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2019年，游戏程序师和航空工程师约翰·卡迈克(John Carmack)宣布了研究通用人工智能的计划。

2019年，游戏程序师和航空工程师约翰·卡迈克(John Carmack)宣布了研究通用人工智能的计划。

==Processing power needed to simulate a brain  模拟人脑所需要的处理能力==

==模拟人脑所需要的处理能力==

 

===全脑模拟===

 

===Whole brain emulation  全脑模拟===

{{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 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。”在以医学研究为目的的背景下，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。能够提供必要详细信息的神经成像技术正在迅速提高，而未来学家雷·库兹韦尔(Ray Kurzweil)在《奇点临近》书中预测，一张高质量的大脑图像将会出现，而同时，实现强人工智能所需的算力也会就绪。

 

[[File:Estimations of Human Brain Emulation Required Performance.svg|thumb|right|400px|Estimates of how much processing power is needed to emulate a human brain at various levels (from Ray Kurzweil, and [[Anders Sandberg]] and [[Nick Bostrom]]), along with the fastest supercomputer from [[TOP500]] mapped by year. Note the logarithmic scale and exponential trendline, which assumes the computational capacity doubles every 1.1 years. Kurzweil believes that mind uploading will be possible at neural simulation, while the Sandberg, Bostrom report is less certain about where [[consciousness]] arises.根据对在不同水平上模拟人类大脑的所需处理能力的估计(来自 Ray Kurzweil，[ Anders Sandberg 和 Nick Bostrom ]) ，以及每年从最快的五百台超级计算机获得的数据，绘制出对数尺度趋势线和指数趋势线。它呈现出计算能力每1.1年增长一倍。库兹韦尔相信，在神经模拟中上传思维是可能的，而桑德伯格和博斯特罗姆的报告对意识从何产生则不太确定。{{sfn|Sandberg|Boström|2008}}]] For low-level brain simulation, an extremely powerful computer would be required. The [[human brain]] has a huge number of [[synapses]]. Each of the 10¹¹ (one hundred billion) [[neurons]] has on average 7,000 synaptic connections (synapses) to other neurons. It has been estimated that the brain of a three-year-old child has about 10¹⁵ synapses (1 quadrillion). This number declines with age, stabilizing by adulthood. Estimates vary for an adult, ranging from 10¹⁴ to 5×10¹⁴ synapses (100 to 500 trillion).{{sfn|Drachman|2005}} An estimate of the brain's processing power, based on a simple switch model for neuron activity, is around 10¹⁴ (100 trillion) synaptic updates per second ([[SUPS]]).{{sfn|Russell|Norvig|2003}} In 1997, Kurzweil looked at various estimates for the hardware required to equal the human brain and adopted a figure of 10¹⁶ computations per second (cps).<ref>In "Mind Children" {{Harvnb|Moravec|1988|page=61}} 10¹⁵ cps is used. More recently, in 1997, <{{cite web|url=http://www.transhumanist.com/volume1/moravec.htm |title=Archived copy |accessdate=23 June 2006 |url-status=dead |archiveurl=https://web.archive.org/web/20060615031852/http://transhumanist.com/volume1/moravec.htm |archivedate=15 June 2006 }}> Moravec argued for 10⁸ MIPS which would roughly correspond to 10¹⁴ cps.  Moravec talks in terms of MIPS, not "cps", which is a non-standard term Kurzweil introduced.</ref> (For comparison, if a "computation" was equivalent to one "[[FLOPS|floating point operation]]" –  a measure used to rate current [[supercomputer]]s – then 10¹⁶ "computations" would be equivalent to 10 [[Peta-|petaFLOPS]], [[FLOPS#Performance records|achieved in 2011]]). He used this figure to predict the necessary hardware would be available sometime between 2015 and 2025, if the exponential growth in computer power at the time of writing continued.

