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第9行： − + − 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>. − − − 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。 − <!-- SUMMARIZING HISTORY --> − <!-- SUMMARIZING HISTORY --> − Artificial intelligence was founded as an academic discipline in 1955, and in the years since has experienced several waves of optimism,<ref name="Optimism of early AI"/><ref name="AI in the 80s"/> followed by disappointment and the loss of funding (known as an "[[AI winter]]"),<ref name="First AI winter"/><ref name="Second AI winter"/> followed by new approaches, success and renewed funding.<ref name="AI in the 80s"/><ref name="AI in 2000s"/> For most of its history, AI research has been divided into sub-fields that often fail to communicate with each other.<ref name="Fragmentation of AI"/> These sub-fields are based on technical considerations, such as particular goals (e.g. "[[robotics]]" or "[[machine learning]]"),<ref name="Problems of AI"/> the use of particular tools ("[[logic]]" or [[artificial neural network]]s), or deep philosophical differences.<ref name="Biological intelligence vs. intelligence in general"/><ref name="Neats vs. scruffies"/><ref name="Symbolic vs. sub-symbolic"/> Sub-fields have also been based on social factors (particular institutions or the work of particular researchers).<ref name="Fragmentation of AI"/>+ − − Artificial intelligence was founded as an academic discipline in 1955, and in the years since has experienced several waves of optimism, followed by disappointment and the loss of funding (known as an "AI winter"), followed by new approaches, success and renewed funding. For most of its history, AI research has been divided into sub-fields that often fail to communicate with each other. These sub-fields are based on technical considerations, such as particular goals (e.g. "robotics" or "machine learning"), the use of particular tools ("logic" or artificial neural networks), or deep philosophical differences. Sub-fields have also been based on social factors (particular institutions or the work of particular researchers). − − 1955年AI作为一门学科被建立起来，后来经历过几段乐观时期与紧随而来的亏损以及缺乏资金的困境（也就是“AI寒冬”），每次又找到了新的出路，取得了新的成果和新的投资。对于大多数描述AI的历史，AI研究被划分为互不关联的子领域。人们通常把技术考量作为这些子领域的划分依据，比如特殊目标（例如“机器人学”或者“机器学习”），特殊工具的使用（“逻辑”或者人工神经网络），或者在哲学层面深层次的区别。子领域的划分也与社会因素有关（比如某些特定机构或者特定研究者所做的工作）。 − − − − − − <!-- SUMMARIZING PROBLEMS, APPROACHES, TOOLS --> − − <!-- SUMMARIZING PROBLEMS, APPROACHES, TOOLS --> − The traditional problems (or goals) of AI research include [[automated reasoning|reasoning]], [[knowledge representation]], [[Automated planning and scheduling|planning]], [[machine learning|learning]], [[natural language processing]], [[machine perception|perception]] and the ability to move and manipulate objects.<ref name="Problems of AI"/> [[artificial general intelligence|General intelligence]] is among the field's long-term goals.<ref name="General intelligence"/> Approaches include [[#Statistical|statistical methods]], [[#Sub-symbolic|computational intelligence]], and [[#Symbolic|traditional symbolic AI]]. Many tools are used in AI, including versions of [[#Search and optimization|search and mathematical optimization]], [[artificial neural network]]s, and [[#Probabilistic methods for uncertain reasoning|methods based on statistics, probability and economics]]. The AI field draws upon [[computer science]], [[Information engineering (field)|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. − − − − − − − − − − − The field was founded on the assumption that [[human intelligence]] "can be so precisely described that a machine can be made to simulate it".<ref>See the [[Dartmouth Workshop|Dartmouth proposal]], under [[#Philosophy|Philosophy]], below.