更改

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.

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。

“通用人工智能”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。通用人工智能的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畴。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'''<font color="#ff8000">《奇点临近》</font>'''（即在2015年至2045年之间）中讨论的时间线是可信的。

{{main|Mind uploading}}

{{main|Mind uploading}}

A popular discussed approach to achieving general intelligent action is [[whole brain emulation]]. A low-level brain model is built by [[brain scanning|scanning]] and [[Brain mapping|mapping]] a biological brain in detail and copying its state into a computer system or another computational device. The computer runs a [[computer simulation|simulation]] model so faithful to the original that it will behave in essentially the same way as the original brain, or for all practical purposes, indistinguishably.<ref name=Roadmap>

A popular discussed approach to achieving general intelligent action is [[whole brain emulation]]. A low-level brain model is built by [[brain scanning|scanning]] and [[Brain mapping|mapping]] a biological brain in detail and copying its state into a computer system or another computational device. The computer runs a [[computer simulation|simulation]] model so faithful to the original that it will behave in essentially the same way as the original brain, or for all practical purposes, indistinguishably.

A popular discussed approach to achieving general intelligent action is whole brain emulation. A low-level brain model is built by scanning and mapping a biological brain in detail and copying its state into a computer system or another computational device. The computer runs a simulation model so faithful to the original that it will behave in essentially the same way as the original brain, or for all practical purposes, indistinguishably.<ref name=Roadmap>

A popular discussed approach to achieving general intelligent action is whole brain emulation. A low-level brain model is built by scanning and mapping a biological brain in detail and copying its state into a computer system or another computational device. The computer runs a simulation model so faithful to the original that it will behave in essentially the same way as the original brain, or for all practical purposes, indistinguishably.<ref name=Roadmap>

{{Harvnb|Sandberg|Boström|2008}}. "The basic idea is to take a particular brain, scan its structure in detail, and construct a software model of it that is so faithful to the original that, when run on appropriate hardware, it will behave in essentially the same way as the original brain."</ref> Whole brain emulation is discussed in [[computational neuroscience]] and [[neuroinformatics]], in the context of [[brain simulation]] for medical research purposes. It is discussed in [[artificial intelligence]] research{{sfn|Goertzel|2007}} as an approach to strong AI. [[Neuroimaging]] technologies that could deliver the necessary detailed understanding are improving rapidly, and [[futurist]] Ray Kurzweil in the book ''The Singularity Is Near''<ref name=K/> predicts that a map of sufficient quality will become available on a similar timescale to the required computing power.

{{Harvnb|Sandberg|Boström|2008}}. "The basic idea is to take a particular brain, scan its structure in detail, and construct a software model of it that is so faithful to the original that, when run on appropriate hardware, it will behave in essentially the same way as the original brain."</ref> Whole brain emulation is discussed in [[computational neuroscience]] and [[neuroinformatics]], in the context of [[brain simulation]] for medical research purposes. It is discussed in [[artificial intelligence]] research{{sfn|Goertzel|2007}} as an approach to strong AI. [[Neuroimaging]] technologies that could deliver the necessary detailed understanding are improving rapidly, and [[futurist]] Ray Kurzweil in the book ''The Singularity Is Near''<ref name=K/> predicts that a map of sufficient quality will become available on a similar timescale to the required computing power.

."The basic idea is to take a particular brain, scan its structure in detail, and construct a software model of it that is so faithful to the original that, when run on appropriate hardware, it will behave in essentially the same way as the original brain."</ref> Whole brain emulation is discussed in computational neuroscience and neuroinformatics, in the context of brain simulation for medical research purposes. It is discussed in artificial intelligence research as an approach to strong AI. Neuroimaging technologies that could deliver the necessary detailed understanding are improving rapidly, and futurist Ray Kurzweil in the book The Singularity Is Near predicts that a map of sufficient quality will become available on a similar timescale to the required computing power.

."The basic idea is to take a particular brain, scan its structure in detail, and construct a software model of it that is so faithful to the original that, when run on appropriate hardware, it will behave in essentially the same way as the original brain."</ref> Whole brain emulation is discussed in computational neuroscience and neuroinformatics, in the context of brain simulation for medical research purposes. It is discussed in artificial intelligence research as an approach to strong AI. Neuroimaging technologies that could deliver the necessary detailed understanding are improving rapidly, and futurist Ray Kurzweil in the book The Singularity Is Near predicts that a map of sufficient quality will become available on a similar timescale to the required computing power.

