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第451行： − 自从1956年开始人工智能研究以来，这一领域的发展速度已经随着时间的推移而放缓，并且阻碍了创造具有人类水平的智能行为的机器的目标。这种延迟的一个可能的解释是计算机缺乏足够的存储空间或处理能力。此外，与人工智能研究过程相关的复杂程度也可能限制人工智能研究的进展。+ − 第460行：
第458行： − 虽然大多数人工智能研究人员认为强大的人工智能可以在未来实现，但也有一些人像休伯特·德雷福斯(Hubert Dreyfus)和罗杰·彭罗斯(Roger Penrose)否认实现强大人工智能的可能性。约翰·麦卡锡(John McCarthy)是众多计算机科学家之一，他们相信人类水平的人工智能将会实现，但是日期无法准确预测。+ − 第468行：
第465行： − 概念上的局限性是人工智能研究缓慢的另一个可能原因。人工智能研究人员可能需要修改他们学科的概念框架，以便为实现强大的人工智能提供一个更强大的基础和贡献。正如威廉·克罗克森(William Clocksin)在2003年写的那样: “这个框架始于魏泽堡的观察，即智力只在特定的社会和文化背景下表现出来。”。+ 第476行：
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第481行： − 过去几十年人工智能研究中遇到的问题进一步阻碍了人工智能的发展。人工智能研究人员所承诺的失败的预测，以及对人类行为缺乏完整理解，已经帮助削弱了人类水平人工智能的最初设想。尽管人工智能研究的进展带来了进步和失望，但大多数研究人员对人工智能在21世纪可能实现的目标持乐观态度。+ 第492行：
第489行： − 还有其他可能的原因可以解释为什么对于强人工智能进行了长时间的研究。错综复杂的科学问题，以及通过心理学和神经生理学充分了解人脑的必要性，限制了许多研究人员在计算机硬件中模拟人脑的功能。许多研究人员倾向于低估与人工智能未来预测有关的任何怀疑，但是如果不认真对待这些问题，人们就会忽视不确定问题的解决方案。+ 第508行：
第505行： − 人们在研究中使用特定的语境时倾向于重新定义抽象的实践，使得研究人员集中在数个概念上。抽象在人工智能研究中最有效的应用来自规划和解决问题。虽然目标是提高计算速度，但是抽象的作用已经对抽象算子的参与提出了问题。+ 第515行：
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第523行： − 许多人工智能研究人员一直在争论机器是否应该带有情感。典型的人工智能模型中没有情感，一些研究人员说，将情感编程到机器中可以让它们拥有自己的思想。情感总结了人类的经历，因为它允许人们记住那些经历。大卫·格勒尼特(David Gelernter)写道: “除非计算机能够模拟人类情感的所有细微差别，否则它不会具有创造力。”这种对情绪的关注给人工智能研究人员带来了一些问题，随着未来人工智能研究的进展，它与强人工智能的概念联系起来。+ − −
→Possible explanations for the slow progress of AI research 人工智能研究进展缓慢的可能解释
虽然意识在强人工智能/通用人工智能中的作用是有争议的，但是很多通用人工智能的研究人员认为研究实现意识的可能性是至关重要的。在早期的努力中，伊格尔·亚历山大(Igor Aleksander)认为如何创造一个有意识的机器的知识已经存在，但是训练这样一个机器去理解语言可能需要四十年。
虽然意识在强人工智能/通用人工智能中的作用是有争议的，但是很多通用人工智能的研究人员认为研究实现意识的可能性是至关重要的。在早期的努力中，伊格尔·亚历山大(Igor Aleksander)认为如何创造一个有意识的机器的知识已经存在，但是训练这样一个机器去理解语言可能需要四十年。
==Possible explanations for the slow progress of AI research 人工智能研究进展缓慢的可能解释==
==对于人工智能研究进展缓慢的可能解释==
{{See also|History of artificial intelligence#The problems}}
{{See also|History of artificial intelligence#The problems}}
Since the launch of AI research in 1956, the growth of this field has slowed down over time and has stalled the aims of creating machines skilled with intelligent action at the human level. A possible explanation for this delay is that computers lack a sufficient scope of memory or processing power. In addition, the level of complexity that connects to the process of AI research may also limit the progress of AI research.
Since the launch of AI research in 1956, the growth of this field has slowed down over time and has stalled the aims of creating machines skilled with intelligent action at the human level. A possible explanation for this delay is that computers lack a sufficient scope of memory or processing power. In addition, the level of complexity that connects to the process of AI research may also limit the progress of AI research.
自从1956年人工智能研究启动以来，这一领域的发展速度已经随着时间的推移而放缓，创造具有人类水平智能机器的目标依然遥不可及。这种延迟的一个可能的解释是计算机缺乏足够的存储空间或处理能力。此外，人工智能研究过程的复杂程度也可能限制人工智能研究的进展。
While most AI researchers believe strong AI can be achieved in the future, there are some individuals like Hubert Dreyfus and Roger Penrose who deny the possibility of achieving strong AI. John McCarthy was one of various computer scientists who believe human-level AI will be accomplished, but a date cannot accurately be predicted.
While most AI researchers believe strong AI can be achieved in the future, there are some individuals like Hubert Dreyfus and Roger Penrose who deny the possibility of achieving strong AI. John McCarthy was one of various computer scientists who believe human-level AI will be accomplished, but a date cannot accurately be predicted.
虽然大多数人工智能研究人员认为强人工智能可以在未来实现，但也有一些像休伯特·德雷福斯(Hubert Dreyfus)和罗杰·彭罗斯(Roger Penrose)那样否认实现强人工智能的可能性。约翰·麦卡锡(John McCarthy)是众多相信人类水平人工智能将会实现的计算机科学家之一，但是日期无法准确预测。
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。”。
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，思想和这些思想的创造者是否被孤立的想法引起了人工智能研究者的兴趣。
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。
The problems that have been encountered in AI research over the past decades have further impeded the progress of AI. The failed predictions that have been promised by AI researchers and the lack of a complete understanding of human behaviors have helped diminish the primary idea of human-level AI. Although the progress of AI research has brought both improvement and disappointment, most investigators have established optimism about potentially achieving the goal of AI in the 21st century.
The problems that have been encountered in AI research over the past decades have further impeded the progress of AI. The failed predictions that have been promised by AI researchers and the lack of a complete understanding of human behaviors have helped diminish the primary idea of human-level AI. Although the progress of AI research has brought both improvement and disappointment, most investigators have established optimism about potentially achieving the goal of AI in the 21st century.
过去几十年人工智能研究中遇到的问题进一步阻碍了人工智能的发展。人工智能研究人员所做的失败预测，以及对人类行为不够完整的理解，削弱了人类水平人工智能的最初设想。尽管人工智能研究的进展带来了进步和失望，但大多数研究人员对人工智能在21世纪可能实现的目标持乐观态度。
Other possible reasons have been proposed for the lengthy research in the progress of strong AI. The intricacy of scientific problems and the need to fully understand the human brain through psychology and neurophysiology have limited many researchers in emulating the function of the human brain in computer hardware. Many researchers tend to underestimate any doubt that is involved with future predictions of AI, but without taking those issues seriously, people can then overlook solutions to problematic questions.
Other possible reasons have been proposed for the lengthy research in the progress of strong AI. The intricacy of scientific problems and the need to fully understand the human brain through psychology and neurophysiology have limited many researchers in emulating the function of the human brain in computer hardware. Many researchers tend to underestimate any doubt that is involved with future predictions of AI, but without taking those issues seriously, people can then overlook solutions to problematic questions.
还有其他可能的原因也能解释为什么强人工智能的研究持续了这么长时间。错综复杂的科学问题，以及用心理学和神经生理学充分了解人脑的需要，限制了许多研究人员在计算机硬件中模拟人脑。许多研究人员倾向于低估对人工智能未来预测有关的任何怀疑，但是如果不认真对待这些问题，人们就会忽视这些问题的解决方案。
The practice of abstraction, which people tend to redefine when working with a particular context in research, provides researchers with a concentration on just a few concepts. The most productive use of abstraction in AI research comes from planning and problem solving. Although the aim is to increase the speed of a computation, the role of abstraction has posed questions about the involvement of abstraction operators.
The practice of abstraction, which people tend to redefine when working with a particular context in research, provides researchers with a concentration on just a few concepts. The most productive use of abstraction in AI research comes from planning and problem solving. Although the aim is to increase the speed of a computation, the role of abstraction has posed questions about the involvement of abstraction operators.
人们倾向于在特定研究领域中使用抽象，这使得研究人员能集中在少数几个概念上。抽象在人工智能研究中最有效的应用来自规划和解决问题。虽然目标是提高计算速度，但是抽象的作用和角色已经在呼唤一种能用哦股计算的抽象操作。
A possible reason for the slowness in AI relates to the acknowledgement by many AI researchers that heuristics is a section that contains a significant breach between computer performance and human performance. The specific functions that are programmed to a computer may be able to account for many of the requirements that allow it to match human intelligence. These explanations are not necessarily guaranteed to be the fundamental causes for the delay in achieving strong AI, but they are widely agreed by numerous researchers.
A possible reason for the slowness in AI relates to the acknowledgement by many AI researchers that heuristics is a section that contains a significant breach between computer performance and human performance. The specific functions that are programmed to a computer may be able to account for many of the requirements that allow it to match human intelligence. These explanations are not necessarily guaranteed to be the fundamental causes for the delay in achieving strong AI, but they are widely agreed by numerous researchers.
-- [[用户:Qige96|Ricky]]([[用户讨论:Qige96|讨论]])这段英文审校也读不懂。
人工智能发展缓慢的一个可能原因是许多人工智能研究人员承认启发式算法是计算机性能和人类表现之间的一个重大缺口。为编入计算机的特定功能可以满足许多要求，使计算机与人类智能相匹配。这些解释并不一定是造成人工智能实现延迟的根本原因，但它们得到了众多研究人员的广泛认同。
人工智能发展缓慢的一个可能原因是许多人工智能研究人员承认启发式算法是计算机性能和人类表现之间的一个重大缺口。为编入计算机的特定功能可以满足许多要求，使计算机与人类智能相匹配。这些解释并不一定是造成人工智能实现延迟的根本原因，但它们得到了众多研究人员的广泛认同。
There have been many AI researchers that debate over the idea whether machines should be created with emotions. There are no emotions in typical models of AI and some researchers say programming emotions into machines allows them to have a mind of their own. Emotion sums up the experiences of humans because it allows them to remember those experiences. David Gelernter writes, "No computer will be creative unless it can simulate all the nuances of human emotion." This concern about emotion has posed problems for AI researchers and it connects to the concept of strong AI as its research progresses into the future.
There have been many AI researchers that debate over the idea whether machines should be created with emotions. There are no emotions in typical models of AI and some researchers say programming emotions into machines allows them to have a mind of their own. Emotion sums up the experiences of humans because it allows them to remember those experiences. David Gelernter writes, "No computer will be creative unless it can simulate all the nuances of human emotion." This concern about emotion has posed problems for AI researchers and it connects to the concept of strong AI as its research progresses into the future.
许多人工智能研究人员一直在争论机器是否应该带有情感。典型的人工智能模型中没有情感，一些研究人员说，将情感编程到机器中可以让它们拥有自己的思想。情感总结了人类的经历，它使得人们记住那些经历。大卫·格勒尼特(David Gelernter)则写道: “除非计算机能够模拟人类情感的所有细微差别，否则它不会具有创造力。”这种对情绪的关注给人工智能研究人员带来了一些问题，随着未来人工智能研究的进展，它与强人工智能的概念联系起来。
==Controversies and dangers 争议和风险==
==Controversies and dangers 争议和风险==