</ref> 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 [[History of AI#AI in myth, fiction and speculation|myth]], [[artificial intelligence in fiction|fiction]] and [[philosophy of AI|philosophy]] since [[ancient history|antiquity]].<ref name="McCorduck's thesis"/> Some people also consider AI to be [[existential risk|a danger to humanity]] if it progresses unabated.<ref>{{cite web|url=https://betanews.com/2016/10/21/artificial-intelligence-stephen-hawking/|title=Stephen Hawking believes AI could be mankind's last accomplishment|date=21 October 2016|website=BetaNews|url-status=live|archiveurl=https://web.archive.org/web/20170828183930/https://betanews.com/2016/10/21/artificial-intelligence-stephen-hawking/|archivedate=28 August 2017|df=dmy-all}}</ref><ref name="pmid31835078">{{cite journal |vauthors=Lombardo P, Boehm I, Nairz K |title=RadioComics – Santa Claus and the future of radiology |journal=Eur J Radiol |volume=122 |issue=1 |pages=108771 |year=2020 |pmid=31835078 |doi=10.1016/j.ejrad.2019.108771|doi-access=free }}</ref> Others believe that AI, unlike previous technological revolutions, will create a [[Technological unemployment#21st century|risk of mass unemployment]].<ref name="guardian jobs debate"/>+ − − The field was founded on the assumption that human intelligence "can be so precisely described that a machine can be made to simulate it". This raises philosophical arguments about the nature of the mind and the ethics of creating artificial beings endowed with human-like intelligence. These issues have been explored by myth, fiction and philosophy since antiquity. Others believe that AI, unlike previous technological revolutions, will create a risk of mass unemployment. − − 这一领域是建立在人类智能“可以被精确描述从而使机器可以模拟”的观点上的。这一观点引出了关于思维的本质和创造具有类人智能AI的伦理方面的哲学争论，于是自古以来就有一些神话、小说以及哲学对此类问题展开过探讨。一些人认为AI的发展不会威胁人类生存；但另一些人认为AI与以前的技术革命不同，它将带来大规模失业的风险。 − − − − <!-- SUMMARIZING APPLICATIONS, STATE OF THE ART --> − − <!-- SUMMARIZING APPLICATIONS, STATE OF THE ART --> − In the twenty-first century, AI techniques have experienced a resurgence following concurrent advances in [[Computer performance|computer power]], large amounts of [[big data|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]].<ref name="AI widely used"/><ref name="AI in 2000s"/>+ − − In the twenty-first century, AI techniques have experienced a resurgence following concurrent advances in computer power, large amounts of data, and theoretical understanding; and AI techniques have become an essential part of the technology industry, helping to solve many challenging problems in computer science, software engineering and operations research. − − 在二十一世纪，随着计算机能力、大量数据和理论认识的同步发展，人工智能技术经历了一次复兴; 人工智能技术已成为技术工业的重要组成部分，帮助解决了计算机科学、软件工程和运筹学中的许多具有挑战性的问题。 第81行：
第37行： − − − − − − <!-- THIS IS A SOCIAL HISTORY. TECHNICAL HISTORY IS COVERED IN THE "APPROACHES" AND "TOOLS" SECTIONS. --> − − <!-- THIS IS A SOCIAL HISTORY. TECHNICAL HISTORY IS COVERED IN THE "APPROACHES" AND "TOOLS" SECTIONS. --> − − − − − 第103行：
第46行： − − − − − − − − <!-- PRE-20TH CENTURY. MAYBE TO BE KEPT SHORT. --> − − <!-- PRE-20TH CENTURY. MAYBE TO BE KEPT SHORT. --> − Thought-capable [[artificial being]]s appeared as [[storytelling device]]s in antiquity,<ref name="AI in myth"/> 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.--><ref name="AI in early science fiction"/> These characters and their fates raised many of the same issues now discussed in the [[ethics of artificial intelligence]].<ref name="McCorduck's thesis"/>+ − − 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。 − − − − − − <!-- MAJOR INTELLECTUAL PRECURSORS: LOGIC, THEORY OF COMPUTATION, CYBERNETICS, INFORMATION THEORY, EARLY NEURAL NETS --> − <!