend to be a huge number of exceptions. Almost nothing is simply true or false in the way that abstract logic requires. AI research has explored a number of solutions to this problem.<ref name="Default reasoning and non-monotonic logic"/>
Default reasoning and the qualification problem: Many of the things people know take the form of "working assumptions". For example, if a bird comes up in conversation, people typically picture an animal that is fist-sized, sings, and flies. None of these things are true about all birds. John McCarthy identified this problem in 1969 as the qualification problem: for any commonsense rule that AI researchers care to represent, there tend to be a huge number of exceptions. Almost nothing is simply true or false in the way that abstract logic requires. AI research has explored a number of solutions to this problem.
'''<font color=#ff8000>缺省推理 Default Reasoning</font>''' 和'''<font color=#ff8000> 限定性问题 Qualification Problem</font>''': 人们对事物的认知常常基于一个可行的假设。提到鸟，人们通常会想象一只拳头大小、会唱歌、会飞的动物。但并不是所有鸟类都有这样的特性。1969年约翰 · 麦卡锡将其归咎于资格问题: 对于AI研究人员所关心的任何常识性规则来说，往往存在大量的例外。几乎没有什么在逻辑角度是完全真或完全假。AI研究探索了许多解决这个问题的方法。
;Breadth of commonsense knowledge: The number of atomic facts that the average person knows is very large. Research projects that attempt to build a complete knowledge base of [[commonsense knowledge]] (e.g., [[Cyc]]) require enormous amounts of laborious [[ontology engineering|ontological engineering]]—they must be built, by hand, one complicated concept at a time.<ref name="Breadth of commonsense knowledge"/>
Breadth of commonsense knowledge: The number of atomic facts that the average person knows is very large. Research projects that attempt to build a complete knowledge base of commonsense knowledge (e.g., Cyc) require enormous amounts of laborious ontological engineering—they must be built, by hand, one complicated concept at a time.
常识的广度: 常人掌握的“元常识”的数量是非常大的。想要建立一个像Cyc一样的完整的常识库，需要大量耗精力的本体工程ーー这些常识必须由人工一个一个地构建。
--[[用户:Thingamabob|Thingamabob]]（[[用户讨论:Thingamabob|讨论]]）。想要建立一个像Cyc一样的完整的常识库 一句为省译
;Subsymbolic form of some commonsense knowledge: Much of what people know is not represented as "facts" or "statements" that they could express verbally. For example, a chess master will avoid a particular chess position because it "feels too exposed"{{sfn|Dreyfus|Dreyfus|1986}} or an art critic can take one look at a statue and realize that it is a fake.{{sfn|Gladwell|2005}} These are non-conscious and sub-symbolic intuitions or tendencies in the human brain.<ref name="Intuition"/> Knowledge like this informs, supports and provides a context for symbolic, conscious knowledge. As with the related problem of sub-symbolic reasoning, it is hoped that [[situated artificial intelligence|situated AI]], [[computational intelligence]], or [[#Statistical|statistical AI]] will provide ways to represent this kind of knowledge.<ref name="Intuition"/>
Subsymbolic form of some commonsense knowledge: Much of what people know is not represented as "facts" or "statements" that they could express verbally. For example, a chess master will avoid a particular chess position because it "feels too exposed" or an art critic can take one look at a statue and realize that it is a fake. These are non-conscious and sub-symbolic intuitions or tendencies in the human brain. Knowledge like this informs, supports and provides a context for symbolic, conscious knowledge. As with the related problem of sub-symbolic reasoning, it is hoped that situated AI, computational intelligence, or statistical AI will provide ways to represent this kind of knowledge.
常识的'''<font color=#ff8000>亚符号 Subsymbolic</font>''' 形式: 人们所知道的许多东西必不能用可以口头表达的“事实”或“陈述”描述。例如，一个国际象棋大师会避免下某个位置，因为觉得这步棋“感觉太激进” ，或者一个艺术评论家可以看一眼雕像，就知道它是假的。这些是人类大脑中无意识和亚符号的直觉。这种知识为符号化的、有意识的知识提供信息和语境。与亚符号推理的相关问题一样，我们希望情境AI、计算智能或统计AI能够表示这类知识。
--[[用户:Thingamabob|Thingamabob]]（[[用户讨论:Thingamabob|讨论]]） 这种知识为符号化的、有意识的知识提供信息和语境 一句为省译
=== Planning ===
=== Planning ===
规划
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! ——这个链接在介绍中——
[[File:Hierarchical-control-system.svg|thumb| A [[hierarchical control system]] is a form of [[control system]] in which a set of devices and governing software is arranged in a hierarchy.]]
A [[hierarchical control system is a form of control system in which a set of devices and governing software is arranged in a hierarchy.]]
分层控制系统是控制系统的一种形式，在这种控制系统中，一组设备和控制软件被放在一个层次结构中
{{Main|Automated planning and scheduling}}
Intelligent agents must be able to set goals and achieve them.<ref name="Planning"/> They need a way to visualize the future—a representation of the state of the world and be able to make predictions about how their actions will change it—and be able to make choices that maximize the [[utility]] (or "value") of available choices.<ref name="Information value theory"/>
Intelligent agents must be able to set goals and achieve them. They need a way to visualize the future—a representation of the state of the world and be able to make predictions about how their actions will change it—and be able to make choices that maximize the utility (or "value") of available choices.
智能体必须能够设定并实现目标。他们就需要将未来可视化——这是一种对其所处环境状况的表述，并能够预测他们的行动将如何改变环境——依此能够选择使效用（或者“价值”）最大化的选项。
--[[用户:Thingamabob|Thingamabob]]（[[用户讨论:Thingamabob|讨论]]） 并能够预测他们的行动将如何改变环境——依此能够选择使效用（或者“价值”）最大化的选项 一句为省译
In classical planning problems, the agent can assume that it is the only system acting in the world, allowing the agent to be certain of the consequences of its actions.<ref name="Classical planning"/> However, if the agent is not the only actor, then it requires that the agent can reason under uncertainty. This calls for an agent that can not only assess its environment and make predictions but also evaluate its predictions and adapt based on its assessment.<ref name="Non-deterministic planning"/>
In classical planning problems, the agent can assume that it is the only system acting in the world, allowing the agent to be certain of the consequences of its actions. However, if the agent is not the only actor, then it requires that the agent can reason under uncertainty. This calls for an agent that can not only assess its environment and make predictions but also evaluate its predictions and adapt based on its assessment.
在经典的规划问题中，智能体可以假设它是世界上唯一运行着的系统，以便于智能体确定其做出某个行为带来的后果。然而，如果智能体不是唯一的参与者，这就要求智能体能够在不确定的情况下进行推理。这需要一智能体不仅能够评估其环境和作出预测，而且还评估其预测和根据其预测做出调整。
[[Multi-agent planning]] uses the [[cooperation]] and competition of many agents to achieve a given goal. [[Emergent behavior]] such as this is used by [[evolutionary algorithms]] and [[swarm intelligence]].<ref name="Multi-agent planning"/>
Multi-agent planning uses the cooperation and competition of many agents to achieve a given goal. Emergent behavior such as this is used by evolutionary algorithms and swarm intelligence.
多智能体规划利用多个智能体之间的协作和竞争来达到目标。进化算法和群体智能会用到类似这样的涌现行为。
=== Learning ===
=== Learning ===
学习
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{{Main|Machine learning}}
Machine learning (ML), a fundamental concept of AI research since the field's inception,<ref>[[Alan Turing]] discussed the centrality of learning as early as 1950, in his classic paper "[[Computing Machinery and Intelligence]]".{{Harv|Turing|1950}} In 1956, at the original Dartmouth AI summer conference, [[Ray Solomonoff]] wrote a report on unsupervised probabilistic machine learning: "An Inductive Inference Machine".{{Harv|Solomonoff|1956}}</ref> is the study of computer algorithms that improve automatically through experience.<ref>This is a form of [[Tom M. Mitchell|Tom Mitchell]]'s widely quoted definition of machine learning: "A computer program is set to learn from an experience ''E'' with respect to some task ''T'' and some performance measure ''P'' if its performance on ''T'' as measured by ''P'' improves with experience ''E''."</ref><ref name="Machine learning"/>
Machine learning (ML), a fundamental concept of AI research since the field's inception, is the study of computer algorithms that improve automatically through experience.
机器学习'''<font color=#ff8000> Machine Learning,ML</font>'''是自AI诞生以来就有的一个基本概念，它研究如何通过经验自动改进计算机算法。
[[Unsupervised learning]] is the ability to find patterns in a stream of input, without requiring a human to label the inputs first. [[Supervised learning]] includes both [[statistical classification|classification]] and numerical [[Regression analysis|regression]], which requires a human to label the input data first. Classification is used to determine what category something belongs in, and occurs after a program sees a number of examples of things from several categories. Regression is the attempt to produce a function that describes the relationship between inputs and outputs and predicts how the outputs should change as the inputs change.<ref name="Machine learning"/> Both classifiers and regression learners can be viewed as "function approximators" trying to learn an unknown (possibly implicit) function; for example, a spam classifier can be viewed as learning a function that maps from the text of an email to one of two categories, "spam" or "not spam". [[Computational learning theory]] can assess learners by [[computational complexity]], by [[sample complexity]] (how much data is required), or by other notions of [[optimization theory|optimization]].<ref>{{cite journal|last1=Jordan|first1=M. I.|last2=Mitchell|first2=T. M.|title=Machine learning: Trends, perspectives, and prospects|journal=Science|date=16 July 2015|volume=349|issue=6245|pages=255–260|doi=10.1126/science.aaa8415|pmid=26185243|bibcode=2015Sci...349..255J}}</ref> In [[reinforcement learning]]<ref name="Reinforcement learning"/> the agent is rewarded for good responses and punished for bad ones. The agent uses this sequence of rewards and punishments to form a strategy for operating in its problem space.
Unsupervised learning is the ability to find patterns in a stream of input, without requiring a human to label the inputs first. Supervised learning includes both classification and numerical regression, which requires a human to label the input data first. Classification is used to determine what category something belongs in, and occurs after a program sees a number of examples of things from several categories. Regression is the attempt to produce a function that describes the relationship between inputs and outputs and predicts how the outputs should change as the inputs change. In reinforcement learning the agent is rewarded for good responses and punished for bad ones. The agent uses this sequence of rewards and punishments to form a strategy for operating in its problem space.
'''<font color=#ff8000>无监督学习 Unsupervised Learning</font>'''可以从输入流中发现某种模式，而不需要人类提前标注输入。'''<font color=#ff8000>监督式学习 Supervised Learning</font>'''包括分类和数值回归，这需要人类首先标注输入数据。分类被用于确定某物属于哪个类别，这需要把大量来自多个类别的例子输入程序。回归用来产生一个描述输入和输出之间的关系的函数，并预测输出会如何随着输入的变化而变化。在强化学习中，智能体会因为好的回应而受到奖励，因为坏的回应而受到惩罚。智能体通过一系列的奖励和惩罚形成了一个在其问题空间中可施行的策略。
=== Planning ===
=== Natural language processing ===
=== Planning ===
=== Natural language processing ===
自然语言处理
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[[File:Hierarchical-control-system.svg|thumb| A [[hierarchical control system]] is a form of [[control system]] in which a set of devices and governing software is arranged in a hierarchy.]]
[[File:ParseTree.svg|thumb| A [[parse tree]] represents the [[syntax|syntactic]] structure of a sentence according to some [[formal grammar]].]]
A [[hierarchical control system is a form of control system in which a set of devices and governing software is arranged in a hierarchy.]]
A [[parse tree represents the syntactic structure of a sentence according to some formal grammar.]]
一个[[根据某种形式语法，解析树表示一个句子的句法结构]
{{Main|Natural language processing}}
[[Natural language processing]]<ref name="Natural language processing"/> (NLP) gives machines the ability to read and [[natural language understanding|understand]] human language. A sufficiently powerful natural language processing system would enable [[natural-language user interface]]s and the acquisition of knowledge directly from human-written sources, such as newswire texts. Some straightforward applications of natural language processing include [[information retrieval]], [[text mining]], [[question answering]]<ref>[https://www.academia.edu/2475776/Versatile_question_answering_systems_seeing_in_synthesis "Versatile question answering systems: seeing in synthesis"] {{webarchive|url=https://web.archive.org/web/20160201125047/http://www.academia.edu/2475776/Versatile_question_answering_systems_seeing_in_synthesis |date=1 February 2016 }}, Mittal et al., IJIIDS, 5(2), 119–142, 2011
Natural language processing (NLP) gives machines the ability to read and understand human language. A sufficiently powerful natural language processing system would enable natural-language user interfaces and the acquisition of knowledge directly from human-written sources, such as newswire texts. Some straightforward applications of natural language processing include information retrieval, text mining, question answering<ref>[https://www.academia.edu/2475776/Versatile_question_answering_systems_seeing_in_synthesis "Versatile question answering systems: seeing in synthesis"] , Mittal et al., IJIIDS, 5(2), 119–142, 2011
自然语言处理(NLP)赋予机器阅读和理解人类语言的能力。一个足够强大的自然语言处理系统可以提供自然语言用户界面，并能直接从如新闻专线文本的人类文字中获取知识。一些简单的自然语言处理的应用包括信息检索、文本挖掘、问答和机器翻译。目前许多方法使用词的共现频率来构建文本的句法表示。用“关键词定位”策略进行搜索很常见且可扩展，但很粗糙;搜索“狗”可能只匹配与含“狗”字的文档，而漏掉与“犬”匹配的文档。“词汇相关性”策略使用如“事故”这样的词出现的频次，评估文本想表达的情感。现代统计NLP方法可以结合所有这些策略以及其他策略，在以页或段落为单位的处理上获得还能让人接受的准确度，但仍然缺乏对单独的句子进行分类所需的语义理解。除了编码语义常识常见的困难外，现有的语义NLP有时可扩展性太差，无法应用到在商业中。而“叙述性”NLP除了达到语义NLP的功能之外，还想最终能做到充分理解常识推理。
</ref> and [[machine translation]].<ref name="Applications of natural language processing"/> Many current approaches use word co-occurrence frequencies to construct syntactic representations of text. "Keyword spotting" strategies for search are popular and scalable but dumb; a search query for "dog" might only match documents with the literal word "dog" and miss a document with the word "poodle". "Lexical affinity" strategies use the occurrence of words such as "accident" to [[sentiment analysis|assess the sentiment]] of a document. Modern statistical NLP approaches can combine all these strategies as well as others, and often achieve acceptable accuracy at the page or paragraph level, but continue to lack the semantic understanding required to classify isolated sentences well. Besides the usual difficulties with encoding semantic commonsense knowledge, existing semantic NLP sometimes scales too poorly to be viable in business applications. Beyond semantic NLP, the ultimate goal of "narrative" NLP is to embody a full understanding of commonsense reasoning.<ref>{{cite journal|last1=Cambria|first1=Erik|last2=White|first2=Bebo|title=Jumping NLP Curves: A Review of Natural Language Processing Research [Review Article]|journal=IEEE Computational Intelligence Magazine|date=May 2014|volume=9|issue=2|pages=48–57|doi=10.1109/MCI.2014.2307227}}</ref>
</ref> and machine translation.
/ ref 和机器翻译。
=== Perception ===
=== Perception ===
知觉
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{{Main|Machine perception|Computer vision|Speech recognition}}
[[File:Ääretuvastuse näide.png|thumb|[[Feature detection (computer vision)|Feature detection]] (pictured: [[edge detection]]) helps AI compose informative abstract structures out of raw data.]]
Feature detection (pictured: edge detection) helps AI compose informative abstract structures out of raw data.]]
[图片: 边缘检测特征提取]帮助AI从原始数据中合成有信息量的抽象结构
[[Machine perception]]<ref name="Machine perception"/> is the ability to use input from sensors (such as cameras (visible spectrum or infrared), microphones, wireless signals, and active [[lidar]], sonar, radar, and [[tactile sensor]]s) to deduce aspects of the world. Applications include [[speech recognition]],<ref name="Speech recognition"/> [[facial recognition system|facial recognition]], and [[object recognition]].<ref name="Object recognition"/> [[Computer vision]] is the ability to analyze visual input. Such input is usually ambiguous; a giant, fifty-meter-tall pedestrian far away may produce exactly the same pixels as a nearby normal-sized pedestrian, requiring the AI to judge the relative likelihood and reasonableness of different interpretations, for example by using its "object model" to assess that fifty-meter pedestrians do not exist.<ref name="Computer vision"/>
Machine perception is the ability to use input from sensors (such as cameras (visible spectrum or infrared), microphones, wireless signals, and active lidar, sonar, radar, and tactile sensors) to deduce aspects of the world. Applications include speech recognition, facial recognition, and object recognition. Computer vision is the ability to analyze visual input. Such input is usually ambiguous; a giant, fifty-meter-tall pedestrian far away may produce exactly the same pixels as a nearby normal-sized pedestrian, requiring the AI to judge the relative likelihood and reasonableness of different interpretations, for example by using its "object model" to assess that fifty-meter pedestrians do not exist.
机器感知是利用传感器(如可见光或红外线摄像头、麦克风、无线信号、激光雷达、声纳、雷达和触觉传感器)的输入来推断世界的不同角度的能力。应用包括语音识别、面部识别和物体识别。计算机视觉是分析可视化输入的能力。这种输入通常是模糊的; 一个在远处50米高的巨人可能会与近处正常大小的行人占据完全相同的像素，这就要求AI判断不同解释的相对可能性和合理性，例如使用”物体模型”来判断50米高的巨人其实是不存在的。
=== Motion and manipulation ===
=== Motion and manipulation ===
运动和操作
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{{Main|Robotics}}
AI is heavily used in [[robotics]].<ref name="Robotics"/> Advanced [[robotic arm]]s and other [[industrial robot]]s, widely used in modern factories, can learn from experience how to move efficiently despite the presence of friction and gear slippage.<ref name="Configuration space"/> A modern mobile robot, when given a small, static, and visible environment, can easily determine its location and [[robotic mapping|map]] its environment; however, dynamic environments, such as (in [[endoscopy]]) the interior of a patient's breathing body, pose a greater challenge. [[Motion planning]] is the process of breaking down a movement task into "primitives" such as individual joint movements. Such movement often involves compliant motion, a process where movement requires maintaining physical contact with an object.{{sfn|Tecuci|2012}}<ref name="Robotic mapping"/><ref>{{cite journal|last1=Cadena|first1=Cesar|last2=Carlone|first2=Luca|last3=Carrillo|first3=Henry|last4=Latif|first4=Yasir|last5=Scaramuzza|first5=Davide|last6=Neira|first6=Jose|last7=Reid|first7=Ian|last8=Leonard|first8=John J.|title=Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age|journal=IEEE Transactions on Robotics|date=December 2016|volume=32|issue=6|pages=1309–1332|doi=10.1109/TRO.2016.2624754|arxiv=1606.05830|bibcode=2016arXiv160605830C}}</ref> [[Moravec's paradox]] generalizes that low-level sensorimotor skills that humans take for granted are, counterintuitively, difficult to program into a robot; the paradox is named after [[Hans Moravec]], who stated in 1988 that "it is comparatively easy to make computers exhibit adult level performance on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one-year-old when it comes to perception and mobility".<ref>{{Cite book| first = Hans | last = Moravec | year = 1988 | title = Mind Children | publisher = Harvard University Press | author-link =Hans Moravec| p=15}}</ref><ref>{{cite news|last1=Chan|first1=Szu Ping|title=This is what will happen when robots take over the world|url=https://www.telegraph.co.uk/finance/economics/11994694/Heres-what-will-happen-when-robots-take-over-the-world.html|accessdate=23 April 2018|date=15 November 2015}}</ref> This is attributed to the fact that, unlike checkers, physical dexterity has been a direct target of [[natural selection]] for millions of years.<ref name="The Economist">{{cite news|title=IKEA furniture and the limits of AI|url=https://www.economist.com/news/leaders/21740735-humans-have-had-good-run-most-recent-breakthrough-robotics-it-clear|accessdate=24 April 2018|work=The Economist|date=2018|language=en}}</ref>
AI is heavily used in robotics. Moravec's paradox generalizes that low-level sensorimotor skills that humans take for granted are, counterintuitively, difficult to program into a robot; the paradox is named after Hans Moravec, who stated in 1988 that "it is comparatively easy to make computers exhibit adult level performance on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one-year-old when it comes to perception and mobility". This is attributed to the fact that, unlike checkers, physical dexterity has been a direct target of natural selection for millions of years.
AI在机器人技术中应用广泛。在现代工厂中广泛使用的高级机械臂和其他工业机器人，可以从经验中学习如何在存在摩擦和齿轮滑移的情况下有效地移动。当处在一个静态且可见的小环境中时，现代移动机器人可以很容易地确定自己的位置并绘制环境地图。然而如果是动态环境，比如用内窥镜检查病人呼吸的身体的内部，难度就会更高。运动规划是将一个运动任务分解为如单个的关节运动这样的“基本任务”的过程。这种运动通常包括顺应运动，在这个过程中需要与物体保持物理接触。
'''<font color=#ff8000>莫拉维克悖论 Moravec's Paradox</font>''' 低级感觉运动技能的概括,人类理所当然,相反,很难计划到一个机器人;这个悖论是以汉斯•莫拉维克的名字命名的，他在1988年表示:“让电脑在智力测试或跳棋中表现出成人水平的表现相对容易，但让它们掌握一岁大的感知和行动能力则很难或不可能。”这归因于这样一个事实:与跳棋不同，数百万年来，身体灵巧性一直是自然选择的直接目标。
莫拉维克的悖论概括了人类理所当然认为低水平的感知运动技能很难在编程给机器人的事实，这个悖论是以汉斯 · 莫拉维克的名字命名的，他在1988年表示: “让计算机在智力测试或下跳棋中展现出成人水平的表现相对容易，但要让计算机拥有一岁小孩的感知和移动能力却很难，甚至不可能。”这是因为，与跳棋不同，身体灵巧性一直在数百万年的自然选择后才形成的。（意译）
--[[用户:Thingamabob|Thingamabob]]（[[用户讨论:Thingamabob|讨论]]）这是因为，与跳棋不同，身体灵巧性一直在数百万年的自然选择后才形成的。一句为意译
=== Natural language processing ===
=== Social intelligence ===
=== Natural language processing ===
=== Social intelligence ===
社会智力
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{{Main|Affective computing}}
{{Main|Natural language processing}}
[[File:Kismet robot at MIT Museum.jpg|thumb|[[Kismet (robot)|Kismet]], a robot with rudimentary social skills{{sfn|''Kismet''}}]]
Kismet, a robot with rudimentary social skills]]
Kismet，一个具有基本社交技能的机器人]]
Moravec's paradox can be extended to many forms of social intelligence.<ref>{{cite magazine |last1=Thompson|first1=Derek|title=What Jobs Will the Robots Take?|url=https://www.theatlantic.com/business/archive/2014/01/what-jobs-will-the-robots-take/283239/|accessdate=24 April 2018|magazine=The Atlantic|date=2018}}</ref><ref>{{cite journal|last1=Scassellati|first1=Brian|title=Theory of mind for a humanoid robot|journal=Autonomous Robots|volume=12|issue=1|year=2002|pages=13–24|doi=10.1023/A:1013298507114}}</ref> Distributed multi-agent coordination of autonomous vehicles remains a difficult problem.<ref>{{cite journal|last1=Cao|first1=Yongcan|last2=Yu|first2=Wenwu|last3=Ren|first3=Wei|last4=Chen|first4=Guanrong|title=An Overview of Recent Progress in the Study of Distributed Multi-Agent Coordination|journal=IEEE Transactions on Industrial Informatics|date=February 2013|volume=9|issue=1|pages=427–438|doi=10.1109/TII.2012.2219061|arxiv=1207.3231}}</ref> [[Affective computing]] is an interdisciplinary umbrella that comprises systems which recognize, interpret, process, or simulate human [[Affect (psychology)|affects]].{{sfn|Thro|1993}}{{sfn|Edelson|1991}}{{sfn|Tao|Tan|2005}} Moderate successes related to affective computing include textual [[sentiment analysis]] and, more recently, multimodal affect analysis (see [[multimodal sentiment analysis]]), wherein AI classifies the affects displayed by a videotaped subject.<ref>{{cite journal|last1=Poria|first1=Soujanya|last2=Cambria|first2=Erik|last3=Bajpai|first3=Rajiv|last4=Hussain|first4=Amir|title=A review of affective computing: From unimodal analysis to multimodal fusion|journal=Information Fusion|date=September 2017|volume=37|pages=98–125|doi=10.1016/j.inffus.2017.02.003|hdl=1893/25490|hdl-access=free}}</ref>
Moravec's paradox can be extended to many forms of social intelligence. Distributed multi-agent coordination of autonomous vehicles remains a difficult problem. Affective computing is an interdisciplinary umbrella that comprises systems which recognize, interpret, process, or simulate human affects. Moderate successes related to affective computing include textual sentiment analysis and, more recently, multimodal affect analysis (see multimodal sentiment analysis), wherein AI classifies the affects displayed by a videotaped subject.
莫拉维克悖论可以扩展到社会智能的许多形式。自动汽车分布式多智能体协调一直是一个难题。情感计算是一个跨学科交叉领域，包括了识别、解释、处理、模拟人的情感的系统。与情感计算相关的一些还算成功的领域有文本情感分析，以及最近的'''<font color=#ff8000>多模态情感分析 Multimodal Affect Analysis</font>''' ，多模态情感分析中AI可以左到将录像中被试表现出的情感分类。
</ref> and machine translation.
In the long run, social skills and an understanding of human emotion and [[game theory]] would be valuable to a social agent. Being able to predict the actions of others by understanding their motives and emotional states would allow an agent to make better decisions. Some computer systems mimic human emotion and expressions to appear more sensitive to the emotional dynamics of human interaction, or to otherwise facilitate [[human–computer interaction]].<ref name="Emotion and affective computing"/> Similarly, some [[virtual assistant]]s are programmed to speak conversationally or even to banter humorously; this tends to give naïve users an unrealistic conception of how intelligent existing computer agents actually are.<ref>{{cite magazine|last1=Waddell|first1=Kaveh|title=Chatbots Have Entered the Uncanny Valley|url=https://www.theatlantic.com/technology/archive/2017/04/uncanny-valley-digital-assistants/523806/|accessdate=24 April 2018|magazine=The Atlantic|date=2018}}</ref>
In the long run, social skills and an understanding of human emotion and game theory would be valuable to a social agent. Being able to predict the actions of others by understanding their motives and emotional states would allow an agent to make better decisions. Some computer systems mimic human emotion and expressions to appear more sensitive to the emotional dynamics of human interaction, or to otherwise facilitate human–computer interaction.
从长远来看，社交技巧以及对人类情感和博弈论的理解对社会智能体的价值很高。能够通过理解他人的动机和情绪状态来预测他人的行为，会让智能体做出更好的决策。有些计算机系统模仿人类的情感和表情，有利于对人类交互的情感动力更敏感，或利于促进人机交互。
=== Perception ===
=== General intelligence ===
=== Perception ===
=== General intelligence ===
通用智能
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{{Main|Machine perception|Computer vision|Speech recognition}}
{{Main|Artificial general intelligence|AI-complete}}
[[File:Ääretuvastuse näide.png|thumb|[[Feature detection (computer vision)|Feature detection]] (pictured: [[edge detection]]) helps AI compose informative abstract structures out of raw data.]]
Historically, projects such as the Cyc knowledge base (1984–) and the massive Japanese [[Fifth generation computer|Fifth Generation Computer Systems]] initiative (1982–1992) attempted to cover the breadth of human cognition. These early projects failed to escape the limitations of non-quantitative symbolic logic models and, in retrospect, greatly underestimated the difficulty of cross-domain AI. Nowadays, the vast majority of current AI researchers work instead on tractable "narrow AI" applications (such as medical diagnosis or automobile navigation).<ref name="contemporary agi">{{cite book|last1=Pennachin|first1=C.|last2=Goertzel|first2=B.|title=Contemporary Approaches to Artificial General Intelligence|journal=Artificial General Intelligence. Cognitive Technologies|date=2007|doi=10.1007/978-3-540-68677-4_1|publisher=Springer|location=Berlin, Heidelberg|series=Cognitive Technologies|isbn=978-3-540-23733-4}}</ref> Many researchers predict that such "narrow AI" work in different individual domains will eventually be incorporated into a machine with [[artificial general intelligence]] (AGI), combining most of the narrow skills mentioned in this article and at some point even exceeding human ability in most or all these areas.<ref name="General intelligence"/><ref name="Roberts">{{cite magazine|last1=Roberts|first1=Jacob|title=Thinking Machines: The Search for Artificial Intelligence|magazine=Distillations|date=2016|volume=2|issue=2|pages=14–23|url=https://www.sciencehistory.org/distillations/magazine/thinking-machines-the-search-for-artificial-intelligence|accessdate=20 March 2018|archive-url=https://web.archive.org/web/20180819152455/https://www.sciencehistory.org/distillations/magazine/thinking-machines-the-search-for-artificial-intelligence|archive-date=19 August 2018|url-status=dead}}</ref> Many advances have general, cross-domain significance. One high-profile example is that [[DeepMind]] in the 2010s developed a "generalized artificial intelligence" that could learn many diverse [[Atari 2600|Atari]] games on its own, and later developed a variant of the system which succeeds at [[Catastrophic interference#The Sequential Learning Problem: McCloskey and Cohen (1989)|sequential learning]].<ref>{{cite news|title=The superhero of artificial intelligence: can this genius keep it in check?|url=https://www.theguardian.com/technology/2016/feb/16/demis-hassabis-artificial-intelligence-deepmind-alphago|accessdate=26 April 2018|work=the Guardian|date=16 February 2016|language=en}}</ref><ref>{{cite journal|last1=Mnih|first1=Volodymyr|last2=Kavukcuoglu|first2=Koray|last3=Silver|first3=David|last4=Rusu|first4=Andrei A.|last5=Veness|first5=Joel|last6=Bellemare|first6=Marc G.|last7=Graves|first7=Alex|last8=Riedmiller|first8=Martin|last9=Fidjeland|first9=Andreas K.|last10=Ostrovski|first10=Georg|last11=Petersen|first11=Stig|last12=Beattie|first12=Charles|last13=Sadik|first13=Amir|last14=Antonoglou|first14=Ioannis|last15=King|first15=Helen|last16=Kumaran|first16=Dharshan|last17=Wierstra|first17=Daan|last18=Legg|first18=Shane|last19=Hassabis|first19=Demis|title=Human-level control through deep reinforcement learning|journal=Nature|date=26 February 2015|volume=518|issue=7540|pages=529–533|doi=10.1038/nature14236|pmid=25719670|bibcode=2015Natur.518..529M}}</ref><ref>{{cite news|last1=Sample|first1=Ian|title=Google's DeepMind makes AI program that can learn like a human|url=https://www.theguardian.com/global/2017/mar/14/googles-deepmind-makes-ai-program-that-can-learn-like-a-human|accessdate=26 April 2018|work=the Guardian|date=14 March 2017|language=en}}</ref> Besides [[transfer learning]],<ref>{{cite news|title=From not working to neural networking|url=https://www.economist.com/news/special-report/21700756-artificial-intelligence-boom-based-old-idea-modern-twist-not|accessdate=26 April 2018|work=The Economist|date=2016|language=en}}</ref> hypothetical AGI breakthroughs could include the development of reflective architectures that can engage in decision-theoretic metareasoning, and figuring out how to "slurp up" a comprehensive knowledge base from the entire unstructured [[World Wide Web|Web]].{{sfn|Russell|Norvig|2009|chapter=27. AI: The Present and Future}} Some argue that some kind of (currently-undiscovered) conceptually straightforward, but mathematically difficult, "Master Algorithm" could lead to AGI.{{sfn|Domingos|2015|chapter=9. The Pieces of the Puzzle Fall into Place}} Finally, a few "emergent" approaches look to simulating human intelligence extremely closely, and believe that [[anthropomorphism|anthropomorphic]] features like an [[artificial brain]] or simulated [[developmental robotics|child development]] may someday reach a critical point where general intelligence emerges.<ref name="Brain simulation"/><ref>{{cite journal|last1=Goertzel|first1=Ben|last2=Lian|first2=Ruiting|last3=Arel|first3=Itamar|last4=de Garis|first4=Hugo|last5=Chen|first5=Shuo|title=A world survey of artificial brain projects, Part II: Biologically inspired cognitive architectures|journal=Neurocomputing|date=December 2010|volume=74|issue=1–3|pages=30–49|doi=10.1016/j.neucom.2010.08.012}}</ref>
Historically, projects such as the Cyc knowledge base (1984–) and the massive Japanese Fifth Generation Computer Systems initiative (1982–1992) attempted to cover the breadth of human cognition. These early projects failed to escape the limitations of non-quantitative symbolic logic models and, in retrospect, greatly underestimated the difficulty of cross-domain AI. Nowadays, the vast majority of current AI researchers work instead on tractable "narrow AI" applications (such as medical diagnosis or automobile navigation). Many researchers predict that such "narrow AI" work in different individual domains will eventually be incorporated into a machine with artificial general intelligence (AGI), combining most of the narrow skills mentioned in this article and at some point even exceeding human ability in most or all these areas. Many advances have general, cross-domain significance. One high-profile example is that DeepMind in the 2010s developed a "generalized artificial intelligence" that could learn many diverse Atari games on its own, and later developed a variant of the system which succeeds at sequential learning. Besides transfer learning, hypothetical AGI breakthroughs could include the development of reflective architectures that can engage in decision-theoretic metareasoning, and figuring out how to "slurp up" a comprehensive knowledge base from the entire unstructured Web. Some argue that some kind of (currently-undiscovered) conceptually straightforward, but mathematically difficult, "Master Algorithm" could lead to AGI. Finally, a few "emergent" approaches look to simulating human intelligence extremely closely, and believe that anthropomorphic features like an artificial brain or simulated child development may someday reach a critical point where general intelligence emerges.
历史上，诸如 Cyc 知识库(1984 -)和大规模的日本第五代计算机系统倡议(1982-1992)等项目试图涵盖人类的所有认知。这些早期的项目未能逃脱非定量符号逻辑模型的限制，现在回过头看，这些项目大大低估了实现跨领域AI的难度。当下绝大多数AI研究人员主要研究易于处理的“狭义AI”应用(如医疗诊断或汽车导航)。许多研究人员预测，不同领域的“狭义AI”工作最终将被整合到一台具有人工通用智能(AGI)的机器中，结合上文提到的大多数狭义功能，甚至在某种程度上在大多数或所有这些领域都超过人类。许多进展具有普遍的、跨领域的意义。一个著名的例子是，21世纪一零年代，DeepMind开发了一种“'''<font color=#ff8000>通用人工智能 Generalized Artificial Intelligence</font>'''” ，它可以自己学习许多不同的 Atari 游戏，后来又开发了这种系统的升级版，在序贯学习方面取得了成功。除了迁移学习，未来AGI 的突破可能包括开发能够进行决策理论元推理的反射架构，以及从整个非结构化的网络中整合一个全面的知识库。一些人认为，某种(目前尚未发现的)概念简单，但在数学上困难的“主算法”可以导致 AGI。最后，一些“涌现”的方法着眼于尽可能地模拟人类智能，并相信如人工大脑或模拟儿童发展等拟人特征，有一天会达到一个临界点，通用智能从此出现。
Many of the problems in this article may also require general intelligence, if machines are to solve the problems as well as people do. For example, even specific straightforward tasks, like [[machine translation]], require that a machine read and write in both languages ([[#Natural language processing|NLP]]), follow the author's argument ([[#Deduction, reasoning, problem solving|reason]]), know what is being talked about ([[#Knowledge representation|knowledge]]), and faithfully reproduce the author's original intent ([[#Social intelligence|social intelligence]]). A problem like machine translation is considered "[[AI-complete]]", because all of these problems need to be solved simultaneously in order to reach human-level machine performance.
Many of the problems in this article may also require general intelligence, if machines are to solve the problems as well as people do. For example, even specific straightforward tasks, like machine translation, require that a machine read and write in both languages (NLP), follow the author's argument (reason), know what is being talked about (knowledge), and faithfully reproduce the author's original intent (social intelligence). A problem like machine translation is considered "AI-complete", because all of these problems need to be solved simultaneously in order to reach human-level machine performance.
如果机器要像人一样解决问题，那么本文中的许多问题也可能需要通用智能。例如，即使是特定的如机器翻译的直接任务，也要求机器用两种语言进行读写(NLP) ，符合作者的观点(推理) ，知道谈论的内容(知识) ，并忠实地再现作者的原始意图(社会智能)。像机器翻译这样的问题被认为是“AI完备”的，因为需要同时解决所有这些问题，机器性能才能达到人类水平。
== Approaches ==
== Approaches ==
方法
=== Motion and manipulation ===
There is no established unifying theory or [[paradigm]] that guides AI research. Researchers disagree about many issues.<ref>[[Nils Nilsson (researcher)|Nils Nilsson]] writes: "Simply put, there is wide disagreement in the field about what AI is all about" {{Harv|Nilsson|1983|p=10}}.</ref> A few of the most long standing questions that have remained unanswered are these: should artificial intelligence simulate natural intelligence by studying [[psychology]] or [[Neuroscience|neurobiology]]? Or is [[human biology]] as irrelevant to AI research as bird biology is to [[aeronautical engineering]]?<ref name="Biological intelligence vs. intelligence in general"/>
There is no established unifying theory or paradigm that guides AI research. Researchers disagree about many issues. A few of the most long standing questions that have remained unanswered are these: should artificial intelligence simulate natural intelligence by studying psychology or neurobiology? Or is human biology as irrelevant to AI research as bird biology is to aeronautical engineering?
目前还没有统一的理论或范式来指导AI的研究。研究人员在许多问题上存在分歧。一些长期悬而未决的问题是: AI是否应该通过研究心理学或神经生物学来模拟自然智能？人类生物学和AI研究的关系和鸟类生物学和航空工程学的关系一样吗？
<!-- This is linked to in the introduction -->
Can intelligent behavior be described using simple, elegant principles (such as [[logic]] or [[optimization (mathematics)|optimization]])? Or does it necessarily require solving a large number of completely unrelated problems?<ref name="Neats vs. scruffies"/>
Can intelligent behavior be described using simple, elegant principles (such as logic or optimization)? Or does it necessarily require solving a large number of completely unrelated problems?
智能行为可以用简单、优雅的原则(如逻辑或优化)来描述吗？还是需要去解决大量完全不相关的问题？
=== Cybernetics and brain simulation ===
=== Cybernetics and brain simulation ===
控制论与大脑模拟
{{Main|Cybernetics|Computational neuroscience}}
In the 1940s and 1950s, a number of researchers explored the connection between [[neurobiology]], [[information theory]], and [[cybernetics]]. Some of them built machines that used electronic networks to exhibit rudimentary intelligence, such as [[W. Grey Walter]]'s [[turtle (robot)|turtles]] and the [[Johns Hopkins Beast]]. Many of these researchers gathered for meetings of the Teleological Society at [[Princeton University]] and the [[Ratio Club]] in England.<ref name="AI's immediate precursors"/> By 1960, this approach was largely abandoned, although elements of it would be revived in the 1980s.
In the 1940s and 1950s, a number of researchers explored the connection between neurobiology, information theory, and cybernetics. Some of them built machines that used electronic networks to exhibit rudimentary intelligence, such as W. Grey Walter's turtles and the Johns Hopkins Beast. Many of these researchers gathered for meetings of the Teleological Society at Princeton University and the Ratio Club in England. By 1960, this approach was largely abandoned, although elements of it would be revived in the 1980s.
在20世纪四五十年代，许多研究人员探索了神经生物学、信息论和控制论之间的联系。他们中的一些人利用电子网络制造机器来表现基本的智能，比如 w · 格雷 · 沃尔特的乌龟和约翰 · 霍普金斯的野兽。这些研究人员中的许多人参加了在普林斯顿大学的目的论社和英格兰的比率俱乐部举办的集会。到了1960年，这种方法基本上被放弃了，直到二十世纪八十年代一些部分又被重新使用。
=== Symbolic ===
=== Symbolic ===
符号化
{{Main|Symbolic AI}}
When access to digital computers became possible in the mid-1950s, AI research began to explore the possibility that human intelligence could be reduced to symbol manipulation. The research was centered in three institutions: [[Carnegie Mellon University]], [[Stanford]] and [[MIT]], and as described below, each one developed its own style of research. [[John Haugeland]] named these symbolic approaches to AI "good old fashioned AI" or "[[GOFAI]]".<ref name="GOFAI"/> During the 1960s, symbolic approaches had achieved great success at simulating high-level "thinking" in small demonstration programs. Approaches based on [[cybernetics]] or [[artificial neural network]]s were abandoned or pushed into the background.<ref>The most dramatic case of sub-symbolic AI being pushed into the background was the devastating critique of [[perceptron]]s by [[Marvin Minsky]] and [[Seymour Papert]] in 1969. See [[History of AI]], [[AI winter]], or [[Frank Rosenblatt]].</ref>
When access to digital computers became possible in the mid-1950s, AI research began to explore the possibility that human intelligence could be reduced to symbol manipulation. The research was centered in three institutions: Carnegie Mellon University, Stanford and MIT, and as described below, each one developed its own style of research. John Haugeland named these symbolic approaches to AI "good old fashioned AI" or "GOFAI".
20世纪50年代中期，当数字计算机成为可能时，AI研究开始探索人类智能可以简化为符号操纵的可能性。这项研究集中在3个机构: 卡内基梅隆大学，斯坦福和麻省理工学院，正如下面所描述的，每个机构都有自己的研究风格。约翰 · 豪格兰德将这些具有象征意义的AI方法命名为“好的老式人工智能 Good Old Fashioned AI”或“ GOFAI”。
Researchers in the 1960s and the 1970s were convinced that symbolic approaches would eventually succeed in creating a machine with [[artificial general intelligence]] and considered this the goal of their field.
Researchers in the 1960s and the 1970s were convinced that symbolic approaches would eventually succeed in creating a machine with artificial general intelligence and considered this the goal of their field.
20世纪六七十年代的研究人员相信，符号方法最终会成功地创造出一台具有人工通用智能的机器，并以此作为他们研究领域的目标。
==== Cognitive simulation ====
==== Cognitive simulation ====
认知模拟
Economist [[Herbert A. Simon|Herbert Simon]] and [[Allen Newell]] studied human problem-solving skills and attempted to formalize them, and their work laid the foundations of the field of artificial intelligence, as well as [[cognitive science]], [[operations research]] and [[management science]]. Their research team used the results of [[psychology|psychological]] experiments to develop programs that simulated the techniques that people used to solve problems. This tradition, centered at [[Carnegie Mellon University]] would eventually culminate in the development of the [[Soar (cognitive architecture)|Soar]] architecture in the middle 1980s.<ref name="AI at CMU in the 60s"/><ref name="Soar"/>
Economist Herbert Simon and Allen Newell studied human problem-solving skills and attempted to formalize them, and their work laid the foundations of the field of artificial intelligence, as well as cognitive science, operations research and management science. Their research team used the results of psychological experiments to develop programs that simulated the techniques that people used to solve problems. This tradition, centered at Carnegie Mellon University would eventually culminate in the development of the Soar architecture in the middle 1980s.
经济学家赫伯特 · 西蒙和艾伦 · 纽厄尔研究了人类解决问题的能力，并试图将其形式化，他们的工作为AI、认知科学、运筹学和管理科学奠定了基础。他们的研究团队利用心理学实验的结果来开发程序，模拟人们用来解决问题的方法。这个以卡内基梅隆大学为中心的研究，最终在20世纪80年代中期的SOAR的开发过程中达到顶峰。
==== Logic-based ====
==== Logic-based ====
基于逻辑的
Unlike Simon and Newell, [[John McCarthy (computer scientist)|John McCarthy]] felt that machines did not need to simulate human thought, but should instead try to find the essence of abstract reasoning and problem-solving, regardless whether people used the same algorithms.<ref name="Biological intelligence vs. intelligence in general"/> His laboratory at [[Stanford University|Stanford]] ([[Stanford Artificial Intelligence Laboratory|SAIL]]) focused on using formal [[logic]] to solve a wide variety of problems, including [[knowledge representation]], [[automated planning and scheduling|planning]] and [[machine learning|learning]].<ref name="AI at Stanford in the 60s"/> Logic was also the focus of the work at the [[University of Edinburgh]] and elsewhere in Europe which led to the development of the programming language [[Prolog]] and the science of [[logic programming]].<ref name="AI at Edinburgh and France in the 60s"/>
Unlike Simon and Newell, John McCarthy felt that machines did not need to simulate human thought, but should instead try to find the essence of abstract reasoning and problem-solving, regardless whether people used the same algorithms. His laboratory at Stanford (SAIL) focused on using formal logic to solve a wide variety of problems, including knowledge representation, planning and learning. Logic was also the focus of the work at the University of Edinburgh and elsewhere in Europe which led to the development of the programming language Prolog and the science of logic programming.
与西蒙和纽厄尔不同，约翰 · 麦卡锡认为机器不需要模拟人类的思维，而是应该尝试寻找抽象推理和解决问题的本质，不管人们是否使用相同的算法。他在斯坦福大学的实验室(SAIL)致力于使用形式逻辑来解决各种各样的问题，包括知识表示、规划和学习。逻辑也是爱丁堡大学和欧洲其他地方工作的重点，这促进了编程语言 Prolog 和逻辑编程科学的发展。
==== Anti-logic or scruffy ====
=== General intelligence ===
==== Anti-logic or scruffy ====
反逻辑的或邋遢的（scruffy）
Researchers at [[MIT]] (such as [[Marvin Minsky]] and [[Seymour Papert]])<ref name="AI at MIT in the 60s"/> found that solving difficult problems in [[computer vision|vision]] and [[natural language processing]] required ad-hoc solutions—they argued that there was no simple and general principle (like [[logic]]) that would capture all the aspects of intelligent behavior. [[Roger Schank]] described their "anti-logic" approaches as "[[Neats vs. scruffies|scruffy]]" (as opposed to the "[[neats vs. scruffies|neat]]" paradigms at [[Carnegie Mellon University|CMU]] and Stanford).<ref name="Neats vs. scruffies"/> [[Commonsense knowledge bases]] (such as [[Doug Lenat]]'s [[Cyc]]) are an example of "scruffy" AI, since they must be built by hand, one complicated concept at a time.<ref name="Cyc"/>
Researchers at MIT (such as Marvin Minsky and Seymour Papert) found that solving difficult problems in vision and natural language processing required ad-hoc solutions—they argued that there was no simple and general principle (like logic) that would capture all the aspects of intelligent behavior. Roger Schank described their "anti-logic" approaches as "scruffy" (as opposed to the "neat" paradigms at CMU and Stanford). Commonsense knowledge bases (such as Doug Lenat's Cyc) are an example of "scruffy" AI, since they must be built by hand, one complicated concept at a time.
麻省理工学院(MIT)的研究人员马文•明斯基和西摩•派珀特等发现，视觉和自然语言处理中的难题需要特定的解决方案——他们认为，没有简单而普遍的原则(如逻辑)可以涵盖智能行为。罗杰•尚克将他们的“反逻辑”方法形容为“邋遢的”(相对于卡内基梅隆大学和斯坦福大学的“整洁”范式)。常识库(如常识知识库的 Cyc)是“邋遢”AI的一个例子，因为它们必须人工一个一个地构建复杂概念。
====Knowledge-based====
====Knowledge-based====
基于知识
When computers with large memories became available around 1970, researchers from all three traditions began to build [[knowledge representation|knowledge]] into AI applications.<ref name="Knowledge revolution"/> This "knowledge revolution" led to the development and deployment of [[expert system]]s (introduced by [[Edward Feigenbaum]]), the first truly successful form of AI software.<ref name="Expert systems"/> A key component of the system architecture for all expert systems is the knowledge base, which stores facts and rules that illustrate AI.<ref>{{Cite journal |last=Frederick |first=Hayes-Roth |last2=William |first2=Murray |last3=Leonard |first3=Adelman |title=Expert systems|journal=AccessScience |language=en |doi=10.1036/1097-8542.248550}}</ref> The knowledge revolution was also driven by the realization that enormous amounts of knowledge would be required by many simple AI applications.
When computers with large memories became available around 1970, researchers from all three traditions began to build knowledge into AI applications. The knowledge revolution was also driven by the realization that enormous amounts of knowledge would be required by many simple AI applications.
1970年左右，当拥有大容量存储器的计算机出现时，来自这三个研究方向的研究人员开始将知识应用于AI领域。推动知识革命的另一个原因是人们认识到，许多简单的AI应用程序也需要大量的知识。
=== Sub-symbolic ===
=== Sub-symbolic ===
亚符号
By the 1980s, progress in symbolic AI seemed to stall and many believed that symbolic systems would never be able to imitate all the processes of human cognition, especially [[machine perception|perception]], [[robotics]], [[machine learning|learning]] and [[pattern recognition]]. A number of researchers began to look into "sub-symbolic" approaches to specific AI problems.<ref name="Symbolic vs. sub-symbolic"/> Sub-symbolic methods manage to approach intelligence without specific representations of knowledge.
By the 1980s, progress in symbolic AI seemed to stall and many believed that symbolic systems would never be able to imitate all the processes of human cognition, especially perception, robotics, learning and pattern recognition. A number of researchers began to look into "sub-symbolic" approaches to specific AI problems. Sub-symbolic methods manage to approach intelligence without specific representations of knowledge.
到了20世纪80年代，符号AI的进步似乎停滞不前，许多人认为符号系统永远无法模仿人类认知的所有过程，尤其在感知、机器人学、学习和模式识别等方面。许多研究人员开始研究针对特定AI问题的“亚符号”方法。亚符号方法能在没有特定知识表示的情况下，做到接近智能。
==== Embodied intelligence ====
==== Embodied intelligence ====
具身智慧
== Approaches ==
This includes [[embodied agent|embodied]], [[situated]], [[behavior-based AI|behavior-based]], and [[nouvelle AI]]. Researchers from the related field of [[robotics]], such as [[Rodney Brooks]], rejected symbolic AI and focused on the basic engineering problems that would allow robots to move and survive.<ref name="Embodied AI"/> Their work revived the non-symbolic point of view of the early [[cybernetic]]s researchers of the 1950s and reintroduced the use of [[control theory]] in AI. This coincided with the development of the [[embodied mind thesis]] in the related field of [[cognitive science]]: the idea that aspects of the body (such as movement, perception and visualization) are required for higher intelligence.
This includes embodied, situated, behavior-based, and nouvelle AI. Researchers from the related field of robotics, such as Rodney Brooks, rejected symbolic AI and focused on the basic engineering problems that would allow robots to move and survive. Their work revived the non-symbolic point of view of the early cybernetics researchers of the 1950s and reintroduced the use of control theory in AI. This coincided with the development of the embodied mind thesis in the related field of cognitive science: the idea that aspects of the body (such as movement, perception and visualization) are required for higher intelligence.
这包括具体化的、情境化的、基于行为的和 nouvelle AI。来自机器人相关领域的研究人员，如罗德尼 · 布鲁克斯，放弃了符号化AI的方法，而专注于使机器人能够移动和生存的基本工程问题。他们的工作重启了20世纪50年代早期控制论研究者的非符号观点，并将控制论重新引入到AI的应用中。这与认知科学相关领域的具身理论的发展相吻合: 认为如运动、感知和视觉等身体的各个功能是高智能所必需的。
Within [[developmental robotics]], developmental learning approaches are elaborated upon to allow robots to accumulate repertoires of novel skills through autonomous self-exploration, social interaction with human teachers, and the use of guidance mechanisms (active learning, maturation, motor synergies, etc.).{{sfn|Weng|McClelland|Pentland|Sporns|2001}}{{sfn|Lungarella|Metta|Pfeifer|Sandini|2003}}{{sfn|Asada|Hosoda|Kuniyoshi|Ishiguro|2009}}{{sfn|Oudeyer|2010}}
Within developmental robotics, developmental learning approaches are elaborated upon to allow robots to accumulate repertoires of novel skills through autonomous self-exploration, social interaction with human teachers, and the use of guidance mechanisms (active learning, maturation, motor synergies, etc.).
在发展型机器人中，人们开发了发展型学习方法，通过自主的自我探索、与人类教师的社会互动，以及使用主动学习、成熟、协同运动等指导机制 ，使机器人积累新技能的能力。
====Computational intelligence and soft computing====
=== Cybernetics and brain simulation ===
====Computational intelligence and soft computing====
计算智能与软计算
Interest in [[Artificial neural network|neural networks]] and "[[connectionism]]" was revived by [[David Rumelhart]] and others in the middle of the 1980s.<ref name="Revival of connectionism"/> [[Artificial neural network]]s are an example of [[soft computing]]—they are solutions to problems which cannot be solved with complete logical certainty, and where an approximate solution is often sufficient. Other [[soft computing]] approaches to AI include [[fuzzy system]]s, [[Grey system theory]], [[evolutionary computation]] and many statistical tools. The application of soft computing to AI is studied collectively by the emerging discipline of [[computational intelligence]].<ref name="Computational intelligence"/>
Interest in neural networks and "connectionism" was revived by David Rumelhart and others in the middle of the 1980s. Artificial neural networks are an example of soft computing—they are solutions to problems which cannot be solved with complete logical certainty, and where an approximate solution is often sufficient. Other soft computing approaches to AI include fuzzy systems, Grey system theory, evolutionary computation and many statistical tools. The application of soft computing to AI is studied collectively by the emerging discipline of computational intelligence.
上世纪80年代中期，大卫•鲁梅尔哈特等人重新激发了人们对神经网络和“'''<font color=#ff8000>连接主义 Connectionism</font>'''”的兴趣。人工神经网络是软计算的一个例子ーー它们解决不能完全用逻辑确定性解决且近似解常常是充分的问题。AI的其他软计算方法包括'''<font color=#ff8000>模糊系统 Fuzzy Systems </font>'''、'''<font color=#ff8000>灰色系统理论 Grey System Theory</font>'''、'''<font color=#ff8000>进化计算 Evolutionary Computation </font>'''和许多统计工具。软计算在AI中的应用是计算智能这一新兴学科的集中研究领域。
=== Statistical learning ===
=== Statistical learning ===
统计学习
Much of traditional [[Symbolic artificial intelligence|GOFAI]] got bogged down on ''ad hoc'' patches to [[symbolic computation]] that worked on their own toy models but failed to generalize to real-world results. However, around the 1990s, AI researchers adopted sophisticated mathematical tools, such as [[hidden Markov model]]s (HMM), [[information theory]], and normative Bayesian [[decision theory]] to compare or to unify competing architectures. The shared mathematical language permitted a high level of collaboration with more established fields (like [[mathematics]], economics or [[operations research]]).{{efn|While such a "victory of the neats" may be a consequence of the field becoming more mature, [[Artificial Intelligence: A Modern Approach|AIMA]] states that in practice both [[neats and scruffies|neat and scruffy]] approaches continue to be necessary in AI research.}} Compared with GOFAI, new "statistical learning" techniques such as HMM and neural networks were gaining higher levels of accuracy in many practical domains such as [[data mining]], without necessarily acquiring a semantic understanding of the datasets. The increased successes with real-world data led to increasing emphasis on comparing different approaches against shared test data to see which approach performed best in a broader context than that provided by idiosyncratic toy models; AI research was becoming more [[scientific method|scientific]]. Nowadays results of experiments are often rigorously measurable, and are sometimes (with difficulty) reproducible.<ref name="Formal methods in AI"/><ref>{{cite news|last1=Hutson|first1=Matthew|title=Artificial intelligence faces reproducibility crisis|url=http://science.sciencemag.org/content/359/6377/725|accessdate=28 April 2018|work=[[Science Magazine|Science]]|date=16 February 2018|pages=725–726|language=en|doi=10.1126/science.359.6377.725|bibcode=2018Sci...359..725H}}</ref> Different statistical learning techniques have different limitations; for example, basic HMM cannot model the infinite possible combinations of natural language.{{sfn|Norvig|2012}} Critics note that the shift from GOFAI to statistical learning is often also a shift away from [[explainable AI]]. In AGI research, some scholars caution against over-reliance on statistical learning, and argue that continuing research into GOFAI will still be necessary to attain general intelligence.{{sfn|Langley|2011}}{{sfn|Katz|2012}}
Much of traditional GOFAI got bogged down on ad hoc patches to symbolic computation that worked on their own toy models but failed to generalize to real-world results. However, around the 1990s, AI researchers adopted sophisticated mathematical tools, such as hidden Markov models (HMM), information theory, and normative Bayesian decision theory to compare or to unify competing architectures. The shared mathematical language permitted a high level of collaboration with more established fields (like mathematics, economics or operations research). Compared with GOFAI, new "statistical learning" techniques such as HMM and neural networks were gaining higher levels of accuracy in many practical domains such as data mining, without necessarily acquiring a semantic understanding of the datasets. The increased successes with real-world data led to increasing emphasis on comparing different approaches against shared test data to see which approach performed best in a broader context than that provided by idiosyncratic toy models; AI research was becoming more scientific. Nowadays results of experiments are often rigorously measurable, and are sometimes (with difficulty) reproducible. Different statistical learning techniques have different limitations; for example, basic HMM cannot model the infinite possible combinations of natural language. Critics note that the shift from GOFAI to statistical learning is often also a shift away from explainable AI. In AGI research, some scholars caution against over-reliance on statistical learning, and argue that continuing research into GOFAI will still be necessary to attain general intelligence.
许多传统的 GOFAI 陷入了不断给在实验模型中行之有效，但不能推广到现实世界的符号计算修补漏洞的困境中。然而，在20世纪90年代前后，AI研究人员采用了复杂的数学工具，如'''<font color=#ff8000>隐马尔可夫模型 Hidden Markov Model,HMM</font>'''、信息论和'''<font color=#ff8000>标准贝叶斯判别理论 Normative Bayesian Decision Theory</font>'''来比较或统一有竞争关系的架构。共通的数学语言允许其与数学、经济学或运筹学等更成熟的领域进行高层次的融合。与 GOFAI 相比，隐马尔可夫模型和神经网络等新的“统计学习”技术在数据挖掘等许多实际领域中不必理解数据集的语义，却能得到更高的精度，随着现实世界数据的日益增加，人们越来越注重用不同的方法测试相同的数据，并进行比较，看哪种方法在比特殊实验室环境更广泛的背景下表现得更好; AI研究正变得更加科学。如今，实验结果一般是严格可测的，有时可以重现（但有难度）。不同的统计学习技术有不同的局限性，例如，基本的 HMM 不能为自然语言的无限可能的组合建模。评论者们指出，从 GOFAI 到统计学习的转变也经常是可解释AI的转变。在 AGI 的研究中，一些学者警告不要过度依赖统计学习，并认为继续研究 GOFAI 仍然是实现通用智能的必要条件。
=== Symbolic ===
=== Integrating the approaches ===
=== Integrating the approaches ===
整合各种方法
;Intelligent agent paradigm: An [[intelligent agent]] is a system that perceives its environment and takes actions that maximize its chances of success. The simplest intelligent agents are programs that solve specific problems. More complicated agents include human beings and organizations of human beings (such as [[firm]]s). The paradigm allows researchers to directly compare or even combine different approaches to isolated problems, by asking which agent is best at maximizing a given "goal function". An agent that solves a specific problem can use any approach that works—some agents are symbolic and logical, some are sub-symbolic [[artificial neural network]]s and others may use new approaches. The paradigm also gives researchers a common language to communicate with other fields—such as [[decision theory]] and economics—that also use concepts of abstract agents. Building a complete agent requires researchers to address realistic problems of integration; for example, because sensory systems give uncertain information about the environment, planning systems must be able to function in the presence of uncertainty. The intelligent agent paradigm became widely accepted during the 1990s.<ref name="Intelligent agents"/>
Intelligent agent paradigm: An intelligent agent is a system that perceives its environment and takes actions that maximize its chances of success. The simplest intelligent agents are programs that solve specific problems. More complicated agents include human beings and organizations of human beings (such as firms). The paradigm allows researchers to directly compare or even combine different approaches to isolated problems, by asking which agent is best at maximizing a given "goal function". An agent that solves a specific problem can use any approach that works—some agents are symbolic and logical, some are sub-symbolic artificial neural networks and others may use new approaches. The paradigm also gives researchers a common language to communicate with other fields—such as decision theory and economics—that also use concepts of abstract agents. Building a complete agent requires researchers to address realistic problems of integration; for example, because sensory systems give uncertain information about the environment, planning systems must be able to function in the presence of uncertainty. The intelligent agent paradigm became widely accepted during the 1990s.
智能体范式: 智能体是一个感知其环境并采取行动，最大限度地提高其成功机会的系统。最简单的智能体是解决特定问题的程序。更复杂的智能体包括人类和人类组织(如公司)。这种范式使得研究人员能通过观察哪一个智能体能最大化给定的“目标函数”直接比较甚至结合不同的方法来解决孤立的问题。解决特定问题的智能体可以使用任何有效的方法ーー可以是是符号化和逻辑化的，也可以是亚符号化的人工神经网络，还可以是新的方法。这种范式还为研究人员提供了一种与其他领域(如决策理论和经济学)进行交流的共同语言，这些领域也使用了抽象智能体的概念。建立一个完整的智能体需要研究人员解决现实的整合协调问题; 例如，由于传感系统提供关于环境的信息不确定，决策系统就必须在不确定性的条件下运作。智能体范式在20世纪90年代被广泛接受。
Researchers in the 1960s and the 1970s were convinced that symbolic approaches would eventually succeed in creating a machine with [[artificial general intelligence]] and considered this the goal of their field.
;[[Agent architecture]]s and [[cognitive architecture]]s:Researchers have designed systems to build intelligent systems out of interacting [[intelligent agent]]s in a [[multi-agent system]].<ref name="Agent architectures"/> A [[hierarchical control system]] provides a bridge between sub-symbolic AI at its lowest, reactive levels and traditional symbolic AI at its highest levels, where relaxed time constraints permit planning and world modeling.<ref name="Hierarchical control system"/> Some cognitive architectures are custom-built to solve a narrow problem; others, such as [[Soar (cognitive architecture)|Soar]], are designed to mimic human cognition and to provide insight into general intelligence. Modern extensions of Soar are [[hybrid intelligent system]]s that include both symbolic and sub-symbolic components.<ref>{{cite journal|last1=Laird|first1=John|title=Extending the Soar cognitive architecture|journal=Frontiers in Artificial Intelligence and Applications|date=2008|volume=171|page=224|citeseerx=10.1.1.77.2473}}</ref><ref>{{cite journal|last1=Lieto|first1=Antonio|last2=Lebiere|first2=Christian|last3=Oltramari|first3=Alessandro|title=The knowledge level in cognitive architectures: Current limitations and possibile developments|journal=Cognitive Systems Research|date=May 2018|volume=48|pages=39–55|doi=10.1016/j.cogsys.2017.05.001|hdl=2318/1665207|hdl-access=free}}</ref><ref>{{cite journal|last1=Lieto|first1=Antonio|last2=Bhatt|first2=Mehul|last3=Oltramari|first3=Alessandro|last4=Vernon|first4=David|title=The role of cognitive architectures in general artificial intelligence|journal=Cognitive Systems Research|date=May 2018|volume=48|pages=1–3|doi=10.1016/j.cogsys.2017.08.003|hdl=2318/1665249|hdl-access=free}}</ref>
Researchers in the 1960s and the 1970s were convinced that symbolic approaches would eventually succeed in creating a machine with artificial general intelligence and considered this the goal of their field.
Agent architectures and cognitive architectures:Researchers have designed systems to build intelligent systems out of interacting intelligent agents in a multi-agent system.
智能体体系结构和认知体系结构: 研究人员已经设计了一些在多智能体系统中利用相互作用的智能体构建智能系统的系统。分层控制系统为亚符号AI、反应层和符号AI提供了一座桥梁，亚符号AI在底层、反应层和符号AI在顶层。
一些认知架构是人为构造用来解决特定问题的；其他比如SOAR，是用来模仿人类的认知，向通用智能更进一步。现在SOAR的扩展是含有符号和亚符号部分的混合智能系统。
== Tools ==
==== Cognitive simulation ====
== Tools ==
工具
AI has developed many tools to solve the most difficult problems in [[computer science]]. A few of the most general of these methods are discussed below.
AI has developed many tools to solve the most difficult problems in computer science. A few of the most general of these methods are discussed below.
AI已经开发出许多工具来解决计算机科学中最困难的问题。下面将讨论其中一些最常用的方法。
=== Search and optimization ===
=== Search and optimization ===
搜索和优化
{{Main|Search algorithm|Mathematical optimization|Evolutionary computation}}
==== Anti-logic or scruffy ====
Many problems in AI can be solved in theory by intelligently searching through many possible solutions:<ref name="Search"/> [[#Deduction, reasoning, problem solving|Reasoning]] can be reduced to performing a search. For example, logical proof can be viewed as searching for a path that leads from [[premise]]s to [[Logical consequence|conclusions]], where each step is the application of an [[inference rule]].<ref name="Logic as search"/> [[Automated planning and scheduling|Planning]] algorithms search through trees of goals and subgoals, attempting to find a path to a target goal, a process called [[means-ends analysis]].<ref name="Planning as search"/> [[Robotics]] algorithms for moving limbs and grasping objects use [[local search (optimization)|local searches]] in [[Configuration space (physics)|configuration space]].<ref name="Configuration space"/> Many [[machine learning|learning]] algorithms use search algorithms based on [[optimization (mathematics)|optimization]].
Many problems in AI can be solved in theory by intelligently searching through many possible solutions: Reasoning can be reduced to performing a search. For example, logical proof can be viewed as searching for a path that leads from premises to conclusions, where each step is the application of an inference rule. Planning algorithms search through trees of goals and subgoals, attempting to find a path to a target goal, a process called means-ends analysis. Robotics algorithms for moving limbs and grasping objects use local searches in configuration space. Many learning algorithms use search algorithms based on optimization.
AI中的许多问题可以通过智能地搜索许多可能的解决方案而在理论上得到解决: 推理可以简化为执行一次搜索。例如，逻辑证明可以看作是寻找一条从前提到结论的路径，其中每一步都用到了推理规则。规划算法通过搜索目标和子目标的树，试图找到一条通往目标的路径，这个过程称为目的手段分析。机器人学中移动肢体和抓取物体的算法使用的是位形空间的局部搜索。许多学习算法使用基于优化的搜索算法。
Simple exhaustive searches<ref name="Uninformed search"/> are rarely sufficient for most real-world problems: the [[search algorithm|search space]] (the number of places to search) quickly grows to [[Astronomically large|astronomical numbers]]. The result is a search that is [[Computation time|too slow]] or never completes. The solution, for many problems, is to use "[[heuristics]]" or "rules of thumb" that prioritize choices in favor of those that are more likely to reach a goal and to do so in a shorter number of steps. In some search methodologies heuristics can also serve to entirely eliminate some choices that are unlikely to lead to a goal (called "[[pruning (algorithm)|pruning]] the [[search tree]]"). [[Heuristics]] supply the program with a "best guess" for the path on which the solution lies.<ref name="Informed search"/> Heuristics limit the search for solutions into a smaller sample size.{{sfn|Tecuci|2012}}
Simple exhaustive searches are rarely sufficient for most real-world problems: the search space (the number of places to search) quickly grows to astronomical numbers. The result is a search that is too slow or never completes. The solution, for many problems, is to use "heuristics" or "rules of thumb" that prioritize choices in favor of those that are more likely to reach a goal and to do so in a shorter number of steps. In some search methodologies heuristics can also serve to entirely eliminate some choices that are unlikely to lead to a goal (called "pruning the search tree"). Heuristics supply the program with a "best guess" for the path on which the solution lies. Heuristics limit the search for solutions into a smaller sample size.
对于大多数真实世界的问题，简单的穷举搜索很难满足要求: 搜索空间(要搜索的位置数)很快就会增加到天文数字。结果就是搜索速度太慢或者永远不能完成。对于许多问题，解决方法是使用“'''<font color=#ff8000>启发式 Heuristics</font>''' ”或“'''<font color=#ff8000>经验法则 Rules of Thumb</font>''' ” ，优先考虑那些更有可能达到目标的选择，并且在较短的步骤内完成。在一些搜索方法中，启发式方法还可以完全移去一些不可能通向目标的选择(称为“修剪搜索树”)。启发式为程序提供了解决方案所在路径的“最佳猜测”。启发式做不到去更小的样本中搜索解。
A very different kind of search came to prominence in the 1990s, based on the mathematical theory of [[optimization (mathematics)|optimization]]. For many problems, it is possible to begin the search with some form of a guess and then refine the guess incrementally until no more refinements can be made. These algorithms can be visualized as blind [[hill climbing]]: we begin the search at a random point on the landscape, and then, by jumps or steps, we keep moving our guess uphill, until we reach the top. Other optimization algorithms are [[simulated annealing]], [[beam search]] and [[random optimization]].<ref name="Optimization search"/>
A very different kind of search came to prominence in the 1990s, based on the mathematical theory of optimization. For many problems, it is possible to begin the search with some form of a guess and then refine the guess incrementally until no more refinements can be made. These algorithms can be visualized as blind hill climbing: we begin the search at a random point on the landscape, and then, by jumps or steps, we keep moving our guess uphill, until we reach the top. Other optimization algorithms are simulated annealing, beam search and random optimization.
====Knowledge-based====
在20世纪90年代，一种非常不同的基于数学最优化理论的搜索引起了人们的注意。对于许多问题，可以从某种形式的猜测开始搜索，然后逐步细化猜测，直到无法进行更多的细化。这些算法可以喻为盲目地爬山: 我们从地形上的一个随机点开始搜索，然后，通过跳跃或登爬，我们将猜测继续向山上移动，直到我们到达山顶。其他的优化算法有 '''<font color=#ff8000>模拟退火算法</font>''' 、'''<font color=#ff8000>定向搜索</font>''' 和'''<font color=#ff8000>随机优化</font>''' 。
[[File:ParticleSwarmArrowsAnimation.gif|thumb|A [[particle swarm optimization|particle swarm]] seeking the [[global minimum]]]]
particle swarm seeking the global minimum]]
[粒子群搜索全局最小]
[[Evolutionary computation]] uses a form of optimization search. For example, they may begin with a population of organisms (the guesses) and then allow them to mutate and recombine, [[artificial selection|selecting]] only the fittest to survive each generation (refining the guesses). Classic [[evolutionary algorithms]] include [[genetic algorithms]], [[gene expression programming]], and [[genetic programming]].<ref name="Genetic programming"/> Alternatively, distributed search processes can coordinate via [[swarm intelligence]] algorithms. Two popular swarm algorithms used in search are [[particle swarm optimization]] (inspired by bird [[flocking (behavior)|flocking]]) and [[ant colony optimization]] (inspired by [[ant trail]]s).<ref name="Society based learning"/><ref>{{cite book|author1=Daniel Merkle|author2=Martin Middendorf|editor1-last=Burke|editor1-first=Edmund K.|editor2-last=Kendall|editor2-first=Graham|title=Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques|date=2013|publisher=Springer Science & Business Media|isbn=978-1-4614-6940-7|language=en|chapter=Swarm Intelligence}}</ref>
Evolutionary computation uses a form of optimization search. For example, they may begin with a population of organisms (the guesses) and then allow them to mutate and recombine, selecting only the fittest to survive each generation (refining the guesses). Classic evolutionary algorithms include genetic algorithms, gene expression programming, and genetic programming.
进化计算用了优化搜索的形式。例如，他们可能从一群有机体(猜测)开始，然后让它们变异和重组，选择适者继续生存 (改进猜测)。经典的进化算法包括遗传算法、基因表达编程和遗传编程。
=== Logic ===
=== Logic ===
逻辑
{{Main|Logic programming|Automated reasoning}}
[[Logic]]<ref name="Logic"/> is used for knowledge representation and problem solving, but it can be applied to other problems as well. For example, the [[satplan]] algorithm uses logic for [[automated planning and scheduling|planning]]<ref name="Satplan"/> and [[inductive logic programming]] is a method for [[machine learning|learning]].<ref name="Symbolic learning techniques"/>
Logic is used for knowledge representation and problem solving, but it can be applied to other problems as well. For example, the satplan algorithm uses logic for planning and inductive logic programming is a method for learning.
逻辑被用来知识表示和解决问题，还可以应用到其他问题上。例如，satplan 算法使用逻辑进行规划，归纳逻辑编程是一种学习方法。
Several different forms of logic are used in AI research. [[Propositional logic]]<ref name="Propositional logic"/> involves [[truth function]]s such as "or" and "not". [[First-order logic]]<ref name="First-order logic"/> adds [[quantifier (logic)|quantifiers]] and [[predicate (mathematical logic)|predicates]], and can express facts about objects, their properties, and their relations with each other. [[Fuzzy set theory]] assigns a "degree of truth" (between 0 and 1) to vague statements such as "Alice is old" (or rich, or tall, or hungry) that are too linguistically imprecise to be completely true or false. [[Fuzzy logic]] is successfully used in [[control system]]s to allow experts to contribute vague rules such as "if you are close to the destination station and moving fast, increase the train's brake pressure"; these vague rules can then be numerically refined within the system. Fuzzy logic fails to scale well in knowledge bases; many AI researchers question the validity of chaining fuzzy-logic inferences.{{efn|"There exist many different types of uncertainty, vagueness, and ignorance... [We] independently confirm the inadequacy of systems for reasoning about uncertainty that propagates numerical factors according to only to which connectives appear in assertions."<ref>{{cite journal|last1=Elkan|first1=Charles|title=The paradoxical success of fuzzy logic|journal=IEEE Expert|date=1994|volume=9|issue=4|pages=3–49|doi=10.1109/64.336150|citeseerx=10.1.1.100.8402}}</ref>}}<ref name="Fuzzy logic"/><ref>{{cite news|title=What is 'fuzzy logic'? Are there computers that are inherently fuzzy and do not apply the usual binary logic?|url=https://www.scientificamerican.com/article/what-is-fuzzy-logic-are-t/|accessdate=5 May 2018|work=Scientific American|language=en}}</ref>
Several different forms of logic are used in AI research. Propositional logic}}
AI研究中使用了多种不同形式的逻辑。命题逻辑包含诸如“或”和“否”这样的真值函数。一阶逻辑增加了量词和谓词，可以表达关于对象、对象属性和对象之间的关系。模糊集合论给诸如“爱丽丝老了”(或是富有的、高的、饥饿的)这样模糊的表述赋予了一个“真实程度”(介于0到1之间)，这些表述在语言上很模糊，不能完全判定为正确或错误。模糊逻辑在控制系统中得到了成功应用，使专家能够制定模糊规则，比如“如果你正以较快的速度接近终点站，那么就增加列车的制动压力”;这些模糊的规则可以在系统内用数值细化。模糊逻辑无助于扩展知识库;许多AI研究者质疑链接模糊逻辑推理的有效性。
====Computational intelligence and soft computing====
[[Default logic]]s, [[non-monotonic logic]]s and [[circumscription (logic)|circumscription]]<ref name="Default reasoning and non-monotonic logic"/> are forms of logic designed to help with default reasoning and the [[qualification problem]]. Several extensions of logic have been designed to handle specific domains of [[knowledge representation|knowledge]], such as: [[description logic]]s;<ref name="Representing categories and relations"/> [[situation calculus]], [[event calculus]] and [[fluent calculus]] (for representing events and time);<ref name="Representing time"/> [[Causality#Causal calculus|causal calculus]];<ref name="Representing causation"/> [[Belief revision|belief calculus (belief revision)]];<ref>"The Belief Calculus and Uncertain Reasoning", Yen-Teh Hsia</ref> and [[modal logic]]s.<ref name="Representing knowledge about knowledge"/> Logics to model contradictory or inconsistent statements arising in multi-agent systems have also been designed, such as [[paraconsistent logic]]s.
Default logics, non-monotonic logics and circumscription and modal logics. Logics to model contradictory or inconsistent statements arising in multi-agent systems have also been designed, such as paraconsistent logics.
'''<font color=#ff8000>缺省逻辑 Default Logics</font>'''、'''<font color=#ff8000>非单调逻辑 Non-monotonic Logics</font>'''、'''<font color=#ff8000>限制逻辑 Circumscription</font>'''和'''<font color=#ff8000>模态逻辑 Modal Logics</font>'''。对多智能体系统中出现的矛盾或不一致的陈述进行建模的逻辑也已经被设计出来，例如'''<font color=#ff8000>次协调逻辑 Paraconsistent Logics.</font>'''。
默认逻辑、非单调逻辑和边界都用逻辑形式来解决缺省推理和限定问题。一些逻辑扩展被用于处理特定的知识领域，例如:描述逻辑;情景演算、事件演算和用于表示事件和时间的流畅演算；因果演算;信念演算(信念修正);和模态逻辑。人们也设计了对多智能体系统中出现的矛盾或不一致陈述进行建模的逻辑，如次协调逻辑。
=== Probabilistic methods for uncertain reasoning ===
=== Probabilistic methods for uncertain reasoning ===
不确定推理的概率方法
{{Main|Bayesian network|Hidden Markov model|Kalman filter|Particle filter|Decision theory|Utility theory}}
[[File:EM Clustering of Old Faithful data.gif|right|frame|[[Expectation-maximization]] clustering of [[Old Faithful]] eruption data starts from a random guess but then successfully converges on an accurate clustering of the two physically distinct modes of eruption.]]
[[Expectation-maximization clustering of Old Faithful eruption data starts from a random guess but then successfully converges on an accurate clustering of the two physically distinct modes of eruption.]]
[[期望-最大化老实泉喷发数据的聚类从一个随机的猜测开始，然后成功地收敛到两个物理上截然不同的喷发模式的精确聚类]]
Many problems in AI (in reasoning, planning, learning, perception, and robotics) require the agent to operate with incomplete or uncertain information. AI researchers have devised a number of powerful tools to solve these problems using methods from [[probability]] theory and economics.<ref name="Stochastic methods for uncertain reasoning"/>
Many problems in AI (in reasoning, planning, learning, perception, and robotics) require the agent to operate with incomplete or uncertain information. AI researchers have devised a number of powerful tools to solve these problems using methods from probability theory and economics.
AI中的许多问题(在推理、规划、学习、感知和机器人技术方面)要求智能体在信息不完整或不确定的情况下进行操作。AI研究人员从概率论和经济学的角度设计了许多强大的工具来解决这些问题。
[[Bayesian network]]s<ref name="Bayesian networks"/> are a very general tool that can be used for various problems: reasoning (using the [[Bayesian inference]] algorithm),<ref name="Bayesian inference"/> [[Machine learning|learning]] (using the [[expectation-maximization algorithm]]),{{efn|Expectation-maximization, one of the most popular algorithms in machine learning, allows clustering in the presence of unknown [[latent variables]]{{sfn|Domingos|2015|p=210}}}}<ref name="Bayesian learning"/> [[Automated planning and scheduling|planning]] (using [[decision network]]s)<ref name="Bayesian decision networks"/> and [[machine perception|perception]] (using [[dynamic Bayesian network]]s).<ref name="Stochastic temporal models"/> Probabilistic algorithms can also be used for filtering, prediction, smoothing and finding explanations for streams of data, helping [[machine perception|perception]] systems to analyze processes that occur over time (e.g., [[hidden Markov model]]s or [[Kalman filter]]s).<ref name="Stochastic temporal models"/> Compared with symbolic logic, formal Bayesian inference is computationally expensive. For inference to be tractable, most observations must be [[conditionally independent]] of one another. Complicated graphs with diamonds or other "loops" (undirected [[cycle (graph theory)|cycles]]) can require a sophisticated method such as [[Markov chain Monte Carlo]], which spreads an ensemble of [[random walk]]ers throughout the Bayesian network and attempts to converge to an assessment of the conditional probabilities. Bayesian networks are used on [[Xbox Live]] to rate and match players; wins and losses are "evidence" of how good a player is{{citation needed|date=July 2019}}. [[Google AdSense|AdSense]] uses a Bayesian network with over 300 million edges to learn which ads to serve.{{sfn|Domingos|2015|loc=chapter 6}}
Bayesian networks are a very general tool that can be used for various problems: reasoning (using the Bayesian inference algorithm), learning (using the expectation-maximization algorithm),}} planning (using decision networks) and perception (using dynamic Bayesian networks). Probabilistic algorithms can also be used for filtering, prediction, smoothing and finding explanations for streams of data, helping perception systems to analyze processes that occur over time (e.g., hidden Markov models or Kalman filters). Compared with symbolic logic, formal Bayesian inference is computationally expensive. For inference to be tractable, most observations must be conditionally independent of one another. Complicated graphs with diamonds or other "loops" (undirected cycles) can require a sophisticated method such as Markov chain Monte Carlo, which spreads an ensemble of random walkers throughout the Bayesian network and attempts to converge to an assessment of the conditional probabilities. Bayesian networks are used on Xbox Live to rate and match players; wins and losses are "evidence" of how good a player is. AdSense uses a Bayesian network with over 300 million edges to learn which ads to serve.
'''<font color=#ff8000>贝叶斯网络 Bayesian Networks </font>''' 是一个非常通用的工具，可用于各种问题: 推理(使用贝叶斯推断算法) ，学习(使用期望最大化算法) ，规划(使用决策网络)和感知(使用动态贝叶斯网络)。概率算法也可以用于滤波、预测、平滑和解释数据流，帮助传感系统分析随时间发生的过程(例如，隐马尔可夫模型或'''<font color=#ff8000>卡尔曼滤波器 Kalman Filters</font>''')。与符号逻辑相比，形式化的贝叶斯推断逻辑运算量很大。为了使推论易于处理，大多数观察值必须彼此有条件地独立。含有方块或其他“圈”(无向循环)的复杂图形可能需要比如马尔科夫蒙特卡洛图的复杂方法，这种方法将一组随机行走遍布整个贝叶斯网络，并试图收敛到对条件概率的评估。贝叶斯网络在 Xbox Live 上被用来评估和匹配玩家; 胜率是证明一个玩家有多有优秀的“证据”。AdSense使用一个有超过3亿条边的贝叶斯网络来学习广告推广的最佳时机。
A key concept from the science of economics is "[[utility]]": a measure of how valuable something is to an intelligent agent. Precise mathematical tools have been developed that analyze how an agent can make choices and plan, using [[decision theory]], [[decision analysis]],<ref name="Decisions theory and analysis"/> and [[applied information economics|information value theory]].<ref name="Information value theory"/> These tools include models such as [[Markov decision process]]es,<ref name="Markov decision process"/> dynamic [[decision network]]s,<ref name="Stochastic temporal models"/> [[game theory]] and [[mechanism design]].<ref name="Game theory and mechanism design"/>
A key concept from the science of economics is "utility": a measure of how valuable something is to an intelligent agent. Precise mathematical tools have been developed that analyze how an agent can make choices and plan, using decision theory, decision analysis, and information value theory. These tools include models such as Markov decision processes, dynamic decision networks, game theory and mechanism design.
经济学中的一个关键概念是“效用” :这是一种衡量某物对于一个智能智能体的价值的方法。人们运用决策理论、决策分析和信息价值理论开发出了精确的数学工具来分析智能体应该如何选择和计划。这些工具包括马尔可夫决策过程、动态决策网络、博弈论和机制设计等模型。
=== Classifiers and statistical learning methods ===
=== Classifiers and statistical learning methods ===
分类器与统计学习方法
{{Main|Classifier (mathematics)|Statistical classification|Machine learning}}
The simplest AI applications can be divided into two types: classifiers ("if shiny then diamond") and controllers ("if shiny then pick up"). Controllers do, however, also classify conditions before inferring actions, and therefore classification forms a central part of many AI systems. [[Classifier (mathematics)|Classifiers]] are functions that use [[pattern matching]] to determine a closest match. They can be tuned according to examples, making them very attractive for use in AI. These examples are known as observations or patterns. In supervised learning, each pattern belongs to a certain predefined class. A class can be seen as a decision that has to be made. All the observations combined with their class labels are known as a data set. When a new observation is received, that observation is classified based on previous experience.<ref name="Classifiers"/>
The simplest AI applications can be divided into two types: classifiers ("if shiny then diamond") and controllers ("if shiny then pick up"). Controllers do, however, also classify conditions before inferring actions, and therefore classification forms a central part of many AI systems. Classifiers are functions that use pattern matching to determine a closest match. They can be tuned according to examples, making them very attractive for use in AI. These examples are known as observations or patterns. In supervised learning, each pattern belongs to a certain predefined class. A class can be seen as a decision that has to be made. All the observations combined with their class labels are known as a data set. When a new observation is received, that observation is classified based on previous experience.
最简单的AI应用程序可以分为两类: '''<font color=#ff8000>分类器 Classifiers</font>''' (“ if shiny then diamond”)和'''<font color=#ff8000>控制器 Controllers</font>''' (“ if shiny then pick up”)。然而，控制器在推断前也对条件进行分类，因此分类构成了许多AI系统的核心部分。分类器是使用匹配模式来判别最接近的类别的函数。它们可以根据例子进行调整，使它们在AI的应用中更有效。这些例子被称为观察或模式。在监督式学习中，每个模式都属于某个预定义的类别。可以把一个类看作是一个必须做出的决定。所有的观测和它们的类标签被称为数据集。当接收一个新观察时，这个观察结果将根据以前的经验被分类。
--[[用户:Thingamabob|Thingamabob]]（[[用户讨论:Thingamabob|讨论]]） 分类器(“ if shiny then diamond”)和控制器(“ if shiny then pick up”) 一句不能准确翻译
A classifier can be trained in various ways; there are many statistical and [[machine learning]] approaches. The [[decision tree learning|decision tree]]<ref name="Decision tree"/> is perhaps the most widely used machine learning algorithm.{{sfn|Domingos|2015|p=88}} Other widely used classifiers are the [[Artificial neural network|neural network]],<ref name="Neural networks"/>
A classifier can be trained in various ways; there are many statistical and machine learning approaches. The decision tree is perhaps the most widely used machine learning algorithm. Other widely used classifiers are the neural network,
分类器可以通过多种方式进行训练;，比如许多统计学和机器学习方法。决策树可能是应用最广泛的机器学习算法。其他使用广泛的分类器还有神经网络。
[[k-nearest neighbor algorithm]],{{efn|The most widely used analogical AI until the mid-1990s{{sfn|Domingos|2015|p=187}}}}<ref name="K-nearest neighbor algorithm"/>
k-nearest neighbor algorithm,}}
K最近邻算法
[[kernel methods]] such as the [[support vector machine]] (SVM),{{efn|SVM displaced k-nearest neighbor in the 1990s{{sfn|Domingos|2015|p=188}}}}<ref name="Kernel methods"/>
kernel methods such as the support vector machine (SVM),}}
例如支持向量机的核心方法
[[Gaussian mixture model]],<ref name="Gaussian mixture model"/> and the extremely popular [[naive Bayes classifier]].{{efn|Naive Bayes is reportedly the "most widely used learner" at Google, due in part to its scalability.{{sfn|Domingos|2015|p=152}}}}<ref name="Naive Bayes classifier"/> Classifier performance depends greatly on the characteristics of the data to be classified, such as the dataset size, distribution of samples across classes, the dimensionality, and the level of noise. Model-based classifiers perform well if the assumed model is an extremely good fit for the actual data. Otherwise, if no matching model is available, and if accuracy (rather than speed or scalability) is the sole concern, conventional wisdom is that discriminative classifiers (especially SVM) tend to be more accurate than model-based classifiers such as "naive Bayes" on most practical data sets.<ref name="Classifier performance"/>{{sfn|Russell|Norvig|2009|loc=18.12: Learning from Examples: Summary}}
Gaussian mixture model, and the extremely popular naive Bayes classifier.}} Classifier performance depends greatly on the characteristics of the data to be classified, such as the dataset size, distribution of samples across classes, the dimensionality, and the level of noise. Model-based classifiers perform well if the assumed model is an extremely good fit for the actual data. Otherwise, if no matching model is available, and if accuracy (rather than speed or scalability) is the sole concern, conventional wisdom is that discriminative classifiers (especially SVM) tend to be more accurate than model-based classifiers such as "naive Bayes" on most practical data sets.
'''<font color=#ff8000> 高斯混合模型 Gaussian Mixture Mode</font>'''，以及非常流行的'''<font color=#ff8000>朴素贝叶斯分类器 Naive Bayes Classifier</font>'''。分类器的分类效果在很大程度上取决于待分类数据的特征，如数据集的大小、样本跨类别的分布、维数和噪声水平。如果假设的模型很符合实际数据，则基于这种模型的分类器就能给出很好的结果。否则，传统观点认为如果没有匹配模型可用，而且只关心准确性(而不是速度或可扩展性) ，在大多数实际数据集上鉴别分类器(尤其是支持向量机)往往比基于模型的分类器(如“朴素贝叶斯”)更准确。
=== Artificial neural networks ===
=== Artificial neural networks ===
人工神经网络
{{Main|Artificial neural network|Connectionism}}
[[File:Artificial neural network.svg|thumb|A neural network is an interconnected group of nodes, akin to the vast network of [[neuron]]s in the [[human brain]].]]
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A neural network is an interconnected group of nodes, akin to the vast network of [[neurons in the human brain.]]
神经网络是一组相互连接的节点，类似于人脑中庞大的神经元网络。
Neural networks were inspired by the architecture of neurons in the human brain. A simple "neuron" ''N'' accepts input from other neurons, each of which, when activated (or "fired"), cast a weighted "vote" for or against whether neuron ''N'' should itself activate. Learning requires an algorithm to adjust these weights based on the training data; one simple algorithm (dubbed "[[Hebbian learning|fire together, wire together]]") is to increase the weight between two connected neurons when the activation of one triggers the successful activation of another. The neural network forms "concepts" that are distributed among a subnetwork of shared{{efn|Each individual neuron is likely to participate in more than one concept.}} neurons that tend to fire together; a concept meaning "leg" might be coupled with a subnetwork meaning "foot" that includes the sound for "foot". Neurons have a continuous spectrum of activation; in addition, neurons can process inputs in a nonlinear way rather than weighing straightforward votes. Modern neural networks can learn both continuous functions and, surprisingly, digital logical operations. Neural networks' early successes included predicting the stock market and (in 1995) a mostly self-driving car.{{efn|Steering for the 1995 "[[History of autonomous cars#1990s|No Hands Across America]]" required "only a few human assists".}}{{sfn|Domingos|2015|loc=Chapter 4}} In the 2010s, advances in neural networks using deep learning thrust AI into widespread public consciousness and contributed to an enormous upshift in corporate AI spending; for example, AI-related [[mergers and acquisitions|M&A]] in 2017 was over 25 times as large as in 2015.<ref>{{cite news|title=Why Deep Learning Is Suddenly Changing Your Life|url=http://fortune.com/ai-artificial-intelligence-deep-machine-learning/|accessdate=12 March 2018|work=Fortune|date=2016}}</ref><ref>{{cite news|title=Google leads in the race to dominate artificial intelligence|url=https://www.economist.com/news/business/21732125-tech-giants-are-investing-billions-transformative-technology-google-leads-race|accessdate=12 March 2018|work=The Economist|date=2017|language=en}}</ref>
Neural networks were inspired by the architecture of neurons in the human brain. A simple "neuron" N accepts input from other neurons, each of which, when activated (or "fired"), cast a weighted "vote" for or against whether neuron N should itself activate. Learning requires an algorithm to adjust these weights based on the training data; one simple algorithm (dubbed "fire together, wire together") is to increase the weight between two connected neurons when the activation of one triggers the successful activation of another. The neural network forms "concepts" that are distributed among a subnetwork of shared neurons that tend to fire together; a concept meaning "leg" might be coupled with a subnetwork meaning "foot" that includes the sound for "foot". Neurons have a continuous spectrum of activation; in addition, neurons can process inputs in a nonlinear way rather than weighing straightforward votes. Modern neural networks can learn both continuous functions and, surprisingly, digital logical operations. Neural networks' early successes included predicting the stock market and (in 1995) a mostly self-driving car. In the 2010s, advances in neural networks using deep learning thrust AI into widespread public consciousness and contributed to an enormous upshift in corporate AI spending; for example, AI-related M&A in 2017 was over 25 times as large as in 2015.
神经网络的诞生受到人脑神经元结构的启发。一个简单的“神经元”N 接受来自其他神经元的输入，每个神经元在被激活(或者说“放电”)时，都会对N是否应该被激活按一定的权重赋上值。学习的过程需要一个根据训练数据调整这些权重的算法; 一个被称为“相互放电，彼此联系”简单的算法在一个神经元激活触发另一个神经元的激活时增加两个连接神经元之间的权重。神经网络中形成一种分布在一个共享的神经元子网络中的”概念”，这些神经元往往一起放电; ”腿”的概念可能和”脚”概念的子网络相结合，后者包括”脚”的发音。神经元有一个连续的激活谱; 此外，神经元还可以用非线性的方式处理输入，而不是简单地加权求和。现代神经网络可以学习连续函数甚至的数字逻辑运算。神经网络早期的成功包括预测股票市场和自动驾驶汽车(1995年)。2010年代，神经网络使用深度学习取得巨大进步，也因此将AI推向了公众视野里，并促使企业对AI投资急速增加; 例如2017年与AI相关的并购交易规模是2015年的25倍多。
The study of non-learning [[artificial neural network]]s<ref name="Neural networks"/> began in the decade before the field of AI research was founded, in the work of [[Walter Pitts]] and [[Warren McCullouch]]. [[Frank Rosenblatt]] invented the [[perceptron]], a learning network with a single layer, similar to the old concept of [[linear regression]]. Early pioneers also include [[Alexey Grigorevich Ivakhnenko]], [[Teuvo Kohonen]], [[Stephen Grossberg]], [[Kunihiko Fukushima]], [[Christoph von der Malsburg]], David Willshaw, [[Shun-Ichi Amari]], [[Bernard Widrow]], [[John Hopfield]], [[Eduardo R. Caianiello]], and others{{citation needed|date=July 2019}}.
The study of non-learning artificial neural networks began in the decade before the field of AI research was founded, in the work of Walter Pitts and Warren McCullouch. Frank Rosenblatt invented the perceptron, a learning network with a single layer, similar to the old concept of linear regression. Early pioneers also include Alexey Grigorevich Ivakhnenko, Teuvo Kohonen, Stephen Grossberg, Kunihiko Fukushima, Christoph von der Malsburg, David Willshaw, Shun-Ichi Amari, Bernard Widrow, John Hopfield, Eduardo R. Caianiello, and others.
沃尔特 · 皮茨和沃伦 · 麦克卢奇共同完成的非学习型人工神经网络的研究比AI研究领域成立早十年。他们发明了'''<font color=#ff8000>感知机 Perceptron</font>'''，这是一个单层的学习网络，类似于线性回归的概念。早期的拓荒者还包括 Alexey Grigorevich Ivakhnenko，Teuvo Kohonen，Stephen Grossberg，Kunihiko Fukushima，Christoph von der Malsburg，David Willshaw，Shun-Ichi Amari，Bernard Widrow，John Hopfield，Eduardo r. Caianiello 等人。
The main categories of networks are acyclic or [[feedforward neural network]]s (where the signal passes in only one direction) and [[recurrent neural network]]s (which allow feedback and short-term memories of previous input events). Among the most popular feedforward networks are [[perceptron]]s, [[multi-layer perceptron]]s and [[radial basis network]]s.<ref name="Feedforward neural networks"/> Neural networks can be applied to the problem of [[intelligent control]] (for robotics) or [[machine learning|learning]], using such techniques as [[Hebbian learning]] ("fire together, wire together"), [[GMDH]] or [[competitive learning]].<ref name="Learning in neural networks"/>
The main categories of networks are acyclic or feedforward neural networks (where the signal passes in only one direction) and recurrent neural networks (which allow feedback and short-term memories of previous input events). Among the most popular feedforward networks are perceptrons, multi-layer perceptrons and radial basis networks. Neural networks can be applied to the problem of intelligent control (for robotics) or learning, using such techniques as Hebbian learning ("fire together, wire together"), GMDH or competitive learning.
网络的主要分为'''<font color=#ff8000> 非循环或前馈神经网络 Acyclic or Feedforward Neural Networks</font>'''(信号只向一个方向传递)和'''<font color=#ff8000>循环神经网络 Recurrent Neural Network</font>''' (允许对以前的输入事件进行反馈和短期记忆)。其中最常用的前馈网络有感知机、'''<font color=#ff8000多层感知机 Multi-layer Perceptrons></font>''' 和'''<font color=#ff8000> 径向基网络 Radial Basis Networks</font>'''。使用'''<font color=#ff8000>赫布型学习 Hebbian Learning </font>''' (“相互放电，共同链接”) ，GMDH 或竞争学习等技术的神经网络可以被应用于智能控制(机器人)或学习问题。
Today, neural networks are often trained by the [[backpropagation]] algorithm, which had been around since 1970 as the reverse mode of [[automatic differentiation]] published by [[Seppo Linnainmaa]],<ref name="lin1970">[[Seppo Linnainmaa]] (1970). The representation of the cumulative rounding error of an algorithm as a Taylor expansion of the local rounding errors. Master's Thesis (in Finnish), Univ. Helsinki, 6–7.</ref><ref name="grie2012">Griewank, Andreas (2012). Who Invented the Reverse Mode of Differentiation?. Optimization Stories, Documenta Matematica, Extra Volume ISMP (2012), 389–400.</ref> and was introduced to neural networks by [[Paul Werbos]].<ref name="WERBOS1974">[[Paul Werbos]], "Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences", ''PhD thesis, Harvard University'', 1974.</ref><ref name="werbos1982">[[Paul Werbos]] (1982). Applications of advances in nonlinear sensitivity analysis. In System modeling and optimization (pp. 762–770). Springer Berlin Heidelberg. [http://werbos.com/Neural/SensitivityIFIPSeptember1981.pdf Online] {{webarchive|url=https://web.archive.org/web/20160414055503/http://werbos.com/Neural/SensitivityIFIPSeptember1981.pdf |date=14 April 2016 }}</ref><ref name="Backpropagation"/>
Today, neural networks are often trained by the backpropagation algorithm, which had been around since 1970 as the reverse mode of automatic differentiation published by Seppo Linnainmaa, and was introduced to neural networks by Paul Werbos.
当下神经网络常用'''<font color=#ff8000>反向传播算法</font>''' 来训练，1970年反向传播算法出现，被认为是 Seppo Linnainmaa提出的自动微分的反向模式出现，被保罗·韦伯引入神经网络。
[[Hierarchical temporal memory]] is an approach that models some of the structural and algorithmic properties of the [[neocortex]].<ref name="Hierarchical temporal memory"/>
Hierarchical temporal memory is an approach that models some of the structural and algorithmic properties of the neocortex.
分级暂时性记忆是一种模拟大脑新皮层结构和算法特性的方法。
To summarize, most neural networks use some form of [[gradient descent]] on a hand-created neural topology. However, some research groups, such as [[Uber]], argue that simple [[neuroevolution]] to mutate new neural network topologies and weights may be competitive with sophisticated gradient descent approaches{{citation needed|date=July 2019}}. One advantage of neuroevolution is that it may be less prone to get caught in "dead ends".<ref>{{cite news|title=Artificial intelligence can 'evolve' to solve problems|url=http://www.sciencemag.org/news/2018/01/artificial-intelligence-can-evolve-solve-problems|accessdate=7 February 2018|work=Science {{!}} AAAS|date=10 January 2018|language=en}}</ref>
To summarize, most neural networks use some form of gradient descent on a hand-created neural topology. However, some research groups, such as Uber, argue that simple neuroevolution to mutate new neural network topologies and weights may be competitive with sophisticated gradient descent approaches. One advantage of neuroevolution is that it may be less prone to get caught in "dead ends".
总之，大多数神经网络都会在人工神经拓扑结构上使用某种形式的'''<font color=#ff8000>梯度下降法 Gradient Descent</font>'''。然而，一些比如 Uber的研究组织，认为通过简单的神经进化改变新神经网络拓扑结构和神经元间的权重可能比复杂的梯度下降法更适用。神经进化的一个优势是，它不容易陷入“死胡同”。
==== Deep feedforward neural networks ====
==== Deep feedforward neural networks ====
深层前馈神经网络
{{Main|Deep learning}}
[[Deep learning]] is any [[artificial neural network]] that can learn a long chain of causal links{{dubious|date=July 2019}}. For example, a feedforward network with six hidden layers can learn a seven-link causal chain (six hidden layers + output layer) and has a [[deep learning#Credit assignment|"credit assignment path"]] (CAP) depth of seven{{citation needed|date=July 2019}}. Many deep learning systems need to be able to learn chains ten or more causal links in length.<ref name="schmidhuber2015"/> Deep learning has transformed many important subfields of artificial intelligence{{why|date=July 2019}}, including [[computer vision]], [[speech recognition]], [[natural language processing]] and others.<ref name="goodfellow2016">Ian Goodfellow, Yoshua Bengio, and Aaron Courville (2016). Deep Learning. MIT Press. [http://www.deeplearningbook.org Online] {{webarchive|url=https://web.archive.org/web/20160416111010/http://www.deeplearningbook.org/ |date=16 April 2016 }}</ref><ref name="HintonDengYu2012">{{cite journal | last1 = Hinton | first1 = G. | last2 = Deng | first2 = L. | last3 = Yu | first3 = D. | last4 = Dahl | first4 = G. | last5 = Mohamed | first5 = A. | last6 = Jaitly | first6 = N. | last7 = Senior | first7 = A. | last8 = Vanhoucke | first8 = V. | last9 = Nguyen | first9 = P. | last10 = Sainath | first10 = T. | last11 = Kingsbury | first11 = B. | year = 2012 | title = Deep Neural Networks for Acoustic Modeling in Speech Recognition – The shared views of four research groups | url = | journal = IEEE Signal Processing Magazine | volume = 29 | issue = 6| pages = 82–97 | doi=10.1109/msp.2012.2205597}}</ref><ref name="schmidhuber2015">{{cite journal |last=Schmidhuber |first=J. |year=2015 |title=Deep Learning in Neural Networks: An Overview |journal=Neural Networks |volume=61 |pages=85–117 |arxiv=1404.7828 |doi=10.1016/j.neunet.2014.09.003|pmid=25462637 }}</ref>
Deep learning is any artificial neural network that can learn a long chain of causal links. For example, a feedforward network with six hidden layers can learn a seven-link causal chain (six hidden layers + output layer) and has a "credit assignment path" (CAP) depth of seven. Many deep learning systems need to be able to learn chains ten or more causal links in length.
深度学习是任何可以学习长因果链的人工神经网络。例如，一个具有六个隐藏层的前馈网络可以学习有七个链接的因果链(六个隐藏层 + 一个输出层) ，并且具“'''<font color=#ff8000>信用分配路径 Credit Assignment Path，CAP</font>''' ”的深度为7。许多深度学习系统需要学习长度在十及以上的因果链。
[[期望-最大化老忠实喷发数据的聚类从一个随机的猜测开始，然后成功地收敛到两个物理上截然不同的喷发模式的精确聚类]]
--[[用户:Thingamabob|Thingamabob]]（[[用户讨论:Thingamabob|讨论]]）Credit Assignment Path未找到标准翻译
According to one overview,<ref name="scholarpedia">{{cite journal | last1 = Schmidhuber | first1 = Jürgen | authorlink = Jürgen Schmidhuber | year = 2015 | title = Deep Learning | journal = Scholarpedia | volume = 10 | issue = 11 | page = 32832 | doi = 10.4249/scholarpedia.32832 | df = dmy-all | bibcode = 2015SchpJ..1032832S | doi-access = free }}</ref> the expression "Deep Learning" was introduced to the [[machine learning]] community by [[Rina Dechter]] in 1986<ref name="dechter1986">[[Rina Dechter]] (1986). Learning while searching in constraint-satisfaction problems. University of California, Computer Science Department, Cognitive Systems Laboratory.[https://www.researchgate.net/publication/221605378_Learning_While_Searching_in_Constraint-Satisfaction-Problems Online] {{webarchive|url=https://web.archive.org/web/20160419054654/https://www.researchgate.net/publication/221605378_Learning_While_Searching_in_Constraint-Satisfaction-Problems |date=19 April 2016 }}</ref> and gained traction after
According to one overview, the expression "Deep Learning" was introduced to the machine learning community by Rina Dechter in 1986 and gained traction after
根据一篇综述，“深度学习”这种表述是在1986年被里纳·德克特引入到机器学习领域的，并在2000年伊克尔·艾森贝格和他的同事将其引入人工神经网络后获得了关注。
Igor Aizenberg and colleagues introduced it to [[artificial neural network]]s in 2000.<ref name="aizenberg2000">Igor Aizenberg, Naum N. Aizenberg, Joos P.L. Vandewalle (2000). Multi-Valued and Universal Binary Neurons: Theory, Learning and Applications. Springer Science & Business Media.</ref> The first functional Deep Learning networks were published by [[Alexey Grigorevich Ivakhnenko]] and V. G. Lapa in 1965.<ref>{{Cite book|title=Cybernetic Predicting Devices|last=Ivakhnenko|first=Alexey|publisher=Naukova Dumka|year=1965|isbn=|location=Kiev|pages=}}</ref>{{page needed|date=December 2016}} These networks are trained one layer at a time. Ivakhnenko's 1971 paper<ref name="ivak1971">{{Cite journal |doi = 10.1109/TSMC.1971.4308320|title = Polynomial Theory of Complex Systems|journal = IEEE Transactions on Systems, Man, and Cybernetics|issue = 4|pages = 364–378|year = 1971|last1 = Ivakhnenko|first1 = A. G.|url = https://semanticscholar.org/paper/b7efb6b6f7e9ffa017e970a098665f76d4dfeca2}}</ref> describes the learning of a deep feedforward multilayer perceptron with eight layers, already much deeper than many later networks. In 2006, a publication by [[Geoffrey Hinton]] and Ruslan Salakhutdinov introduced another way of pre-training many-layered [[feedforward neural network]]s (FNNs) one layer at a time, treating each layer in turn as an [[unsupervised learning|unsupervised]] [[restricted Boltzmann machine]], then using [[supervised learning|supervised]] [[backpropagation]] for fine-tuning.{{sfn|Hinton|2007}} Similar to shallow artificial neural networks, deep neural networks can model complex non-linear relationships. Over the last few years, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks that contain many layers of non-linear hidden units and a very large output layer.<ref>{{cite web|last1=Research|first1=AI|title=Deep Neural Networks for Acoustic Modeling in Speech Recognition|url=http://airesearch.com/ai-research-papers/deep-neural-networks-for-acoustic-modeling-in-speech-recognition/|website=airesearch.com|accessdate=23 October 2015|date=23 October 2015}}</ref>
Igor Aizenberg and colleagues introduced it to artificial neural networks in 2000. The first functional Deep Learning networks were published by Alexey Grigorevich Ivakhnenko and V. G. Lapa in 1965. These networks are trained one layer at a time. Ivakhnenko's 1971 paper describes the learning of a deep feedforward multilayer perceptron with eight layers, already much deeper than many later networks. In 2006, a publication by Geoffrey Hinton and Ruslan Salakhutdinov introduced another way of pre-training many-layered feedforward neural networks (FNNs) one layer at a time, treating each layer in turn as an unsupervised restricted Boltzmann machine, then using supervised backpropagation for fine-tuning. Similar to shallow artificial neural networks, deep neural networks can model complex non-linear relationships. Over the last few years, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks that contain many layers of non-linear hidden units and a very large output layer.
第一个功能性的深度学习网络是由A. G.伊瓦赫年科和V.G.拉帕 在1965年发表的。这些网络每次只训练一层。1971年伊瓦赫年科的论文描述了一个8层的深度前馈多层感知机网络的学习过程，这个网络已经比许多后来的网络要深得多了。2006年，杰弗里•辛顿和特迪诺夫的文章介绍了另一种预训练'''<font color=#ff8000>多层前向神经网络 Many-layered Feedforward Neural Networks, FNNs</font>''' 的方法，一次训练一层，将每一层都视为无监督的受限玻尔兹曼机，然后使用监督式反向传播进行微调。与浅层人工神经网络类似，深层神经网络可以模拟复杂的非线性关系。在过去的几年里，机器学习算法和计算机硬件的进步催生了更有效的方法训练包含许多层非线性隐藏单元和一个非常大的输出层的深层神经网络。
Deep learning often uses [[convolutional neural network]]s (CNNs), whose origins can be traced back to the [[Neocognitron]] introduced by [[Kunihiko Fukushima]] in 1980.<ref name="FUKU1980">{{cite journal | last1 = Fukushima | first1 = K. | year = 1980 | title = Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position | url = | journal = Biological Cybernetics | volume = 36 | issue = 4| pages = 193–202 | doi=10.1007/bf00344251 | pmid=7370364}}</ref> In 1989, [[Yann LeCun]] and colleagues applied [[backpropagation]] to such an architecture. In the early 2000s, in an industrial application, CNNs already processed an estimated 10% to 20% of all the checks written in the US.<ref name="lecun2016slides">[[Yann LeCun]] (2016). Slides on Deep Learning [https://indico.cern.ch/event/510372/ Online] {{webarchive|url=https://web.archive.org/web/20160423021403/https://indico.cern.ch/event/510372/ |date=23 April 2016 }}</ref>
Deep learning often uses convolutional neural networks (CNNs), whose origins can be traced back to the Neocognitron introduced by Kunihiko Fukushima in 1980. In 1989, Yann LeCun and colleagues applied backpropagation to such an architecture. In the early 2000s, in an industrial application, CNNs already processed an estimated 10% to 20% of all the checks written in the US.
深度学习通常使用'<font color=#ff8000>卷积神经网络 ConvolutionalNeural Networks CNNs</font>''' ，其起源可以追溯到1980年由福岛邦彦引进的新认知机。1989年扬·勒丘恩和他的同事将反向传播应用于这样的架构。在21世纪初，在一项工业应用中，CNNs已经处理了美国大约10% 到20%的签发支票。
Since 2011, fast implementations of CNNs on GPUs have
Since 2011, fast implementations of CNNs on GPUs have
自2011年以来，在 GPUs上快速实现的 CNN 赢得了许多视觉模式识别比赛。
won many visual pattern recognition competitions.<ref name="schmidhuber2015"/>
won many visual pattern recognition competitions.
CNNs with 12 convolutional layers were used in conjunction with [[reinforcement learning]] by Deepmind's "[[AlphaGo]] Lee", the program that beat a top [[Go (game)|Go]] champion in 2016.<ref name="Nature2017">{{cite journal |first1=David |last1=Silver|author-link1=David Silver (programmer)|first2= Julian|last2= Schrittwieser|first3= Karen|last3= Simonyan|first4= Ioannis|last4= Antonoglou|first5= Aja|last5= Huang|author-link5=Aja Huang|first6=Arthur|last6= Guez|first7= Thomas|last7= Hubert|first8= Lucas|last8= Baker|first9= Matthew|last9= Lai|first10= Adrian|last10= Bolton|first11= Yutian|last11= Chen|author-link11=Chen Yutian|first12= Timothy|last12= Lillicrap|first13=Hui|last13= Fan|author-link13=Fan Hui|first14= Laurent|last14= Sifre|first15= George van den|last15= Driessche|first16= Thore|last16= Graepel|first17= Demis|last17= Hassabis |author-link17=Demis Hassabis|title=Mastering the game of Go without human knowledge|journal=[[Nature (journal)|Nature]]|issn= 0028-0836|pages=354–359|volume =550|issue =7676|doi =10.1038/nature24270|pmid=29052630|date=19 October 2017|quote=AlphaGo Lee... 12 convolutional layers|bibcode=2017Natur.550..354S|url=http://discovery.ucl.ac.uk/10045895/1/agz_unformatted_nature.pdf}}{{closed access}}</ref>
CNNs with 12 convolutional layers were used in conjunction with reinforcement learning by Deepmind's "AlphaGo Lee", the program that beat a top Go champion in 2016.
2016年Deepmind 的“阿尔法狗李”使用了有12个卷积层的 CNNs 和强化学习，击败了一个顶级围棋冠军。
==== Deep recurrent neural networks ====
==== Deep recurrent neural networks ====
深层递归神经网络
{{Main|Recurrent neural networks}}
Early on, deep learning was also applied to sequence learning with [[recurrent neural network]]s (RNNs)<ref name="Recurrent neural networks"/> which are in theory Turing complete<ref>{{cite journal|last1=Hyötyniemi|first1=Heikki|title=Turing machines are recurrent neural networks|journal=Proceedings of STeP '96/Publications of the Finnish Artificial Intelligence Society|pages=13–24|date=1996}}</ref> and can run arbitrary programs to process arbitrary sequences of inputs. The depth of an RNN is unlimited and depends on the length of its input sequence; thus, an RNN is an example of deep learning.<ref name="schmidhuber2015"/> RNNs can be trained by [[gradient descent]]<ref>P. J. Werbos. Generalization of backpropagation with application to a recurrent gas market model" ''Neural Networks'' 1, 1988.</ref><ref>A. J. Robinson and F. Fallside. The utility driven dynamic error propagation network. Technical Report CUED/F-INFENG/TR.1, Cambridge University Engineering Department, 1987.</ref><ref>R. J. Williams and D. Zipser. Gradient-based learning algorithms for recurrent networks and their computational complexity. In Back-propagation: Theory, Architectures and Applications. Hillsdale, NJ: Erlbaum, 1994.</ref> but suffer from the [[vanishing gradient problem]].<ref name="goodfellow2016"/><ref name="hochreiter1991">[[Sepp Hochreiter]] (1991), [http://people.idsia.ch/~juergen/SeppHochreiter1991ThesisAdvisorSchmidhuber.pdf Untersuchungen zu dynamischen neuronalen Netzen] {{webarchive|url=https://web.archive.org/web/20150306075401/http://people.idsia.ch/~juergen/SeppHochreiter1991ThesisAdvisorSchmidhuber.pdf |date=6 March 2015 }}, Diploma thesis. Institut f. Informatik, Technische Univ. Munich. Advisor: J. Schmidhuber.</ref> In 1992, it was shown that unsupervised pre-training of a stack of [[recurrent neural network]]s can speed up subsequent supervised learning of deep sequential problems.<ref name="SCHMID1992">{{cite journal | last1 = Schmidhuber | first1 = J. | year = 1992 | title = Learning complex, extended sequences using the principle of history compression | url = | journal = Neural Computation | volume = 4 | issue = 2| pages = 234–242 | doi=10.1162/neco.1992.4.2.234| citeseerx = 10.1.1.49.3934}}</ref>
Early on, deep learning was also applied to sequence learning with recurrent neural networks (RNNs) and can run arbitrary programs to process arbitrary sequences of inputs. The depth of an RNN is unlimited and depends on the length of its input sequence; thus, an RNN is an example of deep learning. but suffer from the vanishing gradient problem. In 1992, it was shown that unsupervised pre-training of a stack of recurrent neural networks can speed up subsequent supervised learning of deep sequential problems.
早期，深度学习也被用于'''<font color=#ff8000>循环神经网络 Recurrent Neural Networks,RNNs</font>''' 的序列学习，可以运行任意程序来处理任意的输入序列。一个神经网络的深度是无限制的，取决于其输入序列的长度; 因此，神经网络是一个深度学习的例子，但却存在梯度消失问题。1992年的一项研究表明无监督的预训练循环神经网络可以加速后续的深度序列问题的监督式学习。
[[k-nearest neighbor algorithm]],{{efn|The most widely used analogical AI until the mid-1990s{{sfn|Domingos|2015|p=187}}}}<ref name="K-nearest neighbor algorithm"/>
Numerous researchers now use variants of a deep learning recurrent NN called the [[long short-term memory]] (LSTM) network published by Hochreiter & Schmidhuber in 1997.<ref name=lstm>[[Sepp Hochreiter|Hochreiter, Sepp]]; and [[Jürgen Schmidhuber|Schmidhuber, Jürgen]]; ''Long Short-Term Memory'', Neural Computation, 9(8):1735–1780, 1997</ref> LSTM is often trained by [[Connectionist temporal classification|Connectionist Temporal Classification]] (CTC).<ref name="graves2006">Alex Graves, Santiago Fernandez, Faustino Gomez, and [[Jürgen Schmidhuber]] (2006). Connectionist temporal classification: Labelling unsegmented sequence data with recurrent neural nets. Proceedings of ICML'06, pp. 369–376.</ref> At Google, Microsoft and Baidu this approach has revolutionized [[speech recognition]].<ref name="hannun2014">{{cite arXiv
Numerous researchers now use variants of a deep learning recurrent NN called the long short-term memory (LSTM) network published by Hochreiter & Schmidhuber in 1997. LSTM is often trained by Connectionist Temporal Classification (CTC). At Google, Microsoft and Baidu this approach has revolutionized speech recognition.<ref name="hannun2014">{{cite arXiv
许多研究人员现在使用的是被称为 '''<font color=#ff8000>长短期记忆 Long Short-term Memory, LSTM </font>'''网络——一种深度学习循环神经网络的变体，由霍克赖特和施米德胡贝在1997年提出。人们通常使用'''<font color=#ff8000>连接时序分类Connectionist Temporal Classification, CTC</font>'''训练LSTM。[21谷歌，微软和百度用CTC彻底改变了语音识别。例如,2015年谷歌的语音识别性能大幅提升了49%,现在数十亿智能手机用户都可以通过谷歌声音使用这项技术。谷歌也使用LSTM来改进机器翻译，例如2015年，通过训练的LSTM，谷歌的语音识别性能大幅提升了49%，现在通过谷歌语音可以被数十亿的智能手机用户使用。谷歌还使用LSTM来改进机器翻译、语言建模和多语言语言处理。LSTM与CNNs一起使用改进了自动图像字幕的功能等众多应用。
=== Evaluating progress ===
=== Evaluating progress ===
评估进度
{{Further|Progress in artificial intelligence|Competitions and prizes in artificial intelligence}}
AI, like electricity or the steam engine, is a general purpose technology. There is no consensus on how to characterize which tasks AI tends to excel at.<ref>{{cite news|last1=Brynjolfsson|first1=Erik|last2=Mitchell|first2=Tom|title=What can machine learning do? Workforce implications|url=http://science.sciencemag.org/content/358/6370/1530|accessdate=7 May 2018|work=Science|date=22 December 2017|pages=1530–1534|language=en|doi=10.1126/science.aap8062|bibcode=2017Sci...358.1530B}}</ref> While projects such as [[AlphaZero]] have succeeded in generating their own knowledge from scratch, many other machine learning projects require large training datasets.<ref>{{cite news|last1=Sample|first1=Ian|title='It's able to create knowledge itself': Google unveils AI that learns on its own|url=https://www.theguardian.com/science/2017/oct/18/its-able-to-create-knowledge-itself-google-unveils-ai-learns-all-on-its-own|accessdate=7 May 2018|work=the Guardian|date=18 October 2017|language=en}}</ref><ref>{{cite news|title=The AI revolution in science|url=http://www.sciencemag.org/news/2017/07/ai-revolution-science|accessdate=7 May 2018|work=Science {{!}} AAAS|date=5 July 2017|language=en}}</ref> Researcher [[Andrew Ng]] has suggested, as a "highly imperfect rule of thumb", that "almost anything a typical human can do with less than one second of mental thought, we can probably now or in the near future automate using AI."<ref>{{cite news|title=Will your job still exist in 10 years when the robots arrive?|url=http://www.scmp.com/tech/innovation/article/2098164/robots-are-coming-here-are-some-jobs-wont-exist-10-years|accessdate=7 May 2018|work=[[South China Morning Post]]|date=2017|language=en}}</ref> [[Moravec's paradox]] suggests that AI lags humans at many tasks that the human brain has specifically evolved to perform well.<ref name="The Economist"/>
AI, like electricity or the steam engine, is a general purpose technology. There is no consensus on how to characterize which tasks AI tends to excel at. While projects such as AlphaZero have succeeded in generating their own knowledge from scratch, many other machine learning projects require large training datasets. Researcher Andrew Ng has suggested, as a "highly imperfect rule of thumb", that "almost anything a typical human can do with less than one second of mental thought, we can probably now or in the near future automate using AI." Moravec's paradox suggests that AI lags humans at many tasks that the human brain has specifically evolved to perform well.
AI和电或蒸汽机一样，是一种通用技术。在AI 擅长什么样的任务这个问题上尚未达成共识。虽然像 AlphaZero 这样的项目已经能做到从零开始产生知识，但是许多其他的机器学习项目仍需要大量的训练数据集。研究人员安德鲁 Ng 认为，作为一个“极不完美的经验法则”，“几乎任何普通人只需要不到一秒钟的思考就能做到的事情，我们现在或者在不久的将来都可以使用AI做到。”莫拉维克悖论表明，AI在执行许多人类大脑专门进化出来的、能够很好完成的任务时表现不如人类。
Games provide a well-publicized benchmark for assessing rates of progress. [[AlphaGo]] around 2016 brought the era of classical board-game benchmarks to a close. Games of imperfect knowledge provide new challenges to AI in the area of [[game theory]].<ref>{{cite news|last1=Borowiec|first1=Tracey Lien, Steven|title=AlphaGo beats human Go champ in milestone for artificial intelligence|url=https://www.latimes.com/world/asia/la-fg-korea-alphago-20160312-story.html|accessdate=7 May 2018|work=latimes.com|date=2016}}</ref><ref>{{cite news|last1=Brown|first1=Noam|last2=Sandholm|first2=Tuomas|title=Superhuman AI for heads-up no-limit poker: Libratus beats top professionals|url=http://science.sciencemag.org/content/359/6374/418|accessdate=7 May 2018|work=Science|date=26 January 2018|pages=418–424|language=en|doi=10.1126/science.aao1733}}</ref> [[Esports|E-sports]] such as [[StarCraft]] continue to provide additional public benchmarks.<ref>{{cite journal|last1=Ontanon|first1=Santiago|last2=Synnaeve|first2=Gabriel|last3=Uriarte|first3=Alberto|last4=Richoux|first4=Florian|last5=Churchill|first5=David|last6=Preuss|first6=Mike|title=A Survey of Real-Time Strategy Game AI Research and Competition in StarCraft|journal=IEEE Transactions on Computational Intelligence and AI in Games|date=December 2013|volume=5|issue=4|pages=293–311|doi=10.1109/TCIAIG.2013.2286295|citeseerx=10.1.1.406.2524}}</ref><ref>{{cite news|title=Facebook Quietly Enters StarCraft War for AI Bots, and Loses|url=https://www.wired.com/story/facebook-quietly-enters-starcraft-war-for-ai-bots-and-loses/|accessdate=7 May 2018|work=WIRED|date=2017}}</ref> There are many competitions and prizes, such as the [[ImageNet|Imagenet Challenge]], to promote research in artificial intelligence. The most common areas of competition include general machine intelligence, conversational behavior, data-mining, [[autonomous car|robotic cars]], and robot soccer as well as conventional games.<ref>{{Cite web|url=http://image-net.org/challenges/LSVRC/2017/|title=ILSVRC2017|website=image-net.org|language=en|access-date=2018-11-06}}</ref>
Games provide a well-publicized benchmark for assessing rates of progress. AlphaGo around 2016 brought the era of classical board-game benchmarks to a close. Games of imperfect knowledge provide new challenges to AI in the area of game theory. E-sports such as StarCraft continue to provide additional public benchmarks. There are many competitions and prizes, such as the Imagenet Challenge, to promote research in artificial intelligence. The most common areas of competition include general machine intelligence, conversational behavior, data-mining, robotic cars, and robot soccer as well as conventional games.
游戏评估进步率用的是一个公众皆知的基准。2016年前后，AlphaGo 拉上了传统棋类基准的时代的幕布。不完全知识的游戏在博弈论领域对AI来说是提新的挑战。星际争霸等电子竞技用的是不同的公众基准。它们设立了有许多如 Imagenet 挑战赛的比赛和奖项以促进AI研究。最常见的比赛内容包括通用机器智能、对话行为、数据挖掘、机器人汽车、机器人足球以及传统游戏。
The "imitation game" (an interpretation of the 1950 [[Turing test]] that assesses whether a computer can imitate a human) is nowadays considered too exploitable to be a meaningful benchmark.<ref>{{cite journal|last1=Schoenick|first1=Carissa|last2=Clark|first2=Peter|last3=Tafjord|first3=Oyvind|last4=Turney|first4=Peter|last5=Etzioni|first5=Oren|title=Moving beyond the Turing Test with the Allen AI Science Challenge|journal=Communications of the ACM|date=23 August 2017|volume=60|issue=9|pages=60–64|doi=10.1145/3122814|arxiv=1604.04315}}</ref> A derivative of the Turing test is the Completely Automated Public Turing test to tell Computers and Humans Apart ([[CAPTCHA]]). As the name implies, this helps to determine that a user is an actual person and not a computer posing as a human. In contrast to the standard Turing test, CAPTCHA is administered by a machine and targeted to a human as opposed to being administered by a human and targeted to a machine. A computer asks a user to complete a simple test then generates a grade for that test. Computers are unable to solve the problem, so correct solutions are deemed to be the result of a person taking the test. A common type of CAPTCHA is the test that requires the typing of distorted letters, numbers or symbols that appear in an image undecipherable by a computer.{{sfn|O'Brien|Marakas|2011}}
The "imitation game" (an interpretation of the 1950 Turing test that assesses whether a computer can imitate a human) is nowadays considered too exploitable to be a meaningful benchmark. A derivative of the Turing test is the Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA). As the name implies, this helps to determine that a user is an actual person and not a computer posing as a human. In contrast to the standard Turing test, CAPTCHA is administered by a machine and targeted to a human as opposed to being administered by a human and targeted to a machine. A computer asks a user to complete a simple test then generates a grade for that test. Computers are unable to solve the problem, so correct solutions are deemed to be the result of a person taking the test. A common type of CAPTCHA is the test that requires the typing of distorted letters, numbers or symbols that appear in an image undecipherable by a computer.
“模仿游戏”(对1950年图灵测试的一种解释，用来评估计算机是否可以模仿人类)如今被认为是一个过于灵活而不能成为有意义的基准。图灵测试衍生出了'''<font color=#ff8000>验证码 Completely Automated Public Turing test to tell Computers and Humans Apart,CAPTCHA</font>'''(即全自动区分计算机和人类的图灵测试)。顾名思义，这有助于确定用户是一个真实的人，而不是一台伪装成人的计算机。与标准的图灵测试不同，CAPTCHA 是由机器控制，面向人测试，而不是由人控制的，面向机器测试的。计算机要求用户完成一个简单的测试，然后给测试评出一个等级。计算机无法解决这个问题，所以一般认为只有人参加测试才能得出正确答案。验证码的一个常见类型是要求输入一幅计算机无法破译的图中扭曲的字母，数字或符号测试。
Proposed "universal intelligence" tests aim to compare how well machines, humans, and even non-human animals perform on problem sets that are generic as possible. At an extreme, the test suite can contain every possible problem, weighted by [[Kolmogorov complexity]]; unfortunately, these problem sets tend to be dominated by impoverished pattern-matching exercises where a tuned AI can easily exceed human performance levels.<ref name="Mathematical definitions of intelligence"/><ref>{{cite journal|last1=Hernández-Orallo|first1=José|last2=Dowe|first2=David L.|last3=Hernández-Lloreda|first3=M.Victoria|title=Universal psychometrics: Measuring cognitive abilities in the machine kingdom|journal=Cognitive Systems Research|date=March 2014|volume=27|pages=50–74|doi=10.1016/j.cogsys.2013.06.001|hdl=10251/50244|hdl-access=free}}</ref>
Proposed "universal intelligence" tests aim to compare how well machines, humans, and even non-human animals perform on problem sets that are generic as possible. At an extreme, the test suite can contain every possible problem, weighted by Kolmogorov complexity; unfortunately, these problem sets tend to be dominated by impoverished pattern-matching exercises where a tuned AI can easily exceed human performance levels.
“通用智能”测试旨在比较机器、人类甚至非人类动物在尽可能通用的问题集上的表现。在极端情况下，测试集可以包含所有可能出现的问题，由柯氏复杂性赋权重; 可是这些问题集往往是用有限的模式匹配练习完成的，在这些练习中，优化过的AI可以轻易地超过人类。
== Applications{{anchor|Goals}} ==
== Applications ==
申请
[[File:Automated online assistant.png|thumb|An [[automated online assistant]] providing customer service on a web page – one of many very primitive applications of artificial intelligence]]
An [[automated online assistant providing customer service on a web page – one of many very primitive applications of artificial intelligence]]
AI的初级应用之一：提供客户服务的网页自动化助理]
{{Main|Applications of artificial intelligence}}
AI is relevant to any intellectual task.{{sfn|Russell|Norvig|2009|p=1}} Modern artificial intelligence techniques are pervasive<ref name=":1">{{Cite book|last=|first=|url=https://ec.europa.eu/info/sites/info/files/commission-white-paper-artificial-intelligence-feb2020_en.pdf|title=White Paper: On Artificial Intelligence - A European approach to excellence and trust|publisher=European Commission|year=2020|isbn=|location=Brussels|pages=1}}</ref> and are too numerous to list here. Frequently, when a technique reaches mainstream use, it is no longer considered artificial intelligence; this phenomenon is described as the [[AI effect]].{{sfn|''CNN''|2006}}
AI is relevant to any intellectual task. Modern artificial intelligence techniques are pervasive and are too numerous to list here. Frequently, when a technique reaches mainstream use, it is no longer considered artificial intelligence; this phenomenon is described as the AI effect.
AI与任何智力任务都息息相关。现代AI技术无处不在，数量众多，无法在此列举。通常，当一种技术变成主流应用时，它就不再被认为是AI; 这种现象被称为AI效应。
High-profile examples of AI include autonomous vehicles (such as [[Unmanned aerial vehicle|drones]] and [[self-driving cars]]), medical diagnosis, creating art (such as poetry), proving mathematical theorems, playing games (such as Chess or Go), search engines (such as [[Google search]]), online assistants (such as [[Siri]]), image recognition in photographs, spam filtering, predicting flight delays,<ref>[https://ishti.org/2018/11/19/using-artificial-intelligence-to-predict-flight-delays/ Using AI to predict flight delays], Ishti.org.</ref> prediction of judicial decisions,<ref name="ecthr2016">{{cite journal |author1=N. Aletras |author2=D. Tsarapatsanis |author3=D. Preotiuc-Pietro |author4=V. Lampos |title=Predicting judicial decisions of the European Court of Human Rights: a Natural Language Processing perspective |journal=PeerJ Computer Science |volume=2 |pages=e93 |year=2016 |df=dmy-all |doi=10.7717/peerj-cs.93 |doi-access=free }}</ref> targeting online advertisements, {{sfn|Russell|Norvig|2009|p=1}}<ref>{{cite news|title=The Economist Explains: Why firms are piling into artificial intelligence|url=https://www.economist.com/blogs/economist-explains/2016/04/economist-explains|accessdate=19 May 2016|work=[[The Economist]]|date=31 March 2016|url-status=live|archiveurl=https://web.archive.org/web/20160508010311/http://www.economist.com/blogs/economist-explains/2016/04/economist-explains|archivedate=8 May 2016|df=dmy-all}}</ref><ref>{{cite news|url=https://www.nytimes.com/2016/02/29/technology/the-promise-of-artificial-intelligence-unfolds-in-small-steps.html|title=The Promise of Artificial Intelligence Unfolds in Small Steps|last=Lohr|first=Steve|work=[[The New York Times]]|date=28 February 2016|accessdate=29 February 2016|url-status=live|archiveurl=https://web.archive.org/web/20160229171843/http://www.nytimes.com/2016/02/29/technology/the-promise-of-artificial-intelligence-unfolds-in-small-steps.html|archivedate=29 February 2016|df=dmy-all}}</ref> and [[energy storage]]<ref>{{Cite web|url=https://www.cnbc.com/2019/06/14/the-business-using-ai-to-change-how-we-think-about-energy-storage.html|title=A Californian business is using A.I. to change the way we think about energy storage|last=Frangoul|first=Anmar|date=2019-06-14|website=CNBC|language=en|access-date=2019-11-05}}</ref>
High-profile examples of AI include autonomous vehicles (such as drones and self-driving cars), medical diagnosis, creating art (such as poetry), proving mathematical theorems, playing games (such as Chess or Go), search engines (such as Google search), online assistants (such as Siri), image recognition in photographs, spam filtering, predicting flight delays, prediction of judicial decisions, targeting online advertisements, and energy storage
大众常见的AI包括自动驾驶(如无人机和自动驾驶汽车)、医疗诊断、艺术创作(如诗歌)、证明数学定理、玩游戏(如国际象棋或围棋)、搜索引擎(如谷歌搜索)、在线助手(如 Siri)、图像识别、垃圾邮件过滤、航班延误预测、司法判决预测、投放在线广告和能源储存。
With social media sites overtaking TV as a source for news for young people and news organizations increasingly reliant on social media platforms for generating distribution,<ref>{{cite web|url=https://www.bbc.co.uk/news/uk-36528256|title=Social media 'outstrips TV' as news source for young people|date=15 June 2016|author=Wakefield, Jane|work=BBC News|url-status=live|archiveurl=https://web.archive.org/web/20160624000744/http://www.bbc.co.uk/news/uk-36528256|archivedate=24 June 2016|df=dmy-all}}</ref> major publishers now use artificial intelligence (AI) technology to post stories more effectively and generate higher volumes of traffic.<ref>{{cite web|url=https://www.bbc.co.uk/news/business-36837824|title=So you think you chose to read this article?|date=22 July 2016|author=Smith, Mark|work=BBC News|url-status=live|archiveurl=https://web.archive.org/web/20160725205007/http://www.bbc.co.uk/news/business-36837824|archivedate=25 July 2016|df=dmy-all}}</ref>
With social media sites overtaking TV as a source for news for young people and news organizations increasingly reliant on social media platforms for generating distribution, major publishers now use artificial intelligence (AI) technology to post stories more effectively and generate higher volumes of traffic.
随着社交媒体网站取代电视成为年轻人获取新闻的来源，以及新闻机构越来越依赖社交媒体平台来发布新闻，大型出版商现在使用AI技术发布新闻，这样做效率更高且能带来更多的流量。
[[Hierarchical temporal memory]] is an approach that models some of the structural and algorithmic properties of the [[neocortex]].<ref name="Hierarchical temporal memory"/>
AI can also produce [[Deepfake]]s, a content-altering technology. ZDNet reports, "It presents something that did not actually occur," Though 88% of Americans believe Deepfakes can cause more harm than good, only 47% of them believe they can be targeted. The boom of election year also opens public discourse to threats of videos of falsified politician media.<ref>{{Cite web|url=https://www.zdnet.com/article/half-of-americans-do-not-believe-deepfake-news-could-target-them-online/|title=Half of Americans do not believe deepfake news could target them online|last=Brown|first=Eileen|website=ZDNet|language=en|access-date=2019-12-03}}</ref>
AI can also produce Deepfakes, a content-altering technology. ZDNet reports, "It presents something that did not actually occur," Though 88% of Americans believe Deepfakes can cause more harm than good, only 47% of them believe they can be targeted. The boom of election year also opens public discourse to threats of videos of falsified politician media.
AI还可以用来换脸 ，这是一种改变内容的技术。至顶网报道说，“它展示出一些并没有真正发生的事情。”尽管88% 的美国人认为换脸弊大于利，但只有47%的人认为自己会成为换脸对象。选举年的盛况也让公众开始讨论起虚假政治视频的害处。
=== Healthcare ===
=== Healthcare ===
医疗
{{Main|Artificial intelligence in healthcare}}
[[File:Laproscopic Surgery Robot.jpg|thumb| A patient-side surgical arm of [[Da Vinci Surgical System]]]]AI in healthcare is often used for classification, whether to automate initial evaluation of a CT scan or EKG or to identify high-risk patients for population health. The breadth of applications is rapidly increasing.
A patient-side surgical arm of [[Da Vinci Surgical System]]AI in healthcare is often used for classification, whether to automate initial evaluation of a CT scan or EKG or to identify high-risk patients for population health. The breadth of applications is rapidly increasing.
在医疗保健中， AI通常被用于分类，它既可以自动对 CT 扫描或心电图EKG进行初步评估，又可以在人口健康调查中识别高风险患者。AI的应用范围正在迅速扩大。
As an example, AI is being applied to the high-cost problem of dosage issues—where findings suggested that AI could save $16 billion. In 2016, a groundbreaking study in California found that a mathematical formula developed with the help of AI correctly determined the accurate dose of immunosuppressant drugs to give to organ patients.<ref>{{Cite news|url=https://hbr.org/2018/05/10-promising-ai-applications-in-health-care|title=10 Promising AI Applications in Health Care|date=2018-05-10|work=Harvard Business Review|access-date=2018-08-28|archive-url=https://web.archive.org/web/20181215015645/https://hbr.org/2018/05/10-promising-ai-applications-in-health-care|archive-date=15 December 2018|url-status=dead}}</ref> [[File:X-ray of a hand with automatic bone age calculation.jpg|thumb|[[Projectional radiography|X-ray]] of a hand, with automatic calculation of [[bone age]] by computer software]]
As an example, AI is being applied to the high-cost problem of dosage issues—where findings suggested that AI could save $16 billion. In 2016, a groundbreaking study in California found that a mathematical formula developed with the help of AI correctly determined the accurate dose of immunosuppressant drugs to give to organ patients. X-ray of a hand, with automatic calculation of bone age by computer software]]
例如研究结果表明，AI在高成本的剂量问题上可以节省160亿美元。2016年，加利福尼亚州的一项开创性研究发现，在AI的辅助下得到的一个数学公式给出了器官患者免疫抑制药的准确剂量。
--[[用户:Thingamabob|Thingamabob]]（[[用户讨论:Thingamabob|讨论]]） 例如研究结果表明，AI在高成本的剂量问题上可以节省160亿美元。 为省译
Artificial intelligence is assisting doctors. According to Bloomberg Technology, Microsoft has developed AI to help doctors find the right treatments for cancer.<ref>{{cite news | author=Dina Bass | title=Microsoft Develops AI to Help Cancer Doctors Find the Right Treatments | url=https://www.bloomberg.com/news/articles/2016-09-20/microsoft-develops-ai-to-help-cancer-doctors-find-the-right-treatments | date=20 September 2016 | publisher=Bloomberg | url-status=live | archiveurl=https://web.archive.org/web/20170511103625/https://www.bloomberg.com/news/articles/2016-09-20/microsoft-develops-ai-to-help-cancer-doctors-find-the-right-treatments | archivedate=11 May 2017 | df=dmy-all | newspaper=Bloomberg.com }}</ref> There is a great amount of research and drugs developed relating to cancer. In detail, there are more than 800 medicines and vaccines to treat cancer. This negatively affects the doctors, because there are too many options to choose from, making it more difficult to choose the right drugs for the patients. Microsoft is working on a project to develop a machine called "Hanover"{{citation needed|date=July 2019}}. Its goal is to memorize all the papers necessary to cancer and help predict which combinations of drugs will be most effective for each patient. One project that is being worked on at the moment is fighting [[acute myeloid leukemia|myeloid leukemia]], a fatal cancer where the treatment has not improved in decades. Another study was reported to have found that artificial intelligence was as good as trained doctors in identifying skin cancers.<ref>{{Cite news|url=https://www.bbc.co.uk/news/health-38717928|title=Artificial intelligence 'as good as cancer doctors'|last=Gallagher|first=James|date=26 January 2017|work=BBC News|language=en-GB|access-date=26 January 2017|url-status=live|archiveurl=https://web.archive.org/web/20170126133849/http://www.bbc.co.uk/news/health-38717928|archivedate=26 January 2017|df=dmy-all}}</ref> Another study is using artificial intelligence to try to monitor multiple high-risk patients, and this is done by asking each patient numerous questions based on data acquired from live doctor to patient interactions.<ref>{{Citation|title=Remote monitoring of high-risk patients using artificial intelligence|date=18 Oct 1994|url=https://www.google.com/patents/US5357427|editor-last=Langen|editor2-last=Katz|editor3-last=Dempsey|editor-first=Pauline A.|editor2-first=Jeffrey S.|editor3-first=Gayle|issue=US5357427 A|accessdate=27 February 2017|url-status=live|archiveurl=https://web.archive.org/web/20170228090520/https://www.google.com/patents/US5357427|archivedate=28 February 2017|df=dmy-all}}</ref> One study was done with transfer learning, the machine performed a diagnosis similarly to a well-trained ophthalmologist, and could generate a decision within 30 seconds on whether or not the patient should be referred for treatment, with more than 95% accuracy.<ref>{{Cite journal|url=https://www.cell.com/action/captchaChallenge?redirectUri=%2Fcell%2Fpdf%2FS0092-8674%2818%2930154-5.pdf|title=Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning|last=Kermany|first=D|last2=Goldbaum|first2=M|journal=Cell|access-date=2018-12-18|last3=Zhang|first3=Kang|volume=172|issue=5|pages=1122–1131.e9|pmid=29474911|year=2018|doi=10.1016/j.cell.2018.02.010}}</ref>
Artificial intelligence is assisting doctors. According to Bloomberg Technology, Microsoft has developed AI to help doctors find the right treatments for cancer. There is a great amount of research and drugs developed relating to cancer. In detail, there are more than 800 medicines and vaccines to treat cancer. This negatively affects the doctors, because there are too many options to choose from, making it more difficult to choose the right drugs for the patients. Microsoft is working on a project to develop a machine called "Hanover". Its goal is to memorize all the papers necessary to cancer and help predict which combinations of drugs will be most effective for each patient. One project that is being worked on at the moment is fighting myeloid leukemia, a fatal cancer where the treatment has not improved in decades. Another study was reported to have found that artificial intelligence was as good as trained doctors in identifying skin cancers. Another study is using artificial intelligence to try to monitor multiple high-risk patients, and this is done by asking each patient numerous questions based on data acquired from live doctor to patient interactions. One study was done with transfer learning, the machine performed a diagnosis similarly to a well-trained ophthalmologist, and could generate a decision within 30 seconds on whether or not the patient should be referred for treatment, with more than 95% accuracy.
AI还能协助医生。据彭博科技报道，微软已经开发出帮助医生找到正确的癌症治疗方法的AI。如今有大量的研究和药物开发与癌症有关，准确来说有800多种可以治疗癌症的药物和疫苗。这对医生来说并不是一件好事，因为选项太多，使得为病人选择合适的药物变得更难。微软正在开发一种名为“汉诺威”的机器。它的目标是记住所有与癌症有关的论文，并帮助预测哪些药物的组合对病人最有效。目前正在进行的一个项目是抗击髓系白血病，这是一种致命的癌症，几十年来治疗水平一直没有提高。据报道，另一项研究发现，AI在识别皮肤癌方面与训练有素的医生一样优秀。另一项研究是使用AI通过询问每个高风险患者多个问题监测他们，这些问题是基于从医生与患者的互动中获得的数据产生的。其中一项研究是通过转移学习完成的，机器进行的诊断类似于训练有素的眼科医生，可以在30秒内做出是否应该转诊治疗的决定，准确率超过95% 。
[[Deep learning]] is any [[artificial neural network]] that can learn a long chain of causal links{{dubious|date=July 2019}}. For example, a feedforward network with six hidden layers can learn a seven-link causal chain (six hidden layers + output layer) and has a [[deep learning#Credit assignment|"credit assignment path"]] (CAP) depth of seven{{citation needed|date=July 2019}}. Many deep learning systems need to be able to learn chains ten or more causal links in length.<ref name="schmidhuber2015"/> Deep learning has transformed many important subfields of artificial intelligence{{why|date=July 2019}}, including [[computer vision]], [[speech recognition]], [[natural language processing]] and others.<ref name="goodfellow2016">Ian Goodfellow, Yoshua Bengio, and Aaron Courville (2016). Deep Learning. MIT Press. [http://www.deeplearningbook.org Online] {{webarchive|url=https://web.archive.org/web/20160416111010/http://www.deeplearningbook.org/ |date=16 April 2016 }}</ref><ref name="HintonDengYu2012">{{cite journal | last1 = Hinton | first1 = G. | last2 = Deng | first2 = L. | last3 = Yu | first3 = D. | last4 = Dahl | first4 = G. | last5 = Mohamed | first5 = A. | last6 = Jaitly | first6 = N. | last7 = Senior | first7 = A. | last8 = Vanhoucke | first8 = V. | last9 = Nguyen | first9 = P. | last10 = Sainath | first10 = T. | last11 = Kingsbury | first11 = B. | year = 2012 | title = Deep Neural Networks for Acoustic Modeling in Speech Recognition – The shared views of four research groups | url = | journal = IEEE Signal Processing Magazine | volume = 29 | issue = 6| pages = 82–97 | doi=10.1109/msp.2012.2205597}}</ref><ref name="schmidhuber2015">{{cite journal |last=Schmidhuber |first=J. |year=2015 |title=Deep Learning in Neural Networks: An Overview |journal=Neural Networks |volume=61 |pages=85–117 |arxiv=1404.7828 |doi=10.1016/j.neunet.2014.09.003|pmid=25462637 }}</ref>
According to [[CNN]], a recent study by surgeons at the Children's National Medical Center in Washington successfully demonstrated surgery with an autonomous robot. The team supervised the robot while it performed soft-tissue surgery, stitching together a pig's bowel during open surgery, and doing so better than a human surgeon, the team claimed.<ref>{{cite news|author=Senthilingam, Meera|title=Are Autonomous Robots Your next Surgeons?|work=CNN|publisher=Cable News Network|date=12 May 2016|accessdate=4 December 2016|url=http://www.cnn.com/2016/05/12/health/robot-surgeon-bowel-operation/|url-status=live|archiveurl=https://web.archive.org/web/20161203154119/http://www.cnn.com/2016/05/12/health/robot-surgeon-bowel-operation|archivedate=3 December 2016|df=dmy-all}}</ref> IBM has created its own artificial intelligence computer, the [[IBM Watson]], which has beaten human intelligence (at some levels). Watson has struggled to achieve success and adoption in healthcare.<ref>{{Cite web|url=https://spectrum.ieee.org/biomedical/diagnostics/how-ibm-watson-overpromised-and-underdelivered-on-ai-health-care|title=Full Page Reload|website=IEEE Spectrum: Technology, Engineering, and Science News|language=en|access-date=2019-09-03}}</ref>
According to CNN, a recent study by surgeons at the Children's National Medical Center in Washington successfully demonstrated surgery with an autonomous robot. The team supervised the robot while it performed soft-tissue surgery, stitching together a pig's bowel during open surgery, and doing so better than a human surgeon, the team claimed. IBM has created its own artificial intelligence computer, the IBM Watson, which has beaten human intelligence (at some levels). Watson has struggled to achieve success and adoption in healthcare.
据 CNN 报道，华盛顿国家儿童医疗中心的外科医生最近的一项研究成功演示了一台自主机器人手术。研究组观看了机器人做软组织手术、在开放手术中缝合猪肠的整个过程，并认为比人类外科医生做得更好。IBM已经创造了自己的AI计算机——IBM 沃森，它在某种程度上已经超越了人类智能。沃森一直在努力实现医疗保健领域的应用。
=== Automotive ===
=== Automotive ===
汽车
{{Main|driverless cars}}
Advancements in AI have contributed to the growth of the automotive industry through the creation and evolution of self-driving vehicles. {{as of|2016}}, there are over 30 companies utilizing AI into the creation of [[self-driving car]]s. A few companies involved with AI include [[Tesla Motors|Tesla]], [[Google]], and [[Apple Inc.|Apple]].<ref>"33 Corporations Working On Autonomous Vehicles". CB Insights. N.p., 11 August 2016. 12 November 2016.</ref>
Advancements in AI have contributed to the growth of the automotive industry through the creation and evolution of self-driving vehicles. , there are over 30 companies utilizing AI into the creation of self-driving cars. A few companies involved with AI include Tesla, Google, and Apple.
在AI领域自动驾驶汽车的创造和发展促进了汽车行业的发展。目前有超过30家公司利用AI开发自动驾驶汽车，包括特斯拉、谷歌和苹果等 。
Many components contribute to the functioning of self-driving cars. These vehicles incorporate systems such as braking, lane changing, collision prevention, navigation and mapping. Together, these systems, as well as high-performance computers, are integrated into one complex vehicle.<ref>West, Darrell M. "Moving forward: Self-driving vehicles in China, Europe, Japan, Korea, and the United States". Center for Technology Innovation at Brookings. N.p., September 2016. 12 November 2016.</ref>
Many components contribute to the functioning of self-driving cars. These vehicles incorporate systems such as braking, lane changing, collision prevention, navigation and mapping. Together, these systems, as well as high-performance computers, are integrated into one complex vehicle.
自动驾驶汽车的功能的实现需要很多组件。这些车辆集成了诸如刹车、换车道、防撞、导航和测绘等系统。这些系统以及高性能计算机被装配到一辆复杂的车中。
Recent developments in autonomous automobiles have made the innovation of self-driving trucks possible, though they are still in the testing phase. The UK government has passed legislation to begin testing of self-driving truck platoons in 2018.<ref>{{cite journal|last1=Burgess|first1=Matt|title=The UK is about to Start Testing Self-Driving Truck Platoons|url=https://www.wired.co.uk/article/uk-trial-self-driving-trucks-platoons-roads|journal=Wired UK|accessdate=20 September 2017|url-status=live|archiveurl=https://web.archive.org/web/20170922055917/http://www.wired.co.uk/article/uk-trial-self-driving-trucks-platoons-roads|archivedate=22 September 2017|df=dmy-all|date=2017-08-24}}</ref> Self-driving truck platoons are a fleet of self-driving trucks following the lead of one non-self-driving truck, so the truck platoons aren't entirely autonomous yet. Meanwhile, the Daimler, a German automobile corporation, is testing the Freightliner Inspiration which is a semi-autonomous truck that will only be used on the highway.<ref>{{cite journal|last1=Davies|first1=Alex|title=World's First Self-Driving Semi-Truck Hits the Road|url=https://www.wired.com/2015/05/worlds-first-self-driving-semi-truck-hits-road/|journal=WIRED|accessdate=20 September 2017|url-status=live|archiveurl=https://web.archive.org/web/20171028222802/https://www.wired.com/2015/05/worlds-first-self-driving-semi-truck-hits-road/|archivedate=28 October 2017|df=dmy-all|date=2015-05-05}}</ref>
Recent developments in autonomous automobiles have made the innovation of self-driving trucks possible, though they are still in the testing phase. The UK government has passed legislation to begin testing of self-driving truck platoons in 2018. Self-driving truck platoons are a fleet of self-driving trucks following the lead of one non-self-driving truck, so the truck platoons aren't entirely autonomous yet. Meanwhile, the Daimler, a German automobile corporation, is testing the Freightliner Inspiration which is a semi-autonomous truck that will only be used on the highway.
自动驾驶汽车的最新发展使自动驾驶卡车的创新成为可能，尽管它们仍处于测试阶段。英国政府已通过立法，将于2018年开始测试自动驾驶卡车列队行驶。自动驾驶卡车列队是指一排自动驾驶卡车跟随一辆非自动驾驶卡车，所以卡车排还不是完全自动的。与此同时，德国汽车公司戴姆勒正在测试Freightliner Inspiration，这是一种只在高速公路上行驶的半自动卡车。
One main factor that influences the ability for a driver-less automobile to function is mapping. In general, the vehicle would be pre-programmed with a map of the area being driven. This map would include data on the approximations of street light and curb heights in order for the vehicle to be aware of its surroundings. However, Google has been working on an algorithm with the purpose of eliminating the need for pre-programmed maps and instead, creating a device that would be able to adjust to a variety of new surroundings.<ref>McFarland, Matt. "Google's artificial intelligence breakthrough may have a huge impact on self-driving cars and much more". ''The Washington Post'' 25 February 2015. Infotrac Newsstand. 24 October 2016</ref> Some self-driving cars are not equipped with steering wheels or brake pedals, so there has also been research focused on creating an algorithm that is capable of maintaining a safe environment for the passengers in the vehicle through awareness of speed and driving conditions.<ref>"Programming safety into self-driving cars". National Science Foundation. N.p., 2 February 2015. 24 October 2016.</ref>
One main factor that influences the ability for a driver-less automobile to function is mapping. In general, the vehicle would be pre-programmed with a map of the area being driven. This map would include data on the approximations of street light and curb heights in order for the vehicle to be aware of its surroundings. However, Google has been working on an algorithm with the purpose of eliminating the need for pre-programmed maps and instead, creating a device that would be able to adjust to a variety of new surroundings. Some self-driving cars are not equipped with steering wheels or brake pedals, so there has also been research focused on creating an algorithm that is capable of maintaining a safe environment for the passengers in the vehicle through awareness of speed and driving conditions.
影响无人驾驶汽车性能的一个主要因素是地图。一般来说，车辆将预先编程行驶区域的地图。这张地图将包括街灯和路缘高度的近似数据，让车辆能够感知周围环境。然而谷歌一直在研究一种不需要预编程地图的算法，创造一种能够适应各种新环境的设备。一些自动驾驶汽车没有配备方向盘或刹车踏板，因此也有研究致力于创建感知速度和驾驶条件的算法，为车内乘客提供一个安全的环境。
Another factor that is influencing the ability of a driver-less automobile is the safety of the passenger. To make a driver-less automobile, engineers must program it to handle high-risk situations. These situations could include a head-on collision with pedestrians. The car's main goal should be to make a decision that would avoid hitting the pedestrians and saving the passengers in the car. But there is a possibility the car would need to make a decision that would put someone in danger. In other words, the car would need to decide to save the pedestrians or the passengers.<ref>ArXiv, E. T. (26 October 2015). Why Self-Driving Cars Must Be Programmed to Kill. Retrieved 17 November 2017, from https://www.technologyreview.com/s/542626/why-self-driving-cars-must-be-programmed-to-kill/{{Dead link|date=October 2019 |bot=InternetArchiveBot |fix-attempted=yes }}</ref> The programming of the car in these situations is crucial to a successful driver-less automobile.
Another factor that is influencing the ability of a driver-less automobile is the safety of the passenger. To make a driver-less automobile, engineers must program it to handle high-risk situations. These situations could include a head-on collision with pedestrians. The car's main goal should be to make a decision that would avoid hitting the pedestrians and saving the passengers in the car. But there is a possibility the car would need to make a decision that would put someone in danger. In other words, the car would need to decide to save the pedestrians or the passengers. The programming of the car in these situations is crucial to a successful driver-less automobile.
衡量无人驾驶汽车能力的另一个因素是乘客的安全。工程师们必须对无人驾驶汽车进行编程，使其能够处理比如与行人正面相撞的高风险的情况。这辆车的主要目标应该是做出一个避免撞到行人，保护车内的乘客的决定。但是有时汽车有可能也会将某人置于危险之中。也就是，汽车需要决定是拯救行人还是乘客。汽车在这些情况下的编程对于一辆成功的无人驾驶汽车是至关重要的。
=== Finance and economics ===
=== Finance and economics ===
金融和经济
{{Main|Recurrent neural networks}}
[[Financial institution]]s have long used [[artificial neural network]] systems to detect charges or claims outside of the norm, flagging these for human investigation. The use of AI in [[banking]] can be traced back to 1987 when [[Security Pacific National Bank]] in US set-up a Fraud Prevention Task force to counter the unauthorized use of debit cards.<ref>{{Cite web|url=https://www.latimes.com/archives/la-xpm-1990-01-17-fi-233-story.html|title=Impact of Artificial Intelligence on Banking|last=Christy|first=Charles A.|website=latimes.com|access-date=2019-09-10|date=17 January 1990}}</ref> Programs like Kasisto and Moneystream are using AI in financial services.
Financial institutions have long used artificial neural network systems to detect charges or claims outside of the norm, flagging these for human investigation. The use of AI in banking can be traced back to 1987 when Security Pacific National Bank in US set-up a Fraud Prevention Task force to counter the unauthorized use of debit cards. Programs like Kasisto and Moneystream are using AI in financial services.
长期以来，金融机构一直使用人工神经网络系统来检测超出常规的费用或索赔，并将其标记起来等待人工调查。AI在银行业的应用可以追溯到1987年，当时美国国家安全太平洋银行成立了一个防防诈特别小组，以打击未经授权使用借记卡的行为。金融服务领域的如Kasisto 和 Moneystream等程序正在使用AI技术。
Banks use artificial intelligence systems today to organize operations, maintain book-keeping, invest in stocks, and manage properties. AI can react to changes overnight or when business is not taking place.<ref name="Eleanor">{{cite web|url=https://www.icas.com/ca-today-news/how-accountancy-and-finance-are-using-artificial-intelligence|title=Accounting, automation and AI|first=Eleanor|last=O'Neill|website=icas.com|language=English|date=31 July 2016|access-date=18 November 2016|url-status=live|archiveurl=https://web.archive.org/web/20161118165901/https://www.icas.com/ca-today-news/how-accountancy-and-finance-are-using-artificial-intelligence|archivedate=18 November 2016|df=dmy-all}}</ref> In August 2001, robots beat humans in a simulated [[stock trader|financial trading]] competition.<ref>[http://news.bbc.co.uk/2/hi/business/1481339.stm Robots Beat Humans in Trading Battle.] {{webarchive|url=https://web.archive.org/web/20090909001249/http://news.bbc.co.uk/2/hi/business/1481339.stm |date=9 September 2009 }} BBC.com (8 August 2001)</ref> AI has also reduced fraud and financial crimes by [[Statistical software|monitoring]] [[behavioral pattern]]s of users for any abnormal changes or anomalies.<ref name="fsroundtable.org">{{Cite news|url=http://fsroundtable.org/cto-corner-artificial-intelligence-use-in-financial-services/|title=CTO Corner: Artificial Intelligence Use in Financial Services – Financial Services Roundtable|date=2 April 2015|work=Financial Services Roundtable|language=en-US|access-date=18 November 2016|url-status=dead|archiveurl=https://web.archive.org/web/20161118165842/http://fsroundtable.org/cto-corner-artificial-intelligence-use-in-financial-services/|archivedate=18 November 2016|df=dmy-all}}</ref><ref>{{Cite web|url=https://www.sas.com/en_ae/solutions/ai.html|title=Artificial Intelligence Solutions, AI Solutions|website=www.sas.com}}</ref><ref>{{Cite web|url=https://www.latimes.com/business/la-fi-palantir-sales-ipo-20190107-story.html|title=Palantir once mocked the idea of salespeople. Now it's hiring them|last=Chapman|first=Lizette|website=latimes.com|access-date=2019-02-28|date=7 January 2019}}</ref>
Banks use artificial intelligence systems today to organize operations, maintain book-keeping, invest in stocks, and manage properties. AI can react to changes overnight or when business is not taking place. In August 2001, robots beat humans in a simulated financial trading competition. AI has also reduced fraud and financial crimes by monitoring behavioral patterns of users for any abnormal changes or anomalies.
如今，银行使用AI系统来组织业务、记账、投资股票和管理房地产。AI可以对突然的变化和没有业务的情况做出反应。2001年8月，机器人在一场模拟金融交易竞赛中击败了人类。AI还通过监测用户的行为模式发现异常变化或异常现象，减少了欺诈和金融犯罪。
AI is increasingly being used by [[Corporate finance|corporations]]. [[Jack Ma]] has controversially predicted that AI [[CEO]]'s are 30 years away.<ref>{{Cite web|url=https://money.cnn.com/2017/04/24/technology/alibaba-jack-ma-30-years-pain-robot-ceo/index.html|title=Jack Ma: In 30 years, the best CEO could be a robot|first=Sherisse|last=Pham|date=24 April 2017|website=CNNMoney}}</ref><ref>{{Cite web|url=https://venturebeat.com/2016/10/22/cant-find-a-perfect-ceo-create-an-ai-one-yourself/|title=Can't find a perfect CEO? Create an AI one yourself|date=22 October 2016}}</ref>
AI is increasingly being used by corporations. Jack Ma has controversially predicted that AI CEO's are 30 years away.
人AI正越来越多地被企业所使用。马云发表过一个有争议的预测：距离AI当上CEO还有30年的时间。
The use of AI machines in the market in applications such as online trading and decision making has changed major economic theories.<ref>{{cite book |last1=Marwala |first1= Tshilidzi| last2=Hurwitz |first2= Evan |title=Artificial Intelligence and Economic Theory: Skynet in the Market |year=2017 |publisher=[[Springer Science+Business Media|Springer]] |location=London |isbn=978-3-319-66104-9}}</ref> For example, AI-based buying and selling platforms have changed the law of [[supply and demand]] in that it is now possible to easily estimate individualized demand and supply curves and thus individualized pricing. Furthermore, AI machines reduce [[information asymmetry]] in the market and thus making markets more efficient while reducing the volume of trades{{citation needed|date=July 2019}}. Furthermore, AI in the markets limits the consequences of behavior in the markets again making markets more efficient{{citation needed|date=July 2019}}. Other theories where AI has had impact include in [[rational choice]], [[rational expectations]], [[game theory]], [[Lewis turning point]], [[portfolio optimization]] and [[counterfactual thinking]]{{citation needed|date=July 2019}}.. In August 2019, the [[American Institute of Certified Public Accountants|AICPA]] introduced AI training course for accounting professionals.<ref>{{Cite web|url=https://www.mileseducation.com/finance/artificial_intelligence|title=Miles Education {{!}} Future Of Finance {{!}} Blockchain Fundamentals for F&A Professionals Certificate|website=www.mileseducation.com|access-date=2019-09-26|archive-url=https://web.archive.org/web/20190926102133/https://www.mileseducation.com/finance/artificial_intelligence|archive-date=26 September 2019|url-status=dead}}</ref>
The use of AI machines in the market in applications such as online trading and decision making has changed major economic theories. For example, AI-based buying and selling platforms have changed the law of supply and demand in that it is now possible to easily estimate individualized demand and supply curves and thus individualized pricing. Furthermore, AI machines reduce information asymmetry in the market and thus making markets more efficient while reducing the volume of trades. Furthermore, AI in the markets limits the consequences of behavior in the markets again making markets more efficient. Other theories where AI has had impact include in rational choice, rational expectations, game theory, Lewis turning point, portfolio optimization and counterfactual thinking.. In August 2019, the AICPA introduced AI training course for accounting professionals.
AI机器在市场上如在线交易和决策的应用改变了主流经济理论。例如，基于AI的买卖平台改变了供求规律，因为现在可以通过AI很容易地估计个性化需求和供给曲线，从而实现个性化的定价。此外，AI减少了交易的信息不对称，在使市场更有效率的同时也减少了交易量。此外，AI限定了市场行为的后果，再次提高了交易效率。AI影响的其他理论包括理性选择、理性预期、博弈论、刘易斯转折点、投资组合优化和反事实思维。2019年8月，AICPA 为会计专业人员开设了 AI 培训课程。
=== Cybersecurity ===
=== Cybersecurity ===
网络安全
{{More citations needed section|date=January 2020}}
|last3=Casper |first3=Jared
The [[cybersecurity]] arena faces significant challenges in the form of large-scale hacking attacks of different types that harm organizations of all kinds and create billions of dollars in business damage. Artificial intelligence and Natural Language Processing (NLP) has begun to be used by security companies - for example, SIEM (Security Information and Event Management) solutions. The more advanced of these solutions use AI and NLP to automatically sort the data in networks into high risk and low-risk information. This enables security teams to focus on the attacks that have the potential to do real harm to the organization, and not become victims of attacks such as [[Denial-of-service attack|Denial of Service (DoS)]], [[Malware]] and others.
The cybersecurity arena faces significant challenges in the form of large-scale hacking attacks of different types that harm organizations of all kinds and create billions of dollars in business damage. Artificial intelligence and Natural Language Processing (NLP) has begun to be used by security companies - for example, SIEM (Security Information and Event Management) solutions. The more advanced of these solutions use AI and NLP to automatically sort the data in networks into high risk and low-risk information. This enables security teams to focus on the attacks that have the potential to do real harm to the organization, and not become victims of attacks such as Denial of Service (DoS), Malware and others.
网络安全领域面临着各种大规模黑客攻击的重大挑战，这些攻击损害到了很多组织，造成了数十亿美元的商业损失。网络安全公司已经开始使用AI和自然语言处理(NLP) ，例如，SIEM (Security Information and Event Management,安全信息和事件管理)解决方案。更高级的解决方案使用AI和自然语言处理将网络中的数据划分为高风险和低风险两类信息。这使得安全团队能够专注于对付那些有可能对组织造成真正伤害的攻击，不沦为分布式拒绝服务攻击、恶意软件和其他攻击的受害者。
=== Government ===
=== Government ===
政府
{{Main|Artificial intelligence in government}}
Artificial intelligence in government consists of applications and regulation. Artificial intelligence paired with [[facial recognition system]]s may be used for [[mass surveillance]]. This is already the case in some parts of China.<ref>{{Cite news|url=https://www.nytimes.com/2019/05/22/world/asia/china-surveillance-xinjiang.html|title=How China Uses High-Tech Surveillance to Subdue Minorities|first1=Chris|last1=Buckley|first2=Paul|last2=Mozur|date=22 May 2019|work=The New York Times}}</ref><ref>{{Cite web|url=http://social.techcrunch.com/2019/05/03/china-smart-city-exposed/|title=Security lapse exposed a Chinese smart city surveillance system}}</ref> An artificial intelligence has also competed in the Tama City [[AI mayor|mayoral elections]] in 2018.
Artificial intelligence in government consists of applications and regulation. Artificial intelligence paired with facial recognition systems may be used for mass surveillance. This is already the case in some parts of China. An artificial intelligence has also competed in the Tama City mayoral elections in 2018.
政府AI包括应用和管理。AI与人脸识别系统相结合可用于大规模监控。在中国的一些地区已经开始使用这种技术。一个AI还参与了2018年关都地区市长选举的角逐。
In 2019, the tech city of Bengaluru in India is set to deploy AI managed traffic signal systems across the 387 traffic signals in the city. This system will involve use of cameras to ascertain traffic density and accordingly calculate the time needed to clear the traffic volume which will determine the signal duration for vehicular traffic across streets.<ref>{{Cite web|url=https://nextbigwhat.com/ai-traffic-signals-to-be-installed-in-bengaluru-soon/|title=AI traffic signals to be installed in Bengaluru soon|date=2019-09-24|website=NextBigWhat|language=en-US|access-date=2019-10-01}}</ref>
In 2019, the tech city of Bengaluru in India is set to deploy AI managed traffic signal systems across the 387 traffic signals in the city. This system will involve use of cameras to ascertain traffic density and accordingly calculate the time needed to clear the traffic volume which will determine the signal duration for vehicular traffic across streets.
2019年，印度硅谷班加罗尔将在该市的387个交通信号灯上部署AI控制的交通信号系统。这个系统将使用摄像头来确定交通密度，并据此计算清除交通量所需的时间，决定街道上的车辆交通灯的持续时间。
=== Law-related professions ===
=== Law-related professions ===
与法律有关的专业
{{Main|Legal informatics#Artificial intelligence}}
Artificial intelligence (AI) is becoming a mainstay component of law-related professions. In some circumstances, this analytics-crunching technology is using algorithms and machine learning to do work that was previously done by entry-level lawyers.{{Citation needed|date=December 2019}}
Artificial intelligence (AI) is becoming a mainstay component of law-related professions. In some circumstances, this analytics-crunching technology is using algorithms and machine learning to do work that was previously done by entry-level lawyers.
AI正在成为法律相关专业的主要组成部分。有时人们通过AI分析处理技术使用算法和机器学习来完成以前由初级律师完成的工作。
In [[Electronic discovery|Electronic Discovery (eDiscovery)]], the industry has been focused on machine learning (predictive coding/technology assisted review), which is a subset of AI. To add to the soup of applications, Natural Language Processing (NLP) and Automated Speech Recognition (ASR) are also in vogue in the industry.<ref>{{Cite web|url=https://www.ft.com/content/fef40df0-4a6a-11e9-bde6-79eaea5acb64|title=AI learns to read Korean, so you don't have to|last=Croft|first=Jane|date=2019-05-02|website=Financial Times|language=en-GB|access-date=2019-12-19}}</ref>
In Electronic Discovery (eDiscovery), the industry has been focused on machine learning (predictive coding/technology assisted review), which is a subset of AI. To add to the soup of applications, Natural Language Processing (NLP) and Automated Speech Recognition (ASR) are also in vogue in the industry.
电子资料档案查询(eDiscovery)产业一直侧重机器学习(预测编码 / 技术辅助评审) ，这是AI的一个子领域。自然语言处理(NLP)和自动语音识别(ASR)也正在这个行业流行起来。
=== Video games ===
=== Video games ===
电子游戏
{{Main|Artificial intelligence (video games)}}
In video games, artificial intelligence is routinely used to generate dynamic purposeful behavior in [[non-player character]]s (NPCs). In addition, well-understood AI techniques are routinely used for [[pathfinding]]. Some researchers consider NPC AI in games to be a "solved problem" for most production tasks. Games with more atypical AI include the AI director of ''[[Left 4 Dead]]'' (2008) and the neuroevolutionary training of platoons in ''[[Supreme Commander 2]]'' (2010).<ref>{{cite news|url=https://www.economist.com/news/science-and-technology/21721890-games-help-them-understand-reality-why-ai-researchers-video-games|title=Why AI researchers like video games|website=The Economist|url-status=live|archiveurl=https://web.archive.org/web/20171005051028/https://www.economist.com/news/science-and-technology/21721890-games-help-them-understand-reality-why-ai-researchers-video-games|archivedate=5 October 2017|df=dmy-all}}</ref><ref>Yannakakis, G. N. (2012, May). Game AI revisited. In Proceedings of the 9th conference on Computing Frontiers (pp. 285–292). ACM.</ref>
In video games, artificial intelligence is routinely used to generate dynamic purposeful behavior in non-player characters (NPCs). In addition, well-understood AI techniques are routinely used for pathfinding. Some researchers consider NPC AI in games to be a "solved problem" for most production tasks. Games with more atypical AI include the AI director of Left 4 Dead (2008) and the neuroevolutionary training of platoons in Supreme Commander 2 (2010).
在视频游戏中，AI通常被用来让非玩家角色( non-player characters，NPCs)中做出动态的目的性行为。此外，还常用简单的AI技术寻路。一些研究人员认为，对于大多数生产任务来说，游戏中的 NPC AI 是一个“解决了的问题”。含更多非典型 AI 的游戏有《求生之路》（Left 4 Dead,2008)中的 AI 导演和《最高指挥官2》(Supreme Commander 2,2010)中的 '''<font color=#32cd32>对排神经进化训练</font>'''。
|year=2015
--[[用户:Thingamabob|Thingamabob]]（[[用户讨论:Thingamabob|讨论]]） neuroevolutionary training of platoons 未找到标准翻译
=== Military ===
=== Military ===
军事
{{Further|Artificial intelligence arms race|Lethal autonomous weapon|Unmanned combat aerial vehicle}}
The United States and other nations are developing AI applications for a range of military functions.<ref name=":2">{{Cite book|last=Congressional Research Service|first=|url=https://fas.org/sgp/crs/natsec/R45178.pdf|title=Artificial Intelligence and National Security|publisher=Congressional Research Service|year=2019|isbn=|location=Washington, DC|pages=}}[[Template:PD-notice|PD-notice]]</ref> The main military applications of Artificial Intelligence and Machine Learning are to enhance C2, Communications, Sensors, Integration and Interoperability.<ref name="AI">{{cite web|title=Artificial intelligence as the basis of future control networks.|url=https://www.researchgate.net/publication/334573170|last=Slyusar|first=Vadym|date=2019|work=Preprint}}</ref> AI research is underway in the fields of intelligence collection and analysis, logistics, cyber operations, information operations, command and control, and in a variety of semiautonomous and autonomous vehicles.<ref name=":2" /> Artificial Intelligence technologies enable coordination of sensors and effectors, threat detection and identification, marking of enemy positions, target acquisition, coordination and deconfliction of distributed Join Fires between networked combat vehicles and tanks also inside Manned and Unmanned Teams (MUM-T).<ref name=AI /> AI has been incorporated into military operations in Iraq and Syria.<ref name=":2" />
The United States and other nations are developing AI applications for a range of military functions. The main military applications of Artificial Intelligence and Machine Learning are to enhance C2, Communications, Sensors, Integration and Interoperability. AI research is underway in the fields of intelligence collection and analysis, logistics, cyber operations, information operations, command and control, and in a variety of semiautonomous and autonomous vehicles. Artificial Intelligence technologies enable coordination of sensors and effectors, threat detection and identification, marking of enemy positions, target acquisition, coordination and deconfliction of distributed Join Fires between networked combat vehicles and tanks also inside Manned and Unmanned Teams (MUM-T). AI has been incorporated into military operations in Iraq and Syria.
美国和其他国家正在为一系列军事功能开发AI应用程序。AI和机器学习的主要军事应用是增强 C2、通信、传感器、集成和互操作性。情报收集和分析、后勤、网络操作、信息操作、指挥和控制以及各种半自动和自动车辆等领域正在进行AI研究。AI技术能够协调传感器和效应器、探测威胁和识别、标记敌人阵地、目标获取、协调和消除有人和无人小组(MUM-T)、联网作战车辆和坦克内部的分布式联合火力。伊拉克和叙利亚的军事行动采用了AI。
| cs.CL 类
Worldwide annual military spending on robotics rose from US$5.1 billion in 2010 to US$7.5 billion in 2015.<ref>{{cite news|title=Getting to grips with military robotics|url=https://www.economist.com/news/special-report/21735478-autonomous-robots-and-swarms-will-change-nature-warfare-getting-grips|accessdate=7 February 2018|work=The Economist|date=25 January 2018|language=en}}</ref><ref>{{cite web|title=Autonomous Systems: Infographic|url=https://www.siemens.com/innovation/en/home/pictures-of-the-future/digitalization-and-software/autonomous-systems-infographic.html|website=siemens.com|accessdate=7 February 2018|language=en}}</ref> Military drones capable of autonomous action are widely considered a useful asset.<ref>{{Cite web|url=https://www.cnas.org/publications/reports/understanding-chinas-ai-strategy|title=Understanding China's AI Strategy|last=Allen|first=Gregory|date=February 6, 2019|website=www.cnas.org/publications/reports/understanding-chinas-ai-strategy|publisher=Center for a New American Security|archive-url=https://web.archive.org/web/20190317004017/https://www.cnas.org/publications/reports/understanding-chinas-ai-strategy|archive-date=March 17, 2019|url-status=|access-date=March 17, 2019}}</ref> Many artificial intelligence researchers seek to distance themselves from military applications of AI.<ref>{{cite news|last1=Metz|first1=Cade|title=Pentagon Wants Silicon Valley's Help on A.I.|url=https://www.nytimes.com/2018/03/15/technology/military-artificial-intelligence.html|accessdate=19 March 2018|work=The New York Times|date=15 March 2018}}</ref>
Worldwide annual military spending on robotics rose from US$5.1 billion in 2010 to US$7.5 billion in 2015. Military drones capable of autonomous action are widely considered a useful asset. Many artificial intelligence researchers seek to distance themselves from military applications of AI.
全球每年在机器人方面的军费开支从2010年的51亿美元增加到2015年的75亿美元。人们都认为具有自主行动能力的军用无人机是价值的。许多AI研究人员试图远离AI的军事应用。
=== Hospitality ===
=== Hospitality ===
服务
In the hospitality industry, Artificial Intelligence based solutions are used to reduce staff load and increase efficiency<ref>{{cite web|title=Role of AI in travel and Hospitality Industry|url=https://www.infosys.com/industries/travel-hospitality/documents/ai-travel-hospitality.pdf|accessdate=14 January 2020|work=Infosys|date=2018}}</ref> by cutting repetitive tasks frequency, trends analysis, guest interaction, and customer needs prediction.<ref>{{cite web|title=Advanced analytics in hospitality|url=https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/advanced-analytics-in-hospitality|accessdate=14 January 2020|work=McKinsey & Company|date=2017}}</ref> Hotel services backed by Artificial Intelligence are represented in the form of a chatbot,<ref>{{cite web|title=Current applications of Artificial Intelligence in tourism and hospitality|url=https://www.researchgate.net/publication/333242550|accessdate=14 January 2020|work=Sinteza|date=2019}}</ref> application, virtual voice assistant and service robots.
In the hospitality industry, Artificial Intelligence based solutions are used to reduce staff load and increase efficiency by cutting repetitive tasks frequency, trends analysis, guest interaction, and customer needs prediction. Hotel services backed by Artificial Intelligence are represented in the form of a chatbot, application, virtual voice assistant and service robots.
在服务业，基于AI的解决方案通过减少重复性任务的频率、分析趋势、与客户活动和预测客户需求来减少员工负担和提高效率。使用AI的酒店服务以聊天机器人、应用程序、虚拟语音助手和服务机器人的形式呈现。
=== Audit ===
=== Audit ===
审计
| 标题序列到序列学习与神经网络
For financial statements audit, AI makes continuous audit possible. AI tools could analyze many sets of different information immediately. The potential benefit would be the overall audit risk will be reduced, the level of assurance will be increased and the time duration of audit will be reduced.<ref>{{cite journal|last1=Chang|first1=Hsihui|last2=Kao|first2=Yi-Ching|last3=Mashruwala|first3=Raj|last4=Sorensen|first4=Susan M.|title=Technical Inefficiency, Allocative Inefficiency, and Audit Pricing|journal=Journal of Accounting, Auditing & Finance|volume=33|issue=4|date=10 April 2017|pages=580–600|doi=10.1177/0148558X17696760}}</ref>
For financial statements audit, AI makes continuous audit possible. AI tools could analyze many sets of different information immediately. The potential benefit would be the overall audit risk will be reduced, the level of assurance will be increased and the time duration of audit will be reduced.
在财务报表审计这方面，AI 可以做到持续审计。AI工具可以迅速分析多组不同的信息。可能的好处是减少了总体审计风险，提高审计水平，缩短审计时间。
=== Advertising ===
=== Advertising ===
广告
It is possible to use AI to predict or generalize the behavior of customers from their [[digital footprints]] in order to target them with personalized promotions or build customer personas automatically.<ref name="Matz et al 2017">Matz, S. C., et al. "Psychological targeting as an effective approach to digital mass persuasion." Proceedings of the National Academy of Sciences (2017): 201710966.</ref> A documented case reports that online gambling companies were using AI to improve customer targeting.<ref>{{cite web |last1=Busby |first1=Mattha |title=Revealed: how bookies use AI to keep gamblers hooked |url=https://www.theguardian.com/technology/2018/apr/30/bookies-using-ai-to-keep-gamblers-hooked-insiders-say |website=the Guardian |language=en |date=30 April 2018}}</ref>
It is possible to use AI to predict or generalize the behavior of customers from their digital footprints in order to target them with personalized promotions or build customer personas automatically. A documented case reports that online gambling companies were using AI to improve customer targeting.
AI通过客户的数字足迹预测或归纳客户的行为，投放定制广告或者自动构建顾客角色。有记录报告称，线上赌博公司正在使用AI来改善客户定位功能。
Moreover, the application of [[Personality computing]] AI models can help reducing the cost of advertising campaigns by adding psychological targeting to more traditional sociodemographic or behavioral targeting.<ref name="Celli et al. 2017">Celli, Fabio, Pietro Zani Massani, and Bruno Lepri. "Profilio: Psychometric Profiling to Boost Social Media Advertising." Proceedings of the 2017 ACM on Multimedia Conference. ACM, 2017 [https://www.researchgate.net/publication/320542489_Profilio_Psychometric_Profiling_to_Boost_Social_Media_Advertising]</ref>
Moreover, the application of Personality computing AI models can help reducing the cost of advertising campaigns by adding psychological targeting to more traditional sociodemographic or behavioral targeting.
此外，个性计算AI模型通过结合心理定位和传统的社会人口学或行为定位方法，帮助降低广告投放的成本。
=== Art ===
=== Art ===
艺术
{{Further|Computer art}}
Artificial Intelligence has inspired numerous creative applications including its usage to produce visual art. The exhibition "Thinking Machines: Art and Design in the Computer Age, 1959–1989" at MoMA<ref name="moma">{{Cite web|url=https://www.moma.org/calendar/exhibitions/3863|title=Thinking Machines: Art and Design in the Computer Age, 1959–1989|website=The Museum of Modern Art|language=en|access-date=2019-07-23}}</ref> provides a good overview of the historical applications of AI for art, architecture, and design. Recent exhibitions showcasing the usage of AI to produce art include the Google-sponsored benefit and auction at the Gray Area Foundation in San Francisco, where artists experimented with the [[DeepDream]] algorithm<ref name = wp1>[https://www.washingtonpost.com/news/innovations/wp/2016/03/10/googles-psychedelic-paint-brush-raises-the-oldest-question-in-art/ Retrieved July 29]</ref> and the exhibition "Unhuman: Art in the Age of AI," which took place in Los Angeles and Frankfurt in the fall of 2017.<ref name = sf>{{cite web|url=https://www.statefestival.org/program/2017/unhuman-art-in-the-age-of-ai |title=Unhuman: Art in the Age of AI – State Festival |publisher=Statefestival.org |date= |accessdate=2018-09-13}}</ref><ref name="artsy">{{Cite web|url=https://www.artsy.net/article/artsy-editorial-hard-painting-made-computer-human|title=It's Getting Hard to Tell If a Painting Was Made by a Computer or a Human|last=Chun|first=Rene|date=2017-09-21|website=Artsy|language=en|access-date=2019-07-23}}</ref> In the spring of 2018, the Association of Computing Machinery dedicated a special magazine issue to the subject of computers and art highlighting the role of machine learning in the arts.<ref name = acm>[https://dl.acm.org/citation.cfm?id=3204480.3186697 Retrieved July 29]</ref> The Austrian [[Ars Electronica]] and [[Museum of Applied Arts, Vienna]] opened exhibitions on AI in 2019.<ref name="Ars Electronica Exhibition ''Understanding AI''">{{Cite web|url=https://ars.electronica.art/center/en/exhibitions/ai/ |access-date=September 2019}}</ref><ref name="Museum of Applied Arts Exhibition ''Uncanny Values''">{{Cite web|url=https://www.mak.at/en/program/exhibitions/uncanny_values |access-date=October 2019|title=MAK Wien - MAK Museum Wien}}</ref> The Ars Electronica's 2019 festival "Out of the box" extensively thematized the role of arts for a sustainable societal transformation with AI.<ref name="European Platform for Digital Humanism">{{Cite web|url=https://ars.electronica.art/outofthebox/en/digital-humanism-conf/ |access-date=September 2019}}</ref>
Artificial Intelligence has inspired numerous creative applications including its usage to produce visual art. The exhibition "Thinking Machines: Art and Design in the Computer Age, 1959–1989" at MoMA provides a good overview of the historical applications of AI for art, architecture, and design. Recent exhibitions showcasing the usage of AI to produce art include the Google-sponsored benefit and auction at the Gray Area Foundation in San Francisco, where artists experimented with the DeepDream algorithm and the exhibition "Unhuman: Art in the Age of AI," which took place in Los Angeles and Frankfurt in the fall of 2017. In the spring of 2018, the Association of Computing Machinery dedicated a special magazine issue to the subject of computers and art highlighting the role of machine learning in the arts. The Austrian Ars Electronica and Museum of Applied Arts, Vienna opened exhibitions on AI in 2019. The Ars Electronica's 2019 festival "Out of the box" extensively thematized the role of arts for a sustainable societal transformation with AI.
AI催生了许多在如视觉艺术等领域的创造性应用。在纽约现代艺术博物馆举办的“思考机器: 计算机时代的艺术与设计，1959-1989”展览概述了艺术、建筑和设计的历史中AI的应用。最近的展览展示了AI在艺术创作中的应用，包括谷歌赞助的旧金山灰色地带基金会(Gray Area Foundation)的慈善拍卖会，艺术家们在拍卖会中尝试了 DeepDream 算法，以及2017年秋天在洛杉矶和法兰克福举办的“非人类: AI时代的艺术”展览。2018年春天，计算机协会发行了一期主题为计算机和艺术的特刊，着重展示了机器学习在艺术中的作用。奥地利电子艺术博物馆和维也纳应用艺术博物馆于2019年开设了AI展览。2019年的电子艺术节 “Out of the box”将AI艺术在可持续社会转型中的作用变成了一个主题。
== Philosophy and ethics ==
== Philosophy and ethics ==
哲学和伦理学
{{Main|Philosophy of artificial intelligence|Ethics of artificial intelligence}}
There are three philosophical questions related to AI:
There are three philosophical questions related to AI:
有三个与人工智能相关的哲学问题:
# Is [[artificial general intelligence]] possible? Can a machine solve any problem that a human being can solve using intelligence? Or are there hard limits to what a machine can accomplish?
Is artificial general intelligence possible? Can a machine solve any problem that a human being can solve using intelligence? Or are there hard limits to what a machine can accomplish?
通用人工智能可能实现吗？机器能解决任何人类智能能解决的问题吗？或者一台机器所能完成的事情是否有严格的界限？
# Are intelligent machines dangerous? How can we ensure that machines behave ethically and that they are used ethically?
Are intelligent machines dangerous? How can we ensure that machines behave ethically and that they are used ethically?
智能机器危险吗？我们怎样才能确保机器的行为和使用机器的过程符合道德规范？
# Can a machine have a [[mind]], [[consciousness]] and [[philosophy of mind|mental states]] in exactly the same sense that human beings do? Can a machine be [[Sentience|sentient]], and thus deserve certain rights? Can a machine [[intention]]ally cause harm?
Can a machine have a mind, consciousness and mental states in exactly the same sense that human beings do? Can a machine be sentient, and thus deserve certain rights? Can a machine intentionally cause harm?
机器能否拥有与人类完全相同的思维、意识和精神状态？一台机器是否能拥有直觉，因此得到某些权利？机器会做出刻意伤害吗？
=== The limits of artificial general intelligence ===
=== The limits of artificial general intelligence ===
人工智能的局限性
{{Main|Philosophy of AI|Turing test|Physical symbol systems hypothesis|Dreyfus' critique of AI|The Emperor's New Mind|AI effect}}
Can a machine be intelligent? Can it "think"?
Can a machine be intelligent? Can it "think"?
机器是智能的吗？它能“思考”吗？
| cs.CL 类
;''[[Computing Machinery and Intelligence|Alan Turing's "polite convention"]]'': We need not decide if a machine can "think"; we need only decide if a machine can act as intelligently as a human being. This approach to the philosophical problems associated with artificial intelligence forms the basis of the [[Turing test]].<ref name="Turing test"/>
Alan Turing's "polite convention": We need not decide if a machine can "think"; we need only decide if a machine can act as intelligently as a human being. This approach to the philosophical problems associated with artificial intelligence forms the basis of the Turing test.
阿兰 · 图灵的'''<font color=#32cd32>“礼貌惯例”</font>''' : 我们不需要决定一台机器是否可以“思考” ; 我们只需要决定一台机器是否可以像人一样聪明地行动。这个AI相关的哲学问题的答案成为了图灵测试的基础。
--[[用户:Thingamabob|Thingamabob]]（[[用户讨论:Thingamabob|讨论]]）polite convention未找到标准翻译
;''The [[Dartmouth Workshop|Dartmouth proposal]]'': "Every aspect of learning or any other feature of intelligence can be so precisely described that a machine can be made to simulate it." This conjecture was printed in the proposal for the Dartmouth Conference of 1956.<ref name="Dartmouth proposal"/>
The Dartmouth proposal: "Every aspect of learning or any other feature of intelligence can be so precisely described that a machine can be made to simulate it." This conjecture was printed in the proposal for the Dartmouth Conference of 1956.
达特茅斯学院提出: “可以通过准确地描述学习的每个方面或智能的任何特征，使得一台机器可以模拟学习和智能。”这个猜想被写在了1956年达特茅斯学院会议的提案中。
;''[[Physical symbol system|Newell and Simon's physical symbol system hypothesis]]'': "A physical symbol system has the necessary and sufficient means of general intelligent action." Newell and Simon argue that intelligence consists of formal operations on symbols.<ref name="Physical symbol system hypothesis"/> [[Hubert Dreyfus]] argued that, on the contrary, human expertise depends on unconscious instinct rather than conscious symbol manipulation and on having a "feel" for the situation rather than explicit symbolic knowledge. (See [[Dreyfus' critique of AI]].)<ref>
Newell and Simon's physical symbol system hypothesis: "A physical symbol system has the necessary and sufficient means of general intelligent action." Newell and Simon argue that intelligence consists of formal operations on symbols. Hubert Dreyfus argued that, on the contrary, human expertise depends on unconscious instinct rather than conscious symbol manipulation and on having a "feel" for the situation rather than explicit symbolic knowledge. (See Dreyfus' critique of AI.)<ref>
纽威尔和西蒙的物理符号系统假说: 物理符号系统具有通用智能行为的充要途径。纽威尔和西蒙认为智能由符号形式的运算组成。休伯特·德雷福斯)则相反地认为，人类的知识依赖于无意识的本能，而不是有意识的符号运算;依赖于对情境的“感觉”，而不是明确的符号知识。(参见德雷福斯对人工智能的批评。)
Dreyfus criticized the [[necessary and sufficient|necessary]] condition of the [[physical symbol system]] hypothesis, which he called the "psychological assumption": "The mind can be viewed as a device operating on bits of information according to formal rules." {{Harv|Dreyfus|1992|p=156}}</ref><ref name="Dreyfus' critique"/>
Dreyfus criticized the necessary condition of the physical symbol system hypothesis, which he called the "psychological assumption": "The mind can be viewed as a device operating on bits of information according to formal rules." </ref>
德莱弗斯批评了他称之为“心理假设”物理符号系统假说的必要条件: “头脑可以被看作是一种按照形式化规则，用信息位运算的机器。”
;''Gödelian arguments'': [[Gödel]] himself,<ref name="Gödel himself"/> [[John Lucas (philosopher)|John Lucas]] (in 1961) and [[Roger Penrose]] (in a more detailed argument from 1989 onwards) made highly technical arguments that human mathematicians can consistently see the truth of their own "Gödel statements" and therefore have computational abilities beyond that of mechanical Turing machines.<ref name="The mathematical objection"/> However, some people do not agree with the "Gödelian arguments".<ref>{{cite web|author1=Graham Oppy|title=Gödel's Incompleteness Theorems|url=http://plato.stanford.edu/entries/goedel-incompleteness/#GdeArgAgaMec|website=[[Stanford Encyclopedia of Philosophy]]|accessdate=27 April 2016|date=20 January 2015|quote=These Gödelian anti-mechanist arguments are, however, problematic, and there is wide consensus that they fail.|author1-link=Graham Oppy}}</ref><ref>{{cite book|author1=Stuart J. Russell|author2-link=Peter Norvig|author2=Peter Norvig|title=Artificial Intelligence: A Modern Approach|date=2010|publisher=[[Prentice Hall]]|location=Upper Saddle River, NJ|isbn=978-0-13-604259-4|edition=3rd|chapter=26.1.2: Philosophical Foundations/Weak AI: Can Machines Act Intelligently?/The mathematical objection|quote=even if we grant that computers have limitations on what they can prove, there is no evidence that humans are immune from those limitations.|title-link=Artificial Intelligence: A Modern Approach|author1-link=Stuart J. Russell}}</ref><ref>Mark Colyvan. An introduction to the philosophy of mathematics. [[Cambridge University Press]], 2012. From 2.2.2, 'Philosophical significance of Gödel's incompleteness results': "The accepted wisdom (with which I concur) is that the Lucas-Penrose arguments fail."</ref>
Gödelian arguments: Gödel himself,
哥德尔的观点
哥德尔本人、约翰·卢卡斯(在1961年)和罗杰·彭罗斯(在1989年以后的一个更详细的争论中)提出了高度技术性的论点，认为人类数学家始终可以看到他们自己的“'''<font color=#ff8000>哥德尔不完备定理 Gödel Satements</font>'''”的真实性，因此计算能力超过机械图灵机。然而，也有一些人不同意“哥德尔不完备定理”。
;''The [[artificial brain]] argument'': The brain can be simulated by machines and because brains are intelligent, simulated brains must also be intelligent; thus machines can be intelligent. [[Hans Moravec]], [[Ray Kurzweil]] and others have argued that it is technologically feasible to copy the brain directly into hardware and software and that such a simulation will be essentially identical to the original.<ref name="Brain simulation"/>
The artificial brain argument: The brain can be simulated by machines and because brains are intelligent, simulated brains must also be intelligent; thus machines can be intelligent. Hans Moravec, Ray Kurzweil and others have argued that it is technologically feasible to copy the brain directly into hardware and software and that such a simulation will be essentially identical to the original.
人工大脑的观点: 大脑可以被机器模拟，因为大脑是智能的，模拟的大脑也必须是智能的; 因此机器可以是智能的。汉斯·莫拉维克、雷·库兹韦尔和其他人认为，技术层面直接将大脑复制到硬件和软件上是可行的，而且这些拷贝在本质上和原来的大脑是没有区别的。
;''The [[AI effect]]'': Machines are ''already'' intelligent, but observers have failed to recognize it. When [[Deep Blue (chess computer)|Deep Blue]] beat [[Garry Kasparov]] in chess, the machine was acting intelligently. However, onlookers commonly discount the behavior of an artificial intelligence program by arguing that it is not "real" intelligence after all; thus "real" intelligence is whatever intelligent behavior people can do that machines still cannot. This is known as the AI Effect: "AI is whatever hasn't been done yet."<!--<ref name="AI Effect"/>-->
The AI effect: Machines are already intelligent, but observers have failed to recognize it. When Deep Blue beat Garry Kasparov in chess, the machine was acting intelligently. However, onlookers commonly discount the behavior of an artificial intelligence program by arguing that it is not "real" intelligence after all; thus "real" intelligence is whatever intelligent behavior people can do that machines still cannot. This is known as the AI Effect: "AI is whatever hasn't been done yet."<!---->
AI效应: 机器本来就是智能的，但是观察者却没有意识到这一点。当深蓝在国际象棋比赛中击败加里 · 卡斯帕罗夫时，机器就在做出智能行为。然而，旁观者通常对AI程序的行为不屑一顾，认为它根本不是“真正的”智能; 因此，“真正的”智能就是人任何类能够做到但机器仍然做不到的智能行为。这就是众所周知的AI效应: “AI就是一切尚未完成的事情"。
=== Potential harm{{anchor|Potential_risks_and_moral_reasoning}} ===
=== Potential harm ===
=== Evaluating progress ===
=== Potential harm ===
潜在危害
Widespread use of artificial intelligence could have [[unintended consequences]] that are dangerous or undesirable. Scientists from the [[Future of Life Institute]], among others, described some short-term research goals to see how AI influences the economy, the laws and ethics that are involved with AI and how to minimize AI security risks. In the long-term, the scientists have proposed to continue optimizing function while minimizing possible security risks that come along with new technologies.<ref>Russel, Stuart., Daniel Dewey, and Max Tegmark. Research Priorities for Robust and Beneficial Artificial Intelligence. AI Magazine 36:4 (2015). 8 December 2016.</ref>
Widespread use of artificial intelligence could have unintended consequences that are dangerous or undesirable. Scientists from the Future of Life Institute, among others, described some short-term research goals to see how AI influences the economy, the laws and ethics that are involved with AI and how to minimize AI security risks. In the long-term, the scientists have proposed to continue optimizing function while minimizing possible security risks that come along with new technologies.
AI的广泛使用可能会产生危险或导致意外后果。生命未来研究所(Future of Life Institute)等机构的科学家提出了一些短期研究目标，以此了解AI如何影响经济、与AI相关的法律和道德规范，以及如何将AI的安全风险降到最低。从长远来看，科学家们建议继续优化功能，同时最小化新技术带来的可能的安全风险。
The potential negative effects of AI and automation were a major issue for [[Andrew Yang]]'s [[Andrew Yang 2020 presidential campaign|2020 presidential campaign]] in the United States.<ref>{{Cite journal|url=https://www.wired.com/story/andrew-yangs-presidential-bid-is-so-very-21st-century/|title=Andrew Yang's Presidential Bid Is So Very 21st Century|journal=Wired|first=Matt|last=Simon|date=1 April 2019|via=www.wired.com}}</ref> Irakli Beridze, Head of the Centre for Artificial Intelligence and Robotics at UNICRI, United Nations, has expressed that "I think the dangerous applications for AI, from my point of view, would be criminals or large terrorist organizations using it to disrupt large processes or simply do pure harm. [Terrorists could cause harm] via digital warfare, or it could be a combination of robotics, drones, with AI and other things as well that could be really dangerous. And, of course, other risks come from things like job losses. If we have massive numbers of people losing jobs and don't find a solution, it will be extremely dangerous. Things like lethal autonomous weapons systems should be properly governed — otherwise there's massive potential of misuse."<ref>{{Cite web | url=https://futurism.com/artificial-intelligence-experts-fear/amp |title = Five experts share what scares them the most about AI|date = 5 September 2018}}</ref>
The potential negative effects of AI and automation were a major issue for Andrew Yang's 2020 presidential campaign in the United States. Irakli Beridze, Head of the Centre for Artificial Intelligence and Robotics at UNICRI, United Nations, has expressed that "I think the dangerous applications for AI, from my point of view, would be criminals or large terrorist organizations using it to disrupt large processes or simply do pure harm. [Terrorists could cause harm] via digital warfare, or it could be a combination of robotics, drones, with AI and other things as well that could be really dangerous. And, of course, other risks come from things like job losses. If we have massive numbers of people losing jobs and don't find a solution, it will be extremely dangerous. Things like lethal autonomous weapons systems should be properly governed — otherwise there's massive potential of misuse."
AI和自动化潜在的负面影响在安德鲁杨2020年竞选美国总统的过程中体现出来。联合国 UNICRI AI和机器人中心主任伊拉克利·贝瑞德兹表示: ”我认为AI危害会体现在犯罪分子或大型恐怖组织利用AI破坏大型流程或通过数字战争造成损失，或者可能是机器人、无人机、AI以及其他可能非常危险的东西的结合。当然，其还有失业等风险。如果大量的人失去工作，而且没有解决方案，这将是极其危险的。致命的自主武器系统之类的东西应该得到合适的控制，否则就可能会被大量滥用。”
==== Existential risk ====
==== Existential risk ====
存在风险
{{Main|Existential risk from artificial general intelligence}}
Physicist [[Stephen Hawking]], [[Microsoft]] founder [[Bill Gates]], and [[SpaceX]] founder [[Elon Musk]] have expressed concerns about the possibility that AI could evolve to the point that humans could not control it, with Hawking theorizing that this could "[[Global catastrophic risk|spell the end of the human race]]".<ref>{{cite news|last1=Rawlinson|first1=Kevin|title=Microsoft's Bill Gates insists AI is a threat|url=https://www.bbc.co.uk/news/31047780|work=BBC News|accessdate=30 January 2015|url-status=live|archiveurl=https://web.archive.org/web/20150129183607/http://www.bbc.co.uk/news/31047780|archivedate=29 January 2015|df=dmy-all|date=2015-01-29}}</ref><ref name="Holley">{{Cite news|title = Bill Gates on dangers of artificial intelligence: 'I don't understand why some people are not concerned'|url = https://www.washingtonpost.com/news/the-switch/wp/2015/01/28/bill-gates-on-dangers-of-artificial-intelligence-dont-understand-why-some-people-are-not-concerned/|work= The Washington Post|date = 28 January 2015|access-date = 30 October 2015|issn = 0190-8286|first = Peter|last = Holley|url-status=live|archiveurl = https://web.archive.org/web/20151030054330/https://www.washingtonpost.com/news/the-switch/wp/2015/01/28/bill-gates-on-dangers-of-artificial-intelligence-dont-understand-why-some-people-are-not-concerned/|archivedate = 30 October 2015|df = dmy-all}}</ref><ref>{{Cite news|title = Elon Musk: artificial intelligence is our biggest existential threat|url = https://www.theguardian.com/technology/2014/oct/27/elon-musk-artificial-intelligence-ai-biggest-existential-threat|work= The Guardian|accessdate = 30 October 2015|first = Samuel|last = Gibbs|url-status=live|archiveurl = https://web.archive.org/web/20151030054330/http://www.theguardian.com/technology/2014/oct/27/elon-musk-artificial-intelligence-ai-biggest-existential-threat|archivedate = 30 October 2015|df = dmy-all|date = 2014-10-27}}</ref>
Physicist Stephen Hawking, Microsoft founder Bill Gates, and SpaceX founder Elon Musk have expressed concerns about the possibility that AI could evolve to the point that humans could not control it, with Hawking theorizing that this could "spell the end of the human race".
物理学家斯蒂芬 · 霍金、微软创始人比尔 · 盖茨和 SpaceX 公司创始人埃隆 · 马斯克对AI进化到人类无法控制的程度表示担忧，霍金认为这可能“会导致人类末日”。
The "imitation game" (an interpretation of the 1950 [[Turing test]] that assesses whether a computer can imitate a human) is nowadays considered too exploitable to be a meaningful benchmark.<ref>{{cite journal|last1=Schoenick|first1=Carissa|last2=Clark|first2=Peter|last3=Tafjord|first3=Oyvind|last4=Turney|first4=Peter|last5=Etzioni|first5=Oren|title=Moving beyond the Turing Test with the Allen AI Science Challenge|journal=Communications of the ACM|date=23 August 2017|volume=60|issue=9|pages=60–64|doi=10.1145/3122814|arxiv=1604.04315}}</ref> A derivative of the Turing test is the Completely Automated Public Turing test to tell Computers and Humans Apart ([[CAPTCHA]]). As the name implies, this helps to determine that a user is an actual person and not a computer posing as a human. In contrast to the standard Turing test, CAPTCHA is administered by a machine and targeted to a human as opposed to being administered by a human and targeted to a machine. A computer asks a user to complete a simple test then generates a grade for that test. Computers are unable to solve the problem, so correct solutions are deemed to be the result of a person taking the test. A common type of CAPTCHA is the test that requires the typing of distorted letters, numbers or symbols that appear in an image undecipherable by a computer.{{sfn|O'Brien|Marakas|2011}}
{{quote|text=The development of full artificial intelligence could spell the end of the human race. Once humans develop artificial intelligence, it will take off on its own and redesign itself at an ever-increasing rate. Humans, who are limited by slow biological evolution, couldn't compete and would be superseded.|author=[[Stephen Hawking]]<ref>{{Cite news|title = Stephen Hawking warns artificial intelligence could end mankind|url = https://www.bbc.com/news/technology-30290540|accessdate = 30 October 2015|url-status=live|archiveurl = https://web.archive.org/web/20151030054329/http://www.bbc.com/news/technology-30290540|archivedate = 30 October 2015|df = dmy-all|work = [[BBC News]]|date = 2014-12-02|last1 = Cellan-Jones|first1 = Rory}}</ref>}}
In his book ''[[Superintelligence: Paths, Dangers, Strategies|Superintelligence]]'', philosopher [[Nick Bostrom]] provides an argument that artificial intelligence will pose a threat to humankind. He argues that sufficiently intelligent AI, if it chooses actions based on achieving some goal, will exhibit [[Instrumental convergence|convergent]] behavior such as acquiring resources or protecting itself from being shut down. If this AI's goals do not fully reflect humanity's—one example is an AI told to compute as many digits of pi as possible—it might harm humanity in order to acquire more resources or prevent itself from being shut down, ultimately to better achieve its goal. Bostrom also emphasizes the difficulty of fully conveying humanity's values to an advanced AI. He uses the hypothetical example of giving an AI the goal to make humans smile to illustrate a misguided attempt. If the AI in that scenario were to become superintelligent, Bostrom argues, it may resort to methods that most humans would find horrifying, such as inserting "electrodes into the facial muscles of humans to cause constant, beaming grins" because that would be an efficient way to achieve its goal of making humans smile.<ref>{{cite web|url=https://www.ted.com/talks/nick_bostrom_what_happens_when_our_computers_get_smarter_than_we_are/transcript|title=What happens when our computers get smarter than we are?|first=Nick|last=Bostrom|publisher=[[TED (conference)]]|date=2015}}</ref> In his book ''[[Human Compatible]]'', AI researcher [[Stuart J. Russell]] echoes some of Bostrom's concerns while also proposing [[Human Compatible#Russell's three principles|an approach]] to developing provably beneficial machines focused on uncertainty and deference to humans,<ref name="HC">{{cite book |last=Russell |first=Stuart |date=October 8, 2019 |title=Human Compatible: Artificial Intelligence and the Problem of Control |url= |location=United States |publisher=Viking |page= |isbn=978-0-525-55861-3 |author-link=Stuart J. Russell |oclc=1083694322|title-link=Human Compatible }}</ref>{{rp|173}} possibly involving [[Reinforcement learning#Inverse reinforcement learning|inverse reinforcement learning]].<ref name="HC"/>{{rp|191–193}}
In his book Superintelligence, philosopher Nick Bostrom provides an argument that artificial intelligence will pose a threat to humankind. He argues that sufficiently intelligent AI, if it chooses actions based on achieving some goal, will exhibit convergent behavior such as acquiring resources or protecting itself from being shut down. If this AI's goals do not fully reflect humanity's—one example is an AI told to compute as many digits of pi as possible—it might harm humanity in order to acquire more resources or prevent itself from being shut down, ultimately to better achieve its goal. Bostrom also emphasizes the difficulty of fully conveying humanity's values to an advanced AI. He uses the hypothetical example of giving an AI the goal to make humans smile to illustrate a misguided attempt. If the AI in that scenario were to become superintelligent, Bostrom argues, it may resort to methods that most humans would find horrifying, such as inserting "electrodes into the facial muscles of humans to cause constant, beaming grins" because that would be an efficient way to achieve its goal of making humans smile. In his book Human Compatible, AI researcher Stuart J. Russell echoes some of Bostrom's concerns while also proposing an approach to developing provably beneficial machines focused on uncertainty and deference to humans, possibly involving inverse reinforcement learning.
在《超级智能》一书中，哲学家尼克 · 博斯特罗姆提出了一个AI将对人类构成威胁的论点。他认为，如果足够智能的AI选择有目标地行动，它将表现出收敛的行为，如获取资源或保护自己不被关机。如果这个AI的目标没有人性，比如一个AI被告知要尽可能多地计算圆周率的位数，那么它可能会伤害人类，以便获得更多的资源或者防止自身被关闭，最终更好地实现目标。博斯特罗姆还强调了向高级AI充分传达人类价值观存在的困难。他假设了一个例子来说明一种南辕北辙的尝试: 给AI一个让人类微笑的目标。博斯特罗姆认为，如果这种情况下的AI变得非常聪明，它可能会采用大多数人类都会感到恐怖的方法，比如“在人类面部肌肉中插入电极，使其产生持续的笑容” ，因为这将是实现让人类微笑的目标的有效方法。AI研究人员斯图亚特.J.罗素在他的《人类相容》一书中回应了博斯特罗姆的一些担忧，同时也提出了一种开发可证明有益的机器可能涉及逆强化学习的方法，这种机器侧重解决不确定性和顺从人类的问题。
Concern over risk from artificial intelligence has led to some high-profile donations and investments. A group of prominent tech titans including [[Peter Thiel]], Amazon Web Services and Musk have committed $1 billion to [[OpenAI]], a nonprofit company aimed at championing responsible AI development.<ref>{{cite web|url=https://www.chicagotribune.com/bluesky/technology/ct-tech-titans-against-terminators-20151214-story.html|title=Tech titans like Elon Musk are spending $1 billion to save you from terminators|first=Washington|last=Post|url-status=live|archiveurl=https://web.archive.org/web/20160607121118/http://www.chicagotribune.com/bluesky/technology/ct-tech-titans-against-terminators-20151214-story.html|archivedate=7 June 2016|df=dmy-all}}</ref> The opinion of experts within the field of artificial intelligence is mixed, with sizable fractions both concerned and unconcerned by risk from eventual superhumanly-capable AI.<ref>{{cite journal
Concern over risk from artificial intelligence has led to some high-profile donations and investments. A group of prominent tech titans including Peter Thiel, Amazon Web Services and Musk have committed $1 billion to OpenAI, a nonprofit company aimed at championing responsible AI development. The opinion of experts within the field of artificial intelligence is mixed, with sizable fractions both concerned and unconcerned by risk from eventual superhumanly-capable AI.<ref>{{cite journal
其他技术行业的领导者相信AI在目前的形式下是有益的，并将继续帮助人类。甲骨文首席执行官马克 · 赫德表示，AI“实际上将创造更多的就业机会，而不是减少就业机会” ，因为管理AI系统需要人力。Facebook 首席执行官马克 · 扎克伯格相信AI将“解锁大量正面的东西” ，比如治愈疾病和提高自动驾驶汽车的安全性。2015年1月，马斯克向未来生命研究所捐赠了1000万美元，用于研究AI决策。该研究所的目标是“用智能管理”日益增长的技术力量。马斯克还为 DeepMind 和 Vicarious 等开发AI的公司提供资金，以“跟进AI的发展”因为认为这个领域“可能会产生危险的后果”。
== Applications ==
}}</ref> Other technology industry leaders believe that artificial intelligence is helpful in its current form and will continue to assist humans. Oracle CEO [[Mark Hurd]] has stated that AI "will actually create more jobs, not less jobs" as humans will be needed to manage AI systems.<ref>{{Cite web|url=https://searcherp.techtarget.com/news/252460208/Oracle-CEO-Mark-Hurd-sees-no-reason-to-fear-ERP-AI|title=Oracle CEO Mark Hurd sees no reason to fear ERP AI|website=SearchERP|language=en|access-date=2019-05-06}}</ref> Facebook CEO [[Mark Zuckerberg]] believes AI will "unlock a huge amount of positive things," such as curing disease and increasing the safety of autonomous cars.<ref>{{Cite web|url=https://www.businessinsider.com/mark-zuckerberg-shares-thoughts-elon-musks-ai-2018-5|title=Mark Zuckerberg responds to Elon Musk's paranoia about AI: 'AI is going to... help keep our communities safe.'|last=|first=|date=25 May 2018|website=Business Insider|access-date=2019-05-06}}</ref> In January 2015, Musk donated $10 million to the [[Future of Life Institute]] to fund research on understanding AI decision making. The goal of the institute is to "grow wisdom with which we manage" the growing power of technology. Musk also funds companies developing artificial intelligence such as [[DeepMind]] and [[Vicarious (company)|Vicarious]] to "just keep an eye on what's going on with artificial intelligence.<ref>{{cite web|title = The mysterious artificial intelligence company Elon Musk invested in is developing game-changing smart computers|url = http://www.techinsider.io/mysterious-artificial-intelligence-company-elon-musk-investment-2015-10|website = Tech Insider|accessdate = 30 October 2015|url-status=live|archiveurl = https://web.archive.org/web/20151030165333/http://www.techinsider.io/mysterious-artificial-intelligence-company-elon-musk-investment-2015-10|archivedate = 30 October 2015|df = dmy-all}}</ref> I think there is potentially a dangerous outcome there."<ref>{{cite web|title = Musk-Backed Group Probes Risks Behind Artificial Intelligence|url = https://www.bloomberg.com/news/articles/2015-07-01/musk-backed-group-probes-risks-behind-artificial-intelligence|website = Bloomberg.com|accessdate = 30 October 2015|first = Jack|last = Clark|url-status=live|archiveurl = https://web.archive.org/web/20151030202356/http://www.bloomberg.com/news/articles/2015-07-01/musk-backed-group-probes-risks-behind-artificial-intelligence|archivedate = 30 October 2015|df = dmy-all}}</ref><ref>{{cite web|title = Elon Musk Is Donating $10M Of His Own Money To Artificial Intelligence Research|url = http://www.fastcompany.com/3041007/fast-feed/elon-musk-is-donating-10m-of-his-own-money-to-artificial-intelligence-research|website = Fast Company|accessdate = 30 October 2015|url-status=live|archiveurl = https://web.archive.org/web/20151030202356/http://www.fastcompany.com/3041007/fast-feed/elon-musk-is-donating-10m-of-his-own-money-to-artificial-intelligence-research|archivedate = 30 October 2015|df = dmy-all|date = 2015-01-15}}</ref>
}}</ref> Other technology industry leaders believe that artificial intelligence is helpful in its current form and will continue to assist humans. Oracle CEO Mark Hurd has stated that AI "will actually create more jobs, not less jobs" as humans will be needed to manage AI systems. Facebook CEO Mark Zuckerberg believes AI will "unlock a huge amount of positive things," such as curing disease and increasing the safety of autonomous cars. In January 2015, Musk donated $10 million to the Future of Life Institute to fund research on understanding AI decision making. The goal of the institute is to "grow wisdom with which we manage" the growing power of technology. Musk also funds companies developing artificial intelligence such as DeepMind and Vicarious to "just keep an eye on what's going on with artificial intelligence. I think there is potentially a dangerous outcome there."
如果要实现不受控制的高级AI，假设的AI必须超越或者说在思想上超越整个人类，一小部分专家认为这种可能性在足够遥远未来才会出现，不值得研究。其他反对意见则以AI的角度来看，人类要么具有内在价值，要么具有可交流的价值。
For the danger of uncontrolled advanced AI to be realized, the hypothetical AI would have to overpower or out-think all of humanity, which a minority of experts argue is a possibility far enough in the future to not be worth researching.<ref>{{cite web|title = Is artificial intelligence really an existential threat to humanity?|url = http://thebulletin.org/artificial-intelligence-really-existential-threat-humanity8577|website = Bulletin of the Atomic Scientists|accessdate = 30 October 2015|url-status=live|archiveurl = https://web.archive.org/web/20151030054330/http://thebulletin.org/artificial-intelligence-really-existential-threat-humanity8577|archivedate = 30 October 2015|df = dmy-all|date = 2015-08-09}}</ref><ref>{{cite web|title = The case against killer robots, from a guy actually working on artificial intelligence|url = http://fusion.net/story/54583/the-case-against-killer-robots-from-a-guy-actually-building-ai/|website = Fusion.net|accessdate = 31 January 2016|url-status=live|archiveurl = https://web.archive.org/web/20160204175716/http://fusion.net/story/54583/the-case-against-killer-robots-from-a-guy-actually-building-ai/|archivedate = 4 February 2016|df = dmy-all}}</ref> Other counterarguments revolve around humans being either intrinsically or convergently valuable from the perspective of an artificial intelligence.<ref>{{cite web|title = Will artificial intelligence destroy humanity? Here are 5 reasons not to worry.|url = https://www.vox.com/2014/8/22/6043635/5-reasons-we-shouldnt-worry-about-super-intelligent-computers-taking|website = Vox|accessdate = 30 October 2015|url-status=live|archiveurl = https://web.archive.org/web/20151030092203/http://www.vox.com/2014/8/22/6043635/5-reasons-we-shouldnt-worry-about-super-intelligent-computers-taking|archivedate = 30 October 2015|df = dmy-all|date = 2014-08-22}}</ref>
For the danger of uncontrolled advanced AI to be realized, the hypothetical AI would have to overpower or out-think all of humanity, which a minority of experts argue is a possibility far enough in the future to not be worth researching. Other counterarguments revolve around humans being either intrinsically or convergently valuable from the perspective of an artificial intelligence.
为了实现不受控制的先进人工智能的危险，假设的人工智能必须超越或超越整个人类，一小部分专家认为这种可能性在未来足够遥远，不值得研究。其他反对意见则围绕着从人工智能的角度来看， '''<font color=#32cd32> 人类有内在或可聚合的价值。</font>'''
--[[用户:Thingamabob|Thingamabob]]（[[用户讨论:Thingamabob|讨论]]） 不太能翻译 Other counterarguments revolve around humans being either intrinsically or convergently valuable from the perspective of an artificial intelligence.一句
==== Devaluation of humanity ====
==== Devaluation of humanity ====
人性的贬值
{{Main|Computer Power and Human Reason}}
[[Joseph Weizenbaum]] wrote that AI applications cannot, by definition, successfully simulate genuine human empathy and that the use of AI technology in fields such as [[customer service]] or [[psychotherapy]]<ref>In the early 1970s, [[Kenneth Colby]] presented a version of Weizenbaum's [[ELIZA]] known as DOCTOR which he promoted as a serious therapeutic tool. {{Harv|Crevier|1993|pp=132–144}}</ref> was deeply misguided. Weizenbaum was also bothered that AI researchers (and some philosophers) were willing to view the human mind as nothing more than a computer program (a position now known as [[computationalism]]). To Weizenbaum these points suggest that AI research devalues human life.<ref name="Weizenbaum's critique"/>
Joseph Weizenbaum wrote that AI applications cannot, by definition, successfully simulate genuine human empathy and that the use of AI technology in fields such as customer service or psychotherapy was deeply misguided. Weizenbaum was also bothered that AI researchers (and some philosophers) were willing to view the human mind as nothing more than a computer program (a position now known as computationalism). To Weizenbaum these points suggest that AI research devalues human life.
约瑟夫·维森鲍姆写道，根据定义，AI应用程序不能模拟人类的同理心，并且在诸如客户服务或心理治疗等领域使用AI技术是严重错误。维森鲍姆还对AI研究人员(以及一些哲学家)将人类思维视为一个计算机程序(现在称为计算主义)而感到困扰。对维森鲍姆来说，这些观点表明AI研究贬低了人类的生命价值。
====Social justice====
====Social justice====
社会正义
{{further|Algorithmic bias}}
One concern is that AI programs may be programmed to be biased against certain groups, such as women and minorities, because most of the developers are wealthy Caucasian men.<ref>{{Cite web|url=https://www.channelnewsasia.com/news/commentary/artificial-intelligence-big-data-bias-hiring-loans-key-challenge-11097374|title=Commentary: Bad news. Artificial intelligence is biased|website=CNA}}</ref> Support for artificial intelligence is higher among men (with 47% approving) than women (35% approving).
One concern is that AI programs may be programmed to be biased against certain groups, such as women and minorities, because most of the developers are wealthy Caucasian men. Support for artificial intelligence is higher among men (with 47% approving) than women (35% approving).
人们担心的一个问题是，AI程序可能会对某些群体存在偏见，比如女性和少数民族，因为大多数开发者都是富有的白人男性。男性对AI的支持率(47%)高于女性(35%)。
Algorithms have a host of applications in today's legal system already, assisting officials ranging from judges to parole officers and public defenders in gauging the predicted likelihood of recidivism of defendants.<ref name="propublica.org">{{Cite web|url=https://www.propublica.org/article/how-we-analyzed-the-compas-recidivism-algorithm|title=How We Analyzed the COMPAS Recidivism Algorithm|last=Jeff Larson|first=Julia Angwin|date=2016-05-23|website=ProPublica|language=en|access-date=2019-07-23}}</ref> COMPAS (an acronym for Correctional Offender Management Profiling for Alternative Sanctions) counts among the most widely utilized commercially available solutions.<ref name="propublica.org"/> It has been suggested that COMPAS assigns an exceptionally elevated risk of recidivism to black defendants while, conversely, ascribing low risk estimate to white defendants significantly more often than statistically expected.<ref name="propublica.org"/>
Algorithms have a host of applications in today's legal system already, assisting officials ranging from judges to parole officers and public defenders in gauging the predicted likelihood of recidivism of defendants. COMPAS (an acronym for Correctional Offender Management Profiling for Alternative Sanctions) counts among the most widely utilized commercially available solutions. It has been suggested that COMPAS assigns an exceptionally elevated risk of recidivism to black defendants while, conversely, ascribing low risk estimate to white defendants significantly more often than statistically expected.
算法在今天的法律体系中已经有了大量的应用，它能协助无论是法官还是假释官员，抑或是评估被告再次犯罪的可能性的公设辩护人。COMPAS(Correctional Offender Management Profiling for Alternative Sanctions，替代性制裁的惩罚性罪犯管理分析的首字母缩写)是商业上使用最广泛的解决办法之一。有人指出，COMPAS 对黑人被告累犯风险的评估数值非常高，而相反的，白人被告低风险估计的频率明显高于统计学期望。
=== Healthcare ===
==== Decrease in demand for human labor ====
=== Healthcare ===
==== Decrease in demand for human labor ====
减少对人力劳动的需求
{{Main|Artificial intelligence in healthcare}}
{{Further|Technological unemployment#21st century}}
[[File:Laproscopic Surgery Robot.jpg|thumb| A patient-side surgical arm of [[Da Vinci Surgical System]]]]AI in healthcare is often used for classification, whether to automate initial evaluation of a CT scan or EKG or to identify high-risk patients for population health. The breadth of applications is rapidly increasing.
The relationship between automation and employment is complicated. While automation eliminates old jobs, it also creates new jobs through micro-economic and macro-economic effects.<ref>E McGaughey, 'Will Robots Automate Your Job Away? Full Employment, Basic Income, and Economic Democracy' (2018) [https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3044448 SSRN, part 2(3)]</ref> Unlike previous waves of automation, many middle-class jobs may be eliminated by artificial intelligence; ''[[The Economist]]'' states that "the worry that AI could do to white-collar jobs what steam power did to blue-collar ones during the Industrial Revolution" is "worth taking seriously".<ref>{{cite news|title=Automation and anxiety|url=https://www.economist.com/news/special-report/21700758-will-smarter-machines-cause-mass-unemployment-automation-and-anxiety|accessdate=13 January 2018|work=The Economist|date=9 May 2015}}</ref> Subjective estimates of the risk vary widely; for example, Michael Osborne and [[Carl Benedikt Frey]] estimate 47% of U.S. jobs are at "high risk" of potential automation, while an OECD report classifies only 9% of U.S.<!-- see report p. 33 table 4; 9% is both the OECD average and the US average --> jobs as "high risk".<ref>{{cite news|last1=Lohr|first1=Steve|title=Robots Will Take Jobs, but Not as Fast as Some Fear, New Report Says|url=https://www.nytimes.com/2017/01/12/technology/robots-will-take-jobs-but-not-as-fast-as-some-fear-new-report-says.html|accessdate=13 January 2018|work=The New York Times|date=2017}}</ref><ref>{{Cite journal|date=1 January 2017|title=The future of employment: How susceptible are jobs to computerisation?|journal=Technological Forecasting and Social Change|volume=114|pages=254–280|doi=10.1016/j.techfore.2016.08.019|issn=0040-1625|last1=Frey|first1=Carl Benedikt|last2=Osborne|first2=Michael A|citeseerx=10.1.1.395.416}}</ref><ref>Arntz, Melanie, Terry Gregory, and Ulrich Zierahn. "The risk of automation for jobs in OECD countries: A comparative analysis." OECD Social, Employment, and Migration Working Papers 189 (2016). p. 33.</ref> Jobs at extreme risk range from paralegals to fast food cooks, while job demand is likely to increase for care-related professions ranging from personal healthcare to the clergy.<ref>{{cite news|last1=Mahdawi|first1=Arwa|title=What jobs will still be around in 20 years? Read this to prepare your future|url=https://www.theguardian.com/us-news/2017/jun/26/jobs-future-automation-robots-skills-creative-health|accessdate=13 January 2018|work=The Guardian|date=26 June 2017}}</ref> Author [[Martin Ford (author)|Martin Ford]] and others go further and argue that many jobs are routine, repetitive and (to an AI) predictable; Ford warns that these jobs may be automated in the next couple of decades, and that many of the new jobs may not be "accessible to people with average capability", even with retraining. Economists point out that in the past technology has tended to increase rather than reduce total employment, but acknowledge that "we're in uncharted territory" with AI.<ref name="guardian jobs debate">{{cite news|last1=Ford|first1=Martin|last2=Colvin|first2=Geoff|title=Will robots create more jobs than they destroy?|url=https://www.theguardian.com/technology/2015/sep/06/will-robots-create-destroy-jobs|accessdate=13 January 2018|work=The Guardian|date=6 September 2015}}</ref>
The relationship between automation and employment is complicated. While automation eliminates old jobs, it also creates new jobs through micro-economic and macro-economic effects. Unlike previous waves of automation, many middle-class jobs may be eliminated by artificial intelligence; The Economist states that "the worry that AI could do to white-collar jobs what steam power did to blue-collar ones during the Industrial Revolution" is "worth taking seriously". Subjective estimates of the risk vary widely; for example, Michael Osborne and Carl Benedikt Frey estimate 47% of U.S. jobs are at "high risk" of potential automation, while an OECD report classifies only 9% of U.S.<!-- see report p. 33 table 4; 9% is both the OECD average and the US average --> jobs as "high risk". Jobs at extreme risk range from paralegals to fast food cooks, while job demand is likely to increase for care-related professions ranging from personal healthcare to the clergy. Author Martin Ford and others go further and argue that many jobs are routine, repetitive and (to an AI) predictable; Ford warns that these jobs may be automated in the next couple of decades, and that many of the new jobs may not be "accessible to people with average capability", even with retraining. Economists point out that in the past technology has tended to increase rather than reduce total employment, but acknowledge that "we're in uncharted territory" with AI.
自动化与就业的关系是复杂的。自动化在减少过时工作的同时，也通过微观经济和宏观经济效应创造了新的就业机会。与以往的自动化浪潮不同，许多中产阶级的工作可能会被AI淘汰; 《经济学家》指出，“AI对白领工作的影响，就像工业革命时期蒸汽动力对蓝领工作的影响一样，需要我们正视”。对风险的主观估计差别很大，例如，迈克尔 · 奥斯本和卡尔 · 贝内迪克特 · 弗雷估计，美国47% 的工作有较高风险被自动化取代 ，而经合组织的报告认为美国仅有9% 的工作处于“高风险”状态。从律师助理到快餐厨师等职业都面临着极大的风险，而个人医疗保健、神职人员等护理相关职业的就业需求可能会增加。作家马丁•福特和其他人进一步指出，许多工作都是常规、重复的，对AI而言是可以预测的。福特警告道，这些工作可能在未来几十年内实现自动化，而且即便对失业人员进行再培训，许多能力一般的人也不能获得新工作。经济学家指出，在过去技术往往会增加而不是减少总就业人数，但他们承认，AI“正处于未知领域”。
==== Autonomous weapons ====
==== Autonomous weapons ====
自动化武器
{{See also|Lethal autonomous weapon}}
Currently, 50+ countries are researching battlefield robots, including the United States, China, Russia, and the United Kingdom. Many people concerned about risk from superintelligent AI also want to limit the use of artificial soldiers and drones.<ref>{{cite web|title = Stephen Hawking, Elon Musk, and Bill Gates Warn About Artificial Intelligence|url = http://observer.com/2015/08/stephen-hawking-elon-musk-and-bill-gates-warn-about-artificial-intelligence/|website = Observer|accessdate = 30 October 2015|url-status=live|archiveurl = https://web.archive.org/web/20151030053323/http://observer.com/2015/08/stephen-hawking-elon-musk-and-bill-gates-warn-about-artificial-intelligence/|archivedate = 30 October 2015|df = dmy-all|date = 2015-08-19}}</ref>
Currently, 50+ countries are researching battlefield robots, including the United States, China, Russia, and the United Kingdom. Many people concerned about risk from superintelligent AI also want to limit the use of artificial soldiers and drones.
目前，包括美国、中国、俄罗斯和英国在内的50多个国家正在研究战场机器人。许多人担心来自超级智能AI的风险，也希望限制人造士兵和无人机的使用。
=== Ethical machines ===
=== Ethical machines ===
道德的机器
Machines with intelligence have the potential to use their intelligence to prevent harm and minimize the risks; they may have the ability to use [[ethics|ethical reasoning]] to better choose their actions in the world. As such, there is a need for policy making to devise policies for and regulate artificial intelligence and robotics.<ref>{{Cite journal|last=Iphofen|first=Ron|last2=Kritikos|first2=Mihalis|date=2019-01-03|title=Regulating artificial intelligence and robotics: ethics by design in a digital society|journal=Contemporary Social Science|pages=1–15|doi=10.1080/21582041.2018.1563803|issn=2158-2041}}</ref> Research in this area includes [[machine ethics]], [[artificial moral agents]], [[friendly AI]] and discussion towards building a [[human rights]] framework is also in talks.<ref>{{cite_web|url=https://www.voanews.com/episode/ethical-ai-learns-human-rights-framework-4087171|title=Ethical AI Learns Human Rights Framework|accessdate=10 November 2019|website=Voice of America}}</ref>
Machines with intelligence have the potential to use their intelligence to prevent harm and minimize the risks; they may have the ability to use ethical reasoning to better choose their actions in the world. As such, there is a need for policy making to devise policies for and regulate artificial intelligence and robotics. Research in this area includes machine ethics, artificial moral agents, friendly AI and discussion towards building a human rights framework is also in talks.
具有智能的机器可能会利用它们的智能来防止伤害和减少风险; 它们可能能利用伦理推理来更好地做出它们在世界上的行动。因此，有必要为AI和机器人制定和规范政策。这一领域的研究包括机器伦理学、人工道德主题、友好AI以及关于建立人权框架的讨论。
==== Artificial moral agents ====
=== Automotive ===
==== Artificial moral agents ====
人工道德智能体
Wendell Wallach introduced the concept of [[artificial moral agents]] (AMA) in his book ''Moral Machines''<ref>Wendell Wallach (2010). ''Moral Machines'', Oxford University Press.</ref> For Wallach, AMAs have become a part of the research landscape of artificial intelligence as guided by its two central questions which he identifies as "Does Humanity Want Computers Making Moral Decisions"<ref>Wallach, pp 37–54.</ref> and "Can (Ro)bots Really Be Moral".<ref>Wallach, pp 55–73.</ref> For Wallach, the question is not centered on the issue of ''whether'' machines can demonstrate the equivalent of moral behavior in contrast to the ''constraints'' which society may place on the development of AMAs.<ref>Wallach, Introduction chapter.</ref>
Wendell Wallach introduced the concept of artificial moral agents (AMA) in his book Moral Machines For Wallach, AMAs have become a part of the research landscape of artificial intelligence as guided by its two central questions which he identifies as "Does Humanity Want Computers Making Moral Decisions" and "Can (Ro)bots Really Be Moral". For Wallach, the question is not centered on the issue of whether machines can demonstrate the equivalent of moral behavior in contrast to the constraints which society may place on the development of AMAs.
温德尔•沃勒克在他的著作《沃勒克的道德机器》(Moral Machines For Wallach)中提出了人工道德智能体(AMA)的概念，在两个核心问题的指导下，AMA 已经成为AI研究领域的一部分。他将这两个核心问题定义为“人类是否希望计算机做出道德决策”和“机器人真的可以拥有道德吗”。对于沃勒克来说，这个问题的重点不是机器是否能够表现出与社会对AMAs发展的限制相对应的道德行为。
==== Machine ethics ====
==== Machine ethics ====
机器伦理学
{{Main|Machine ethics}}
The field of machine ethics is concerned with giving machines ethical principles, or a procedure for discovering a way to resolve the ethical dilemmas they might encounter, enabling them to function in an ethically responsible manner through their own ethical decision making.<ref name="autogenerated1">Michael Anderson and Susan Leigh Anderson (2011), Machine Ethics, Cambridge University Press.</ref> The field was delineated in the AAAI Fall 2005 Symposium on Machine Ethics: "Past research concerning the relationship between technology and ethics has largely focused on responsible and irresponsible use of technology by human beings, with a few people being interested in how human beings ought to treat machines. In all cases, only human beings have engaged in ethical reasoning. The time has come for adding an ethical dimension to at least some machines. Recognition of the ethical ramifications of behavior involving machines, as well as recent and potential developments in machine autonomy, necessitate this. In contrast to computer hacking, software property issues, privacy issues and other topics normally ascribed to computer ethics, machine ethics is concerned with the behavior of machines towards human users and other machines. Research in machine ethics is key to alleviating concerns with autonomous systems—it could be argued that the notion of autonomous machines without such a dimension is at the root of all fear concerning machine intelligence. Further, investigation of machine ethics could enable the discovery of problems with current ethical theories, advancing our thinking about Ethics."<ref name="autogenerated2">{{cite web|url=http://www.aaai.org/Library/Symposia/Fall/fs05-06 |title=Machine Ethics |work=aaai.org |url-status=dead |archiveurl=https://web.archive.org/web/20141129044821/http://www.aaai.org/Library/Symposia/Fall/fs05-06 |archivedate=29 November 2014 }}</ref> Machine ethics is sometimes referred to as machine morality, computational ethics or computational morality. A variety of perspectives of this nascent field can be found in the collected edition "Machine Ethics"<ref name="autogenerated1"/> that stems from the AAAI Fall 2005 Symposium on Machine Ethics.<ref name="autogenerated2"/>
The field of machine ethics is concerned with giving machines ethical principles, or a procedure for discovering a way to resolve the ethical dilemmas they might encounter, enabling them to function in an ethically responsible manner through their own ethical decision making. The field was delineated in the AAAI Fall 2005 Symposium on Machine Ethics: "Past research concerning the relationship between technology and ethics has largely focused on responsible and irresponsible use of technology by human beings, with a few people being interested in how human beings ought to treat machines. In all cases, only human beings have engaged in ethical reasoning. The time has come for adding an ethical dimension to at least some machines. Recognition of the ethical ramifications of behavior involving machines, as well as recent and potential developments in machine autonomy, necessitate this. In contrast to computer hacking, software property issues, privacy issues and other topics normally ascribed to computer ethics, machine ethics is concerned with the behavior of machines towards human users and other machines. Research in machine ethics is key to alleviating concerns with autonomous systems—it could be argued that the notion of autonomous machines without such a dimension is at the root of all fear concerning machine intelligence. Further, investigation of machine ethics could enable the discovery of problems with current ethical theories, advancing our thinking about Ethics." Machine ethics is sometimes referred to as machine morality, computational ethics or computational morality. A variety of perspectives of this nascent field can be found in the collected edition "Machine Ethics" that stems from the AAAI Fall 2005 Symposium on Machine Ethics.
机器伦理学领域关注的是给予机器伦理原则，或者一种用于解决它们可能遇到的伦理困境的方法，使它们能够通过自己的伦理决策以一种符合伦理的方式运作。2005年秋季AAAI机器伦理研讨会阐述了这一领域: ”过去关于技术与伦理学之间关系的研究主要侧重于人类对技术的使用是否应该负责，只有少数人对人类应当如何对待机器感兴趣。任何时候都只有人类会参与伦理推理。现在是时候给至少一些机器增加道德层面了。认识到机器行为的道德后果，以及机器自主性领域最新和潜在的发展，使这成为必要。与计算机黑客行为、软件产权问题、隐私问题和其他通常归因于计算机道德的主题不同，机器道德关注的是机器对人类用户和其他机器的行为。机器伦理学的研究是减轻人们对自主系统担忧的关键——可以说，人们对机器智能担忧的根源是自主机器概念没有道德维度。此外，在机器伦理学的研究中可以发现当前伦理学理论存在的问题，加深我们对伦理学的思考。”机器伦理学有时被称为机器道德、计算伦理学或计算伦理学。这个新兴领域的各种观点可以在 AAAI 秋季2005年机器伦理学研讨会上收集的“机器伦理学”版本中找到。
==== Malevolent and friendly AI ====
==== Malevolent and friendly AI ====
善恶AI
{{Main|Friendly AI}}
Political scientist [[Charles T. Rubin]] believes that AI can be neither designed nor guaranteed to be benevolent.<ref>{{cite journal|last=Rubin |first=Charles |authorlink=Charles T. Rubin |date=Spring 2003 |title=Artificial Intelligence and Human Nature|journal=The New Atlantis |volume=1 |pages=88–100 |url=http://www.thenewatlantis.com/publications/artificial-intelligence-and-human-nature |url-status=dead |archiveurl=https://web.archive.org/web/20120611115223/http://www.thenewatlantis.com/publications/artificial-intelligence-and-human-nature |archivedate=11 June 2012 |df=dmy}}</ref> He argues that "any sufficiently advanced benevolence may be indistinguishable from malevolence." Humans should not assume machines or robots would treat us favorably because there is no ''a priori'' reason to believe that they would be sympathetic to our system of morality, which has evolved along with our particular biology (which AIs would not share). Hyper-intelligent software may not necessarily decide to support the continued existence of humanity and would be extremely difficult to stop. This topic has also recently begun to be discussed in academic publications as a real source of risks to civilization, humans, and planet Earth.
Political scientist Charles T. Rubin believes that AI can be neither designed nor guaranteed to be benevolent. He argues that "any sufficiently advanced benevolence may be indistinguishable from malevolence." Humans should not assume machines or robots would treat us favorably because there is no a priori reason to believe that they would be sympathetic to our system of morality, which has evolved along with our particular biology (which AIs would not share). Hyper-intelligent software may not necessarily decide to support the continued existence of humanity and would be extremely difficult to stop. This topic has also recently begun to be discussed in academic publications as a real source of risks to civilization, humans, and planet Earth.
政治科学家查尔斯 · 鲁宾认为，AI既不能被设计，也不能保证是友好的。他认为“任何足够的友善都可能难以与邪恶区分。”人类不应该假设机器或机器人会对我们友好，因为没有先验理由认为他们会对我们的道德体系有共鸣感，这个体系是在我们特定的生物进化过程中产生的(AI没有这个过程)。超智能软件不一定会认同人类的继续存在，并且将极难停止。最近一些学术出版物也开始讨论这个话题，认为它是对文明、人类和地球造成风险的真正来源。
One proposal to deal with this is to ensure that the first generally intelligent AI is '[[Friendly AI]]' and will be able to control subsequently developed AIs. Some question whether this kind of check could actually remain in place.
One proposal to deal with this is to ensure that the first generally intelligent AI is 'Friendly AI' and will be able to control subsequently developed AIs. Some question whether this kind of check could actually remain in place.
解决这个问题的一个建议是确保第一个具有通用智能的AI是“友好的AI”，并能够控制后面研发的AI。一些人质疑这种“友好”是否真的能够保持不变。
Leading AI researcher [[Rodney Brooks]] writes, "I think it is a mistake to be worrying about us developing malevolent AI anytime in the next few hundred years. I think the worry stems from a fundamental error in not distinguishing the difference between the very real recent advances in a particular aspect of AI and the enormity and complexity of building sentient volitional intelligence."<ref>{{cite web|last=Brooks|first=Rodney|title=artificial intelligence is a tool, not a threat|date=10 November 2014|url=http://www.rethinkrobotics.com/artificial-intelligence-tool-threat/|url-status=dead|archiveurl=https://web.archive.org/web/20141112130954/http://www.rethinkrobotics.com/artificial-intelligence-tool-threat/|archivedate=12 November 2014|df=dmy-all}}</ref>
Leading AI researcher Rodney Brooks writes, "I think it is a mistake to be worrying about us developing malevolent AI anytime in the next few hundred years. I think the worry stems from a fundamental error in not distinguishing the difference between the very real recent advances in a particular aspect of AI and the enormity and complexity of building sentient volitional intelligence."
首席AI研究员罗德尼 · 布鲁克斯写道: “我认为担心我们在未来几百年的研发出邪恶AI是无稽之谈。我认为，这种担忧源于一个根本性的错误，即没有认识到AI在某些领域进展可以很快但构建有意识有感情的智能是件庞杂且艰巨的任务。”
--[[用户:Thingamabob|Thingamabob]]（[[用户讨论:Thingamabob|讨论]]）“我认为担心我们在未来几百年的研发出邪恶AI是无稽之谈。我认为，这种担忧源于一个根本性的错误，即没有认识到AI在某些领域进展可以很快但构建有意识有感情的智能是件庞杂且艰巨的任务。”该句为意译
=== Machine consciousness, sentience and mind ===
=== Machine consciousness, sentience and mind ===
机器意识、知觉和思维
{{Main|Artificial consciousness}}
[[Financial institution]]s have long used [[artificial neural network]] systems to detect charges or claims outside of the norm, flagging these for human investigation. The use of AI in [[banking]] can be traced back to 1987 when [[Security Pacific National Bank]] in US set-up a Fraud Prevention Task force to counter the unauthorized use of debit cards.<ref>{{Cite web|url=https://www.latimes.com/archives/la-xpm-1990-01-17-fi-233-story.html|title=Impact of Artificial Intelligence on Banking|last=Christy|first=Charles A.|website=latimes.com|access-date=2019-09-10|date=17 January 1990}}</ref> Programs like Kasisto and Moneystream are using AI in financial services.
If an AI system replicates all key aspects of human intelligence, will that system also be [[Sentience|sentient]]—will it have a [[mind]] which has [[consciousness|conscious experiences]]? This question is closely related to the philosophical problem as to the nature of human consciousness, generally referred to as the [[hard problem of consciousness]].
If an AI system replicates all key aspects of human intelligence, will that system also be sentient—will it have a mind which has conscious experiences? This question is closely related to the philosophical problem as to the nature of human consciousness, generally referred to as the hard problem of consciousness.
如果一个AI系统复制了人类智能的所有关键部分，那么这个系统是否也能有意识——它是否能拥有一个有意识体验的头脑？这个问题与人类意识本质的哲学问题密切相关，一般称之为意识难题。
==== Consciousness ====
==== Consciousness ====
意识
{{Main|Hard problem of consciousness|Theory of mind}}
[[David Chalmers]] identified two problems in understanding the mind, which he named the "hard" and "easy" problems of consciousness.<ref name=Chalmers>{{cite journal |url=http://www.imprint.co.uk/chalmers.html |title=Facing up to the problem of consciousness |last=Chalmers |first=David |authorlink=David Chalmers |journal=[[Journal of Consciousness Studies]] |volume= 2 |issue=3 |year=1995 |pages=200–219}} See also [http://consc.net/papers/facing.html this link]
David Chalmers identified two problems in understanding the mind, which he named the "hard" and "easy" problems of consciousness.<ref name=Chalmers> See also [http://consc.net/papers/facing.html this link]
大卫 · 查尔默斯在理解心智方面提出了两个问题，他称之为意识的“困难”和“容易”问题。
</ref> The easy problem is understanding how the brain processes signals, makes plans and controls behavior. The hard problem is explaining how this ''feels'' or why it should feel like anything at all. Human [[information processing]] is easy to explain, however human [[subjective experience]] is difficult to explain.
</ref> The easy problem is understanding how the brain processes signals, makes plans and controls behavior. The hard problem is explaining how this feels or why it should feel like anything at all. Human information processing is easy to explain, however human subjective experience is difficult to explain.
简单的问题是理解大脑如何处理信号，制定计划和控制行为。困难的问题是如何解释这种感觉或者为什么它会有这种感觉。人类的信息处理过程很容易解释，然而人类的主观体验却很难解释。
For example, consider what happens when a person is shown a color swatch and identifies it, saying "it's red". The easy problem only requires understanding the machinery in the brain that makes it possible for a person to know that the color swatch is red. The hard problem is that people also know something else—they also know ''what red looks like''. (Consider that a person born blind can know that something is red without knowing what red looks like.){{efn|This is based on [[Mary's Room]], a thought experiment first proposed by [[Frank Cameron Jackson|Frank Jackson]] in 1982}} Everyone knows subjective experience exists, because they do it every day (e.g., all sighted people know what red looks like). The hard problem is explaining how the brain creates it, why it exists, and how it is different from knowledge and other aspects of the brain.
For example, consider what happens when a person is shown a color swatch and identifies it, saying "it's red". The easy problem only requires understanding the machinery in the brain that makes it possible for a person to know that the color swatch is red. The hard problem is that people also know something else—they also know what red looks like. (Consider that a person born blind can know that something is red without knowing what red looks like.) Everyone knows subjective experience exists, because they do it every day (e.g., all sighted people know what red looks like). The hard problem is explaining how the brain creates it, why it exists, and how it is different from knowledge and other aspects of the brain.
例如当一个人看到一张色卡并识别它，说“它是红色的”时会发生什么。这个简单的问题只需要知道这个人大脑中认出色卡是红色的机制。困难的问题是，人们还知道其他一些东西——他们还知道红色长什么样。(想象一下，一个天生失明的人，即使不知道红色是什么样子，也能知道什么是红色。)每个人都知道主观体验的存在，因为他们每天都有主观体验(例如，所有视力正常的人都知道红色是什么样子)。困难的问题是解释大脑如何创造它，为什么它存在，以及它如何区别于知识和大脑的其他功能。
=== Cybersecurity ===
==== Computationalism and functionalism ====
=== Cybersecurity ===
==== Computationalism and functionalism ====
计算主义和功能主义
{{More citations needed section|date=January 2020}}
{{Main|Computationalism|Functionalism (philosophy of mind)}}
Computationalism is the position in the [[philosophy of mind]] that the human mind or the human brain (or both) is an information processing system and that thinking is a form of computing.<ref>[[Steven Horst|Horst, Steven]], (2005) [http://plato.stanford.edu/entries/computational-mind/ "The Computational Theory of Mind"] in ''The Stanford Encyclopedia of Philosophy''</ref> Computationalism argues that the relationship between mind and body is similar or identical to the relationship between software and hardware and thus may be a solution to the [[mind-body problem]]. This philosophical position was inspired by the work of AI researchers and cognitive scientists in the 1960s and was originally proposed by philosophers [[Jerry Fodor]] and [[Hilary Putnam]].
Computationalism is the position in the philosophy of mind that the human mind or the human brain (or both) is an information processing system and that thinking is a form of computing. Computationalism argues that the relationship between mind and body is similar or identical to the relationship between software and hardware and thus may be a solution to the mind-body problem. This philosophical position was inspired by the work of AI researchers and cognitive scientists in the 1960s and was originally proposed by philosophers Jerry Fodor and Hilary Putnam.
计算主义站在心智哲学的立场，认为人类心智或人类大脑(都)是一个信息处理系统，思维是一种计算形式。计算主义认为，思想和身体之间的关系与软件和硬件之间的关系是相似或相同的，因此这也许能帮助解决“意识和身体问题”。这一哲学立场受20世纪60年代AI研究人员和认知科学家的工作的启发，最初由哲学家杰里 · 福多和希拉里 · 普特南提出。
==== Strong AI hypothesis ====
=== Government ===
==== Strong AI hypothesis ====
强人工智能假说
{{Main|Chinese room}}
The philosophical position that [[John Searle]] has named [[strong AI hypothesis|"strong AI"]] states: "The appropriately programmed computer with the right inputs and outputs would thereby have a mind in exactly the same sense human beings have minds."<ref name="Searle's strong AI"/> Searle counters this assertion with his [[Chinese room]] argument, which asks us to look ''inside'' the computer and try to find where the "mind" might be.<ref name="Chinese room"/>
The philosophical position that John Searle has named "strong AI" states: "The appropriately programmed computer with the right inputs and outputs would thereby have a mind in exactly the same sense human beings have minds." Searle counters this assertion with his Chinese room argument, which asks us to look inside the computer and try to find where the "mind" might be.
约翰 · 塞尔称之为“强人工智能”的哲学立场指出: “具有正确输入和输出程序的计算机，将因此拥有与人脑意义完全相同的头脑。”塞尔用他的中文房间论点反驳了这种说法，他让人们看看电脑内部，并试图找出“思维”可能在哪里。
==== Robot rights ====
==== Robot rights ====
机器人的权利
{{Main|Robot rights}}
If a machine can be created that has intelligence, could it also ''[[sentience|feel]]''? If it can feel, does it have the same rights as a human? This issue, now known as "[[robot rights]]", is currently being considered by, for example, California's [[Institute for the Future]], although many critics believe that the discussion is premature.<ref name="Robot rights"/> Some critics of [[transhumanism]] argue that any hypothetical robot rights would lie on a spectrum with [[animal rights]] and human rights. <ref Name="Evans 2015">{{cite journal | last = Evans | first = Woody | authorlink = Woody Evans | title = Posthuman Rights: Dimensions of Transhuman Worlds | journal = Teknokultura | volume = 12 | issue = 2 | date = 2015 | df = dmy-all | doi = 10.5209/rev_TK.2015.v12.n2.49072 | doi-access = free }}</ref> The subject is profoundly discussed in the 2010 documentary film ''[[Plug & Pray]]'',<ref>{{cite web|url=http://www.plugandpray-film.de/en/content.html|title=Content: Plug & Pray Film – Artificial Intelligence – Robots -|author=maschafilm|work=plugandpray-film.de|url-status=live|archiveurl=https://web.archive.org/web/20160212040134/http://www.plugandpray-film.de/en/content.html|archivedate=12 February 2016|df=dmy-all}}</ref> and many sci fi media such as [[Star Trek]] Next Generation, with the character of [[Commander Data]], who fought being disassembled for research, and wanted to "become human", and the robotic holograms in Voyager.
If a machine can be created that has intelligence, could it also feel? If it can feel, does it have the same rights as a human? This issue, now known as "robot rights", is currently being considered by, for example, California's Institute for the Future, although many critics believe that the discussion is premature. The subject is profoundly discussed in the 2010 documentary film Plug & Pray, and many sci fi media such as Star Trek Next Generation, with the character of Commander Data, who fought being disassembled for research, and wanted to "become human", and the robotic holograms in Voyager.
如果可以创造出一台有智能的机器，那么它是否也有感觉呢？如果它有感觉，它是否拥有与人类同样的权利？这个目前被称为“机器人权利”的问题正在被人们考虑，例如，加利福尼亚的未来研究所就在从事相关研究，尽管许多批评论家认为这种讨论为时过早。2010年的纪录片《插头与祷告》(Plug & Pray)以及《星际迷航: 下一代》(Star Trek Next Generation)等许多科幻媒体都对这个主题进行了深入讨论。《星际迷航》中有个指挥官角色叫戴塔(Data) ，他希望“变成人类”和为了旅行者号上的机器人全息图而抵抗不被人拆解。
=== Law-related professions ===
=== Superintelligence ===
=== Law-related professions ===
=== Superintelligence ===
超级智能
{{Main|Legal informatics#Artificial intelligence}}
{{Main|Superintelligence}}
Are there limits to how intelligent machines—or human-machine hybrids—can be? A superintelligence, hyperintelligence, or superhuman intelligence is a hypothetical agent that would possess intelligence far surpassing that of the brightest and most gifted human mind. ''Superintelligence'' may also refer to the form or degree of intelligence possessed by such an agent.<ref name="Roberts"/>
Are there limits to how intelligent machines—or human-machine hybrids—can be? A superintelligence, hyperintelligence, or superhuman intelligence is a hypothetical agent that would possess intelligence far surpassing that of the brightest and most gifted human mind. Superintelligence may also refer to the form or degree of intelligence possessed by such an agent.
智能机器——或者说人机混合体——能达到的程度有限吗？超级智能、超智能或者超人智能是一种假想的智能体，它拥有的智能远远超过最聪明、最有天赋的人类智慧。超级智能也可以指这种智能体所拥有的智能的形式或程度。
==== Technological singularity ====
==== Technological singularity ====
技术奇异点
{{Main|Technological singularity|Moore's law}}
If research into [[artificial general intelligence|Strong AI]] produced sufficiently intelligent software, it might be able to reprogram and improve itself. The improved software would be even better at improving itself, leading to [[Intelligence explosion|recursive self-improvement]].<ref name="recurse"/> The new intelligence could thus increase exponentially and dramatically surpass humans. Science fiction writer [[Vernor Vinge]] named this scenario "[[technological singularity|singularity]]".<ref name=Singularity/> Technological singularity is when accelerating progress in technologies will cause a runaway effect wherein artificial intelligence will exceed human intellectual capacity and control, thus radically changing or even ending civilization. Because the capabilities of such an intelligence may be impossible to comprehend, the technological singularity is an occurrence beyond which events are unpredictable or even unfathomable.<ref name=Singularity/><ref name="Roberts"/>
If research into Strong AI produced sufficiently intelligent software, it might be able to reprogram and improve itself. The improved software would be even better at improving itself, leading to recursive self-improvement. The new intelligence could thus increase exponentially and dramatically surpass humans. Science fiction writer Vernor Vinge named this scenario "singularity". Technological singularity is when accelerating progress in technologies will cause a runaway effect wherein artificial intelligence will exceed human intellectual capacity and control, thus radically changing or even ending civilization. Because the capabilities of such an intelligence may be impossible to comprehend, the technological singularity is an occurrence beyond which events are unpredictable or even unfathomable.
如果对强人工智能的研究造出了足够智能的软件，那么它也许能做到重新编程并改进自己。改进后的软件甚至可以更好地改进自己，从而实现递归的自我改进。这种新的智能因此可以呈指数增长，并大大超过人类。科幻作家弗诺·文奇将这种情况命名为“奇异点”。技术的加速发展将导致AI超越人类智力和控制能力的失控局面，从而彻底改变甚至终结人类文明。因为这样的智能人类难以理解，所有技术奇异点出现后发生的事是不可预测，或者说深不可测的。
[[Ray Kurzweil]] has used [[Moore's law]] (which describes the relentless exponential improvement in digital technology) to calculate that [[desktop computer]]s will have the same processing power as human brains by the year 2029, and predicts that the singularity will occur in 2045.<ref name=Singularity/>
Ray Kurzweil has used Moore's law (which describes the relentless exponential improvement in digital technology) to calculate that desktop computers will have the same processing power as human brains by the year 2029, and predicts that the singularity will occur in 2045.
雷·库兹韦尔利用摩尔定律(描述了数字技术指数增长的现象)计算出，到2029年，台式电脑的处理能力将与人类大脑相当，并预测奇点将出现在2045年。
==== Transhumanism ====
==== Transhumanism ====
超人类主义
{{Main|Transhumanism}}
Robot designer [[Hans Moravec]], cyberneticist [[Kevin Warwick]] and inventor [[Ray Kurzweil]] have predicted that humans and machines will merge in the future into [[cyborg]]s that are more capable and powerful than either.<ref name="Transhumanism"/> This idea, called [[transhumanism]], has roots in [[Aldous Huxley]] and [[Robert Ettinger]].
Robot designer Hans Moravec, cyberneticist Kevin Warwick and inventor Ray Kurzweil have predicted that humans and machines will merge in the future into cyborgs that are more capable and powerful than either. This idea, called transhumanism, has roots in Aldous Huxley and Robert Ettinger.
机器人设计师汉斯 · 莫拉维克、控制论专家凯文 · 沃里克和发明家雷 · 库兹韦尔预言，人类和机器将在未来融合成为比两者都更强的半机器人。这种观点被称为“超人类主义”，这种观点起源于阿道司.赫胥黎和罗伯特•艾廷格。
[[Edward Fredkin]] argues that "artificial intelligence is the next stage in evolution", an idea first proposed by [[Samuel Butler (novelist)|Samuel Butler]]'s "[[Darwin among the Machines]]" as far back as 1863, and expanded upon by [[George Dyson (science historian)|George Dyson]] in his book of the same name in 1998.<ref name="AI as evolution"/>
Edward Fredkin argues that "artificial intelligence is the next stage in evolution", an idea first proposed by Samuel Butler's "Darwin among the Machines" as far back as 1863, and expanded upon by George Dyson in his book of the same name in 1998.
爱德华•弗雷德金认为，“人工智能是进化的下一个阶段”。早在1863年，塞缪尔•巴特勒的《机器中的达尔文》(Darwin among the Machines)就首次提出了这一观点，乔治•戴森在1998年的同名著作中对其进行了延伸。
== Economics ==
== Economics ==
经济学
The long-term economic effects of AI are uncertain. A survey of economists showed disagreement about whether the increasing use of robots and AI will cause a substantial increase in long-term [[unemployment]], but they generally agree that it could be a net benefit, if [[productivity]] gains are [[Redistribution of income and wealth|redistributed]].<ref>{{Cite web|url=http://www.igmchicago.org/surveys/robots-and-artificial-intelligence|title=Robots and Artificial Intelligence|last=|first=|date=|website=www.igmchicago.org|access-date=2019-07-03}}</ref> A February 2020 European Union white paper on artificial intelligence advocated for artificial intelligence for economic benefits, including "improving healthcare (e.g. making diagnosis more precise, enabling better prevention of diseases), increasing the efficiency of farming, contributing to climate change mitigation and adaptation, [and] improving the efficiency of production systems through predictive maintenance", while acknowledging potential risks.<ref name=":1" />
The long-term economic effects of AI are uncertain. A survey of economists showed disagreement about whether the increasing use of robots and AI will cause a substantial increase in long-term unemployment, but they generally agree that it could be a net benefit, if productivity gains are redistributed. A February 2020 European Union white paper on artificial intelligence advocated for artificial intelligence for economic benefits, including "improving healthcare (e.g. making diagnosis more precise, enabling better prevention of diseases), increasing the efficiency of farming, contributing to climate change mitigation and adaptation, [and] improving the efficiency of production systems through predictive maintenance", while acknowledging potential risks.
人工智能的长期经济效应是不确定的。一项对经济学家的调查显示，在机器人和AI的使用的日益增加是否会导致长期失业率大幅上升的问题上，人们的意见存在分歧。但他们普遍认为，如果生产率成果得到重新分配，也许这不是一件坏事。2020年2月，欧盟发表了一份关于AI的白皮书，主张为了增加经济利益而使用AI，其中包括“改善医疗保健(例如:使诊断更加精确，能够更好地预防疾病) ，提高耕作效率，有减缓和适应气候变化，以及通过预测性维护提高生产系统的效率”，同时也承认AI有潜在风险。
== Regulation ==
== Regulation ==
规例
{{Main|Regulation of artificial intelligence|Regulation of algorithms}}
The development of public sector policies for promoting and regulating artificial intelligence (AI) is considered necessary to both encourage AI and manage associated risks, but challenging.<ref>{{Cite journal|last=Wirtz|first=Bernd W.|last2=Weyerer|first2=Jan C.|last3=Geyer|first3=Carolin|date=2018-07-24|title=Artificial Intelligence and the Public Sector—Applications and Challenges|journal=International Journal of Public Administration|volume=42|issue=7|pages=596–615|doi=10.1080/01900692.2018.1498103|issn=0190-0692}}</ref> In 2017 [[Elon Musk]] called for regulation of AI development.<ref>{{cite news|url=https://www.npr.org/sections/thetwo-way/2017/07/17/537686649/elon-musk-warns-governors-artificial-intelligence-poses-existential-risk|title=Elon Musk Warns Governors: Artificial Intelligence Poses 'Existential Risk'|work=NPR.org|accessdate=27 November 2017|language=en}}</ref> Multiple states now have national policies under development or in place,<ref>{{Cite book|last=Campbell|first=Thomas A.|url=http://www.unicri.it/in_focus/files/Report_AI-An_Overview_of_State_Initiatives_FutureGrasp_7-23-19.pdf|title=Artificial Intelligence: An Overview of State Initiatives|publisher=FutureGrasp, LLC|year=2019|isbn=|location=Evergreen, CO|pages=}}</ref> and in February 2020, the European Union published its draft strategy paper for promoting and regulating AI.<ref name=":12">{{Cite book|last=|first=|url=https://ec.europa.eu/info/sites/info/files/commission-white-paper-artificial-intelligence-feb2020_en.pdf|title=White Paper: On Artificial Intelligence - A European approach to excellence and trust|publisher=European Commission|year=2020|isbn=|location=Brussels|pages=1}}</ref>
The development of public sector policies for promoting and regulating artificial intelligence (AI) is considered necessary to both encourage AI and manage associated risks, but challenging. In 2017 Elon Musk called for regulation of AI development. Multiple states now have national policies under development or in place, and in February 2020, the European Union published its draft strategy paper for promoting and regulating AI.
促进和规范AI的公共部门政策被认为对鼓励人工智能和控制相关风险是必要的，但具有挑战性。2017年，埃隆 · 马斯克呼吁监管AI的发展。多个国家现在正在制定或实施国家性政策，2020年2月，欧盟公布了促进和管理AI的战略文件草案。
== In fiction ==
== In fiction ==
在小说里
{{Main|Artificial intelligence in fiction}}
[[File:Capek play.jpg|thumb|The word "robot" itself was coined by [[Karel Čapek]] in his 1921 play ''[[R.U.R.]]'', the title standing for "[[Rossum's Universal Robots]]"]]
The word "robot" itself was coined by [[Karel Čapek in his 1921 play R.U.R., the title standing for "Rossum's Universal Robots"]]
“机器人”这个词本身是由[[ 卡雷尔·恰佩克 在他1921年的戏剧《R.U.R》中创造的，剧名代表“Rossum 的万能机器人”(Rossum's Universal Robots)]
Thought-capable artificial beings appeared as storytelling devices since antiquity,<ref name="AI in myth"/>
Thought-capable artificial beings appeared as storytelling devices since antiquity,
具有思维能力的人造生命在古代故事中出现，在小说中也很常见
and have been a persistent theme in [[science fiction]].
and have been a persistent theme in science fiction.
一直是科幻小说中的一个永恒主题。
A common [[Trope (literature)|trope]] in these works began with [[Mary Shelley]]'s ''[[Frankenstein]]'', where a human creation becomes a threat to its masters. This includes such works as [[2001: A Space Odyssey (novel)|Arthur C. Clarke's]] and [[2001: A Space Odyssey (film)|Stanley Kubrick's]] ''[[2001: A Space Odyssey]]'' (both 1968), with [[HAL 9000]], the murderous computer in charge of the ''[[Discovery One]]'' spaceship, as well as ''[[The Terminator]]'' (1984) and ''[[The Matrix]]'' (1999). In contrast, the rare loyal robots such as Gort from ''[[The Day the Earth Stood Still]]'' (1951) and Bishop from ''[[Aliens (film)|Aliens]]'' (1986) are less prominent in popular culture.<ref>{{cite journal|last1=Buttazzo|first1=G.|title=Artificial consciousness: Utopia or real possibility?|journal=[[Computer (magazine)|Computer]]|date=July 2001|volume=34|issue=7|pages=24–30|doi=10.1109/2.933500|df=dmy-all}}</ref>
A common trope in these works began with Mary Shelley's Frankenstein, where a human creation becomes a threat to its masters. This includes such works as Arthur C. Clarke's and Stanley Kubrick's 2001: A Space Odyssey (both 1968), with HAL 9000, the murderous computer in charge of the Discovery One spaceship, as well as The Terminator (1984) and The Matrix (1999). In contrast, the rare loyal robots such as Gort from The Day the Earth Stood Still (1951) and Bishop from Aliens (1986) are less prominent in popular culture.
在这些作品中，玛丽 · 雪莱的《弗兰肯斯坦》最先使用了这种常见的比喻 ，在这部作品中，人造物对其主人产生了威胁。这些作品包括亚瑟·查理斯·克拉克斯坦利 · 库布里克的《2001: 太空漫游》(2001: a Space Odyssey，都是1968年出品) ，包括哈尔9000(HAL 9000) ，负责发现一号飞船的凶残计算机，以及《终结者》(The Terminator，1984)和《黑客帝国》(The Matrix，1999)。相比之下，像《地球停止转动的日子》(1951)中的格特和《异形》(1986)中的主教这样罕见的忠诚机器人在流行文化中就不那么突出了。
=== Art ===
[[Isaac Asimov]] introduced the [[Three Laws of Robotics]] in many books and stories, most notably the "Multivac" series about a super-intelligent computer of the same name. Asimov's laws are often brought up during lay discussions of machine ethics;<ref>Anderson, Susan Leigh. "Asimov's "three laws of robotics" and machine metaethics." AI & Society 22.4 (2008): 477–493.</ref> while almost all artificial intelligence researchers are familiar with Asimov's laws through popular culture, they generally consider the laws useless for many reasons, one of which is their ambiguity.<ref>{{cite journal | last1 = McCauley | first1 = Lee | year = 2007 | title = AI armageddon and the three laws of robotics | url = | journal = Ethics and Information Technology | volume = 9 | issue = 2| pages = 153–164 | doi=10.1007/s10676-007-9138-2| citeseerx = 10.1.1.85.8904}}</ref>
Isaac Asimov introduced the Three Laws of Robotics in many books and stories, most notably the "Multivac" series about a super-intelligent computer of the same name. Asimov's laws are often brought up during lay discussions of machine ethics; while almost all artificial intelligence researchers are familiar with Asimov's laws through popular culture, they generally consider the laws useless for many reasons, one of which is their ambiguity.
艾萨克 · 阿西莫夫在许多书籍和故事中介绍了机器人三定律，最著名的是关于同名的“Multitvac”超级智能计算机系列。阿西莫夫定律经常在茶余饭后对机器伦理的讨论中被提起。几乎所有的AI研究人员都通过流行文化熟悉阿西莫夫定律，但他们通常认为这些定律因为许多原因而无用，其中一个原因就是它们的描述过于模糊。
Transhumanism (the merging of humans and machines) is explored in the manga Ghost in the Shell and the science-fiction series Dune. In the 1980s, artist Hajime Sorayama's Sexy Robots series were painted and published in Japan depicting the actual organic human form with lifelike muscular metallic skins and later "the Gynoids" book followed that was used by or influenced movie makers including George Lucas and other creatives. Sorayama never considered these organic robots to be real part of nature but always unnatural product of the human mind, a fantasy existing in the mind even when realized in actual form.
Transhumanism (the merging of humans and machines) is explored in the manga Ghost in the Shell and the science-fiction series Dune. In the 1980s, artist Hajime Sorayama's Sexy Robots series were painted and published in Japan depicting the actual organic human form with lifelike muscular metallic skins and later "the Gynoids" book followed that was used by or influenced movie makers including George Lucas and other creatives. Sorayama never considered these organic robots to be real part of nature but always unnatural product of the human mind, a fantasy existing in the mind even when realized in actual form.
漫画《攻壳机动队》(manga Ghost in the Shell)和科幻小说《沙丘》(Dune)探讨了超人类主义(人类和机器的结合)。20世纪80年代，艺术家空山基的性感机器人系列在日本绘制并出版，描绘了真实的有机人类形体，拥有栩栩如生的金属肌肉皮肤，后来又出版了《雌蕊》一书，该书被乔治 · 卢卡斯等电影制作人使用。空山基从来不认为这些有机机器人是自然的一部分，而是非自然的人类心智的产品，一个存在于头脑中，也许能以实体形式实现的幻想。
Several works use AI to force us to confront the fundamental question of what makes us human, showing us artificial beings that have the ability to feel, and thus to suffer. This appears in Karel Čapek's R.U.R., the films A.I. Artificial Intelligence and Ex Machina, as well as the novel Do Androids Dream of Electric Sheep?, by Philip K. Dick. Dick considers the idea that our understanding of human subjectivity is altered by technology created with artificial intelligence.
Several works use AI to force us to confront the fundamental question of what makes us human, showing us artificial beings that have the ability to feel, and thus to suffer. This appears in Karel Čapek's R.U.R., the films A.I. Artificial Intelligence and Ex Machina, as well as the novel Do Androids Dream of Electric Sheep?, by Philip K. Dick. Dick considers the idea that our understanding of human subjectivity is altered by technology created with artificial intelligence.
一些作品向我们展示了有感知的能力，因此也有遭受苦难的能力的AI，迫使我们面对是什么让我们成为人类这一根本问题。这些都在卡雷尔 · 阿佩克的电影《AI》、AI和机器人，以及菲利普·K·迪克的小说《机器人会梦见电子羊吗?》中都有出现。迪克认为，AI创造的技术改变了我们对人类主观性的理解。
{{div col end}}
{{div col end}}
* [[Abductive reasoning]]
* [[溯因推理 Abductive reasoning]]
* [[Artificial intelligence arms race]]
* ''[[爱,死亡,机器人 A.I. Rising]]''
* [[Behavior selection algorithm]]
* [[人工智能武器装备竞赛 Artificial intelligence arms race]]
* [[Business process automation]]
* [[行为选择算法 Behavior selection algorithm]]
* [[Case-based reasoning]]
* [[交易处理自动机 Business process automation]]
* [[Citizen science#Plastics and pollution|Citizen Science]]
* [[基于案例的推理 Case-based reasoning]]
* [[Commonsense reasoning]]
* [[常识推理 Commonsense reasoning]]
* [[Emergent algorithm]]
* [[涌现算法 Emergent algorithm]]
* [[Evolutionary computation]]
* [[进化计算 Evolutionary computation]]
* [[Female gendering of AI technologies]]
* [[人工智能技术的女性 Female gendering of AI technologies]]
* [[Glossary of artificial intelligence]]
* [[人工智能术语表 Glossary of artificial intelligence]]
* [[Machine learning]]
* [[机器学习 Machine learning]]
* [[Mathematical optimization]]
* [[数学优化 Mathematical optimization]]
* [[Multi-agent system]]
* [[多主体系统 Multi-agent system]]
* [[Personality computing]]
* [[个性化计算 Personality computing]]
* [[Regulation of artificial intelligence]]
* [[人工智能规范 Regulation of artificial intelligence]]
* [[Robotic process automation]]
* [[机器处理自动机 Robotic process automation]]
* [[Universal basic income]]
* [[通用基础收入 Universal basic income]]
* [[Weak AI]]
* [[弱人工智能Weak AI]]