“基本思路是，取一个特定的大脑，详细地扫描其结构，并构建一个无比还原的原始大脑的软件模型，以至于在适当的硬件上运行时，它基本上与原始大脑的行为方式相同。基于医学研究的大脑模拟背景下，全脑模拟在计算神经科学和神经信息学医学期刊上被讨论过。它是人工智能研究中讨论的一种强人工智能的方法。可提供必要详细的理解的神经成像技术正在迅速提高，未来学家雷·库兹韦尔(Ray Kurzweil)在《奇点临近》书中预测，一张质量足够高的地图将在类似的时间尺度上达到所需的计算能力。

“基本思路是，取一个特定的大脑，详细地扫描其结构，并构建一个无比还原的原始大脑的软件模型，以至于在适当的硬件上运行时，它基本上与原始大脑的行为方式相同。”基于医学研究的大脑模拟背景下，全脑模拟在计算神经科学和神经信息学医学期刊上被讨论过。它是人工智能研究中讨论的一种强人工智能的方法。可提供必要详细的理解的神经成像技术正在迅速提高，未来学家雷·库兹韦尔(Ray Kurzweil)在《奇点临近》书中预测，一张质量足够高的地图将在类似的时间尺度上达到所需的计算能力。

The artificial neuron model assumed by Kurzweil and used in many current artificial neural network implementations is simple compared with biological neurons. A brain simulation would likely have to capture the detailed cellular behaviour of biological neurons, presently understood only in the broadest of outlines. The overhead introduced by full modeling of the biological, chemical, and physical details of neural behaviour (especially on a molecular scale) would require computational powers several orders of magnitude larger than Kurzweil's estimate.  In addition the estimates do not account for glial cells, which are at least as numerous as neurons, and which may outnumber neurons by as much as 10:1, and are now known to play a role in cognitive processes.

The artificial neuron model assumed by Kurzweil and used in many current artificial neural network implementations is simple compared with biological neurons. A brain simulation would likely have to capture the detailed cellular behaviour of biological neurons, presently understood only in the broadest of outlines. The overhead introduced by full modeling of the biological, chemical, and physical details of neural behaviour (especially on a molecular scale) would require computational powers several orders of magnitude larger than Kurzweil's estimate.  In addition the estimates do not account for glial cells, which are at least as numerous as neurons, and which may outnumber neurons by as much as 10:1, and are now known to play a role in cognitive processes.

与生物神经元相比，库兹韦尔假设的人工神经元模型在当前许多人工神经网络实现中的应用是简单的。大脑模拟可能需要捕捉生物神经元细胞行为的细节，目前只能从最广泛的概要中理解。对神经行为的生物、化学和物理细节(特别是在分子尺度上)进行全面建模所需要的计算能力将比库兹韦尔的估计大数个数量级。此外，这些估计没有考虑到至少和神经元一样多的胶质细胞，其数量可能比神经元多十分之一，且现已知它们在认知过程中发挥作用。

与生物神经元相比，库兹韦尔假设的人工神经元模型在当前许多'''<font color="#ff8000">人工神经网络(artificial neural network)</font>'''实现中的应用是简单的。大脑模拟可能需要捕捉生物神经元细胞行为的细节，目前只能从最广泛的概要中理解。对神经行为的生物、化学和物理细节(特别是在分子尺度上)进行全面建模所需要的计算能力将比库兹韦尔的估计大数个数量级。此外，这些估计没有考虑到至少和神经元一样多的'''<font color="#ff8000">胶质细胞(glial cells)</font>'''，其数量可能比神经元多十分之一，且现已知它们在认知过程中发挥作用。

There are some research projects that are investigating brain simulation using more sophisticated neural models, implemented on conventional computing architectures. The Artificial Intelligence System project implemented non-real time simulations of a "brain" (with 10¹¹ neurons) in 2005. It took 50 days on a cluster of 27 processors to simulate 1 second of a model. The Blue Brain project used one of the fastest supercomputer architectures in the world, IBM's Blue Gene platform, to create a real time simulation of a single rat neocortical column consisting of approximately 10,000 neurons and 10⁸ synapses in 2006. A longer term goal is to build a detailed, functional simulation of the physiological processes in the human brain: "It is not impossible to build a human brain and we can do it in 10 years," Henry Markram, director of the Blue Brain Project said in 2009 at the TED conference in Oxford. There have also been controversial claims to have simulated a cat brain. Neuro-silicon interfaces have been proposed as an alternative implementation strategy that may scale better.

There are some research projects that are investigating brain simulation using more sophisticated neural models, implemented on conventional computing architectures. The Artificial Intelligence System project implemented non-real time simulations of a "brain" (with 10¹¹ neurons) in 2005. It took 50 days on a cluster of 27 processors to simulate 1 second of a model. The Blue Brain project used one of the fastest supercomputer architectures in the world, IBM's Blue Gene platform, to create a real time simulation of a single rat neocortical column consisting of approximately 10,000 neurons and 10⁸ synapses in 2006. A longer term goal is to build a detailed, functional simulation of the physiological processes in the human brain: "It is not impossible to build a human brain and we can do it in 10 years," Henry Markram, director of the Blue Brain Project said in 2009 at the TED conference in Oxford. There have also been controversial claims to have simulated a cat brain. Neuro-silicon interfaces have been proposed as an alternative implementation strategy that may scale better.

有一些研究项目正在使用更复杂的神经模型研究大脑模拟，这些模型是在传统的计算机体系结构上实现的。人工智能系统项目在2005年实现了对一个“大脑”(有10个 sup 11 / sup 神经元)的非实时模拟。在一个由27个处理器组成的集群上，模拟一个模型的一秒钟花费了50天时间。2006年，蓝脑项目利用世界上最快的超级计算机架构之一——IBM 的蓝色基因平台，创建了一个包含大约10,000个神经元和10个 sup 8 / sup 突触的单个大鼠的新皮质柱的实时模拟。一个更长期的目标是建立一个人脑生理过程的详细的功能模拟: “建立一个人脑并不是不可能的，我们可以在10年内完成，”蓝脑项目主任亨利·马克拉姆(Henry Markram)于2009年在牛津举行的 TED 大会上说道。还有一些有争议的说法是模拟猫的大脑。神经-硅接口已作为一种可替代的实施策略被提出，它可能会更好地进行模拟。

有一些研究项目正在使用更复杂的神经模型研究大脑模拟，这些模型是在传统的计算机体系结构上实现的。人工智能系统项目在2005年实现了对一个“大脑”(有10个 sup 11 / sup 神经元)的非实时模拟。在一个由27个处理器组成的集群上，模拟一个模型的一秒钟花费了50天时间。2006年，蓝脑项目利用世界上最快的超级计算机架构之一——IBM 的蓝色基因平台，创建了一个包含大约10,000个神经元和10个 sup 8 / sup 突触的单个大鼠的'''<font color="#ff8000">新皮质柱(neocortical column)</font>'''的实时模拟。一个更长期的目标是建立一个人脑生理过程的详细的功能模拟: “建立一个人脑并不是不可能的，我们可以在10年内完成，”蓝脑项目主任亨利·马克拉姆(Henry Markram)于2009年在牛津举行的 TED 大会上说道。还有一些有争议的说法是模拟猫的大脑。神经-硅接口已作为一种可替代的实施策略被提出，它可能会更好地进行模拟。

The brain of an organism, while critical, may not be an appropriate boundary for a cognitive model.  To simulate a bee brain, it may be necessary to simulate the body, and the environment. The Extended Mind thesis formalizes the philosophical concept, and research into cephalopods has demonstrated clear examples of a decentralized system.

The brain of an organism, while critical, may not be an appropriate boundary for a cognitive model.  To simulate a bee brain, it may be necessary to simulate the body, and the environment. The Extended Mind thesis formalizes the philosophical concept, and research into cephalopods has demonstrated clear examples of a decentralized system.

The first one is called "the strong AI hypothesis" and the second is "the weak AI hypothesis" because the first one makes the stronger statement: it assumes something special has happened to the machine that goes beyond all its abilities that we can test. Searle referred to the "strong AI hypothesis" as "strong AI". This usage is also common in academic AI research and textbooks.<ref>For example:

The first one is called "the strong AI hypothesis" and the second is "the weak AI hypothesis" because the first one makes the stronger statement: it assumes something special has happened to the machine that goes beyond all its abilities that we can test. Searle referred to the "strong AI hypothesis" as "strong AI". This usage is also common in academic AI research and textbooks.<ref>For example:

第一条被称为“强人工智能假设” ，第二条被称为“弱人工智能假设”，因为第一条假设提出了更强的陈述: 它假定机器发生了某种特殊的事件，超出了我们能够测试的所有能力。塞尔将“强人工智能假说”称为“强人工智能”。这种用法在人工智能学术研究和教科书中也很常见。例如:

第一条被称为“强人工智能假设” ，第二条被称为“弱人工智能假设”，因为第一条假设提出了更强的陈述: 它假定机器发生了某种特殊的事件，超出了我们能够测试的所有能力。塞尔将“'''<font color="#ff8000">强人工智能假说(strong AI hypothesis)</font>'''”称为“强人工智能”。这种用法在人工智能学术研究和教科书中也很常见。例如:

* {{Harvnb|Russell|Norvig|2003}},

* {{Harvnb|Russell|Norvig|2003}},

===Feasibility===

===Feasibility 可能性===

{{expand section|date=February 2016}}

{{expand section|date=February 2016}}

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."

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，通用人工智能短期内不太可能成功。计算机科学家戈登·贝尔(Gordon Bell)认为人类在到达技术奇点之前就已经自我毁灭了。戈登·摩尔(Gordon Moore)，摩尔定律的最初提出者，宣称“我是一个怀疑论者。我不认为技术奇点会发生，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。”百度副总裁吴恩达(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。

现有的许多批评认为，通用人工智能短期内不太可能成功。计算机科学家戈登·贝尔(Gordon Bell)认为人类在到达'''<font color="#ff8000">技术奇点(technological singularity)</font>'''之前就已经自我毁灭了。戈登·摩尔(Gordon Moore)，'''<font color="#ff8000">摩尔定律(Moore's Law)</font>'''的最初提出者，宣称“我是一个怀疑论者。我不认为技术奇点会发生，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。”百度副总裁吴恩达(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。

{{div col|colwidth=30em}}

{{div col|colwidth=30em}}

* [[Automated machine learning]]

* [[Automated machine learning]] 自动机器学习

* [[Machine ethics]]

* [[Machine ethics]] 机器伦理

* [[Multi-task learning]]

* [[Multi-task learning]] 多任务学习

* [[Superintelligence: Paths, Dangers, Strategies|Superintelligence]]

* [[Superintelligence: Paths, Dangers, Strategies|Superintelligence]] 超级智能

* [[Nick Bostrom]]

* [[Nick Bostrom]] 尼克·博斯特罗姆

* [[Eliezer Yudkowsky]]

* [[Eliezer Yudkowsky]] 埃利泽·尤德科夫斯基

* [[Future of Humanity Institute]]

* [[Future of Humanity Institute]] 人类未来研究所

* [[Outline of artificial intelligence]]

* [[Outline of artificial intelligence]] 人工智能概要

* [[Artificial brain]]

* [[Artificial brain]] 人工大脑

* [[Transfer learning]]

* [[Transfer learning]] 学习迁移

* [[Outline of transhumanism]]

* [[Outline of transhumanism]] 超人类主义概要

* [[General game playing]]

* [[General game playing]] 一般博弈

* [[Synthetic intelligence]]

* [[Synthetic intelligence]] 合成智能

* [[Intelligence amplification]] (IA), the use of information technology in augmenting human intelligence instead of creating an external autonomous "AGI"{{div col end}}

* [[Intelligence amplification]] (IA), the use of information technology in augmenting human intelligence instead of creating an external autonomous "AGI"{{div col end}}

 

==Notes 附注==

==Notes==

{{reflist|colwidth=30em}}

{{reflist|colwidth=30em}}

==References==

==References 参考文献==

• Stages of Artificial Intelligence"[https://www.computerscience0.xyz/2020/04/ai-artificial-intelligence-stages-of.html Computer Science]" on 2nd April, 2020.{{refbegin|2}}

• Stages of Artificial Intelligence"[https://www.computerscience0.xyz/2020/04/ai-artificial-intelligence-stages-of.html Computer Science]" on 2nd April, 2020.{{refbegin|2}}

==External links==

==External links 拓展链接==

* [https://cis.temple.edu/~pwang/AGI-Intro.html The AGI portal maintained by Pei Wang]

* [https://cis.temple.edu/~pwang/AGI-Intro.html The AGI portal maintained by Pei Wang]