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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>.

'''<font color=#ff000090>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),</font>'''

'''<font color=#00ff00>以下搬运存在尾端缺失！</font>'''

'''<font color=#ff0000>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),</font>'''

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往往不再被认为属于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。

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”的任务被一个个从AI范畴中除名。这就是所谓的AI效应。'''<font color=#f32cd32>泰斯勒定理</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往往不再被认为属于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顺利理解人类口语，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，内容分发网络和军事模拟的智能规划。

   --[[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；

   --[[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).

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。

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>'''，'''<font color=#ff8000>学习 Learning</font>'''，'''<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统计学、概率论和经济学的算法。AI领域涉及计算机科学，信息工程，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.

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。

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。

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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。

具有思维能力的人造生物在古代以故事讲述者的方式出现，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《罗素姆的万能机器人》(Rossum's Universal Robots，R.U.R.) ——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。

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”。

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，就能模拟任何通过数学推演可以想到的过程，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“人工神经元”。

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&nbsp;... 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&nbsp;... 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。”

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。他们和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) ，解决代数应用题，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。”

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.

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。

20世纪80年代，'''<font color=#ff8000>互补金属氧化物半导体；Complementary Metal Oxide Semiconductor，CMOS</font>'''技术的出现带来了'''<font color=#ff8000>金属氧化物半导体；Metal Oxide Semiconductor，MOS</font>''''''<font color=#ff8000>超大规模集成电路；Very Large Scale Integration，VLSI</font>'''的发展，使实用的'''<font color=#ff8000>人工神经网络；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 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。

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在三番棋的对决当中赢得了蝉联两届世界冠军的柯洁。这标志着AI发展的一个重要里程碑的完成，因为围棋是一种比国际象棋更复杂的游戏。

}}</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.

  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,

如果你的对手在一个角落里布子，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,