-- MAJOR INTELLECTUAL PRECURSORS: LOGIC, THEORY OF COMPUTATION, CYBERNETICS, INFORMATION THEORY, EARLY NEURAL NETS --> − The study of mechanical or [[formal reasoning|"formal" reasoning]] began with [[philosopher]]s and mathematicians in antiquity. The study of mathematical logic led directly to [[Alan Turing]]'s [[theory of computation]], which suggested that a machine, by shuffling symbols as simple as "0" and "1", could simulate any conceivable act of mathematical deduction. This insight, that digital computers can simulate any process of formal reasoning, is known as the [[Church–Turing thesis]].<ref name="Formal reasoning"/> Along with concurrent discoveries in [[Neuroscience|neurobiology]], [[information theory]] and [[cybernetic]]s, this led researchers to consider the possibility of building an electronic brain. Turing proposed changing the question from whether a machine was intelligent, to "whether or not it is possible for machinery to show intelligent behaviour".<ref>{{Citation | last = Turing | first = Alan | authorlink=Alan Turing | year=1948 | chapter=Machine Intelligence | title = The Essential Turing: The ideas that gave birth to the computer age | editor=Copeland, B. Jack | isbn = 978-0-19-825080-7 | publisher = Oxford University Press | location = Oxford | page = 412 }}</ref> The first work that is now generally recognized as AI was [[Warren McCullouch|McCullouch]] and [[Walter Pitts|Pitts]]' 1943 formal design for [[Turing-complete]] "artificial neurons".{{sfn|Russell|Norvig|2009|p=16}}+ − − The study of mechanical or "formal" reasoning began with philosophers and mathematicians in antiquity. The study of mathematical logic led directly to Alan Turing's theory of computation, which suggested that a machine, by shuffling symbols as simple as "0" and "1", could simulate any conceivable act of mathematical deduction. This insight, that digital computers can simulate any process of formal reasoning, is known as the Church–Turing thesis. The first work that is now generally recognized as AI was McCullouch and Pitts' 1943 formal design for Turing-complete "artificial neurons". − − 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 "GOLDEN YEARS" 1956-1974 --> − − <!-- THE "GOLDEN YEARS" 1956-1974 -->
无编辑摘要
'''<font color=#ff8000>人工智能 Artificial Intelligence,AI</font>'''，在[计算机科学]中亦称'''<font color=#ff8000>机器智能 Machine Intelligence</font>'''。与人和其他动物表现出的'''<font color=#ff8000>自然智能 Nature Intelligence</font>'''相反，AI指由人制造出来的机器所表现出来的智能。前沿AI的教科书把AI定义为对“智能体”的研究：智能体指任何感知周围环境并采取行动以最大化其成功实现目标的机会的机器。<ref name="Definition of AI"/>通俗来说，“AI”就是机器模仿人类与人类大脑相关的“认知”功能：例如“学习”和“解决问题”。
'''<font color=#ff8000>人工智能 Artificial Intelligence,AI</font>'''，在[计算机科学]中亦称'''<font color=#ff8000>机器智能 Machine Intelligence</font>'''。与人和其他动物表现出的'''<font color=#ff8000>自然智能 Nature Intelligence</font>'''相反，AI指由人制造出来的机器所表现出来的智能。前沿AI的教科书把AI定义为对“智能体”的研究：智能体指任何感知周围环境并采取行动以最大化其成功实现目标的机会的机器。<ref name="Definition of AI"/>通俗来说，“AI”就是机器模仿人类与人类大脑相关的“认知”功能：例如“学习”和“解决问题”。
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。'''<ref>{{Harvnb|McCorduck|2004|p=204}}</ref><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。<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>”所以比如光学字符识别就往往不再被认为属于AI行列<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>，而已经成为了一种常规技术。<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>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）<ref name="bbc-alphago"/>,自动驾驶汽车，内容分发网络和军事模拟的智能规划<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>。
--[[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；
'''<font color=#0000ff>已解决！</font>'''
'''<font color=#0000ff>已解决！</font>'''
! ——总结历史——
! ——总结历史——
1955年AI作为一门学科被建立起来，后来经历过几段乐观时期<ref name="Optimism of early AI"/><ref name="AI in the 80s"/>与紧随而来的亏损以及缺乏资金的困境（也就是“AI寒冬”<ref name="First AI winter"/><ref name="Second AI winter"/>），每次又找到了新的出路，取得了新的成果和新的投资<ref name="AI in the 80s"/><ref name="AI in 2000s"/>。对于大多数描述AI的历史，AI研究被划分为互不关联的子领域<ref name="Fragmentation of AI"/>。人们通常把技术考量作为这些子领域的划分依据，比如特殊目标（例如“机器人学”或者“机器学习”<ref name="Problems of AI"/>），特殊工具的使用（“逻辑”或者人工神经网络），或者在哲学层面深层次的区别<ref name="Biological intelligence vs. intelligence in general"/><ref name="Neats vs. scruffies"/><ref name="Symbolic vs. sub-symbolic"/>。子领域的划分也与社会因素有关（比如某些特定机构或者特定研究者所做的工作）。<ref name="Fragmentation of AI"/>
! ——总结问题、方法、工具——
! ——总结问题、方法、工具——
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<ref name="Problems of AI"/>。实现通用AI目前仍然是该领域的长远目标。<ref name="General intelligence"/> 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。
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。
<!-- SUMMARISING FICTION/SPECULATION, PHILOSOPHY, HISTORY -->
<!-- SUMMARISING FICTION/SPECULATION, PHILOSOPHY, HISTORY -->
! ——总结小说 / 推测，哲学，历史——
! ——总结小说 / 推测，哲学，历史——
这一领域是建立在人类智能“可以被精确描述从而使机器可以模拟”的观点上的。<ref>See the [[Dartmouth Workshop|Dartmouth proposal]], under [[#Philosophy|Philosophy]], below.</ref>这一观点引出了关于思维的本质和创造具有类人智能AI的伦理方面的哲学争论，于是自古以来<ref name="McCorduck's thesis"/>就有一些神话、小说以及哲学对此类问题展开过探讨。<ref>{{cite web|url=https://betanews.com/2016/10/21/artificial-intelligence-stephen-hawking/|title=Stephen Hawking believes AI could be mankind's last accomplishment|date=21 October 2016|website=BetaNews|url-status=live|archiveurl=https://web.archive.org/web/20170828183930/https://betanews.com/2016/10/21/artificial-intelligence-stephen-hawking/|archivedate=28 August 2017|df=dmy-all}}</ref><ref name="pmid31835078">{{cite journal |vauthors=Lombardo P, Boehm I, Nairz K |title=RadioComics – Santa Claus and the future of radiology |journal=Eur J Radiol |volume=122 |issue=1 |pages=108771 |year=2020 |pmid=31835078 |doi=10.1016/j.ejrad.2019.108771|doi-access=free }}</ref>一些人认为AI的发展不会威胁人类生存；但另一些人认为AI与以前的技术革命不同，它将带来大规模失业的风险。<ref name="guardian jobs debate"/>
! ——总结应用，最新进展——
! ——总结应用，最新进展——
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。<ref name="AI widely used"/><ref name="AI in 2000s"/>
{{toclimit|3}}
{{toclimit|3}}
== 历史 History ==
== 历史 History ==
! ——这是一部社会史。“方法”和“工具”部分介绍了技术历史。-->
! ——这是一部社会史。“方法”和“工具”部分介绍了技术历史。-->
{{Main|History of artificial intelligence|Timeline of artificial intelligence}}
{{Main|History of artificial intelligence|Timeline of artificial intelligence}}
Silver [[didrachma from Crete depicting Talos, an ancient mythical automaton with artificial intelligence]]
Silver [[didrachma from Crete depicting Talos, an ancient mythical automaton with artificial intelligence]]
! -- 20世纪前。也许是为了保持简短。-->
! -- 20世纪前。也许是为了保持简短。-->
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，<ref name="AI in myth"/> 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.)<ref name="AI in early science fiction"/> ——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。<ref name="McCorduck's thesis"/>
! ——主要智能前体: 逻辑学、计算理论、控制论、信息论、早期神经网络
! ——主要智能前体: 逻辑学、计算理论、控制论、信息论、早期神经网络
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>'''<ref name="Formal reasoning"/>。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”。<ref>{{Citation | last = Turing | first = Alan | authorlink=Alan Turing | year=1948 | chapter=Machine Intelligence | title = The Essential Turing: The ideas that gave birth to the computer age | editor=Copeland, B. Jack | isbn = 978-0-19-825080-7 | publisher = Oxford University Press | location = Oxford | page = 412 }}</ref>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”。
--[[用户:Thingamabob|Thingamabob]]（[[用户讨论:Thingamabob|讨论]]）图灵提出“如果人类无法区分机器和人类的回应，那么机器可以被认为是“智能的” 一句为从原版wiki上补充的
--[[用户:Thingamabob|Thingamabob]]（[[用户讨论:Thingamabob|讨论]]）图灵提出“如果人类无法区分机器和人类的回应，那么机器可以被认为是“智能的” 一句为从原版wiki上补充的
! -- “黄金年代”1956 -- 1974 --
! -- “黄金年代”1956 -- 1974 --