For low-level brain simulation, an extremely powerful computer would be required. The [[human brain]] has a huge number of [[synapses]]. Each of the 10¹¹ (one hundred billion) [[neurons]] has on average 7,000 synaptic connections (synapses) to other neurons. It has been estimated that the brain of a three-year-old child has about 10¹⁵ synapses (1 quadrillion). This number declines with age, stabilizing by adulthood. Estimates vary for an adult, ranging from 10¹⁴ to 5×10¹⁴ synapses (100 to 500 trillion).{{sfn|Drachman|2005}} An estimate of the brain's processing power, based on a simple switch model for neuron activity, is around 10¹⁴ (100 trillion) synaptic updates per second ([[SUPS]]).{{sfn|Russell|Norvig|2003}} In 1997, Kurzweil looked at various estimates for the hardware required to equal the human brain and adopted a figure of 10¹⁶ computations per second (cps).<ref>In "Mind Children" {{Harvnb|Moravec|1988|page=61}} 10¹⁵ cps is used. More recently, in 1997, <{{cite web|url=http://www.transhumanist.com/volume1/moravec.htm |title=Archived copy |accessdate=23 June 2006 |url-status=dead |archiveurl=https://web.archive.org/web/20060615031852/http://transhumanist.com/volume1/moravec.htm |archivedate=15 June 2006 }}> Moravec argued for 10⁸ MIPS which would roughly correspond to 10¹⁴ cps.  Moravec talks in terms of MIPS, not "cps", which is a non-standard term Kurzweil introduced.</ref> (For comparison, if a "computation" was equivalent to one "[[FLOPS|floating point operation]]" –  a measure used to rate current [[supercomputer]]s – then 10¹⁶ "computations" would be equivalent to 10 [[Peta-|petaFLOPS]], [[FLOPS#Performance records|achieved in 2011]]). He used this figure to predict the necessary hardware would be available sometime between 2015 and 2025, if the exponential growth in computer power at the time of writing continued.

Estimates of how much processing power is needed to emulate a human brain at various levels (from Ray Kurzweil, and [[Anders Sandberg and Nick Bostrom), along with the fastest supercomputer from TOP500 mapped by year. Note the logarithmic scale and exponential trendline, which assumes the computational capacity doubles every 1.1 years. Kurzweil believes that mind uploading will be possible at neural simulation, while the Sandberg, Bostrom report is less certain about where consciousness arises.]] For low-level brain simulation, an extremely powerful computer would be required. The human brain has a huge number of synapses. Each of the 10¹¹ (one hundred billion) neurons has on average 7,000 synaptic connections (synapses) to other neurons. It has been estimated that the brain of a three-year-old child has about 10¹⁵ synapses (1 quadrillion). This number declines with age, stabilizing by adulthood. Estimates vary for an adult, ranging from 10¹⁴ to 5×10¹⁴ synapses (100 to 500 trillion). An estimate of the brain's processing power, based on a simple switch model for neuron activity, is around 10¹⁴ (100 trillion) synaptic updates per second (SUPS). In 1997, Kurzweil looked at various estimates for the hardware required to equal the human brain and adopted a figure of 10¹⁶ computations per second (cps). (For comparison, if a "computation" was equivalent to one "floating point operation" –  a measure used to rate current supercomputers – then 10¹⁶ "computations" would be equivalent to 10 petaFLOPS, achieved in 2011). He used this figure to predict the necessary hardware would be available sometime between 2015 and 2025, if the exponential growth in computer power at the time of writing continued.

Estimates of how much processing power is needed to emulate a human brain at various levels (from Ray Kurzweil, and [[Anders Sandberg and Nick Bostrom), along with the fastest supercomputer from TOP500 mapped by year. Note the logarithmic scale and exponential trendline, which assumes the computational capacity doubles every 1.1 years. Kurzweil believes that mind uploading will be possible at neural simulation, while the Sandberg, Bostrom report is less certain about where consciousness arises.]] For low-level brain simulation, an extremely powerful computer would be required. The human brain has a huge number of synapses. Each of the 10¹¹ (one hundred billion) neurons has on average 7,000 synaptic connections (synapses) to other neurons. It has been estimated that the brain of a three-year-old child has about 10¹⁵ synapses (1 quadrillion). This number declines with age, stabilizing by adulthood. Estimates vary for an adult, ranging from 10¹⁴ to 5×10¹⁴ synapses (100 to 500 trillion). An estimate of the brain's processing power, based on a simple switch model for neuron activity, is around 10¹⁴ (100 trillion) synaptic updates per second (SUPS). In 1997, Kurzweil looked at various estimates for the hardware required to equal the human brain and adopted a figure of 10¹⁶ computations per second (cps). (For comparison, if a "computation" was equivalent to one "floating point operation" –  a measure used to rate current supercomputers – then 10¹⁶ "computations" would be equivalent to 10 petaFLOPS, achieved in 2011). He used this figure to predict the necessary hardware would be available sometime between 2015 and 2025, if the exponential growth in computer power at the time of writing continued.

根据对在不同水平上模拟人类大脑的所需处理能力的估计(来自 Ray Kurzweil，[ Anders Sandberg 和 Nick Bostrom ]) ，以及每年从最快的五百台超级计算机获得的数据，绘制出对数尺度趋势线和指数趋势线。它呈现出计算能力每1.1年增长一倍。库兹韦尔相信，在神经模拟中上传思维是可能的，而桑德伯格和博斯特罗姆的报告对意识从何产生则不太确定。为进行低层次的大脑模拟，需要一个非常强大的计算机。人类的大脑有大量的突触。每10个 sup 11 / sup (1000亿)神经元平均与其他神经元有7000个突触连接(突触)。据估计，一个三岁儿童的大脑约有10个 sup 15 / sup 突触(1千万亿)。这个数字随着年龄的增长而下降，成年后趋于稳定。而每个成年人的估计情况互不相同，从10个 sup 14 / sup 到5个 sup 10 sup 14 / sup 突触(100万亿到500万亿)不等。基于神经元活动的简单开关模型，对大脑处理能力的估计大约是每秒10次 / 秒(100万亿)突触更新(SUPS)。1997年，库兹韦尔研究了等价模拟人脑所需硬件的各种估计，并采纳了每秒10 sup 16 / sup 计算(cps)这个估计结果。(作为比较，如果一次“计算”相当于一次“浮点运算”——一种用于对当前超级计算机进行评级的措施——那么10 sup 16 / sup次“计算”相当于2011年达到的的每秒10000亿次浮点运算)。他用这个数字来预测，如果在撰写本文时计算机能力方面的指数增长继续下去的话，那么在2015年到2025年之间的某个时候，必要的硬件将会出现。

[[File:Estimations of Human Brain Emulation Required Performance.svg|thumb|right|400px|根据对在不同水平上模拟人类大脑的所需处理能力的估计(来自 Ray Kurzweil，[ Anders Sandberg 和 Nick Bostrom ]) ，以及每年从最快的五百台超级计算机获得的数据，绘制出对数尺度趋势线和指数趋势线。它呈现出计算能力每1.1年增长一倍。库兹韦尔相信，在神经模拟中上传思维是可能的，而桑德伯格和博斯特罗姆的报告对意识从何产生则不太确定。]]

===Modelling the neurons in more detail  对神经元的更精细的模拟===

 

===对神经元的更精细的模拟===

The [[artificial neuron]] model assumed by Kurzweil and used in many current [[artificial neural network]] implementations is simple compared with [[biological neuron model|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.<ref name="Discover2011JanFeb">{{Cite journal|author=Swaminathan, Nikhil|title=Glia—the other brain cells|journal=Discover|date=Jan–Feb 2011|url=http://discovermagazine.com/2011/jan-feb/62|access-date=24 January 2014|archive-url=https://web.archive.org/web/20140208071350/http://discovermagazine.com/2011/jan-feb/62|archive-date=8 February 2014|url-status=live}}</ref>

The [[artificial neuron]] model assumed by Kurzweil and used in many current [[artificial neural network]] implementations is simple compared with [[biological neuron model|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.<ref name="Discover2011JanFeb">{{Cite journal|author=Swaminathan, Nikhil|title=Glia—the other brain cells|journal=Discover|date=Jan–Feb 2011|url=http://discovermagazine.com/2011/jan-feb/62|access-date=24 January 2014|archive-url=https://web.archive.org/web/20140208071350/http://discovermagazine.com/2011/jan-feb/62|archive-date=8 February 2014|url-status=live}}</ref>

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'''<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。

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>'''，其数量可能是神经元的10倍，且现已知它们在认知过程中发挥作用。

 

===研究现状===

=== Current research  研究现状===

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.<ref>{{cite journal |last=Izhikevich |first=Eugene M. |last2=Edelman |first2=Gerald M. |date=4 March 2008 |title=Large-scale model of mammalian thalamocortical systems |url=http://vesicle.nsi.edu/users/izhikevich/publications/large-scale_model_of_human_brain.pdf |journal=PNAS |volume=105 |issue=9 |pages=3593–3598 |doi= 10.1073/pnas.0712231105|access-date=23 June 2015 |archive-url=https://web.archive.org/web/20090612095651/http://vesicle.nsi.edu/users/izhikevich/publications/large-scale_model_of_human_brain.pdf |archive-date=12 June 2009 |pmid=18292226 |pmc=2265160|bibcode=2008PNAS..105.3593I }}</ref> 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 [[Neocortex|neocortical column]] consisting of approximately 10,000 neurons and 10⁸ synapses in 2006.<ref>{{cite web|url=http://bluebrain.epfl.ch/Jahia/site/bluebrain/op/edit/pid/19085|title=Project Milestones|work=Blue Brain|accessdate=11 August 2008}}</ref> 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)|TED conference]] in Oxford.<ref>{{Cite news |url=http://news.bbc.co.uk/1/hi/technology/8164060.stm |title=Artificial brain '10 years away' 2009 BBC news |date=22 July 2009 |access-date=25 July 2009 |archive-url=https://web.archive.org/web/20170726040959/http://news.bbc.co.uk/1/hi/technology/8164060.stm |archive-date=26 July 2017 |url-status=live }}</ref> There have also been controversial claims to have simulated a [[cat intelligence#Computer simulation of the cat brain|cat brain]]. Neuro-silicon interfaces have been proposed as an alternative implementation strategy that may scale better.<ref>[http://gauntlet.ucalgary.ca/story/10343 University of Calgary news] {{Webarchive|url=https://web.archive.org/web/20090818081044/http://gauntlet.ucalgary.ca/story/10343 |date=18 August 2009 }}, [http://www.nbcnews.com/id/12037941 NBC News news] {{Webarchive|url=https://web.archive.org/web/20170704063922/http://www.nbcnews.com/id/12037941/ |date=4 July 2017 }}</ref>

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.<ref>{{cite journal |last=Izhikevich |first=Eugene M. |last2=Edelman |first2=Gerald M. |date=4 March 2008 |title=Large-scale model of mammalian thalamocortical systems |url=http://vesicle.nsi.edu/users/izhikevich/publications/large-scale_model_of_human_brain.pdf |journal=PNAS |volume=105 |issue=9 |pages=3593–3598 |doi= 10.1073/pnas.0712231105|access-date=23 June 2015 |archive-url=https://web.archive.org/web/20090612095651/http://vesicle.nsi.edu/users/izhikevich/publications/large-scale_model_of_human_brain.pdf |archive-date=12 June 2009 |pmid=18292226 |pmc=2265160|bibcode=2008PNAS..105.3593I }}</ref> 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 [[Neocortex|neocortical column]] consisting of approximately 10,000 neurons and 10⁸ synapses in 2006.<ref>{{cite web|url=http://bluebrain.epfl.ch/Jahia/site/bluebrain/op/edit/pid/19085|title=Project Milestones|work=Blue Brain|accessdate=11 August 2008}}</ref> 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)|TED conference]] in Oxford.<ref>{{Cite news |url=http://news.bbc.co.uk/1/hi/technology/8164060.stm |title=Artificial brain '10 years away' 2009 BBC news |date=22 July 2009 |access-date=25 July 2009 |archive-url=https://web.archive.org/web/20170726040959/http://news.bbc.co.uk/1/hi/technology/8164060.stm |archive-date=26 July 2017 |url-status=live }}</ref> There have also been controversial claims to have simulated a [[cat intelligence#Computer simulation of the cat brain|cat brain]]. Neuro-silicon interfaces have been proposed as an alternative implementation strategy that may scale better.<ref>[http://gauntlet.ucalgary.ca/story/10343 University of Calgary news] {{Webarchive|url=https://web.archive.org/web/20090818081044/http://gauntlet.ucalgary.ca/story/10343 |date=18 August 2009 }}, [http://www.nbcnews.com/id/12037941 NBC News news] {{Webarchive|url=https://web.archive.org/web/20170704063922/http://www.nbcnews.com/id/12037941/ |date=4 July 2017 }}</ref>

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'''<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。

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 ¹¹ 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 ⁸ 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 actual complexity of modeling biological neurons has been explored in OpenWorm project that was aimed on complete simulation of a worm that has only 302 neurons in its neural network (among about 1000 cells in total). The animal's neural network has been well documented before the start of the project. However, although the task seemed simple at the beginning, the models based on a generic neural network did not work. Currently, the efforts are focused on precise emulation of biological neurons (partly on the molecular level), but the result cannot be called a total success yet. Even if the number of issues to be solved in a human-brain-scale model is not proportional to the number of neurons, the amount of work along this path is obvious.

The actual complexity of modeling biological neurons has been explored in OpenWorm project that was aimed on complete simulation of a worm that has only 302 neurons in its neural network (among about 1000 cells in total). The animal's neural network has been well documented before the start of the project. However, although the task seemed simple at the beginning, the models based on a generic neural network did not work. Currently, the efforts are focused on precise emulation of biological neurons (partly on the molecular level), but the result cannot be called a total success yet. Even if the number of issues to be solved in a human-brain-scale model is not proportional to the number of neurons, the amount of work along this path is obvious.

OpenWorm 项目已经探讨了建模生物神经元的实际复杂性。该项目旨在完全模拟一个蠕虫，其神经网络中只有302个神经元(在总共约1000个细胞中)。项目开始之前，蠕虫的神经网络已经被很好地记录了下来。然而，尽管任务一开始看起来很简单，基于一般神经网络的模型并不起作用。目前，研究的重点是精确模拟生物神经元(部分在分子水平上) ，但结果还不能被称为完全成功。即使在人脑尺度的模型中需要解决的问题的数量与神经元的数量不成比例，沿着这条路径走下去的工作量也是显而易见的。

OpenWorm 项目已经探讨了建模生物神经元的实际复杂性。该项目旨在完全模拟一个蠕虫，其神经网络中只有302个神经元(在总共约1000个细胞中)。项目开始之前，蠕虫的神经网络已经被很好地记录了下来。然而，尽管任务一开始看起来很简单，基于一般神经网络的模型并不起作用。目前，研究的重点是精确模拟生物神经元(部分在分子水平上) ，但结果还不能被称为完全成功。即使在人脑尺度的模型中需要解决的问题的数量，与神经元的数量不成比例，沿着这条路径走下去的工作量也是显而易见的。

===Criticisms of simulation-based approaches  对基于模拟的研究方法的批评===

===对基于模拟的研究方法的批评===

A fundamental criticism of the simulated brain approach derives from [[embodied cognition]] where human embodiment is taken as an essential aspect of human intelligence. Many researchers believe that embodiment is necessary to ground meaning.<ref>{{Harvnb|de Vega|Glenberg|Graesser|2008}}. A wide range of views in current research, all of which require grounding to some degree</ref> If this view is correct, any fully functional brain model will need to encompass more than just the neurons (i.e., a robotic body). Goertzel{{sfn|Goertzel|2007}} proposes virtual embodiment (like in ''[[Second Life]]''), but it is not yet known whether this would be sufficient.

A fundamental criticism of the simulated brain approach derives from [[embodied cognition]] where human embodiment is taken as an essential aspect of human intelligence. Many researchers believe that embodiment is necessary to ground meaning.<ref>{{Harvnb|de Vega|Glenberg|Graesser|2008}}. A wide range of views in current research, all of which require grounding to some degree</ref> If this view is correct, any fully functional brain model will need to encompass more than just the neurons (i.e., a robotic body). Goertzel{{sfn|Goertzel|2007}} proposes virtual embodiment (like in ''[[Second Life]]''), but it is not yet known whether this would be sufficient.

A fundamental criticism of the simulated brain approach derives from embodied cognition where human embodiment is taken as an essential aspect of human intelligence. Many researchers believe that embodiment is necessary to ground meaning. If this view is correct, any fully functional brain model will need to encompass more than just the neurons (i.e., a robotic body). Goertzel proposes virtual embodiment (like in Second Life), but it is not yet known whether this would be sufficient.

A fundamental criticism of the simulated brain approach derives from embodied cognition where human embodiment is taken as an essential aspect of human intelligence. Many researchers believe that embodiment is necessary to ground meaning. If this view is correct, any fully functional brain model will need to encompass more than just the neurons (i.e., a robotic body). Goertzel proposes virtual embodiment (like in Second Life), but it is not yet known whether this would be sufficient.

对模拟大脑的方法的一个基本批评来自具象认知，其中人形化被视为人类智力的一个重要方面。许多研究者认为，具体化是必要的基础意义。如果这种观点是正确的，那么任何功能齐全的大脑模型除了神经元还要包含更多东西(例如，一个机器人身体)。格兹尔提出了虚拟体(就像在《第二人生》中那样) ，但是目前还不知道这是否足够。

对模拟大脑的方法的一个基本批评来自具象认知，其中人形被视为人类智力的一个重要方面。许多研究者认为，具象化是必要的基础意义。如果这种观点是正确的，那么任何功能齐全的大脑模型除了神经元还要包含更多东西(例如，一个机器人身体)。格兹尔提出了虚拟体(就像在《第二人生》中那样) ，但是目前还不知道这是否足够。

Desktop computers using microprocessors capable of more than 10⁹ cps (Kurzweil's non-standard unit "computations per second", see above) have been available since 2005. According to the brain power estimates used by Kurzweil (and Moravec), this computer should be capable of supporting a simulation of a bee brain, but despite some interest no such simulation exists . There are at least three reasons for this:

Desktop computers using microprocessors capable of more than 10⁹ cps (Kurzweil's non-standard unit "computations per second", see above) have been available since 2005. According to the brain power estimates used by Kurzweil (and Moravec), this computer should be capable of supporting a simulation of a bee brain, but despite some interest no such simulation exists . There are at least three reasons for this:

自2005年以来，台式计算机使用的微处理器能够超过10 sup 9 / sup cps (库兹韦尔的非标准单位“每秒计算”，见上文)。根据库兹韦尔(和莫拉维克)使用的大脑能量估算，这台计算机应该能够支持蜜蜂大脑的模拟，但是尽管有些人感兴趣，这样的模拟并不存在。这至少有三个原因:

自2005年以来，台式计算机使用的微处理器能够超过10 ⁹ cps (库兹韦尔的非标准单位“每秒计算”，见上文)。根据库兹韦尔(和莫拉维克)使用的大脑能量估算，这台计算机应该能够支持蜜蜂大脑的模拟，但是尽管有些人感兴趣，这样的模拟却并不存在。这至少有三个原因:

#The neuron model seems to be oversimplified (see next section).

#The neuron model seems to be oversimplified (see next section).

The neuron model seems to be oversimplified (see next section).

The neuron model seems to be oversimplified (see next section).

 

#There is insufficient understanding of higher cognitive processes{{refn|In Goertzels' AGI book ([[#CITEREFYudkowsky2006|Yudkowsky 2006]]), Yudkowsky proposes 5 levels of organisation that must be understood – code/data, sensory modality, concept & category, thought, and deliberation (consciousness) – in order to use the available hardware}} to establish accurately what the brain's neural activity, observed using techniques such as [[Neuroimaging#Functional magnetic resonance imaging|functional magnetic resonance imaging]], correlates with.

#There is insufficient understanding of higher cognitive processes{{refn|In Goertzels' AGI book ([[#CITEREFYudkowsky2006|Yudkowsky 2006]]), Yudkowsky proposes 5 levels of organisation that must be understood – code/data, sensory modality, concept & category, thought, and deliberation (consciousness) – in order to use the available hardware}} to establish accurately what the brain's neural activity, observed using techniques such as [[Neuroimaging#Functional magnetic resonance imaging|functional magnetic resonance imaging]], correlates with.

There is insufficient understanding of higher cognitive processes to establish accurately what the brain's neural activity, observed using techniques such as functional magnetic resonance imaging, correlates with.

There is insufficient understanding of higher cognitive processes to establish accurately what the brain's neural activity, observed using techniques such as functional magnetic resonance imaging, correlates with.

 

#Even if our understanding of cognition advances sufficiently, early simulation programs are likely to be very inefficient and will, therefore, need considerably more hardware.

#Even if our understanding of cognition advances sufficiently, early simulation programs are likely to be very inefficient and will, therefore, need considerably more hardware.

Even if our understanding of cognition advances sufficiently, early simulation programs are likely to be very inefficient and will, therefore, need considerably more hardware.

Even if our understanding of cognition advances sufficiently, early simulation programs are likely to be very inefficient and will, therefore, need considerably more hardware.

#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 [[Cephalopoda|cephalopods]] has demonstrated clear examples of a decentralized system.<ref>{{cite journal | pmid = 15829594 | doi=10.1152/jn.00684.2004 | volume=94 | issue=2 | title=Dynamic model of the octopus arm. I. Biomechanics of the octopus reaching movement |date=August 2005 | journal=J. Neurophysiol. | pages=1443–58 | last1 = Yekutieli | first1 = Y | last2 = Sagiv-Zohar | first2 = R | last3 = Aharonov | first3 = R | last4 = Engel | first4 = Y | last5 = Hochner | first5 = B | last6 = Flash | first6 = T}}</ref>

#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 [[Cephalopoda|cephalopods]] has demonstrated clear examples of a decentralized system.<ref>{{cite journal | pmid = 15829594 | doi=10.1152/jn.00684.2004 | volume=94 | issue=2 | title=Dynamic model of the octopus arm. I. Biomechanics of the octopus reaching movement |date=August 2005 | journal=J. Neurophysiol. | pages=1443–58 | last1 = Yekutieli | first1 = Y | last2 = Sagiv-Zohar | first2 = R | last3 = Aharonov | first3 = R | last4 = Engel | first4 = Y | last5 = Hochner | first5 = B | last6 = Flash | first6 = T}}</ref>

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.

有机体的大脑虽然关键，但可能不是认知模型的合适边界。为了模拟蜜蜂的大脑，可能需要模拟身体和环境。'''<font color="#ff8000">延展心灵论题(The Extended Mind thesis)</font>'''形式化了哲学概念，对头足类动物的研究已经展示了分散系统的明显的例子。

 

 

 

 

#有机体的大脑虽然关键，但可能不是认知模型的合适边界。为了模拟蜜蜂的大脑，可能需要模拟身体和环境。'''<font color="#ff8000">延展心灵论题(The Extended Mind thesis)</font>'''形式化了哲学概念，对头足类动物的研究已经展示了分散系统的明显的例子。

In addition, the scale of the human brain is not currently well-constrained. One estimate puts the human brain at about 100 billion neurons and 100 trillion synapses. Another estimate is 86 billion neurons of which 16.3 billion are in the cerebral cortex and 69 billion in the cerebellum. Glial cell synapses are currently unquantified but are known to be extremely numerous.

In addition, the scale of the human brain is not currently well-constrained. One estimate puts the human brain at about 100 billion neurons and 100 trillion synapses. Another estimate is 86 billion neurons of which 16.3 billion are in the cerebral cortex and 69 billion in the cerebellum. Glial cell synapses are currently unquantified but are known to be extremely numerous.

 

 

==Strong AI and consciousness  强人工智能和意识==

==Strong AI and consciousness  强人工智能和意识==