更改

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.

估计需要多少处理能力才能在不同水平上模拟人类大脑(来自 Ray Kurzweil，[ Anders Sandberg 和 Nick Bostrom ]) ，以及每年从 TOP500绘制出的最快超级计算机。请注意对数尺度趋势线和指数趋势线，它假设计算能力每1.1年翻一番。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在哪里产生则不太确定。]对于低层次的大脑模拟，需要一个非常强大的计算机。人类的大脑有大量的突触。每个10个 sup 11 / sup (1000亿)神经元平均有7000个突触连接(突触)到其他神经元。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。对于一个成年人的估计有所不同，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，库兹韦尔研究了相当于人脑所需硬件的各种估计，并采用了每秒10 sup 16 / sup 计算(cps)的数字。(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“计算”相当于2011年完成的10petaflops)。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。

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。

===Modelling the neurons in more detail===

===Modelling the neurons in more detail 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，Kurzweil 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轮廓中理解。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 Kurzweil 的估计大几百万数量级。此外，这些估计没有考虑胶质细胞，胶质细胞至少和神经元一样多，数量可能比神经元多10:1，现在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。

=== Current research===

=== 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 突触的单个大鼠皮层柱的实时模拟。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，”2009年在牛津举行的 TED 大会上，蓝脑项目主任亨利 · 马克拉姆说。还有一些有争议的说法是模拟猫的大脑。神经硅接口已被提出作为一种替代的实施策略，可能会更好地伸缩。

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。

Hans Moravec addressed the above arguments ("brains are more complicated", "neurons have to be modeled in more detail") in his 1997 paper "When will computer hardware match the human brain?". He measured the ability of existing software to simulate the functionality of neural tissue, specifically the retina.  His results do not depend on the number of glial cells, nor on what kinds of processing neurons perform where.

Hans Moravec addressed the above arguments ("brains are more complicated", "neurons have to be modeled in more detail") in his 1997 paper "When will computer hardware match the human brain?". He measured the ability of existing software to simulate the functionality of neural tissue, specifically the retina.  His results do not depend on the number of glial cells, nor on what kinds of processing neurons perform where.

Hans Moravec 在他1997年的论文《计算机硬件何时能与人脑相匹配? 》中提出了上述观点(“大脑更复杂” ，“神经元必须建模得更详细”) .他测量了现有软件模拟神经组织，特别是视网膜功能的能力。他的研究结果并不取决于神经胶质细胞的数量，也不取决于处理神经元在哪里工作。

汉斯·莫拉维克(Hans Moravec)在他1997年的论文《计算机硬件何时能与人脑匹敌》中提出了上述观点(“大脑更复杂” ，“神经元的建模必须更详细”) .他测量了现有软件模拟神经组织，特别是视网膜功能的能力。他的研究结果并不取决于神经胶质细胞的数量，也不取决于处理神经元在哪里工作。

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.

在 OpenWorm 项目中，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)。在项目开始之前，动物的神经网络已经被很好地记录了下来。然而，尽管一开始任务看起来很简单，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，沿着这条路径所做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===

===Criticisms of simulation-based approaches 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.

对模拟大脑方法的一个基本批评来自具身认知，在那里人体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都需要包含更多的神经元(例如，一个机器人身体)。Goertzel 提出了虚拟化身(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。

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 Kurzweil (和 Moravec)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 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:

#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 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.

人们对高级认知过程的理解不够充分，无法准确地确定大脑的神经活动---- 使用功能性磁共振成像等技术观察到的活动---- 与之相关。

人们对高级认知过程的理解不够充分，无法准确地确定大脑的神经活动---- 与之相关的是使用功能性磁共振成像等技术观察到的大脑活动。

#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 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.

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==

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

{{See also|Philosophy of artificial intelligence|Turing test}}

{{See also|Philosophy of artificial intelligence|Turing test}}

In 1980, philosopher John Searle coined the term "strong AI" as part of his Chinese room argument. He wanted to distinguish between two different hypotheses about artificial intelligence:

In 1980, philosopher John Searle coined the term "strong AI" as part of his Chinese room argument. He wanted to distinguish between two different hypotheses about artificial intelligence:

1980年，哲学家约翰•塞尔(John Searle)将“强人工智能”(strong AI)一词作为他在中文房间里辩论的一部分。他想要区分关于人工智能的两种不同假设:

1980年，哲学家约翰•塞尔(John Searle)将“强人工智能”(strong AI)一词作为他在中文屋论证的一部分。他想要区分关于人工智能的两种不同假设:

* An artificial intelligence system can ''think'' and have a ''mind''.  (The word "mind" has a specific meaning for philosophers, as used in "the [[mind body problem]]" or "the [[philosophy of mind]]".)

* An artificial intelligence system can ''think'' and have a ''mind''.  (The word "mind" has a specific meaning for philosophers, as used in "the [[mind body problem]]" or "the [[philosophy of mind]]".)

* An artificial intelligence system can (only) ''act like'' it thinks and has a mind.

* An artificial intelligence system can (only) ''act like'' it thinks and has a mind.

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: