“通用人工智能”的版本间的差异

2020年10月10日 (六) 23:33的版本

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。

Artificial general intelligence (AGI) is the hypothetical^[1] intelligence of a machine that has the capacity to understand or learn any intellectual task that a human being can. It is a primary goal of some artificial intelligence research and a common topic in science fiction and futures studies. AGI can also be referred to as strong AI,^[2]^[3]引用错误：没有找到与</ref>对应的<ref>标签 full AI,^[4]

|first=Mike|last=Treder|work=Responsible Nanotechnology|date=10 August 2005 |archive-url=https://web.archive.org/web/20191016214415/https://crnano.typepad.com/crnblog/2005/08/advanced_human_.html%7Carchive-date=16 October 2019 |url-status=live}}</ref> full AI,

2005年8月10日2019年10月 https://web.archive.org/web/20191016214415/https://crnano.typepad.com/crnblog/2005/08/advanced_human_.html%7Carchive-date=16,

or general intelligent action.模板:Sfn

or general intelligent action.

或者是通用智能行为。

Some academic sources reserve the term "strong AI" for machines that can experience consciousness.模板:Sfn Today's AI is speculated to be many years, if not decades, away from AGI.^[5]^[6]

Some academic sources reserve the term "strong AI" for machines that can experience consciousness. Today's AI is speculated to be many years, if not decades, away from AGI.

一些学术资源保留了“强人工智能”这个术语，用来形容能够体会意识的机器。据推测，如果不是几十年的话，今天的人工智能将在很多年之后才能达到通用人工智能的地步。

Some authorities emphasize a distinction between strong AI and applied AI,^[7] also called narrow AI^[3] or weak AI.^[8] In contrast to strong AI, weak AI is not intended to perform human cognitive abilities. Rather, weak AI is limited to the use of software to study or accomplish specific problem solving or reasoning tasks.

Some authorities emphasize a distinction between strong AI and applied AI, also called narrow AI In contrast to strong AI, weak AI is not intended to perform human cognitive abilities. Rather, weak AI is limited to the use of software to study or accomplish specific problem solving or reasoning tasks.

一些权威机构强调 强人工智能 和 应用人工智能 之间的区别，也称为 狭义人工智能 与 强人工智能 相比，弱人工智能并不是为了执行人类的认知能力。相反，弱人工智能仅限于使用软件来研究或完成特定问题的解决或完成推理任务。

As of 2017, over forty organizations are researching AGI.^[9]

As of 2017, over forty organizations are researching AGI.

截止到2017年，已经有超过四十家机构在研究 AGI。

Requirements 判定要求

Various criteria for intelligence have been proposed (most famously the Turing test) but to date, there is no definition that satisfies everyone.^[10] However, there is wide agreement among artificial intelligence researchers that intelligence is required to do the following:引用错误：没有找到与</ref>对应的<ref>标签

</ref>

/ 参考

reason, use strategy, solve puzzles, and make judgments under uncertainty;使用策略，解决问题，并且在不确定条件下做出决策。

represent knowledge, including commonsense knowledge;展现知识，包括常识。

plan;规划。

learn;学习。

communicate in natural language;使用自然语言进行交流。

and integrate all these skills towards common goals.综合运用所有技巧以达到某个目的。

Other important capabilities include the ability to sense (e.g. see) and the ability to act (e.g. move and manipulate objects) in the world where intelligent behaviour is to be observed.^[11] This would include an ability to detect and respond to hazard.^[12] Many interdisciplinary approaches to intelligence (e.g. cognitive science, computational intelligence and decision making) tend to emphasise the need to consider additional traits such as imagination (taken as the ability to form mental images and concepts that were not programmed in)^[13] and autonomy.^[14]

Other important capabilities include the ability to sense (e.g. see) and the ability to act (e.g. move and manipulate objects) in the world where intelligent behaviour is to be observed. This would include an ability to detect and respond to hazard. Many interdisciplinary approaches to intelligence (e.g. cognitive science, computational intelligence and decision making) tend to emphasise the need to consider additional traits such as imagination (taken as the ability to form mental images and concepts that were not programmed in) and autonomy.

其他重要的能力包括在客观世界感知(例如:视觉）和行动(例如:移动和操纵物体)的能力。智能行为在客观世界中是可观测的。这将包括检测和应对危险的能力。许多跨学科的智力研究方法(例如:。认知科学、计算智能和决策)倾向于强调考虑额外特征的必要性，例如想象力(被认为是未编入程序的形成意象和概念的能力)和自主性。

Computer based systems that exhibit many of these capabilities do exist (e.g. see computational creativity, automated reasoning, decision support system, robot, evolutionary computation, intelligent agent), but not yet at human levels.

基于计算机的系统，展示了许多这些能力确实存在(例如:。参见计算创造性、自动推理、决策支持系统、机器人、进化计算、智能代理) ，但还没有达到人类的水平。

Tests for confirming human-level AGI模板:Anchor

The following tests to confirm human-level AGI have been considered:^[15]^[16]

The following tests to confirm human-level AGI have been considered:

考虑了下列测试以确认人类水平 AGI:

The Turing Test (Turing)

The Turing Test (Turing)

图灵测试(图灵)

A machine and a human both converse sight unseen with a second human, who must evaluate which of the two is the machine, which passes the test if it can fool the evaluator a significant fraction of the time. Note: Turing does not prescribe what should qualify as intelligence, only that knowing that it is a machine should disqualify it.

A machine and a human both converse sight unseen with a second human, who must evaluate which of the two is the machine, which passes the test if it can fool the evaluator a significant fraction of the time. Note: Turing does not prescribe what should qualify as intelligence, only that knowing that it is a machine should disqualify it.

一个机器人和一个人类都与另一个人类进行眼神交流，后者必须评估两者中哪一个是机器，如果它能骗过评估者很大一部分时间，那么机器就通过了测试。注意: 图灵并没有规定什么是智能，只要能认出它是一台机器就不应该认为它是智能的。

The Coffee Test (Wozniak)

The Coffee Test (Wozniak)

咖啡测试(沃兹尼亚克)

A machine is required to enter an average American home and figure out how to make coffee: find the coffee machine, find the coffee, add water, find a mug, and brew the coffee by pushing the proper buttons.

A machine is required to enter an average American home and figure out how to make coffee: find the coffee machine, find the coffee, add water, find a mug, and brew the coffee by pushing the proper buttons.

一台机器需要进入一个普通的美国家庭，并弄清楚如何制作咖啡: 找到咖啡机，找到咖啡，加水，找到一个马克杯，并通过按下正确的按钮来煮咖啡。

The Robot College Student Test (Goertzel)

The Robot College Student Test (Goertzel)

机器人大学生考试(格兹尔)

A machine enrolls in a university, taking and passing the same classes that humans would, and obtaining a degree.

A machine enrolls in a university, taking and passing the same classes that humans would, and obtaining a degree.

一台机器进入一所大学，学习并通过与人类相同的课程，并获得学位。

The Employment Test (Nilsson)

The Employment Test (Nilsson)

就业测试(尼尔森)

A machine works an economically important job, performing at least as well as humans in the same job.

A machine works an economically important job, performing at least as well as humans in the same job.

机器从事一项经济上重要的工作，在同一项工作中表现至少和人类一样好。

Problems requiring AGI to solve 等待通用人工智能解决的问题

The most difficult problems for computers are informally known as "AI-complete" or "AI-hard", implying that solving them is equivalent to the general aptitude of human intelligence, or strong AI, beyond the capabilities of a purpose-specific algorithm.^[17]

The most difficult problems for computers are informally known as "AI-complete" or "AI-hard", implying that solving them is equivalent to the general aptitude of human intelligence, or strong AI, beyond the capabilities of a purpose-specific algorithm.

对于计算机来说，最困难的问题被非正式地称为“ AI完全问题”或“ AI困难问题” ，这意味着解决这些问题相当于人类智能的一般才能，或强人工智能，超出了特定目的算法的能力。

AI-complete problems are hypothesised to include general computer vision, natural language understanding, and dealing with unexpected circumstances while solving any real world problem.^[18]

AI-complete problems are hypothesised to include general computer vision, natural language understanding, and dealing with unexpected circumstances while solving any real world problem.

人工智能完全问题假设包括一般的计算机视觉，自然语言理解，以及在解决任何现实世界问题的同时处理意外情况。

AI-complete problems cannot be solved with current computer technology alone, and also require human computation. This property could be useful, for example, to test for the presence of humans, as CAPTCHAs aim to do; and for computer security to repel brute-force attacks.^[19]^[20]

AI-complete problems cannot be solved with current computer technology alone, and also require human computation. This property could be useful, for example, to test for the presence of humans, as CAPTCHAs aim to do; and for computer security to repel brute-force attacks.

目前的计算机技术不能单独解决AI完全问题，而且还需要人工计算。例如，这个特性可以用来测试人类是否存在(CAPTCHAs 的目标就是这样做) ，以及应用于计算机安全以抵御强力攻击。

History 历史

Classical AI 经典人工智能

Modern AI research began in the mid 1950s.^[21] The first generation of AI researchers were convinced that artificial general intelligence was possible and that it would exist in just a few decades. AI pioneer Herbert A. Simon wrote in 1965: "machines will be capable, within twenty years, of doing any work a man can do."^[22] Their predictions were the inspiration for Stanley Kubrick and Arthur C. Clarke's character HAL 9000, who embodied what AI researchers believed they could create by the year 2001. AI pioneer Marvin Minsky was a consultant^[23] on the project of making HAL 9000 as realistic as possible according to the consensus predictions of the time; Crevier quotes him as having said on the subject in 1967, "Within a generation ... the problem of creating 'artificial intelligence' will substantially be solved,"^[24] although Minsky states that he was misquoted.^{[citation needed]}

Modern AI research began in the mid 1950s. The first generation of AI researchers were convinced that artificial general intelligence was possible and that it would exist in just a few decades. AI pioneer Herbert A. Simon wrote in 1965: "machines will be capable, within twenty years, of doing any work a man can do." Their predictions were the inspiration for Stanley Kubrick and Arthur C. Clarke's character HAL 9000, who embodied what AI researchers believed they could create by the year 2001. AI pioneer Marvin Minsky was a consultant on the project of making HAL 9000 as realistic as possible according to the consensus prediction of the time; Crevier quotes him as having said on the subject in 1967, "Within a generation ... the problem of creating 'artificial intelligence' will substantially be solved," although Minsky states that he was misquoted.

现代人工智能研究始于20世纪50年代中期。第一代人工智能研究人员确信，通用人工智能是可能的，并将在短短几十年内出现。人工智能的先驱赫伯特·A·西蒙(Herbert A. Simon)在1965年写道: “机器将在20年内拥有完成人类能做的任何工作的能力。”他们的预言启发了斯坦利·库布里克和亚瑟·查理斯·克拉克塑造的角色哈尔9000，它代表了人工智能研究人员相信他们截至2001年能够创造出的东西。人工智能先驱马文·明斯基(Marvin Minsky)是一个项目顾问，该项目旨在根据当时的一致预测，使哈尔9000尽可能逼真; 克里维尔援引他在1967年关于这个问题的话说，“在一代人的时间里... ... 创造‘人工智能’的问题将大体上得到解决，”尽管明斯基声称，他的话被错误引用了。

However, in the early 1970s, it became obvious that researchers had grossly underestimated the difficulty of the project. Funding agencies became skeptical of AGI and put researchers under increasing pressure to produce useful "applied AI".^[25] As the 1980s began, Japan's Fifth Generation Computer Project revived interest in AGI, setting out a ten-year timeline that included AGI goals like "carry on a casual conversation".^[26] In response to this and the success of expert systems, both industry and government pumped money back into the field.^[27] However, confidence in AI spectacularly collapsed in the late 1980s, and the goals of the Fifth Generation Computer Project were never fulfilled.^[28] For the second time in 20 years, AI researchers who had predicted the imminent achievement of AGI had been shown to be fundamentally mistaken. By the 1990s, AI researchers had gained a reputation for making vain promises. They became reluctant to make predictions at all^[29] and to avoid any mention of "human level" artificial intelligence for fear of being labeled "wild-eyed dreamer[s]."^[30]

However, in the early 1970s, it became obvious that researchers had grossly underestimated the difficulty of the project. Funding agencies became skeptical of AGI and put researchers under increasing pressure to produce useful "applied AI". As the 1980s began, Japan's Fifth Generation Computer Project revived interest in AGI, setting out a ten-year timeline that included AGI goals like "carry on a casual conversation". In response to this and the success of expert systems, both industry and government pumped money back into the field. However, confidence in AI spectacularly collapsed in the late 1980s, and the goals of the Fifth Generation Computer Project were never fulfilled. For the second time in 20 years, AI researchers who had predicted the imminent achievement of AGI had been shown to be fundamentally mistaken. By the 1990s, AI researchers had gained a reputation for making vain promises. They became reluctant to make predictions at all and to avoid any mention of "human level" artificial intelligence for fear of being labeled "wild-eyed dreamer[s]."

然而，在20世纪70年代初，很明显，研究人员严重低估了该项目的难度。资助机构开始对通用人工智能持怀疑态度，并对研究人员施加越来越大的压力，要求他们生产出有用的“应用人工智能”。随着20世纪80年代的开始，日本的第五代计算机项目(Fifth Generation Computer Project)重新唤起了人们对通用人工智能的兴趣，并设定了一个长达10年的时间线，其中包括通用人工智能的目标，比如“进行一次随意的交谈”。为了应对这种情况和专家系统的成功建立，工业界和政府都重新将资金投入这一领域。然而，人们对人工智能的信心在20世纪80年代末大幅下降，第五代计算机项目的目标从未实现。20年来的第二次，人工智能研究人员预测通用人工智能即将取得的成果已经被证明根本是错误的。到了20世纪90年代，人工智能研究人员因做出虚假承诺而臭名昭著。他们根本不愿意做预测，也不愿意提及“人类水平”的人工智能，因为他们害怕被贴上“狂热梦想家”的标签

Narrow AI research 狭义人工智能的研究

In the 1990s and early 21st century, mainstream AI achieved far greater commercial success and academic respectability by focusing on specific sub-problems where they can produce verifiable results and commercial applications, such as artificial neural networks and statistical machine learning.^[31] These "applied AI" systems are now used extensively throughout the technology industry, and research in this vein is very heavily funded in both academia and industry. Currently, development on this field is considered an emerging trend, and a mature stage is expected to happen in more than 10 years.^[32]

In the 1990s and early 21st century, mainstream AI achieved far greater commercial success and academic respectability by focusing on specific sub-problems where they can produce verifiable results and commercial applications, such as artificial neural networks and statistical machine learning. These "applied AI" systems are now used extensively throughout the technology industry, and research in this vein is very heavily funded in both academia and industry. Currently, development on this field is considered an emerging trend, and a mature stage is expected to happen in more than 10 years.

在1990年代和21世纪初，主流人工智能取得了更大的商业成功和学术声望，因为它们把重点放在能够产生可验证结果和商业应用的具体子问题上，例如人工神经网络和统计机器学习。这些“应用人工智能”系统现在在整个技术产业中得到广泛应用，这方面的研究得到了学术界和产业界的大量资助。目前，这一领域的发展被认为是一个新兴的趋势，并有望在10多年内进入一个成熟的阶段。

Most mainstream AI researchers hope that strong AI can be developed by combining the programs that solve various sub-problems. Hans Moravec wrote in 1988:

"I am confident that this bottom-up route to artificial intelligence will one day meet the traditional top-down route more than half way, ready to provide the real world competence and the commonsense knowledge that has been so frustratingly elusive in reasoning programs. Fully intelligent machines will result when the metaphorical golden spike is driven uniting the two efforts."^[33]

Most mainstream AI researchers hope that strong AI can be developed by combining the programs that solve various sub-problems. Hans Moravec wrote in 1988:

"I am confident that this bottom-up route to artificial intelligence will one day meet the traditional top-down route more than half way, ready to provide the real world competence and the commonsense knowledge that has been so frustratingly elusive in reasoning programs. Fully intelligent machines will result when the metaphorical golden spike is driven uniting the two efforts."

大多数主流人工智能研究人员希望，通过结合解决各种子问题的项目，可以开发出强人工智能。汉斯·莫拉维克(Hans Moravec)在1988年写道: “我相信，这种自下而上的人工智能路线，终有一天会与传统的自上而下的路线在后半程相遇。令人沮丧的是，当下，真实世界的能力和常识知识在推理程序中一直难以捉摸。而这两种路线结合的人工智能将能为我们解决这些疑难。当一个黄金钉一样的东西将二者结合起来时，就会产生完全智能的机器。” / blockquote

However, even this fundamental philosophy has been disputed; for example, Stevan Harnad of Princeton concluded his 1990 paper on the Symbol Grounding Hypothesis by stating:

"The expectation has often been voiced that "top-down" (symbolic) approaches to modeling cognition will somehow meet "bottom-up" (sensory) approaches somewhere in between. If the grounding considerations in this paper are valid, then this expectation is hopelessly modular and there is really only one viable route from sense to symbols: from the ground up. A free-floating symbolic level like the software level of a computer will never be reached by this route (or vice versa) – nor is it clear why we should even try to reach such a level, since it looks as if getting there would just amount to uprooting our symbols from their intrinsic meanings (thereby merely reducing ourselves to the functional equivalent of a programmable computer)."^[34]

However, even this fundamental philosophy has been disputed; for example, Stevan Harnad of Princeton concluded his 1990 paper on the Symbol Grounding Hypothesis by stating:

"The expectation has often been voiced that "top-down" (symbolic) approaches to modeling cognition will somehow meet "bottom-up" (sensory) approaches somewhere in between. If the grounding considerations in this paper are valid, then this expectation is hopelessly modular and there is really only one viable route from sense to symbols: from the ground up. A free-floating symbolic level like the software level of a computer will never be reached by this route (or vice versa) – nor is it clear why we should even try to reach such a level, since it looks as if getting there would just amount to uprooting our symbols from their intrinsic meanings (thereby merely reducing ourselves to the functional equivalent of a programmable computer)."

然而，连如此基本的哲学问题也存在争议; 例如，普林斯顿大学的斯蒂文·哈纳德(Stevan Harnad)在1990年关于符号基础假说(the Symbol Grounding Hypothesis)的论文中总结道: “人们经常提出这样的期望，即建立“自上而下”(符号)的认知模型的方法将在某种程度上与“自下而上”(感官)的方法在建模过程中的某处相会。如果本文中的基本考虑是正确的，那么绝望的是，这种期望是模块化的，并且从认知到符号真的只有一条可行的路径: 从头开始。类似计算机软件级别的自由浮动的符号永远不可能通过这条路径实现，反之亦然——甚至也不清楚为什么我们应该尝试达到这样一个级别，因为它看起来就像是把我们的符号从它们的内在意义上连根拔起(从而仅仅把我们自己降低为可编程计算机的功能等价物)。” / blockquote

Modern artificial general intelligence research 现代通用人工智能的研究

The term "artificial general intelligence" was used as early as 1997, by Mark Gubrud^[35] in a discussion of the implications of fully automated military production and operations. The term was re-introduced and popularized by Shane Legg and Ben Goertzel around 2002.^[36] The research objective is much older, for example Doug Lenat's Cyc project (that began in 1984), and Allen Newell's Soar project are regarded as within the scope of AGI. AGI research activity in 2006 was described by Pei Wang and Ben Goertzel^[37] as "producing publications and preliminary results". The first summer school in AGI was organized in Xiamen, China in 2009^[38] by the Xiamen university's Artificial Brain Laboratory and OpenCog. The first university course was given in 2010^[39] and 2011^[40] at Plovdiv University, Bulgaria by Todor Arnaudov. MIT presented a course in AGI in 2018, organized by Lex Fridman and featuring a number of guest lecturers. However, as yet, most AI researchers have devoted little attention to AGI, with some claiming that intelligence is too complex to be completely replicated in the near term. However, a small number of computer scientists are active in AGI research, and many of this group are contributing to a series of AGI conferences. The research is extremely diverse and often pioneering in nature. In the introduction to his book,模板:Sfn Goertzel says that estimates of the time needed before a truly flexible AGI is built vary from 10 years to over a century, but the consensus in the AGI research community seems to be that the timeline discussed by Ray Kurzweil in The Singularity is Near^[41] (i.e. between 2015 and 2045) is plausible.模板:Sfn

The term "artificial general intelligence" was used as early as 1997, by Mark Gubrud in a discussion of the implications of fully automated military production and operations. The term was re-introduced and popularized by Shane Legg and Ben Goertzel around 2002. The research objective is much older, for example Doug Lenat's Cyc project (that began in 1984), and Allen Newell's Soar project are regarded as within the scope of AGI. AGI research activity in 2006 was described by Pei Wang and Ben Goertzel as "producing publications and preliminary results". The first summer school in AGI was organized in Xiamen, China in 2009 by the Xiamen university's Artificial Brain Laboratory and OpenCog. The first university course was given in 2010 and 2011 at Plovdiv University, Bulgaria by Todor Arnaudov. MIT presented a course in AGI in 2018, organized by Lex Fridman and featuring a number of guest lecturers. However, as yet, most AI researchers have devoted little attention to AGI, with some claiming that intelligence is too complex to be completely replicated in the near term. However, a small number of computer scientists are active in AGI research, and many of this group are contributing to a series of AGI conferences. The research is extremely diverse and often pioneering in nature. In the introduction to his book, Goertzel says that estimates of the time needed before a truly flexible AGI is built vary from 10 years to over a century, but the consensus in the AGI research community seems to be that the timeline discussed by Ray Kurzweil in The Singularity is Near (i.e. between 2015 and 2045) is plausible.

”人工通用智能”一词早在1997年就由马克·古布鲁德(Mark Gubrud)在讨论全自动化军事生产和作业的影响时使用。这个术语在2002年左右被肖恩·莱格(Shane Legg)和本·格兹尔(Ben Goertzel)重新引入并推广。研究目标要古老得多，例如道格•雷纳特(Doug Lenat)的 Cyc 项目(始于1984年) ，以及艾伦•纽厄尔(Allen Newell)的 Soar 项目被认为属于通用人工智能的范围。王培(Pei Wang)和本·格兹尔将2006年的通用人工智能研究活动描述为“发表论文和取得初步成果”。2009年，厦门大学人工脑实验室和 OpenCog 在中国厦门组织了通用人工智能的第一个暑期学校。第一个大学课程于2010年和2011年在保加利亚普罗夫迪夫大学由托多尔·阿瑙多夫(Todor Arnaudov)开设。2018年，麻省理工学院开设了一门通用人工智能课程，由莱克斯·弗里德曼(Lex Fridman)组织，并邀请了一些客座讲师。然而，迄今为止，大多数人工智能研究人员对通用人工智能关注甚少，一些人声称，智能过于复杂，在短期内无法完全复制。然而，少数计算机科学家积极参与通用人工智能的研究，其中许多人正在为通用人工智能的一系列会议做出贡献。这项研究极其多样化，而且往往具有开创性。格兹尔在他的书的序言中，说，制造一个真正灵活的通用人工智能所需的时间约为10年到超过一个世纪不等，但是通用人工智能研究团体的似乎一致认为雷·库兹韦尔(Ray Kurzweil)在《奇点临近》（即在2015年至2045年之间）中讨论的时间线是可信的。

However, most mainstream AI researchers doubt that progress will be this rapid.^{[citation needed]} Organizations explicitly pursuing AGI include the Swiss AI lab IDSIA,^{[citation needed]} Nnaisense,^[42] Vicarious, Maluuba,^[9] the OpenCog Foundation, Adaptive AI, LIDA, and Numenta and the associated Redwood Neuroscience Institute.^[43] In addition, organizations such as the Machine Intelligence Research Institute^[44] and OpenAI^[45] have been founded to influence the development path of AGI. Finally, projects such as the Human Brain Project^[46] have the goal of building a functioning simulation of the human brain. A 2017 survey of AGI categorized forty-five known "active R&D projects" that explicitly or implicitly (through published research) research AGI, with the largest three being DeepMind, the Human Brain Project, and OpenAI.^[9]

However, most mainstream AI researchers doubt that progress will be this rapid. Organizations explicitly pursuing AGI include the Swiss AI lab IDSIA, Nnaisense, Vicarious,</ref>^[47]

Many of the scholars who are concerned about existential risk believe that the best way forward would be to conduct (possibly massive) research into solving the difficult "control problem" to answer the question: what types of safeguards, algorithms, or architectures can programmers implement to maximize the probability that their recursively-improving AI would continue to behave in a friendly, rather than destructive, manner after it reaches superintelligence?

许多关注世界末日的学者认为，最好的方法是进行(可能是大规模的)研究，解决困难的“控制问题” ，以回答这个问题: 程序员可以实现哪些类型的保障措施、算法或架构，以最大限度地提高其递归改进的人工智能在达到超级智能后继续以友好而不是破坏性的方式运行的可能性？

The thesis that AI can pose existential risk also has many strong detractors. Skeptics sometimes charge that the thesis is crypto-religious, with an irrational belief in the possibility of superintelligence replacing an irrational belief in an omnipotent God; at an extreme, Jaron Lanier argues that the whole concept that current machines are in any way intelligent is "an illusion" and a "stupendous con" by the wealthy.^[48]

The thesis that AI can pose existential risk also has many strong detractors. Skeptics sometimes charge that the thesis is crypto-religious, with an irrational belief in the possibility of superintelligence replacing an irrational belief in an omnipotent God; at an extreme, Jaron Lanier argues that the whole concept that current machines are in any way intelligent is "an illusion" and a "stupendous con" by the wealthy.

认为人工智能可以提出世界末日的观点也遭到了许多强烈的反对。怀疑论者有时指责该论点是秘密宗教性的，他们非理性地相信超级智能可能取代对万能的上帝的非理性信仰; 在极端情况下，杰伦 · 拉尼尔(Jaron Lanier)认为，目前的机器以任何方式具有智能的整个概念是“一种幻觉” ，是富人的“惊人骗局”。

Much of existing criticism argues that AGI is unlikely in the short term. Computer scientist Gordon Bell argues that the human race will already destroy itself before it reaches the technological singularity. Gordon Moore, the original proponent of Moore's Law, declares that "I am a skeptic. I don't believe [a technological singularity] is likely to happen, at least for a long time. And I don't know why I feel that way."^[49] Baidu Vice President Andrew Ng states AI existential risk is "like worrying about overpopulation on Mars when we have not even set foot on the planet yet."^[50]

Much of existing criticism argues that AGI is unlikely in the short term. Computer scientist Gordon Bell argues that the human race will already destroy itself before it reaches the technological singularity. Gordon Moore, the original proponent of Moore's Law, declares that "I am a skeptic. I don't believe [a technological singularity] is likely to happen, at least for a long time. And I don't know why I feel that way." Baidu Vice President Andrew Ng states AI existential risk is "like worrying about overpopulation on Mars when we have not even set foot on the planet yet."

现有的许多批评认为，德盛安联短期内不太可能成功。计算机科学家 Gordon Bell 认为人类在到达技术奇异点之前就已经自我毁灭了。戈登 · 摩尔，摩尔定律的最初倡导者，宣称“我是一个怀疑论者。我不认为技术奇异点会发生，至少在很长一段时间内不会。我不知道为什么会有这种感觉。”百度副总裁 Andrew Ng 说，人工智能世界末日就像是在担心火星人口过剩，而我们甚至还没有踏上这个星球

Notes

↑ "DeepMind and Google: the battle to control artificial intelligence". The Economist (1843 (magazine)). 2019. Retrieved 15 March 2020. AGI stands for Artificial General Intelligence, a hypothetical computer program...
↑ Kurzweil, Singularity (2005) p. 260
↑ ^3.0 ^3.1 Kurzweil, Ray (5 August 2005), "Long Live AI", Forbes: Kurzweil describes strong AI as "machine intelligence with the full range of human intelligence."
↑ "The Age of Artificial Intelligence: George John at TEDxLondonBusinessSchool 2013". Archived from the original on 26 February 2014. Retrieved 22 February 2014.
↑ europarl.europa.eu: How artificial intelligence works, "Concluding remarks: Today's AI is powerful and useful, but remains far from speculated AGI or ASI.", European Parliamentary Research Service, retrieved March 3, 2020
↑ Grace, Katja; Salvatier, John; Dafoe, Allan; Zhang, Baobao; Evans, Owain (31 July 2018). "Viewpoint: When Will AI Exceed Human Performance? Evidence from AI Experts". Journal of Artificial Intelligence Research. 62: 729–754. doi:10.1613/jair.1.11222. ISSN 1076-9757.
↑ Encyclopædia Britannica Strong AI, applied AI, and cognitive simulation -{zh-cn:互联网档案馆; zh-tw:網際網路檔案館; zh-hk:互聯網檔案館;}-的存檔，存档日期15 October 2007. or Jack Copeland What is artificial intelligence? -{zh-cn:互联网档案馆; zh-tw:網際網路檔案館; zh-hk:互聯網檔案館;}-的存檔，存档日期18 August 2007. on AlanTuring.net
↑ "The Open University on Strong and Weak AI". Archived from the original on 25 September 2009. Retrieved 8 October 2007. https://en.wikipedia.org/wiki/Defekte_Weblinks?dwl={{{url}}} Seite nicht mehr abrufbar], Suche in Webarchiven: Kategorie:Wikipedia:Weblink offline (andere Namensräume)[http://timetravel.mementoweb.org/list/2010/Kategorie:Wikipedia:Vorlagenfehler/Vorlage:Toter Link/URL_fehlt
↑ ^9.0 ^9.1 ^9.2 Baum, Seth (12 November 2017). "Baum, Seth, A Survey of Artificial General Intelligence Projects for Ethics, Risk, and Policy (November 12, 2017). Global Catastrophic Risk Institute Working Paper 17-1". {{cite journal}}: Cite journal requires |journal= (help)
↑ AI founder John McCarthy writes: "we cannot yet characterize in general what kinds of computational procedures we want to call intelligent." McCarthy, John (2007). "Basic Questions". Stanford University. Archived from the original on 26 October 2007. Retrieved 6 December 2007. (For a discussion of some definitions of intelligence used by artificial intelligence researchers, see philosophy of artificial intelligence.)

↑ Pfeifer, R. and Bongard J. C., How the body shapes the way we think: a new view of intelligence (The MIT Press, 2007).

↑ White, R. W. (1959). "Motivation reconsidered: The concept of competence". Psychological Review. 66 (5): 297–333. doi:10.1037/h0040934. PMID 13844397.
↑ 脚本错误：没有“Footnotes”这个模块。
↑ deCharms, R. (1968). Personal causation. New York: Academic Press.
↑ Muehlhauser, Luke (11 August 2013). "What is AGI?". Machine Intelligence Research Institute. Archived from the original on 25 April 2014. Retrieved 1 May 2014.
↑ "What is Artificial General Intelligence (AGI)? | 4 Tests For Ensuring Artificial General Intelligence". Talky Blog (in English). 13 July 2019. Archived from the original on 17 July 2019. Retrieved 17 July 2019.
↑ Shapiro, Stuart C. (1992). Artificial Intelligence -{zh-cn:互联网档案馆; zh-tw:網際網路檔案館; zh-hk:互聯網檔案館;}-的存檔，存档日期1 February 2016. In Stuart C. Shapiro (Ed.), Encyclopedia of Artificial Intelligence (Second Edition, pp. 54–57). New York: John Wiley. (Section 4 is on "AI-Complete Tasks".)
↑ Roman V. Yampolskiy. Turing Test as a Defining Feature of AI-Completeness. In Artificial Intelligence, Evolutionary Computation and Metaheuristics (AIECM) --In the footsteps of Alan Turing. Xin-She Yang (Ed.). pp. 3–17. (Chapter 1). Springer, London. 2013. http://cecs.louisville.edu/ry/TuringTestasaDefiningFeature04270003.pdf -{zh-cn:互联网档案馆; zh-tw:網際網路檔案館; zh-hk:互聯網檔案館;}-的存檔，存档日期22 May 2013.
↑ Luis von Ahn, Manuel Blum, Nicholas Hopper, and John Langford. CAPTCHA: Using Hard AI Problems for Security -{zh-cn:互联网档案馆; zh-tw:網際網路檔案館; zh-hk:互聯網檔案館;}-的存檔，存档日期4 March 2016.. In Proceedings of Eurocrypt, Vol. 2656 (2003), pp. 294–311.
↑ Bergmair, Richard (7 January 2006). "Natural Language Steganography and an "AI-complete" Security Primitive". CiteSeerX 10.1.1.105.129. {{cite journal}}: Cite journal requires |journal= (help) (unpublished?)
↑ 脚本错误：没有“Footnotes”这个模块。
↑ 脚本错误：没有“Footnotes”这个模块。 quoted in 脚本错误：没有“Footnotes”这个模块。
↑ "Scientist on the Set: An Interview with Marvin Minsky". Archived from the original on 16 July 2012. Retrieved 5 April 2008.
↑ Marvin Minsky to 脚本错误：没有“Footnotes”这个模块。, quoted in 脚本错误：没有“Footnotes”这个模块。.
↑ The Lighthill report specifically criticized AI's "grandiose objectives" and led the dismantling of AI research in England. (脚本错误：没有“Footnotes”这个模块。; 脚本错误：没有“Footnotes”这个模块。) In the U.S., DARPA became determined to fund only "mission-oriented direct research, rather than basic undirected research". See 模板:Harv under "Shift to Applied Research Increases Investment". See also 模板:Harv and 模板:Harv
↑ 脚本错误：没有“Footnotes”这个模块。, 脚本错误：没有“Footnotes”这个模块。 and see also 脚本错误：没有“Footnotes”这个模块。
↑ 脚本错误：没有“Footnotes”这个模块。; 脚本错误：没有“Footnotes”这个模块。; 脚本错误：没有“Footnotes”这个模块。
↑ 脚本错误：没有“Footnotes”这个模块。
↑ As AI founder John McCarthy writes "it would be a great relief to the rest of the workers in AI if the inventors of new general formalisms would express their hopes in a more guarded form than has sometimes been the case." McCarthy, John (2000). "Reply to Lighthill". Stanford University. Archived from the original on 30 September 2008. Retrieved 29 September 2007.
↑ "At its low point, some computer scientists and software engineers avoided the term artificial intelligence for fear of being viewed as wild-eyed dreamers."Markoff, John (14 October 2005). "Behind Artificial Intelligence, a Squadron of Bright Real People". The New York Times.
↑ 脚本错误：没有“Footnotes”这个模块。
↑ "Trends in the Emerging Tech Hype Cycle". Gartner Reports. Archived from the original on 22 May 2019. Retrieved 7 May 2019.
↑ 脚本错误：没有“Footnotes”这个模块。
↑ Harnad, S (1990). "The Symbol Grounding Problem". Physica D. 42 (1–3): 335–346. arXiv:cs/9906002. Bibcode:1990PhyD...42..335H. doi:10.1016/0167-2789(90)90087-6.
↑ 脚本错误：没有“Footnotes”这个模块。
↑ "Who coined the term "AGI"? » goertzel.org" (in English). Archived from the original on 28 December 2018. Retrieved 28 December 2018., via Life 3.0: 'The term "AGI" was popularized by... Shane Legg, Mark Gubrud and Ben Goertzel'
↑ 脚本错误：没有“Footnotes”这个模块。. See also 脚本错误：没有“Footnotes”这个模块。 with an up-to-date summary and lots of links.
↑ https://goertzel.org/AGI_Summer_School_2009.htm
↑ http://fmi-plovdiv.org/index.jsp?id=1054&ln=1
↑ http://fmi.uni-plovdiv.bg/index.jsp?id=1139&ln=1
↑ 模板:Harv or see Advanced Human Intelligence -{zh-cn:互联网档案馆; zh-tw:網際網路檔案館; zh-hk:互聯網檔案館;}-的存檔，存档日期30 June 2011. where he defines strong AI as "machine intelligence with the full range of human intelligence."
↑ Markoff, John (27 November 2016). "When A.I. Matures, It May Call Jürgen Schmidhuber 'Dad'". The New York Times. Archived from the original on 26 December 2017. Retrieved 26 December 2017.
↑ James Barrat (2013). "Chapter 11: A Hard Takeoff". Our Final Invention: Artificial Intelligence and the End of the Human Era (First ed.). New York: St. Martin's Press. ISBN 9780312622374.
↑ "About the Machine Intelligence Research Institute". Machine Intelligence Research Institute. Archived from the original on 21 January 2018. Retrieved 26 December 2017.
↑ "About OpenAI". OpenAI (in English). Archived from the original on 22 December 2017. Retrieved 26 December 2017.
↑ Theil, Stefan. "Trouble in Mind". Scientific American (in English). pp. 36–42. Bibcode:2015SciAm.313d..36T. doi:10.1038/scientificamerican1015-36. Archived from the original on 9 November 2017. Retrieved 26 December 2017.
↑ Kaj Sotala; Roman Yampolskiy (19 December 2014). "Responses to catastrophic AGI risk: a survey". Physica Scripta. 90 (1).
↑ "But What Would the End of Humanity Mean for Me?". The Atlantic. 9 May 2014. Retrieved 12 December 2015.
↑ "Tech Luminaries Address Singularity". IEEE Spectrum: Technology, Engineering, and Science News (in English). No. SPECIAL REPORT: THE SINGULARITY. 1 June 2008. Retrieved 8 April 2020.
↑ Shermer, Michael (1 March 2017). "Apocalypse AI". Scientific American (in English). p. 77. Bibcode:2017SciAm.316c..77S. doi:10.1038/scientificamerican0317-77. Retrieved 27 November 2017.

References

• Stages of Artificial Intelligence"Computer Science" on 2nd April, 2020.

•2020年4月2日，《人工智能的阶段》[ https://www.computerscience0.xyz/2020/04/ai-Artificial-Intelligence-Stages-of.html 计算机科学]。

Halal, William E. "TechCast Article Series: The Automation of Thought" (PDF). Archived from the original (PDF) on 6 June 2013.

Aleksander, Igor (1996), Impossible Minds, World Scientific Publishing Company, ISBN 978-1-86094-036-1

Omohundro, Steve (2008), The Nature of Self-Improving Artificial Intelligence, presented and distributed at the 2007 Singularity Summit, San Francisco, CA.

Sandberg, Anders; Boström, Nick (2008), Whole Brain Emulation: A Roadmap (PDF), Technical Report #2008‐3, Future of Humanity Institute, Oxford University, retrieved 5 April 2009

Azevedo FA, Carvalho LR, Grinberg LT, et al. (April 2009), "Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain", The Journal of Comparative Neurology, 513 (5): 532–41, doi:10.1002/cne.21974, PMID 19226510, retrieved 4 September 2013

Berglas

最后一个贝格拉斯, Anthony

首先是安东尼 (2008

2008年), [http://berglas.org/Articles/AIKillGrandchildren/AIKillGrandchildren.html

Http://berglas.org/articles/aikillgrandchildren/aikillgrandchildren.html Artificial Intelligence will Kill our Grandchildren 人工智能会杀死我们的孙子] {{citation}}: Check |url= value (help); Check date values in: |year= (help); line feed character in |first= at position 8 (help); line feed character in |last= at position 8 (help); line feed character in |title= at position 52 (help); line feed character in |url= at position 73 (help); line feed character in |year= at position 5 (help)

}}

Chalmers, David (1996), The Conscious Mind, Oxford University Press.

Clocksin, William (August 2003), "Artificial intelligence and the future", Philosophical Transactions of the Royal Society A, 361 (1809): 1721–1748, Bibcode:2003RSPTA.361.1721C, doi:10.1098/rsta.2003.1232, PMID 12952683.

模板:Crevier 1993

Darrach, Brad (20 November 1970), "Meet Shakey, the First Electronic Person", Life Magazine, pp. 58–68.

Drachman, D (2005), "Do we have brain to spare?", Neurology, 64 (12): 2004–5, doi:10.1212/01.WNL.0000166914.38327.BB, PMID 15985565.

Feigenbaum, Edward A.; McCorduck, Pamela (1983), The Fifth Generation: Artificial Intelligence and Japan's Computer Challenge to the World, Michael Joseph, ISBN 978-0-7181-2401-4

Gelernter, David

最后的盖兰特 (2010

2010年), Dream-logic, the Internet and Artificial Thought 梦的逻辑，互联网和人工思维, retrieved 25 July 2010

Http://www.edge.org/3rd_culture/gelernter10.1/gelernter10.1_index.html : 2010年7月25日 {{citation}}: Check date values in: |accessdate= and |year= (help); External link in |accessdate= (help); Text "第一个大卫" ignored (help); line feed character in |accessdate= at position 13 (help); line feed character in |first= at position 6 (help); line feed character in |title= at position 49 (help); line feed character in |year= at position 5 (help)

}}

Goertzel, Ben; Pennachin, Cassio, eds. (2006

2006年), Artificial General Intelligence 人工通用智能 (PDF), Springer

出版商斯普林格, ISBN [[Special:BookSources/978-3-540-23733-4

[国际标准图书馆编号978-3-540-23733-4]|978-3-540-23733-4 [国际标准图书馆编号978-3-540-23733-4]]], archived from [http://people.inf.elte.hu/csizsekp/ai/books/artificial-general-intelligence-cognitive-technologies.9783540237334.27156.pdf

Http://people.inf.elte.hu/csizsekp/ai/books/artificial-general-intelligence-cognitive-technologies.9783540237334.27156.pdf the original] (PDF) on 20 March 2013 {{citation}}: Check |isbn= value: invalid character (help); Check |url= value (help); Check date values in: |year= (help); Text "authorlink Ben Goertzel" ignored (help); Text "档案-url https://web.archive.org/web/20130320184603/http://people.inf.elte.hu/csizsekp/ai/books/artificial-general-intelligence-cognitive-technologies.9783540237334.27156.pdf" ignored (help); Text "档案-日期2013年3月20日" ignored (help); Text "编辑2-first Cassio" ignored (help); Text "编辑2-last Pennachin" ignored (help); line feed character in |authorlink= at position 13 (help); line feed character in |isbn= at position 18 (help); line feed character in |publisher= at position 10 (help); line feed character in |title= at position 32 (help); line feed character in |url= at position 123 (help); line feed character in |year= at position 5 (help)

}}

[[Ben Goertzel

作者: Ben Goertzel|Goertzel, Ben]]; Wang, Pei

2 Wang (2006

2006年), [http://sites.google.com/site/narswang/publications/wang-goertzel.AGI_Aspects.pdf?attredirects=1

Http://sites.google.com/site/narswang/publications/wang-goertzel.agi_aspects.pdf?attredirects=1 Introduction: Aspects of Artificial General Intelligence] (PDF) {{citation}}: Check |url= value (help); Check date values in: |year= (help); Text "first2 Pei" ignored (help); Text "题目简介: 人工通用智能的方方面面" ignored (help); line feed character in |authorlink= at position 13 (help); line feed character in |first2= at position 4 (help); line feed character in |url= at position 96 (help); line feed character in |year= at position 5 (help)

}}

Goertzel, Ben (December 2007), "Human-level artificial general intelligence and the possibility of a technological singularity: a reaction to Ray Kurzweil's The Singularity Is Near, and McDermott's critique of Kurzweil", Artificial Intelligence, 171 (18, Special Review Issue): 1161–1173, doi:10.1016/j.artint.2007.10.011, retrieved 1 April 2009.

Gubrud, Mark (November 1997), "Nanotechnology and International Security", Fifth Foresight Conference on Molecular Nanotechnology, retrieved 7 May 2011

Holte, RC; Choueiry, BY (2003), "Abstraction and reformulation in artificial intelligence", Philosophical Transactions of the Royal Society B, 358 (1435): 1197–1204, doi:10.1098/rstb.2003.1317, PMC 1693218, PMID 12903653.

Howe, J. (November 1994), Artificial Intelligence at Edinburgh University : a Perspective, retrieved 30 August 2007

Johnson, Mark (1987), The body in the mind, Chicago, ISBN 978-0-226-40317-5

Kurzweil, Ray (2005), The Singularity is Near, Viking Press

Lighthill, Professor Sir James (1973), "Artificial Intelligence: A General Survey", Artificial Intelligence: a paper symposium, Science Research Council

Luger, George; Stubblefield, William (2004), Artificial Intelligence: Structures and Strategies for Complex Problem Solving (5th ed.), The Benjamin/Cummings Publishing Company, Inc., p. 720, ISBN 978-0-8053-4780-7

McCarthy, John (October 2007), "From here to human-level AI", Artificial Intelligence, 171 (18): 1174–1182, doi:10.1016/j.artint.2007.10.009.

模板:McCorduck 2004

Moravec, Hans (1976), The Role of Raw Power in Intelligence, archived from the original on 3 March 2016, retrieved 29 September 2007

Moravec, Hans (1988), Mind Children, Harvard University Press

Nagel (1974), "What Is it Like to Be a Bat" (PDF), Philosophical Review, 83 (4): 435–50, doi:10.2307/2183914, JSTOR 2183914.

Newell, Allen; Simon, H. A. (1963), "GPS: A Program that Simulates Human Thought", in Feigenbaum, E.A.; Feldman, J. (eds.), Computers and Thought, New York: McGraw-Hill

Newell, Allen; Simon, H. A. (1976). "Computer Science as Empirical Inquiry: Symbols and Search". Communications of the ACM. 19 (3): 113–126. doi:10.1145/360018.360022. {{cite journal}}: Invalid |ref=harv (help)

Nilsson, Nils (1998), Artificial Intelligence: A New Synthesis, Morgan Kaufmann Publishers, ISBN 978-1-55860-467-4

模板:Russell Norvig 2003

NRC (1999), "Developments in Artificial Intelligence", Funding a Revolution: Government Support for Computing Research, National Academy Press

Poole, David; Mackworth, Alan; Goebel, Randy (1998), Computational Intelligence: A Logical Approach, New York: Oxford University Press

Searle, John (1980), "Minds, Brains and Programs", Behavioral and Brain Sciences, 3 (3): 417–457, doi:10.1017/S0140525X00005756

Simon, H. A. (1965), The Shape of Automation for Men and Management, New York: Harper & Row

Sutherland, J.G. (1990), "Holographic Model of Memory, Learning, and Expression", International Journal of Neural Systems, 1–3: 256–267.

Williams RW, Herrup K (1988), "The control of neuron number", Annual Review of Neuroscience, 11: 423–53, doi:10.1146/annurev.ne.11.030188.002231, PMID 3284447.

de Vega, Manuel; Glenberg, Arthur

2- 最后的格伦伯格2- 第一个亚瑟; Graesser, Arthur, eds. (2008

2008年), Symbols and Embodiment: Debates on meaning and cognition 标题符号与具体化: 关于意义与认知的争论, Oxford University Press

牛津大学出版社, ISBN [[Special:BookSources/978-0-19-921727-4

[国际标准图书编号978-0-19-921727-4]|978-0-19-921727-4 [国际标准图书编号978-0-19-921727-4]]] {{citation}}: Check |isbn= value: invalid character (help); Check date values in: |year= (help); Text "编辑1-first Manuel" ignored (help); Text "编辑1-last de Vega" ignored (help); Text "编辑3-first Arthur" ignored (help); Text "编辑3-last Graesser" ignored (help); line feed character in |editor2-first= at position 7 (help); line feed character in |isbn= at position 18 (help); line feed character in |publisher= at position 24 (help); line feed character in |title= at position 57 (help); line feed character in |year= at position 5 (help)

}}

Yudkowsky, Eliezer (2006), Goertzel, Ben; Pennachin, Cassio (eds.), "Artificial General Intelligence" (PDF), Annual Review of Psychology, Springer, 49: 585–612, doi:10.1146/annurev.psych.49.1.585, ISBN 978-3-540-23733-4, PMID 9496632, archived from the original (PDF) on 11 April 2009

Zucker, Jean-Daniel (July 2003), "A grounded theory of abstraction in artificial intelligence", Philosophical Transactions of the Royal Society B, 358 (1435): 1293–1309, doi:10.1098/rstb.2003.1308, PMC 1693211, PMID 12903672.

Yudkowsky, Eliezer (2008), "Artificial Intelligence as a Positive and Negative Factor in Global Risk", Global Catastrophic Risks, Bibcode:2008gcr..book..303Y.

External links

The AGI portal maintained by Pei Wang

The Genesis Group at MIT's CSAIL – Modern research on the computations that underlay human intelligence

OpenCog – open source project to develop a human-level AI

Simulating logical human thought

What Do We Know about AI Timelines? – Literature review

模板:Existential risk from artificial intelligence

Category:Hypothetical technology

类别: 假设技术

Category:Artificial intelligence

类别: 人工智能

Category:Computational neuroscience

类别: 计算神经科学

fr:Intelligence artificielle#Intelligence artificielle forte

智力人工 # 智力人工强项

This page was moved from wikipedia:en:Artificial general intelligence. Its edit history can be viewed at 通用人工智能/edithistory

[1] "DeepMind and Google: the battle to control artificial intelligence". The Economist (1843 (magazine)). 2019. Retrieved 15 March 2020. AGI stands for Artificial General Intelligence, a hypothetical computer program...

[2] Kurzweil, Singularity (2005) p. 260

[Kurzweil_2005-08-05-3] 3.0 ^3.1 Kurzweil, Ray (5 August 2005), "Long Live AI", Forbes: Kurzweil describes strong AI as "machine intelligence with the full range of human intelligence."

[4] "The Age of Artificial Intelligence: George John at TEDxLondonBusinessSchool 2013". Archived from the original on 26 February 2014. Retrieved 22 February 2014.

[5] uroparl.europa.eu: How artificial intelligence works, "Concluding remarks: Today's AI is powerful and useful, but remains far from speculated AGI or ASI.", European Parliamentary Research Service, retrieved March 3, 2020

[6] Grace, Katja; Salvatier, John; Dafoe, Allan; Zhang, Baobao; Evans, Owain (31 July 2018). "Viewpoint: When Will AI Exceed Human Performance? Evidence from AI Experts". Journal of Artificial Intelligence Research. 62: 729–754. doi:10.1613/jair.1.11222. ISSN 1076-9757.

[7] Encyclopædia Britannica Strong AI, applied AI, and cognitive simulation -{zh-cn:互联网档案馆; zh-tw:網際網路檔案館; zh-hk:互聯網檔案館;}-的存檔，存档日期15 October 2007. or Jack Copeland What is artificial intelligence? -{zh-cn:互联网档案馆; zh-tw:網際網路檔案館; zh-hk:互聯網檔案館;}-的存檔，存档日期18 August 2007. on AlanTuring.net

[8] "The Open University on Strong and Weak AI". Archived from the original on 25 September 2009. Retrieved 8 October 2007. https://en.wikipedia.org/wiki/Defekte_Weblinks?dwl={{{url}}} Seite nicht mehr abrufbar], Suche in Webarchiven: Kategorie:Wikipedia:Weblink offline (andere Namensräume)[http://timetravel.mementoweb.org/list/2010/Kategorie:Wikipedia:Vorlagenfehler/Vorlage:Toter Link/URL_fehlt

[baum-9] 9.0 ^9.1 ^9.2 Baum, Seth (12 November 2017). "Baum, Seth, A Survey of Artificial General Intelligence Projects for Ethics, Risk, and Policy (November 12, 2017). Global Catastrophic Risk Institute Working Paper 17-1". {{cite journal}}: Cite journal requires |journal= (help)

[10] AI founder John McCarthy writes: "we cannot yet characterize in general what kinds of computational procedures we want to call intelligent." McCarthy, John (2007). "Basic Questions". Stanford University. Archived from the original on 26 October 2007. Retrieved 6 December 2007. (For a discussion of some definitions of intelligence used by artificial intelligence researchers, see philosophy of artificial intelligence.)

[11] Pfeifer, R. and Bongard J. C., How the body shapes the way we think: a new view of intelligence (The MIT Press, 2007).
v
t
e
Inline tags
Attribution

{{Attribution needed}}

{{By whom}}

{{Weasel-inline}}

{{Which?}}

{{Who}}

Citation

{{Better source}}

{{Citation needed}}
{{Citation needed span}}

{{Reference necessary}}

{{Citation needed (lead)}}

{{Failed verification}}

{{Full}}

{{Primary source-inline}}

{{Request quotation}}

{{Retracted}}

{{Third-party-inline}}

Incomplete

{{Author missing}}

{{Author incomplete}}

{{Date missing}}

{{ISBN missing}}

{{Page needed}}

{{Publisher missing}}

{{Title incomplete}}

{{Year missing}}

{{Dead link}}

Content

{{Contradict-inline}}

{{Contradiction-inline}}

{{Dubious|talk page section name}}

{{Examples}}

{{Inconsistent}}

{{List fact}}

{{Lopsided}}

{{POV-statement}}

Date and place

{{Clarify timeframe}}

{{Quantify}}

{{Update after}}

{{Update-small}}

{{When}}

{{Where}}

{{Year needed}}

Wikification

{{Disambiguation needed}}

{{Pronunciation needed}}

Wording

{{Ambiguous}}

{{Awkward}}

{{Buzz}}

{{Clarify}}

{{Clarify span}}

{{Definition}}

{{Elucidate}}

{{Expand acronym}}

{{Technical-statement}}

{{Vague|optional message to be displayed on mouseover}}

{{Why?}}

Category

[12] v

[13] t

[14] e

[15] {{Attribution needed}}

[16] {{By whom}}

[17] {{Weasel-inline}}

[18] {{Which?}}

[19] {{Who}}

[20] {{Better source}}

[21] {{Citation needed}}
{{Citation needed span}}

{{Reference necessary}}

[22] {{Citation needed span}}

[23] {{Reference necessary}}

[24] {{Citation needed (lead)}}

[25] {{Failed verification}}

[26] {{Full}}

[27] {{Primary source-inline}}

[28] {{Request quotation}}

[29] {{Retracted}}

[30] {{Third-party-inline}}

[31] {{Author missing}}

[32] {{Author incomplete}}

[33] {{Date missing}}

[34] {{ISBN missing}}

[35] {{Page needed}}

[36] {{Publisher missing}}

[37] {{Title incomplete}}

[38] {{Year missing}}

[39] {{Dead link}}

[40] {{Contradict-inline}}

[41] {{Contradiction-inline}}

[42] {{Dubious|talk page section name}}

[43] {{Examples}}

[44] {{Inconsistent}}

[45] {{List fact}}

[46] {{Lopsided}}

[47] {{POV-statement}}

[48] {{Clarify timeframe}}

[49] {{Quantify}}

[50] {{Update after}}

[51] {{Update-small}}

[52] {{When}}

[53] {{Where}}

[54] {{Year needed}}

[55] {{Disambiguation needed}}

[56] {{Pronunciation needed}}

[57] {{Ambiguous}}

[58] {{Awkward}}

[59] {{Buzz}}

[60] {{Clarify}}

[61] {{Clarify span}}

[62] {{Definition}}

[63] {{Elucidate}}

[64] {{Expand acronym}}

[65] {{Technical-statement}}

[66] {{Vague|optional message to be displayed on mouseover}}

[67] {{Why?}}

[12] White, R. W. (1959). "Motivation reconsidered: The concept of competence". Psychological Review. 66 (5): 297–333. doi:10.1037/h0040934. PMID 13844397.

[13] 脚本错误：没有“Footnotes”这个模块。

[14] Charms, R. (1968). Personal causation. New York: Academic Press.

[15] Muehlhauser, Luke (11 August 2013). "What is AGI?". Machine Intelligence Research Institute. Archived from the original on 25 April 2014. Retrieved 1 May 2014.

[16] "What is Artificial General Intelligence (AGI)? | 4 Tests For Ensuring Artificial General Intelligence". Talky Blog (in English). 13 July 2019. Archived from the original on 17 July 2019. Retrieved 17 July 2019.

[Shapiro92-17] Shapiro, Stuart C. (1992). Artificial Intelligence -{zh-cn:互联网档案馆; zh-tw:網際網路檔案館; zh-hk:互聯網檔案館;}-的存檔，存档日期1 February 2016. In Stuart C. Shapiro (Ed.), Encyclopedia of Artificial Intelligence (Second Edition, pp. 54–57). New York: John Wiley. (Section 4 is on "AI-Complete Tasks".)

[18] Roman V. Yampolskiy. Turing Test as a Defining Feature of AI-Completeness. In Artificial Intelligence, Evolutionary Computation and Metaheuristics (AIECM) --In the footsteps of Alan Turing. Xin-She Yang (Ed.). pp. 3–17. (Chapter 1). Springer, London. 2013. http://cecs.louisville.edu/ry/TuringTestasaDefiningFeature04270003.pdf -{zh-cn:互联网档案馆; zh-tw:網際網路檔案館; zh-hk:互聯網檔案館;}-的存檔，存档日期22 May 2013.

[19] Luis von Ahn, Manuel Blum, Nicholas Hopper, and John Langford. CAPTCHA: Using Hard AI Problems for Security -{zh-cn:互联网档案馆; zh-tw:網際網路檔案館; zh-hk:互聯網檔案館;}-的存檔，存档日期4 March 2016.. In Proceedings of Eurocrypt, Vol. 2656 (2003), pp. 294–311.

[20] Bergmair, Richard (7 January 2006). "Natural Language Steganography and an "AI-complete" Security Primitive". CiteSeerX 10.1.1.105.129. {{cite journal}}: Cite journal requires |journal= (help) (unpublished?)

[21] 脚本错误：没有“Footnotes”这个模块。

[22] 脚本错误：没有“Footnotes”这个模块。 quoted in 脚本错误：没有“Footnotes”这个模块。

[23] "Scientist on the Set: An Interview with Marvin Minsky". Archived from the original on 16 July 2012. Retrieved 5 April 2008.

[24] Marvin Minsky to 脚本错误：没有“Footnotes”这个模块。, quoted in 脚本错误：没有“Footnotes”这个模块。.

[25] The Lighthill report specifically criticized AI's "grandiose objectives" and led the dismantling of AI research in England. (脚本错误：没有“Footnotes”这个模块。; 脚本错误：没有“Footnotes”这个模块。) In the U.S., DARPA became determined to fund only "mission-oriented direct research, rather than basic undirected research". See 模板:Harv under "Shift to Applied Research Increases Investment". See also 模板:Harv and 模板:Harv

[26] 脚本错误：没有“Footnotes”这个模块。, 脚本错误：没有“Footnotes”这个模块。 and see also 脚本错误：没有“Footnotes”这个模块。

[27] 脚本错误：没有“Footnotes”这个模块。; 脚本错误：没有“Footnotes”这个模块。; 脚本错误：没有“Footnotes”这个模块。

[28] 脚本错误：没有“Footnotes”这个模块。

[29] As AI founder John McCarthy writes "it would be a great relief to the rest of the workers in AI if the inventors of new general formalisms would express their hopes in a more guarded form than has sometimes been the case." McCarthy, John (2000). "Reply to Lighthill". Stanford University. Archived from the original on 30 September 2008. Retrieved 29 September 2007.

[30] "At its low point, some computer scientists and software engineers avoided the term artificial intelligence for fear of being viewed as wild-eyed dreamers."Markoff, John (14 October 2005). "Behind Artificial Intelligence, a Squadron of Bright Real People". The New York Times.

[31] 脚本错误：没有“Footnotes”这个模块。

[32] "Trends in the Emerging Tech Hype Cycle". Gartner Reports. Archived from the original on 22 May 2019. Retrieved 7 May 2019.

[33] 脚本错误：没有“Footnotes”这个模块。

[34] Harnad, S (1990). "The Symbol Grounding Problem". Physica D. 42 (1–3): 335–346. arXiv:cs/9906002. Bibcode:1990PhyD...42..335H. doi:10.1016/0167-2789(90)90087-6.

[35] 脚本错误：没有“Footnotes”这个模块。

[36] "Who coined the term "AGI"? » goertzel.org" (in English). Archived from the original on 28 December 2018. Retrieved 28 December 2018., via Life 3.0: 'The term "AGI" was popularized by... Shane Legg, Mark Gubrud and Ben Goertzel'

[37] 脚本错误：没有“Footnotes”这个模块。. See also 脚本错误：没有“Footnotes”这个模块。 with an up-to-date summary and lots of links.

[38] ttps://goertzel.org/AGI_Summer_School_2009.htm

[39] ttp://fmi-plovdiv.org/index.jsp?id=1054&ln=1

[40] ttp://fmi.uni-plovdiv.bg/index.jsp?id=1139&ln=1

[K-41] 模板:Harv or see Advanced Human Intelligence -{zh-cn:互联网档案馆; zh-tw:網際網路檔案館; zh-hk:互聯網檔案館;}-的存檔，存档日期30 June 2011. where he defines strong AI as "machine intelligence with the full range of human intelligence."

[42] Markoff, John (27 November 2016). "When A.I. Matures, It May Call Jürgen Schmidhuber 'Dad'". The New York Times. Archived from the original on 26 December 2017. Retrieved 26 December 2017.

[43] James Barrat (2013). "Chapter 11: A Hard Takeoff". Our Final Invention: Artificial Intelligence and the End of the Human Era (First ed.). New York: St. Martin's Press. ISBN 9780312622374.

[44] "About the Machine Intelligence Research Institute". Machine Intelligence Research Institute. Archived from the original on 21 January 2018. Retrieved 26 December 2017.

[45] "About OpenAI". OpenAI (in English). Archived from the original on 22 December 2017. Retrieved 26 December 2017.

[46] Theil, Stefan. "Trouble in Mind". Scientific American (in English). pp. 36–42. Bibcode:2015SciAm.313d..36T. doi:10.1038/scientificamerican1015-36. Archived from the original on 9 November 2017. Retrieved 26 December 2017.

[physica_scripta-47] Kaj Sotala; Roman Yampolskiy (19 December 2014). "Responses to catastrophic AGI risk: a survey". Physica Scripta. 90 (1).

[atlantic-but-what-48] "But What Would the End of Humanity Mean for Me?". The Atlantic. 9 May 2014. Retrieved 12 December 2015.

[49] "Tech Luminaries Address Singularity". IEEE Spectrum: Technology, Engineering, and Science News (in English). No. SPECIAL REPORT: THE SINGULARITY. 1 June 2008. Retrieved 8 April 2020.

[shermer-50] Shermer, Michael (1 March 2017). "Apocalypse AI". Scientific American (in English). p. 77. Bibcode:2017SciAm.316c..77S. doi:10.1038/scientificamerican0317-77. Retrieved 27 November 2017.

[1]

[2]

[3]

[4]

[5]

[6]

[7]

[8]

[9]

[10]

[11]

[12]

[13]

[14]

[15]

[16]

[17]

[18]

[19]

[20]

[21]

[22]

[23]

[24]

[25]

[26]

[27]

[28]

[29]

[30]

[31]

[32]

[33]

[34]

[35]

[36]

[37]

[38]

[39]

[40]

[41]

[42]

[43]

[44]

[45]

[46]

[47]

[48]

[49]

[50]

@@ 第329行： / 第329行： @@
 Estimates of how much processing power is needed to emulate a human brain at various levels (from Ray Kurzweil, and [[Anders Sandberg and Nick Bostrom), along with the fastest supercomputer from TOP500 mapped by year. Note the logarithmic scale and exponential trendline, which assumes the computational capacity doubles every 1.1 years. Kurzweil believes that mind uploading will be possible at neural simulation, while the Sandberg, Bostrom report is less certain about where consciousness arises.]] For low-level brain simulation, an extremely powerful computer would be required. The human brain has a huge number of synapses. Each of the 10<sup>11</sup> (one hundred billion) neurons has on average 7,000 synaptic connections (synapses) to other neurons. It has been estimated that the brain of a three-year-old child has about 10<sup>15</sup> synapses (1 quadrillion). This number declines with age, stabilizing by adulthood. Estimates vary for an adult, ranging from 10<sup>14</sup> to 5×10<sup>14</sup> synapses (100 to 500 trillion). An estimate of the brain's processing power, based on a simple switch model for neuron activity, is around 10<sup>14</sup> (100 trillion) synaptic updates per second (SUPS). In 1997, Kurzweil looked at various estimates for the hardware required to equal the human brain and adopted a figure of 10<sup>16</sup> computations per second (cps). (For comparison, if a "computation" was equivalent to one "floating point operation" –  a measure used to rate current supercomputers – then 10<sup>16</sup> "computations" would be equivalent to 10 petaFLOPS, achieved in 2011). He used this figure to predict the necessary hardware would be available sometime between 2015 and 2025, if the exponential growth in computer power at the time of writing continued.
-估计需要多少处理能力才能在不同水平上模拟人类大脑(来自 Ray Kurzweil，[ Anders Sandberg 和 Nick Bostrom ]) ，以及每年从 TOP500绘制出的最快超级计算机。请注意对数尺度趋势线和指数趋势线，它假设计算能力每1.1年翻一番。库兹韦尔相信，在神经模拟中上传思维是可能的，而桑德伯格和博斯特罗姆的报告对意识在哪里产生则不太确定。]对于低层次的大脑模拟，需要一个非常强大的计算机。人类的大脑有大量的突触。每个10个 sup 11 / sup (1000亿)神经元平均有7000个突触连接(突触)到其他神经元。据估计，一个三岁儿童的大脑约有10个 sup 15 / sup 突触(1千万亿)。这个数字随着年龄的增长而下降，成年后趋于稳定。对于一个成年人的估计有所不同，从10个 sup 14 / sup 到5个 sup 10 sup 14 / sup 突触(100万亿到500万亿)不等。基于神经元活动的简单开关模型，对大脑处理能力的估计大约是每秒10次 / 秒(100万亿)突触更新(SUPS)。1997年，库兹韦尔研究了相当于人脑所需硬件的各种估计，并采用了每秒10 sup 16 / sup 计算(cps)的数字。(作为比较，如果一次“计算”相当于一次“浮点运算”——一种用于对当前超级计算机进行评级的措施——那么10 sup 16 / sup“计算”相当于2011年完成的10petaflops)。他用这个数字来预测，如果在撰写本文时计算机能力方面的指数增长继续下去的话，那么在2015年到2025年之间的某个时候，必要的硬件将会出现。
+根据对在不同水平上模拟人类大脑的所需处理能力的估计(来自 Ray Kurzweil，[ Anders Sandberg 和 Nick Bostrom ]) ，以及每年从最快的五百台超级计算机获得的数据，绘制出对数尺度趋势线和指数趋势线。它呈现出计算能力每1.1年增长一倍。库兹韦尔相信，在神经模拟中上传思维是可能的，而桑德伯格和博斯特罗姆的报告对意识从何产生则不太确定。]为进行低层次的大脑模拟，需要一个非常强大的计算机。人类的大脑有大量的突触。每10个 sup 11 / sup (1000亿)神经元平均与其他神经元有7000个突触连接(突触)。据估计，一个三岁儿童的大脑约有10个 sup 15 / sup 突触(1千万亿)。这个数字随着年龄的增长而下降，成年后趋于稳定。而每个成年人的估计情况互不相同，从10个 sup 14 / sup 到5个 sup 10 sup 14 / sup 突触(100万亿到500万亿)不等。基于神经元活动的简单开关模型，对大脑处理能力的估计大约是每秒10次 / 秒(100万亿)突触更新(SUPS)。1997年，库兹韦尔研究了等价模拟人脑所需硬件的各种估计，并采纳了每秒10 sup 16 / sup 计算(cps)这个估计结果。(作为比较，如果一次“计算”相当于一次“浮点运算”——一种用于对当前超级计算机进行评级的措施——那么10 sup 16 / sup次“计算”相当于2011年达到的的每秒10000亿次浮点运算)。他用这个数字来预测，如果在撰写本文时计算机能力方面的指数增长继续下去的话，那么在2015年到2025年之间的某个时候，必要的硬件将会出现。
-===Modelling the neurons in more detail===
+===Modelling the neurons in more detail  对神经元的更精细的模拟===
 The [[artificial neuron]] model assumed by Kurzweil and used in many current [[artificial neural network]] implementations is simple compared with [[biological neuron model|biological neurons]]. A brain simulation would likely have to capture the detailed cellular behaviour of biological [[neurons]], presently understood only in the broadest of outlines. The overhead introduced by full modeling of the biological, chemical, and physical details of neural behaviour (especially on a molecular scale) would require computational powers several orders of magnitude larger than Kurzweil's estimate.  In addition the estimates do not account for [[glial cells]], which are at least as numerous as neurons, and which may outnumber neurons by as much as 10:1, and are now known to play a role in cognitive processes.<ref name="Discover2011JanFeb">{{Cite journal|author=Swaminathan, Nikhil|title=Glia—the other brain cells|journal=Discover|date=Jan–Feb 2011|url=http://discovermagazine.com/2011/jan-feb/62|access-date=24 January 2014|archive-url=https://web.archive.org/web/20140208071350/http://discovermagazine.com/2011/jan-feb/62|archive-date=8 February 2014|url-status=live}}</ref>
@@ 第339行： / 第339行： @@
 The artificial neuron model assumed by Kurzweil and used in many current artificial neural network implementations is simple compared with biological neurons. A brain simulation would likely have to capture the detailed cellular behaviour of biological neurons, presently understood only in the broadest of outlines. The overhead introduced by full modeling of the biological, chemical, and physical details of neural behaviour (especially on a molecular scale) would require computational powers several orders of magnitude larger than Kurzweil's estimate.  In addition the estimates do not account for glial cells, which are at least as numerous as neurons, and which may outnumber neurons by as much as 10:1, and are now known to play a role in cognitive processes.
-与生物神经元相比，Kurzweil 假设的人工神经元模型在当前许多人工神经网络实现中的应用是简单的。大脑模拟可能需要捕捉生物神经元细胞行为的细节，目前只能从最广泛的轮廓中理解。对神经行为的生物、化学和物理细节(特别是在分子尺度上)进行全面建模所需要的计算能力将比 Kurzweil 的估计大几百万数量级。此外，这些估计没有考虑胶质细胞，胶质细胞至少和神经元一样多，数量可能比神经元多10:1，现在已知它们在认知过程中发挥作用。
+与生物神经元相比，库兹韦尔假设的人工神经元模型在当前许多人工神经网络实现中的应用是简单的。大脑模拟可能需要捕捉生物神经元细胞行为的细节，目前只能从最广泛的概要中理解。对神经行为的生物、化学和物理细节(特别是在分子尺度上)进行全面建模所需要的计算能力将比库兹韦尔的估计大数个数量级。此外，这些估计没有考虑到至少和神经元一样多的胶质细胞，其数量可能比神经元多十分之一，且现已知它们在认知过程中发挥作用。
-=== Current research===
+=== Current research  研究现状===
 There are some research projects that are investigating brain simulation using more sophisticated neural models, implemented on conventional computing architectures. The [[Artificial Intelligence System]] project implemented non-real time simulations of a "brain" (with 10<sup>11</sup> neurons) in 2005. It took 50 days on a cluster of 27 processors to simulate 1 second of a model.<ref>{{cite journal |last=Izhikevich |first=Eugene M. |last2=Edelman |first2=Gerald M. |date=4 March 2008 |title=Large-scale model of mammalian thalamocortical systems |url=http://vesicle.nsi.edu/users/izhikevich/publications/large-scale_model_of_human_brain.pdf |journal=PNAS |volume=105 |issue=9 |pages=3593–3598 |doi= 10.1073/pnas.0712231105|access-date=23 June 2015 |archive-url=https://web.archive.org/web/20090612095651/http://vesicle.nsi.edu/users/izhikevich/publications/large-scale_model_of_human_brain.pdf |archive-date=12 June 2009 |pmid=18292226 |pmc=2265160|bibcode=2008PNAS..105.3593I }}</ref> The [[Blue Brain]] project used one of the fastest supercomputer architectures in the world, [[IBM]]'s [[Blue Gene]] platform, to create a real time simulation of a single rat [[Neocortex|neocortical column]] consisting of approximately 10,000 neurons and 10<sup>8</sup> synapses in 2006.<ref>{{cite web|url=http://bluebrain.epfl.ch/Jahia/site/bluebrain/op/edit/pid/19085|title=Project Milestones|work=Blue Brain|accessdate=11 August 2008}}</ref> A longer term goal is to build a detailed, functional simulation of the physiological processes in the human brain: "It is not impossible to build a human brain and we can do it in 10 years," [[Henry Markram]], director of the Blue Brain Project said in 2009 at the [[TED (conference)|TED conference]] in Oxford.<ref>{{Cite news |url=http://news.bbc.co.uk/1/hi/technology/8164060.stm |title=Artificial brain '10 years away' 2009 BBC news |date=22 July 2009 |access-date=25 July 2009 |archive-url=https://web.archive.org/web/20170726040959/http://news.bbc.co.uk/1/hi/technology/8164060.stm |archive-date=26 July 2017 |url-status=live }}</ref> There have also been controversial claims to have simulated a [[cat intelligence#Computer simulation of the cat brain|cat brain]]. Neuro-silicon interfaces have been proposed as an alternative implementation strategy that may scale better.<ref>[http://gauntlet.ucalgary.ca/story/10343 University of Calgary news] {{Webarchive|url=https://web.archive.org/web/20090818081044/http://gauntlet.ucalgary.ca/story/10343 |date=18 August 2009 }}, [http://www.nbcnews.com/id/12037941 NBC News news] {{Webarchive|url=https://web.archive.org/web/20170704063922/http://www.nbcnews.com/id/12037941/ |date=4 July 2017 }}</ref>
@@ 第349行： / 第349行： @@
 There are some research projects that are investigating brain simulation using more sophisticated neural models, implemented on conventional computing architectures. The Artificial Intelligence System project implemented non-real time simulations of a "brain" (with 10<sup>11</sup> neurons) in 2005. It took 50 days on a cluster of 27 processors to simulate 1 second of a model. The Blue Brain project used one of the fastest supercomputer architectures in the world, IBM's Blue Gene platform, to create a real time simulation of a single rat neocortical column consisting of approximately 10,000 neurons and 10<sup>8</sup> synapses in 2006. A longer term goal is to build a detailed, functional simulation of the physiological processes in the human brain: "It is not impossible to build a human brain and we can do it in 10 years," Henry Markram, director of the Blue Brain Project said in 2009 at the TED conference in Oxford. There have also been controversial claims to have simulated a cat brain. Neuro-silicon interfaces have been proposed as an alternative implementation strategy that may scale better.
-有一些研究项目正在使用更复杂的神经模型研究大脑模拟，这些模型是在传统的计算机体系结构上实现的。人工智能系统项目在2005年实现了一个“大脑”(有10个 sup 11 / sup 神经元)的非实时模拟。在一个由27个处理器组成的集群上，模拟一个模型的一秒钟花费了50天时间。2006年，蓝脑项目利用世界上最快的超级计算机架构之一，IBM 的蓝色基因平台，创建了一个包含大约10,000个神经元和10个 sup 8 / sup 突触的单个大鼠皮层柱的实时模拟。一个更长期的目标是建立一个人脑生理过程的详细的功能模拟: “建立一个人脑并不是不可能的，我们可以在10年内完成，”2009年在牛津举行的 TED 大会上，蓝脑项目主任亨利 · 马克拉姆说。还有一些有争议的说法是模拟猫的大脑。神经硅接口已被提出作为一种替代的实施策略，可能会更好地伸缩。
+有一些研究项目正在使用更复杂的神经模型研究大脑模拟，这些模型是在传统的计算机体系结构上实现的。人工智能系统项目在2005年实现了对一个“大脑”(有10个 sup 11 / sup 神经元)的非实时模拟。在一个由27个处理器组成的集群上，模拟一个模型的一秒钟花费了50天时间。2006年，蓝脑项目利用世界上最快的超级计算机架构之一——IBM 的蓝色基因平台，创建了一个包含大约10,000个神经元和10个 sup 8 / sup 突触的单个大鼠的新皮质柱的实时模拟。一个更长期的目标是建立一个人脑生理过程的详细的功能模拟: “建立一个人脑并不是不可能的，我们可以在10年内完成，”蓝脑项目主任亨利·马克拉姆(Henry Markram)于2009年在牛津举行的 TED 大会上说道。还有一些有争议的说法是模拟猫的大脑。神经-硅接口已作为一种可替代的实施策略被提出，它可能会更好地进行模拟。
@@ 第357行： / 第357行： @@
 Hans Moravec addressed the above arguments ("brains are more complicated", "neurons have to be modeled in more detail") in his 1997 paper "When will computer hardware match the human brain?". He measured the ability of existing software to simulate the functionality of neural tissue, specifically the retina.  His results do not depend on the number of glial cells, nor on what kinds of processing neurons perform where.
-Hans Moravec 在他1997年的论文《计算机硬件何时能与人脑相匹配? 》中提出了上述观点(“大脑更复杂” ，“神经元必须建模得更详细”) .他测量了现有软件模拟神经组织，特别是视网膜功能的能力。他的研究结果并不取决于神经胶质细胞的数量，也不取决于处理神经元在哪里工作。
+汉斯·莫拉维克(Hans Moravec)在他1997年的论文《计算机硬件何时能与人脑匹敌》中提出了上述观点(“大脑更复杂” ，“神经元的建模必须更详细”) .他测量了现有软件模拟神经组织，特别是视网膜功能的能力。他的研究结果并不取决于神经胶质细胞的数量，也不取决于处理神经元在哪里工作。
@@ 第365行： / 第365行： @@
 The actual complexity of modeling biological neurons has been explored in OpenWorm project that was aimed on complete simulation of a worm that has only 302 neurons in its neural network (among about 1000 cells in total). The animal's neural network has been well documented before the start of the project. However, although the task seemed simple at the beginning, the models based on a generic neural network did not work. Currently, the efforts are focused on precise emulation of biological neurons (partly on the molecular level), but the result cannot be called a total success yet. Even if the number of issues to be solved in a human-brain-scale model is not proportional to the number of neurons, the amount of work along this path is obvious.
-在 OpenWorm 项目中，已经探讨了建模生物神经元的实际复杂性，该项目旨在完全模拟一个蠕虫，其神经网络中只有302个神经元(在总共约1000个细胞中)。在项目开始之前，动物的神经网络已经被很好地记录了下来。然而，尽管一开始任务看起来很简单，基于一般神经网络的模型并不起作用。目前，研究的重点是精确模拟生物神经元(部分在分子水平上) ，但结果还不能被称为完全成功。即使在人脑尺度模型中需要解决的问题的数量与神经元的数量不成比例，沿着这条路径所做的工作量也是显而易见的。
+OpenWorm 项目已经探讨了建模生物神经元的实际复杂性。该项目旨在完全模拟一个蠕虫，其神经网络中只有302个神经元(在总共约1000个细胞中)。项目开始之前，蠕虫的神经网络已经被很好地记录了下来。然而，尽管任务一开始看起来很简单，基于一般神经网络的模型并不起作用。目前，研究的重点是精确模拟生物神经元(部分在分子水平上) ，但结果还不能被称为完全成功。即使在人脑尺度的模型中需要解决的问题的数量与神经元的数量不成比例，沿着这条路径走下去的工作量也是显而易见的。
-===Criticisms of simulation-based approaches===
+===Criticisms of simulation-based approaches  对基于模拟的研究方法的批评===
 A fundamental criticism of the simulated brain approach derives from [[embodied cognition]] where human embodiment is taken as an essential aspect of human intelligence. Many researchers believe that embodiment is necessary to ground meaning.<ref>{{Harvnb|de Vega|Glenberg|Graesser|2008}}. A wide range of views in current research, all of which require grounding to some degree</ref> If this view is correct, any fully functional brain model will need to encompass more than just the neurons (i.e., a robotic body). Goertzel{{sfn|Goertzel|2007}} proposes virtual embodiment (like in ''[[Second Life]]''), but it is not yet known whether this would be sufficient.
@@ 第375行： / 第375行： @@
 A fundamental criticism of the simulated brain approach derives from embodied cognition where human embodiment is taken as an essential aspect of human intelligence. Many researchers believe that embodiment is necessary to ground meaning. If this view is correct, any fully functional brain model will need to encompass more than just the neurons (i.e., a robotic body). Goertzel proposes virtual embodiment (like in Second Life), but it is not yet known whether this would be sufficient.
-对模拟大脑方法的一个基本批评来自具身认知，在那里人体化被视为人类智力的一个重要方面。许多研究者认为，具体化是必要的基础意义。如果这种观点是正确的，那么任何功能齐全的大脑模型都需要包含更多的神经元(例如，一个机器人身体)。Goertzel 提出了虚拟化身(就像在《第二人生》中那样) ，但是目前还不知道这是否足够。
+对模拟大脑的方法的一个基本批评来自具象认知，其中人形化被视为人类智力的一个重要方面。许多研究者认为，具体化是必要的基础意义。如果这种观点是正确的，那么任何功能齐全的大脑模型除了神经元还要包含更多东西(例如，一个机器人身体)。格兹尔提出了虚拟体(就像在《第二人生》中那样) ，但是目前还不知道这是否足够。
@@ 第383行： / 第383行： @@
 Desktop computers using microprocessors capable of more than 10<sup>9</sup> cps (Kurzweil's non-standard unit "computations per second", see above) have been available since 2005. According to the brain power estimates used by Kurzweil (and Moravec), this computer should be capable of supporting a simulation of a bee brain, but despite some interest no such simulation exists . There are at least three reasons for this:
-自2005年以来，台式计算机使用的微处理器能够超过10 sup 9 / sup cps (库兹韦尔的非标准单位“每秒计算” ，见上文)。根据 Kurzweil (和 Moravec)使用的大脑能量估算，这台计算机应该能够支持蜜蜂大脑的模拟，但是尽管有些人感兴趣，这样的模拟并不存在。这至少有三个原因:
+自2005年以来，台式计算机使用的微处理器能够超过10 sup 9 / sup cps (库兹韦尔的非标准单位“每秒计算”，见上文)。根据库兹韦尔(和莫拉维克)使用的大脑能量估算，这台计算机应该能够支持蜜蜂大脑的模拟，但是尽管有些人感兴趣，这样的模拟并不存在。这至少有三个原因:
 #The neuron model seems to be oversimplified (see next section).
@@ 第395行： / 第395行： @@
 There is insufficient understanding of higher cognitive processes to establish accurately what the brain's neural activity, observed using techniques such as functional magnetic resonance imaging, correlates with.
-人们对高级认知过程的理解不够充分，无法准确地确定大脑的神经活动---- 使用功能性磁共振成像等技术观察到的活动---- 与之相关。
+人们对高级认知过程的理解不够充分，无法准确地确定大脑的神经活动---- 与之相关的是使用功能性磁共振成像等技术观察到的大脑活动。
 #Even if our understanding of cognition advances sufficiently, early simulation programs are likely to be very inefficient and will, therefore, need considerably more hardware.
@@ 第407行： / 第407行： @@
 The brain of an organism, while critical, may not be an appropriate boundary for a cognitive model.  To simulate a bee brain, it may be necessary to simulate the body, and the environment. The Extended Mind thesis formalizes the philosophical concept, and research into cephalopods has demonstrated clear examples of a decentralized system.
-有机体的大脑虽然关键，但可能不是认知模型的合适边界。为了模拟蜜蜂的大脑，可能需要模拟身体和环境。扩展心智论文形式化了哲学概念，对头足类动物的研究已经证明了分散系统的明显例子。
+有机体的大脑虽然关键，但可能不是认知模型的合适边界。为了模拟蜜蜂的大脑，可能需要模拟身体和环境。扩展理智论点形式化了哲学概念，对头足类动物的研究已经展示了分散系统的明显的例子。
@@ 第415行： / 第415行： @@
 In addition, the scale of the human brain is not currently well-constrained. One estimate puts the human brain at about 100 billion neurons and 100 trillion synapses. Another estimate is 86 billion neurons of which 16.3 billion are in the cerebral cortex and 69 billion in the cerebellum. Glial cell synapses are currently unquantified but are known to be extremely numerous.
-此外，人类大脑的规模目前还没有得到很好的限制。据估计，人类大脑大约有1000亿个神经元和100万亿个突触。另一个估计是860亿个神经元，其中163亿在大脑皮层，690亿在小脑。神经胶质细胞突触目前尚未定量，但已知数量极多。
+此外，人类大脑的规模目前还没有得到很好的限制。据估计，人类大脑大约有1000亿个神经元和100万亿个突触。另一个估计是860亿个神经元，其中163亿个在大脑皮层，690亿个在小脑。神经胶质细胞突触目前尚未定量，但已知数量极多。
-==Strong AI and consciousness==
+==Strong AI and consciousness  强人工智能和意识==
 {{See also|Philosophy of artificial intelligence|Turing test}}
@@ 第429行： / 第429行： @@
 In 1980, philosopher John Searle coined the term "strong AI" as part of his Chinese room argument. He wanted to distinguish between two different hypotheses about artificial intelligence:
-年，哲学家约翰•塞尔(John Searle)将“强人工智能”(strong AI)一词作为他在中文房间里辩论的一部分。他想要区分关于人工智能的两种不同假设:
+年，哲学家约翰•塞尔(John Searle)将“强人工智能”(strong AI)一词作为他在中文屋论证的一部分。他想要区分关于人工智能的两种不同假设:
 * An artificial intelligence system can ''think'' and have a ''mind''.  (The word "mind" has a specific meaning for philosophers, as used in "the [[mind body problem]]" or "the [[philosophy of mind]]".)
+*一个人工智能系统可以思考并拥有心灵。（词语“心灵”对哲学家来说有特殊意义，正如在“身心问题”或“心灵哲学”中的使用一样。）
 * An artificial intelligence system can (only) ''act like'' it thinks and has a mind.
+*一个人工智能系统可以（仅仅）按它所想而“行动”，并且拥有心灵。
 The first one is called "the ''strong'' AI hypothesis" and the second is "the ''weak'' AI hypothesis" because the first one makes the ''stronger'' statement: it assumes something special has happened to the machine that goes beyond all its abilities that we can test. Searle referred to the "strong AI hypothesis" as "strong AI". This usage is also common in academic AI research and textbooks.<ref>For example: