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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.

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'''，或者说'''狭义人工智能'''与'''强人工智能'''之间的区别：弱人工智能并不需要动用人类的认知能力。相反，弱人工智能仅限于用在通过软件来研究或解决特定问题，或完成推理任务。

一些权威机构强调'''强人工智能'''和'''应用人工智能'''，或者说''''''<font color="#ff8000">狭义人工智能(weak AI)</font>''''''与'''强人工智能'''之间的区别：弱人工智能并不需要动用人类的认知能力。相反，弱人工智能仅限于用在通过软件来研究或解决特定问题，或完成推理任务。

Various criteria for [[intelligence]] have been proposed (most famously the [[Turing test]]) but to date, there is no definition that satisfies everyone.<ref>AI founder [[John McCarthy (computer scientist)|John McCarthy]] writes: "we cannot yet characterize in general what kinds of computational procedures we want to call intelligent." {{cite web| url=http://www-formal.stanford.edu/jmc/whatisai/node1.html| title=Basic Questions| last=McCarthy| first=John| authorlink=John McCarthy (computer scientist)| publisher=[[Stanford University]]| year=2007| access-date=6 December 2007| archive-url=https://web.archive.org/web/20071026100601/http://www-formal.stanford.edu/jmc/whatisai/node1.html| archive-date=26 October 2007| url-status=live}} (For a discussion of some definitions of intelligence used by [[artificial intelligence]] researchers, see [[philosophy of artificial intelligence]].)</ref> However, there ''is'' wide agreement among artificial intelligence researchers that intelligence is required to do the following:

Various criteria for [[intelligence]] have been proposed (most famously the [[Turing test]]) but to date, there is no definition that satisfies everyone.<ref>AI founder [[John McCarthy (computer scientist)|John McCarthy]] writes: "we cannot yet characterize in general what kinds of computational procedures we want to call intelligent." {{cite web| url=http://www-formal.stanford.edu/jmc/whatisai/node1.html| title=Basic Questions| last=McCarthy| first=John| authorlink=John McCarthy (computer scientist)| publisher=[[Stanford University]]| year=2007| access-date=6 December 2007| archive-url=https://web.archive.org/web/20071026100601/http://www-formal.stanford.edu/jmc/whatisai/node1.html| archive-date=26 October 2007| url-status=live}} (For a discussion of some definitions of intelligence used by [[artificial intelligence]] researchers, see [[philosophy of artificial intelligence]].)</ref> However, there ''is'' wide agreement among artificial intelligence researchers that intelligence is required to do the following:

人们提出了各种各样的智能标准(最著名的是图灵测试) ，但到目前为止，还没有一个定义能使所有人满意。然而，人工智能研究人员普遍认为，智能需要做到以下几点:  

人们提出了各种各样的智能标准(最著名的是'''<font color="#ff8000">图灵测试(Turing test)</font>''' ，但到目前为止，还没有一个定义能使所有人满意。然而，人工智能研究人员普遍认为，智能需要做到以下几点:  

<ref>

<ref>

This list of intelligent traits is based on the topics covered by major AI textbooks, including:

This list of intelligent traits is based on the topics covered by major AI textbooks, including:

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.

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.

==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===

==='''<font color="#ff8000">全脑模拟</font>'''===

{{main|Mind uploading}}

{{main|Mind uploading}}

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.

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.

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。<ref>For example:

第一条被称为“'''<font color="#ff8000">强人工智能假设(the strong AI hypothesis)</font>'''” ，第二条被称为“'''<font color="#ff8000">弱人工智能假设(the weak AI hypothesis)</font>'''”，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。<ref>For example:

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

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

* [http://www.encyclopedia.com/doc/1O87-strongAI.html Oxford University Press Dictionary of Psychology] {{Webarchive|url=https://web.archive.org/web/20071203103022/http://www.encyclopedia.com/doc/1O87-strongAI.html |date=3 December 2007 }} (quoted in "High Beam Encyclopedia"),

* [http://www.encyclopedia.com/doc/1O87-strongAI.html Oxford University Press Dictionary of Psychology] {{Webarchive|url=https://web.archive.org/web/20071203103022/http://www.encyclopedia.com/doc/1O87-strongAI.html |date=3 December 2007 }} (quoted in "High Beam Encyclopedia"),

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".

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，以便为实现强人工智能提供一个更强大的基础。正如威廉·克罗克森(William Clocksin)在2003年写的那样: “这个框架始于Weizenbaum的观察，即智能只在特定的社会文化背景下才能表现出来。”。

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，以便为实现强人工智能提供一个更强大的基础。正如威廉·克罗克森(William Clocksin)在2003年写的那样: “这个框架始于魏岑鲍姆(Weizenbaum)的观察，即智能只在特定的社会文化背景下才能表现出来。”。

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.

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，人工智能研究者已经开始探究是否思想和这些思想的创造者是分开的。

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('''<font color="#ff8000">莫拉维克悖论(Moravec's paradox)</font>''')。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，人工智能研究者已经开始探究是否思想和这些思想的创造者是分开的。

   -- [[用户:Qige96|Ricky]]([[用户讨论:Qige96|讨论]])这段英文审校也读不懂。

   -- [[用户:Qige96|Ricky]]([[用户讨论:Qige96|讨论]])这段英文审校也读不懂。