更改

=== 通用智能 ===

=== 通用智能 ===

Historically, projects such as the Cyc knowledge base (1984–) and the massive Japanese [[Fifth generation computer|Fifth Generation Computer Systems]] initiative (1982–1992) attempted to cover the breadth of human cognition. These early projects failed to escape the limitations of non-quantitative symbolic logic models and, in retrospect, greatly underestimated the difficulty of cross-domain AI. Nowadays, the vast majority of current AI researchers work instead on tractable "narrow AI" applications (such as medical diagnosis or automobile navigation).<ref name="contemporary agi">{{cite book|last1=Pennachin|first1=C.|last2=Goertzel|first2=B.|title=Contemporary Approaches to Artificial General Intelligence|journal=Artificial General Intelligence. Cognitive Technologies|date=2007|doi=10.1007/978-3-540-68677-4_1|publisher=Springer|location=Berlin, Heidelberg|series=Cognitive Technologies|isbn=978-3-540-23733-4}}</ref> Many researchers predict that such "narrow AI" work in different individual domains will eventually be incorporated into a machine with [[artificial general intelligence]] (AGI), combining most of the narrow skills mentioned in this article and at some point even exceeding human ability in most or all these areas.<ref name="General intelligence"/><ref name="Roberts">{{cite magazine|last1=Roberts|first1=Jacob|title=Thinking Machines: The Search for Artificial Intelligence|magazine=Distillations|date=2016|volume=2|issue=2|pages=14–23|url=https://www.sciencehistory.org/distillations/magazine/thinking-machines-the-search-for-artificial-intelligence|accessdate=20 March 2018|archive-url=https://web.archive.org/web/20180819152455/https://www.sciencehistory.org/distillations/magazine/thinking-machines-the-search-for-artificial-intelligence|archive-date=19 August 2018|url-status=dead}}</ref> Many advances have general, cross-domain significance. One high-profile example is that [[DeepMind]] in the 2010s developed a "generalized artificial intelligence" that could learn many diverse [[Atari 2600|Atari]] games on its own, and later developed a variant of the system which succeeds at [[Catastrophic interference#The Sequential Learning Problem: McCloskey and Cohen (1989)|sequential learning]].<ref>{{cite news|title=The superhero of artificial intelligence: can this genius keep it in check?|url=https://www.theguardian.com/technology/2016/feb/16/demis-hassabis-artificial-intelligence-deepmind-alphago|accessdate=26 April 2018|work=the Guardian|date=16 February 2016|language=en}}</ref><ref>{{cite journal|last1=Mnih|first1=Volodymyr|last2=Kavukcuoglu|first2=Koray|last3=Silver|first3=David|last4=Rusu|first4=Andrei A.|last5=Veness|first5=Joel|last6=Bellemare|first6=Marc G.|last7=Graves|first7=Alex|last8=Riedmiller|first8=Martin|last9=Fidjeland|first9=Andreas K.|last10=Ostrovski|first10=Georg|last11=Petersen|first11=Stig|last12=Beattie|first12=Charles|last13=Sadik|first13=Amir|last14=Antonoglou|first14=Ioannis|last15=King|first15=Helen|last16=Kumaran|first16=Dharshan|last17=Wierstra|first17=Daan|last18=Legg|first18=Shane|last19=Hassabis|first19=Demis|title=Human-level control through deep reinforcement learning|journal=Nature|date=26 February 2015|volume=518|issue=7540|pages=529–533|doi=10.1038/nature14236|pmid=25719670|bibcode=2015Natur.518..529M}}</ref><ref>{{cite news|last1=Sample|first1=Ian|title=Google's DeepMind makes AI program that can learn like a human|url=https://www.theguardian.com/global/2017/mar/14/googles-deepmind-makes-ai-program-that-can-learn-like-a-human|accessdate=26 April 2018|work=the Guardian|date=14 March 2017|language=en}}</ref> Besides [[transfer learning]],<ref>{{cite news|title=From not working to neural networking|url=https://www.economist.com/news/special-report/21700756-artificial-intelligence-boom-based-old-idea-modern-twist-not|accessdate=26 April 2018|work=The Economist|date=2016|language=en}}</ref> hypothetical AGI breakthroughs could include the development of reflective architectures that can engage in decision-theoretic metareasoning, and figuring out how to "slurp up" a comprehensive knowledge base from the entire unstructured [[World Wide Web|Web]].{{sfn|Russell|Norvig|2009|chapter=27. AI: The Present and Future}} Some argue that some kind of (currently-undiscovered) conceptually straightforward, but mathematically difficult, "Master Algorithm" could lead to AGI.{{sfn|Domingos|2015|chapter=9. The Pieces of the Puzzle Fall into Place}} Finally, a few "emergent" approaches look to simulating human intelligence extremely closely, and believe that [[anthropomorphism|anthropomorphic]] features like an [[artificial brain]] or simulated [[developmental robotics|child development]] may someday reach a critical point where general intelligence emerges.<ref name="Brain simulation"/><ref>{{cite journal|last1=Goertzel|first1=Ben|last2=Lian|first2=Ruiting|last3=Arel|first3=Itamar|last4=de Garis|first4=Hugo|last5=Chen|first5=Shuo|title=A world survey of artificial brain projects, Part II: Biologically inspired cognitive architectures|journal=Neurocomputing|date=December 2010|volume=74|issue=1–3|pages=30–49|doi=10.1016/j.neucom.2010.08.012}}</ref>

历史上，诸如 Cyc 知识库(1984 -)和大规模的日本第五代计算机系统倡议(1982-1992)等项目试图涵盖人类的所有认知。这些早期的项目未能逃脱非定量符号逻辑模型的限制，现在回过头看，这些项目大大低估了实现跨领域AI的难度。当下绝大多数AI研究人员主要研究易于处理的“狭义AI”应用(如医疗诊断或汽车导航)<ref name="contemporary agi">{{cite book|last1=Pennachin|first1=C.|last2=Goertzel|first2=B.|title=Contemporary Approaches to Artificial General Intelligence|journal=Artificial General Intelligence. Cognitive Technologies|date=2007|doi=10.1007/978-3-540-68677-4_1|publisher=Springer|location=Berlin, Heidelberg|series=Cognitive Technologies|isbn=978-3-540-23733-4}}</ref>。许多研究人员预测，不同领域的“狭义AI”工作最终将被整合到一台具有人工通用智能(AGI)的机器中，结合上文提到的大多数狭义功能，甚至在某种程度上在大多数或所有这些领域都超过人类。<ref name="Kurzweil 1999"/><ref name="Kurzweil 2005"/><ref name="Roberts">{{cite magazine|last1=Roberts|first1=Jacob|title=Thinking Machines: The Search for Artificial Intelligence|magazine=Distillations|date=2016|volume=2|issue=2|pages=14–23|url=https://www.sciencehistory.org/distillations/magazine/thinking-machines-the-search-for-artificial-intelligence|accessdate=20 March 2018|archive-url=https://web.archive.org/web/20180819152455/https://www.sciencehistory.org/distillations/magazine/thinking-machines-the-search-for-artificial-intelligence|archive-date=19 August 2018|url-status=dead}}</ref>通用人工智能（或“AGI”）子领域专门研究通用智能。<ref name="Artificial General Intelligence">{{cite book|last1=Pennachin|first1=C.|last2=Goertzel|first2=B.|chapter=Contemporary Approaches to Artificial General Intelligence|title=Artificial General Intelligence |date=2007 |doi=10.1007/978-3-540-68677-4_1 |publisher=Springer|location=Berlin, Heidelberg |series=Cognitive Technologies |isbn=978-3-540-23733-4}}</ref>

 

历史上，诸如 Cyc 知识库(1984 -)和大规模的日本第五代计算机系统倡议(1982-1992)等项目试图涵盖人类的所有认知。这些早期的项目未能逃脱非定量符号逻辑模型的限制，现在回过头看，这些项目大大低估了实现跨领域AI的难度。当下绝大多数AI研究人员主要研究易于处理的“狭义AI”应用(如医疗诊断或汽车导航)<ref name="contemporary agi">{{cite book|last1=Pennachin|first1=C.|last2=Goertzel|first2=B.|title=Contemporary Approaches to Artificial General Intelligence|journal=Artificial General Intelligence. Cognitive Technologies|date=2007|doi=10.1007/978-3-540-68677-4_1|publisher=Springer|location=Berlin, Heidelberg|series=Cognitive Technologies|isbn=978-3-540-23733-4}}</ref>。许多研究人员预测，不同领域的“狭义AI”工作最终将被整合到一台具有人工通用智能(AGI)的机器中，结合上文提到的大多数狭义功能，甚至在某种程度上在大多数或所有这些领域都超过人类。<ref name="Kurzweil 1999"/><ref name="Kurzweil 2005"/><ref name="Roberts">{{cite magazine|last1=Roberts|first1=Jacob|title=Thinking Machines: The Search for Artificial Intelligence|magazine=Distillations|date=2016|volume=2|issue=2|pages=14–23|url=https://www.sciencehistory.org/distillations/magazine/thinking-machines-the-search-for-artificial-intelligence|accessdate=20 March 2018|archive-url=https://web.archive.org/web/20180819152455/https://www.sciencehistory.org/distillations/magazine/thinking-machines-the-search-for-artificial-intelligence|archive-date=19 August 2018|url-status=dead}}</ref>

 

== 方法 ==

== 方法 ==