“通用人工智能”的版本间的差异

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However, Bill Joy, among others, argues a machine with these traits may be a threat to human life or dignity. It remains to be shown whether any of these traits are necessary for strong AI. The role of consciousness is not clear, and currently there is no agreed test for its presence. If a machine is built with a device that simulates the neural correlates of consciousness, would it automatically have self-awareness? It is also possible that some of these properties, such as sentience, naturally emerge from a fully intelligent machine, or that it becomes natural to ascribe these properties to machines once they begin to act in a way that is clearly intelligent. For example, intelligent action may be sufficient for sentience, rather than the other way around.
 
However, Bill Joy, among others, argues a machine with these traits may be a threat to human life or dignity. It remains to be shown whether any of these traits are necessary for strong AI. The role of consciousness is not clear, and currently there is no agreed test for its presence. If a machine is built with a device that simulates the neural correlates of consciousness, would it automatically have self-awareness? It is also possible that some of these properties, such as sentience, naturally emerge from a fully intelligent machine, or that it becomes natural to ascribe these properties to machines once they begin to act in a way that is clearly intelligent. For example, intelligent action may be sufficient for sentience, rather than the other way around.
  
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然而，比尔 · 乔伊等人认为，具有这些特征的机器可能会威胁到人类的生命或尊严。这些特征对于强 AI 来说是否是必要的还有待证明。意识的作用并不清楚，目前也没有对其存在的一致的测试。如果一台机器装有一个模拟意识相关神经区的装置，它会自动具有自我意识吗？也有可能这些特性中的一些，比如感知能力，自然而然地从一个完全智能的机器中产生，或者一旦机器开始以一种明显的智能方式行动，人们就会自然而然地把这些特性归因于机器。例如，智能行为可能足以产生知觉，而不是相反。
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然而，比尔·乔伊(Bill Joy)等人认为，具有这些特征的机器可能会威胁到人类的生命或尊严。这些特征对于强人工智能来说是否是必要的还有待证明。意识的作用并不清楚，目前也没有针对其存在而进行的一致的测试。如果一台机器装有一个模拟与意识相关的神经的装置，它会自动具有自我意识吗？也有可能这些特性中的一些，比如感知能力，自然而然地从一个完全智能的机器中产生，或者一旦机器开始以一种明显智能的方式行动，人们就会自然而然地认为这些特性是机器自主产生的。
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  --~~~“或者一旦机器开始以一种明显智能的方式行动，，人们就会自然而然地认为这些特性是机器自主产生的。”对应原句“or that it becomes natural to ascribe these properties to machines once they begin to act in a way that is clearly intelligent.”与原句在语序和措辞上略有不同，是译者考虑到中文的阅读习惯在不改变原意的条件下意译得出的。
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例如，智能行为可能足以判定机器产生了知觉，而非反过来。
  
  
  
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===Artificial consciousness research===
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===Artificial consciousness research 人工意识研究===
  
 
{{Main|Artificial consciousness}}
 
{{Main|Artificial consciousness}}
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Although the role of consciousness in strong AI/AGI is debatable, many AGI researchers regard research that investigates possibilities for implementing consciousness as vital. In an early effort Igor Aleksander argued that the principles for creating a conscious machine already existed but that it would take forty years to train such a machine to understand language.
 
Although the role of consciousness in strong AI/AGI is debatable, many AGI researchers regard research that investigates possibilities for implementing consciousness as vital. In an early effort Igor Aleksander argued that the principles for creating a conscious machine already existed but that it would take forty years to train such a machine to understand language.
  
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虽然意识在强 ai / AGI 中的作用是有争议的，但是很多 AGI 的研究人员认为研究实现意识的可能性是至关重要的。在早期的努力中，Igor Aleksander 认为创造一个有意识的机器的原则已经存在，但是训练这样一个机器去理解语言需要四十年。
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虽然意识在强人工智能/通用人工智能中的作用是有争议的，但是很多通用人工智能的研究人员认为研究实现意识的可能性是至关重要的。在早期的努力中，伊格尔·亚历山大(Igor Aleksander)认为创造一个有意识的机器的原则已经存在，但是训练这样一个机器去理解语言可能需要四十年。
  
  
  
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==Possible explanations for the slow progress of AI research==
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==Possible explanations for the slow progress of AI research 人工智能研究进展缓慢的可能解释==
  
 
{{See also|History of artificial intelligence#The problems}}
 
{{See also|History of artificial intelligence#The problems}}
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While most AI researchers believe strong AI can be achieved in the future, there are some individuals like Hubert Dreyfus and Roger Penrose who deny the possibility of achieving strong AI. John McCarthy was one of various computer scientists who believe human-level AI will be accomplished, but a date cannot accurately be predicted.
 
While most AI researchers believe strong AI can be achieved in the future, there are some individuals like Hubert Dreyfus and Roger Penrose who deny the possibility of achieving strong AI. John McCarthy was one of various computer scientists who believe human-level AI will be accomplished, but a date cannot accurately be predicted.
  
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虽然大多数人工智能研究人员认为强大的人工智能可以在未来实现，但也有一些人像休伯特 · 德雷福斯和罗杰 · 彭罗斯否认实现强大人工智能的可能性。约翰 · 麦卡锡是众多计算机科学家之一，他们相信人类水平的人工智能将会实现，但是日期无法准确预测。
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虽然大多数人工智能研究人员认为强大的人工智能可以在未来实现，但也有一些人像休伯特·德雷福斯(Hubert Dreyfus)和罗杰·彭罗斯(Roger Penrose)否认实现强大人工智能的可能性。约翰·麦卡锡(John McCarthy)是众多计算机科学家之一，他们相信人类水平的人工智能将会实现，但是日期无法准确预测。
  
  
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Conceptual limitations are another possible reason for the slowness in AI research. AI researchers may need to modify the conceptual framework of their discipline in order to provide a stronger base and contribution to the quest of achieving strong AI. As William Clocksin wrote in 2003: "the framework starts from Weizenbaum's observation that intelligence manifests itself only relative to specific social and cultural contexts".
 
Conceptual limitations are another possible reason for the slowness in AI research. AI researchers may need to modify the conceptual framework of their discipline in order to provide a stronger base and contribution to the quest of achieving strong AI. As William Clocksin wrote in 2003: "the framework starts from Weizenbaum's observation that intelligence manifests itself only relative to specific social and cultural contexts".
  
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概念上的局限性是人工智能研究缓慢的另一个可能原因。人工智能研究人员可能需要修改他们学科的概念框架，以便为实现强大的人工智能提供一个更强大的基础和贡献。正如 William Clocksin 在2003年写的那样: “这个框架始于 Weizenbaum 的观察，即智力只在特定的社会和文化背景下表现出来。”。
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概念上的局限性是人工智能研究缓慢的另一个可能原因。人工智能研究人员可能需要修改他们学科的概念框架，以便为实现强大的人工智能提供一个更强大的基础和贡献。正如威廉·克罗克森(William Clocksin)在2003年写的那样: “这个框架始于魏泽堡的观察，即智力只在特定的社会和文化背景下表现出来。”。
  
  
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Furthermore, AI researchers have been able to create computers that can perform jobs that are complicated for people to do, such as mathematics, but conversely they have struggled to develop a computer that is capable of carrying out tasks that are simple for humans to do, such as walking (Moravec's paradox). A problem described by David Gelernter is that some people assume thinking and reasoning are equivalent. However, the idea of whether thoughts and the creator of those thoughts are isolated individually has intrigued AI researchers.
 
Furthermore, AI researchers have been able to create computers that can perform jobs that are complicated for people to do, such as mathematics, but conversely they have struggled to develop a computer that is capable of carrying out tasks that are simple for humans to do, such as walking (Moravec's paradox). A problem described by David Gelernter is that some people assume thinking and reasoning are equivalent. However, the idea of whether thoughts and the creator of those thoughts are isolated individually has intrigued AI researchers.
  
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此外，人工智能研究人员已经能够创造出能够执行复杂工作(如数学)的计算机，但相反地，他们却难以开发出能够执行人类简单任务(如行走)的计算机(莫拉维克悖论)。大卫 · 格勒尼特描述的一个问题是，有些人认为思考和推理是等价的。然而，思想和这些思想的创造者是否被孤立的想法引起了人工智能研究者的兴趣。
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此外，人工智能研究人员已经能够创造出能够执行对人类而言复杂的工作(如数学)的计算机，但相反地，他们却难以开发出能够执行人类简单任务(如行走)的计算机(莫拉维克悖论)。大卫·格勒尼特(David Gelernter)描述的一个问题是，有些人认为思考和推理是等价的。然而，思想和这些思想的创造者是否被孤立的想法引起了人工智能研究者的兴趣。
  
  
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The problems that have been encountered in AI research over the past decades have further impeded the progress of AI. The failed predictions that have been promised by AI researchers and the lack of a complete understanding of human behaviors have helped diminish the primary idea of human-level AI. Although the progress of AI research has brought both improvement and disappointment, most investigators have established optimism about potentially achieving the goal of AI in the 21st century.
 
The problems that have been encountered in AI research over the past decades have further impeded the progress of AI. The failed predictions that have been promised by AI researchers and the lack of a complete understanding of human behaviors have helped diminish the primary idea of human-level AI. Although the progress of AI research has brought both improvement and disappointment, most investigators have established optimism about potentially achieving the goal of AI in the 21st century.
  
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过去几十年人工智能研究中遇到的问题进一步阻碍了人工智能的发展。人工智能研究人员所承诺的失败的预测，以及对人类行为缺乏完整理解，已经帮助削弱了人类水平人工智能的基本概念。尽管人工智能研究的进展带来了进步和失望，但大多数研究人员对人工智能在21世纪可能实现的目标持乐观态度。
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过去几十年人工智能研究中遇到的问题进一步阻碍了人工智能的发展。人工智能研究人员所承诺的失败的预测，以及对人类行为缺乏完整理解，已经帮助削弱了人类水平人工智能的最初设想。尽管人工智能研究的进展带来了进步和失望，但大多数研究人员对人工智能在21世纪可能实现的目标持乐观态度。
  
  
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Other possible reasons have been proposed for the lengthy research in the progress of strong AI. The intricacy of scientific problems and the need to fully understand the human brain through psychology and neurophysiology have limited many researchers in emulating the function of the human brain in computer hardware. Many researchers tend to underestimate any doubt that is involved with future predictions of AI, but without taking those issues seriously, people can then overlook solutions to problematic questions.
 
Other possible reasons have been proposed for the lengthy research in the progress of strong AI. The intricacy of scientific problems and the need to fully understand the human brain through psychology and neurophysiology have limited many researchers in emulating the function of the human brain in computer hardware. Many researchers tend to underestimate any doubt that is involved with future predictions of AI, but without taking those issues seriously, people can then overlook solutions to problematic questions.
  
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还有其他可能的原因可以解释为什么对于强人工智能进行了长时间的研究。错综复杂的科学问题，以及通过心理学和神经生理学充分了解人脑的必要性，限制了许多研究人员在计算机硬件中模拟人脑的功能。许多研究人员倾向于低估与人工智能未来预测有关的任何怀疑，但是如果不认真对待这些问题，人们就会忽视问题的解决方案。
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还有其他可能的原因可以解释为什么对于强人工智能进行了长时间的研究。错综复杂的科学问题，以及通过心理学和神经生理学充分了解人脑的必要性，限制了许多研究人员在计算机硬件中模拟人脑的功能。许多研究人员倾向于低估与人工智能未来预测有关的任何怀疑，但是如果不认真对待这些问题，人们就会忽视不确定问题的解决方案。
  
  
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Clocksin says that a conceptual limitation that may impede the progress of AI research is that people may be using the wrong techniques for computer programs and implementation of equipment. When AI researchers first began to aim for the goal of artificial intelligence, a main interest was human reasoning. Researchers hoped to establish computational models of human knowledge through reasoning and to find out how to design a computer with a specific cognitive task.
 
Clocksin says that a conceptual limitation that may impede the progress of AI research is that people may be using the wrong techniques for computer programs and implementation of equipment. When AI researchers first began to aim for the goal of artificial intelligence, a main interest was human reasoning. Researchers hoped to establish computational models of human knowledge through reasoning and to find out how to design a computer with a specific cognitive task.
  
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Clocksin 说，阻碍人工智能研究进展的一个概念上的限制是，人们可能在计算机程序和设备实现方面使用了错误的技术。当人工智能研究人员第一次开始瞄准人工智能的目标时，主要的兴趣是人类推理。研究人员希望通过推理建立人类知识的计算模型，并找出如何设计一台具有特定认知任务的计算机。
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克罗克森说，阻碍人工智能研究进展的一个概念上的限制是，人们可能在计算机程序和安装设备方面使用了错误的技术。当人工智能研究人员第一次开始瞄准人工智能的目标时，主要的兴趣是人类推理。研究人员希望通过推理建立人类知识的计算模型，并找出如何设计一台具有特定认知任务的计算机。
  
  
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The practice of abstraction, which people tend to redefine when working with a particular context in research, provides researchers with a concentration on just a few concepts. The most productive use of abstraction in AI research comes from planning and problem solving. Although the aim is to increase the speed of a computation, the role of abstraction has posed questions about the involvement of abstraction operators.
 
The practice of abstraction, which people tend to redefine when working with a particular context in research, provides researchers with a concentration on just a few concepts. The most productive use of abstraction in AI research comes from planning and problem solving. Although the aim is to increase the speed of a computation, the role of abstraction has posed questions about the involvement of abstraction operators.
  
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抽象的实践，人们在研究中使用特定的语境时倾向于重新定义，为研究人员提供了集中在几个概念上的机会。抽象在人工智能研究中最有效的应用来自规划和解决问题。虽然目标是提高计算速度，但是抽象的作用已经对抽象操作符的参与提出了问题。
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抽象的实践，人们在研究中使用特定的语境时倾向于重新定义，使得研究人员集中在数个概念上。抽象在人工智能研究中最有效的应用来自规划和解决问题。虽然目标是提高计算速度，但是抽象的作用已经对抽象算子的参与提出了问题。
  
  
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A possible reason for the slowness in AI relates to the acknowledgement by many AI researchers that heuristics is a section that contains a significant breach between computer performance and human performance. The specific functions that are programmed to a computer may be able to account for many of the requirements that allow it to match human intelligence. These explanations are not necessarily guaranteed to be the fundamental causes for the delay in achieving strong AI, but they are widely agreed by numerous researchers.
 
A possible reason for the slowness in AI relates to the acknowledgement by many AI researchers that heuristics is a section that contains a significant breach between computer performance and human performance. The specific functions that are programmed to a computer may be able to account for many of the requirements that allow it to match human intelligence. These explanations are not necessarily guaranteed to be the fundamental causes for the delay in achieving strong AI, but they are widely agreed by numerous researchers.
  
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人工智能发展缓慢的一个可能原因是许多人工智能研究人员承认启发式是计算机性能和人类性能之间的一个重大缺口。为计算机编程的特定功能可以满足许多要求，使计算机与人类智能相匹配。这些解释并不一定是造成人工智能实现延迟的根本原因，但它们得到了众多研究人员的广泛认同。
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人工智能发展缓慢的一个可能原因是许多人工智能研究人员承认启发式算法是计算机性能和人类表现之间的一个重大缺口。为编入计算机的特定功能可以满足许多要求，使计算机与人类智能相匹配。这些解释并不一定是造成人工智能实现延迟的根本原因，但它们得到了众多研究人员的广泛认同。
  
  

2020年10月12日 (一) 17:02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。

模板:Use British English

模板:Artificial intelligence

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

Main article: Cognitive science


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。


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.

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

Main article: AI-complete


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

Main article: History of artificial intelligence

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

Main article: Artificial intelligence


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


See also

  • Intelligence amplification (IA), the use of information technology in augmenting human intelligence instead of creating an external autonomous "AGI"


Notes

  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
  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. ↑ 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
  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
  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).
  12. ↑ White, R. W. (1959). "Motivation reconsidered: The concept of competence". Psychological Review. 66 (5): 297–333. doi:10.1037/h0040934. PMID 13844397.
  13. ↑ Johnson 1987
  14. ↑ deCharms, 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.
  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. ↑ Crevier 1993, pp. 48–50
  22. ↑ Simon 1965, p. 96 quoted in Crevier 1993, p. 109
  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 Darrach (1970), quoted in Crevier (1993, p. 109).
  25. ↑ The Lighthill report specifically criticized AI's "grandiose objectives" and led the dismantling of AI research in England. (Lighthill 1973; Howe 1994) 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. ↑ Crevier 1993, p. 211, Russell & Norvig 2003, p. 24 and see also Feigenbaum & McCorduck 1983
  27. ↑ Crevier 1993, pp. 161–162, 197–203, 240; Russell & Norvig 2003, p. 25; NRC 1999, under "Shift to Applied Research Increases Investment"
  28. ↑ Crevier 1993, pp. 209–212
  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. ↑ Russell & Norvig 2003, pp. 25–26
  32. ↑ "Trends in the Emerging Tech Hype Cycle". Gartner Reports. Archived from the original on 22 May 2019. Retrieved 7 May 2019.
  33. ↑ Moravec 1988, p. 20
  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. ↑ Gubrud 1997
  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. ↑ Goertzel & Wang 2006. See also Wang (2006) with an up-to-date summary and lots of links.
  38. ↑ https://goertzel.org/AGI_Summer_School_2009.htm
  39. ↑ http://fmi-plovdiv.org/index.jsp?id=1054&ln=1
  40. ↑ http://fmi.uni-plovdiv.bg/index.jsp?id=1139&ln=1
  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.
  47. ↑ Kaj Sotala; Roman Yampolskiy (19 December 2014). "Responses to catastrophic AGI risk: a survey". Physica Scripta. 90 (1).
  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.
  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.


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[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)

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