技术奇点

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"The Singularity" redirects here. For other uses, see Singularity (disambiguation).

"奇点" redirects here. For other uses, see 奇点（释疑).

{{简介}技术增长变得不可控制和不可逆转的假设时间点}}

The technological singularity—also, simply, the singularity—is a hypothetical point in time at which technological growth becomes uncontrollable and irreversible, resulting in unforeseeable changes to human civilization. According to the most popular version of the singularity hypothesis, called intelligence explosion, an upgradable intelligent agent will eventually enter a "runaway reaction" of self-improvement cycles, each new and more intelligent generation appearing more and more rapidly, causing an "explosion" in intelligence and resulting in a powerful superintelligence that qualitatively far surpasses all human intelligence.

The technological singularity—also, simply, the singularity—is a hypothetical point in time at which technological growth becomes uncontrollable and irreversible, resulting in unforeseeable changes to human civilization. According to the most popular version of the singularity hypothesis, called intelligence explosion, an upgradable intelligent agent will eventually enter a "runaway reaction" of self-improvement cycles, each new and more intelligent generation appearing more and more rapidly, causing an "explosion" in intelligence and resulting in a powerful superintelligence that qualitatively far surpasses all human intelligence.

技术奇点Technological singularity——简称 奇点Singularity——是一个假设的时间点，在这个时间点上，技术增长变得不可控制和不可逆转，从而导致人类文明发生无法预见的变化。根据最流行的奇点假说，称为智能爆炸，一个可升级的智能体最终将进入自我完善周期的“失控反应”，每一个新的、更智能的一代出现得越来越快，在智力上引起“爆炸”，并产生一种在质量上远远超过所有人类智力的强大的超智能。

The first use of the concept of a "singularity" in the technological context was John von Neumann. Stanislaw Ulam reports a discussion with von Neumann "centered on the accelerating progress of technology and changes in the mode of human life, which gives the appearance of approaching some essential singularity in the history of the race beyond which human affairs, as we know them, could not continue". Subsequent authors have echoed this viewpoint.

The first use of the concept of a "singularity" in the technological context was John von Neumann. Stanislaw Ulam reports a discussion with von Neumann "centered on the accelerating progress of technology and changes in the mode of human life, which gives the appearance of approaching some essential singularity in the history of the race beyond which human affairs, as we know them, could not continue".

“奇点”概念在技术领域的第一次使用是约翰·冯·诺依曼。Stanislaw Ulam报告了与冯·诺依曼的一次讨论，“集中在技术的加速进步和人类生活方式的变化上，这给人一种接近种族历史上某些基本奇点的表象，在这些奇点之外，我们所知的人类事务将无法继续下去”。

I. J. Good's "intelligence explosion" model predicts that a future superintelligence will trigger a singularity.

I. J. Good's "intelligence explosion" model predicts that a future superintelligence will trigger a singularity.

I. j.古德的“智能爆炸”模型预测，未来的超级智能将触发一个奇点。

The concept and the term "singularity" were popularized by Vernor Vinge in his 1993 essay The Coming Technological Singularity, in which he wrote that it would signal the end of the human era, as the new superintelligence would continue to upgrade itself and would advance technologically at an incomprehensible rate. He wrote that he would be surprised if it occurred before 2005 or after 2030.

The concept and the term "singularity" were popularized by Vernor Vinge in his 1993 essay The Coming Technological Singularity, in which he wrote that it would signal the end of the human era, as the new superintelligence would continue to upgrade itself and would advance technologically at an incomprehensible rate. He wrote that he would be surprised if it occurred before 2005 or after 2030. The consequences of the singularity and its potential benefit or harm to the human race have been intensely debated.

这个概念和术语“奇点”是由 Vernor Vinge 在他1993年的文章《即将到来的技术奇异点》中推广的，他在文中写道，这将标志着人类时代的终结，因为新的超级智能将继续自我升级，并以一种不可思议的速度在技术上进步。他写道，如果它发生在2005年之前或2030年之后，他会感到惊讶。奇点的后果及其对人类的潜在利益或伤害已经引起了激烈的争论。

Public figures such as Stephen Hawking and Elon Musk have expressed concern that full artificial intelligence (AI) could result in human extinction. The consequences of the singularity and its potential benefit or harm to the human race have been intensely debated.

Four polls of AI researchers, conducted in 2012 and 2013 by Nick Bostrom and Vincent C. Müller, suggested a median probability estimate of 50% that artificial general intelligence (AGI) would be developed by 2040–2050.

2012年和2013年，尼克 · 博斯特罗姆(Nick Bostrom)和文森特 · c · 穆勒(Vincent c. Müller)对人工智能研究人员进行了四次民意调查，结果显示，到2040年至2050年，人工通用智能(AGI)开发的概率中值估计为50% 。

Four polls of AI researchers, conducted in 2012 and 2013 by Nick Bostrom and Vincent C. Müller, suggested a median probability estimate of 50% that artificial general intelligence (AGI) would be developed by 2040–2050.

Although technological progress has been accelerating, it has been limited by the basic intelligence of the human brain, which has not, according to Paul R. Ehrlich, changed significantly for millennia. However, with the increasing power of computers and other technologies, it might eventually be possible to build a machine that is significantly more intelligent than humans.

虽然技术进步一直在加速，但是它受到人类大脑基本智能的限制，据保罗 · r · 埃利希说，几千年来人类大脑并没有发生显著的变化。然而，随着计算机和其他技术能力的增强，最终可能会造出一台比人类智能得多的机器。

Background背景

Although technological progress has been accelerating, it has been limited by the basic intelligence of the human brain, which has not, according to Paul R. Ehrlich, changed significantly for millennia. However, with the increasing power of computers and other technologies, it might eventually be possible to build a machine that is significantly more intelligent than humans.

虽然技术进步一直在加速，但它受到人脑基本智力的限制，而根据Paul R.Ehrlich的说法，人类大脑的基本智力在几千年来没有发生显著变化。然而，随着计算机和其他技术的日益强大，最终有可能制造出一台比人类更智能得多的机器。

If a superhuman intelligence were to be invented—either through the amplification of human intelligence or through artificial intelligence—it would bring to bear greater problem-solving and inventive skills than current humans are capable of. Such an AI is referred to as Seed AI because if an AI were created with engineering capabilities that matched or surpassed those of its human creators, it would have the potential to autonomously improve its own software and hardware or design an even more capable machine. This more capable machine could then go on to design a machine of yet greater capability. These iterations of recursive self-improvement could accelerate, potentially allowing enormous qualitative change before any upper limits imposed by the laws of physics or theoretical computation set in. It is speculated that over many iterations, such an AI would far surpass human cognitive abilities.

如果通过扩大人类智能或人工智能来发明一种超人类智能，那么它将比现在的人类拥有更强的解决问题和创造能力。这种人工智能被称为种子人工智能，因为如果人工智能的工程能力与人类创造者的能力相匹配或超越人类，那么它就有潜力自主改进自己的软件和硬件，或者设计出更强大的机器。这台能力更强的机器可以继续设计一台能力更强的机器。这些递归自我改进的迭代可以加速，在物理定律或理论计算设置的任何上限之前，潜在地允许巨大的定性变化。据推测，经过多次迭代，这样的人工智能将远远超过人类的认知能力。

If a superhuman intelligence were to be invented—either through the amplification of human intelligence or through artificial intelligence—it would bring to bear greater problem-solving and inventive skills than current humans are capable of. Such an AI is referred to as Seed AI because if an AI were created with engineering capabilities that matched or surpassed those of its human creators, it would have the potential to autonomously improve its own software and hardware or design an even more capable machine. This more capable machine could then go on to design a machine of yet greater capability. These iterations of recursive self-improvement could accelerate, potentially allowing enormous qualitative change before any upper limits imposed by the laws of physics or theoretical computation set in. It is speculated that over many iterations, such an AI would far surpass human cognitive abilities.

如果一种超人智能是通过人类智能的放大或人工智能发明的，那么它将带来比现在人类所能具备的更大的解决问题和发明的能力。这样的人工智能被称为“种子人工智能” For example, with a million-fold increase in the speed of information processing relative to that of humans, a subjective year would pass in 30 physical seconds. Such a difference in information processing speed could drive the singularity.

速度超级智能描述了一个人工智能，它可以做任何人类能做的事情，唯一的区别是机器运行得更快。例如，与人类相比，信息处理速度提高了100万倍，一个主观的一年将在30个物理秒内过去。 Such a difference in information processing speed could drive the singularity.

Ray Kurzweil writes that, due to [[paradigm shifts, a trend of exponential growth extends Moore's law from integrated circuits to earlier transistors, vacuum tubes, relays, and electromechanical computers. He predicts that the exponential growth will continue, and that in a few decades the computing power of all computers will exceed that of ("unenhanced") human brains, with superhuman artificial intelligence appearing around the same time.]]

雷 · 库兹韦尔Ray Kurzweil写道，由于[[范式的转变，指数增长的趋势将摩尔定律从集成电路扩展到早期的晶体管、真空管、继电器和机电计算机。他预测，这种指数增长将继续下去，在几十年内，所有计算机的计算能力将超过（“未增强的”）人脑，超人人工智能将同时出现]]

Kurzweil's graph). The 7 most recent data points are all NVIDIA GPUs.]]

Kurzweil's graph).最近的7个数据点都是 NVIDIA GPU。]

Plausibility合理性

Many prominent technologists and academics dispute the plausibility of a technological singularity, including Paul Allen, Jeff Hawkins, John Holland, Jaron Lanier, and Gordon Moore, whose law is often cited in support of the concept.

许多著名的技术专家和学者都对技术奇点的合理性提出质疑，包括Paul Allen、Jeff Hawkins、 John Holland、Jaron Lanier和Gordon Moore，他的摩尔定律经常被引用来支持这一概念。

The exponential growth in computing technology suggested by Moore's law is commonly cited as a reason to expect a singularity in the relatively near future, and a number of authors have proposed generalizations of Moore's law. Computer scientist and futurist Hans Moravec proposed in a 1998 book that the exponential growth curve could be extended back through earlier computing technologies prior to the integrated circuit.

摩尔定律所显示的计算技术的指数增长通常被认为是在相对不远的将来出现奇点的一个理由，许多作者已经提出了摩尔定律的推广。计算机科学家和未来主义者汉斯·莫拉维奇在1998年的一本书中提出，指数增长曲线可以通过集成电路出现之前的早期计算技术进行延伸。

Most proposed methods for creating superhuman or transhuman minds fall into one of two categories: intelligence amplification of human brains and artificial intelligence. The speculated ways to produce intelligence augmentation are many, and include bioengineering, genetic engineering, nootropic drugs, AI assistants, direct brain–computer interfaces and mind uploading. Because multiple paths to an intelligence explosion are being explored, it makes a singularity more likely; for a singularity to not occur they would all have to fail.

大多数被提议的创造超人或跨人类头脑的方法分为两类：人脑的智能放大和人工智能。据推测，产生智能增强的方法很多，包括生物工程、基因工程、nootropic药物、AI助手、直接大脑-计算机接口和思维上传。因为人们正在探索通向智能爆炸的多种途径，这使得奇点更有可能；要想不出现奇点，所有这些都必须失败。

Ray Kurzweil postulates a law of accelerating returns in which the speed of technological change (and more generally, all evolutionary processes On the other hand, it has been argued that the global acceleration pattern having the 21st century singularity as its parameter should be characterized as hyperbolic rather than exponential.

雷 · 库兹韦尔假定了一个技术变革的速度(更广泛地说，所有的进化过程)的加速收益定律。另一方面，有人认为，以21世纪奇点为参数的全球加速模式应该被描述为双曲型而不是指数型。

Robin Hanson expressed skepticism of human intelligence augmentation, writing that once the "low-hanging fruit" of easy methods for increasing human intelligence have been exhausted, further improvements will become increasingly difficult to find. Despite all of the speculated ways for amplifying human intelligence, non-human artificial intelligence (specifically seed AI) is the most popular option among the hypotheses that would advance the singularity.

Robin Hanson对人类智能增强表示怀疑，他写道，一旦提高人类智力的简单方法的“低挂果实”用尽，进一步的改进将变得越来越难找到。尽管有各种增强人类智能的推测方法，但非人类人工智能（特别是种子人工智能）是最受欢迎的选择，这些假设将促进奇异性。

Kurzweil reserves the term "singularity" for a rapid increase in artificial intelligence (as opposed to other technologies), writing for example that "The Singularity will allow us to transcend these limitations of our biological bodies and brains ... There will be no distinction, post-Singularity, between human and machine". Kurzweil believes that the singularity will occur by approximately 2045. His predictions differ from Vinge's in that he predicts a gradual ascent to the singularity, rather than Vinge's rapidly self-improving superhuman intelligence.

库兹韦尔将“奇点”一词用于描述人工智能(相对于其他技术)的快速增长，例如他写道: “奇点将允许我们超越生物体和大脑的这些局限... ..。后奇点时代，人类与机器之间将不再有区别。”。库兹韦尔相信奇点将在大约2045年出现。他的预测与 Vinge 的不同之处在于，Vinge 预测的是一个逐渐上升到奇点的过程，而不是 Vinge 的快速自我完善的超人智慧。

Whether or not an intelligence explosion occurs depends on three factors. The first accelerating factor is the new intelligence enhancements made possible by each previous improvement. Contrariwise, as the intelligences become more advanced, further advances will become more and more complicated, possibly overcoming the advantage of increased intelligence. Each improvement should beget at least one more improvement, on average, for movement towards singularity to continue. Finally, the laws of physics will eventually prevent any further improvements.

智能爆炸是否发生取决于三个因素。 第一个加速因素是以前的每一次改进都使新的智能增强成为可能。相反，随着智力的进步，进一步的发展将变得越来越复杂，可能会抵消智力增长的优势。平均来说，每一次改进都应该至少再带来一次改进，以便继续朝着奇点的方向发展。最后，物理定律最终会阻止任何进一步的改进。

Oft-cited dangers include those commonly associated with molecular nanotechnology and genetic engineering. These threats are major issues for both singularity advocates and critics, and were the subject of Bill Joy's Wired magazine article "Why the future doesn't need us".

经常被引用的危险包括那些通常与分子纳米技术和基因工程有关的危险。这些威胁是奇点倡导者和批评者的主要问题，也是比尔 · 乔伊在《连线》杂志上发表文章《为什么未来不需要我们》的主题。

There are two logically independent, but mutually reinforcing, causes of intelligence improvements: increases in the speed of computation, and improvements to the algorithms used. The former is predicted by Moore's Law and the forecasted improvements in hardware, and is comparatively similar to previous technological advances. But there are some AI researchers模板:Who who believe software is more important than hardware.

智能改进有两个逻辑上独立但又相互加强的原因：计算速度的提高和使用的算法的改进。 前者由摩尔定律和硬件方面的预测改进进行预测， 与以前的技术进步比较相似。但也有一些人工智能研究人员认为软件比硬件更重要。

There are substantial dangers associated with an intelligence explosion singularity originating from a recursively self-improving set of algorithms. First, the goal structure of the AI might not be invariant under self-improvement, potentially causing the AI to optimise for something other than what was originally intended. Secondly, AIs could compete for the same scarce resources mankind uses to survive.

由递归自我改进的算法集合引起的智能爆炸存在实质性的危险。首先，人工智能的目标结构可能不会在自我完善的情况下保持不变，这可能会导致人工智能为其他目标进行优化。其次，人工智能可以竞争人类赖以生存的稀缺资源。

A 2017 email survey of authors with publications at the 2015 NeurIPS and ICML machine learning conferences asked about the chance of an intelligence explosion. Of the respondents, 12% said it was "quite likely", 17% said it was "likely", 21% said it was "about even", 24% said it was "unlikely" and 26% said it was "quite unlikely".

2017年对2015年 NeurIPS和 ICML机器学习会议上发表文章的作者的电子邮件调查询问了智能爆炸的可能性。在受访者中，12%的人认为“很有可能”，17%的人说“很可能”，21%的人说“差不多”，24%的人说“不太可能”，26%的人说“不太可能”。

While not actively malicious, there is no reason to think that AIs would actively promote human goals unless they could be programmed as such, and if not, might use the resources currently used to support mankind to promote its own goals, causing human extinction.

尽管人工智能并非主动怀有恶意，但我们没有理由认为它们会主动促进人类目标，除非它们能够被编入这样的程序，否则它们可能会利用目前用于支持人类的资源来促进其自身目标，从而导致人类灭绝。

Speed improvements速度改进

Carl Shulman and Anders Sandberg suggest that algorithm improvements may be the limiting factor for a singularity; while hardware efficiency tends to improve at a steady pace, software innovations are more unpredictable and may be bottlenecked by serial, cumulative research. They suggest that in the case of a software-limited singularity, intelligence explosion would actually become more likely than with a hardware-limited singularity, because in the software-limited case, once human-level AI is developed, it could run serially on very fast hardware, and the abundance of cheap hardware would make AI research less constrained. An abundance of accumulated hardware that can be unleashed once the software figures out how to use it has been called "computing overhang."

卡尔 · 舒尔曼和安德斯 · 桑德伯格认为，算法改进可能是奇点的限制因素; 虽然硬件效率趋向于稳步提高，但软件创新更加不可预测，可能会被连续的、累积的研究所阻碍。他们认为，在软件受限的奇点情况下，智能爆炸实际上比硬件受限的奇点更有可能发生，因为在软件受限的情况下，一旦开发出人类水平的人工智能，它可以在非常快速的硬件上连续运行，而大量廉价的硬件将使人工智能研究受到更少的限制。一旦软件知道如何使用，就可以释放大量积累的硬件，这被称为“计算过剩”

Both for human and artificial intelligence, hardware improvements increase the rate of future hardware improvements. Simply put, Moore's Law suggests that if the first doubling of speed took 18 months, the second would take 18 subjective months; or 9 external months, whereafter, four months, two months, and so on towards a speed singularity. An upper limit on speed may eventually be reached, although it is unclear how high this would be. Jeff Hawkins has stated that a self-improving computer system would inevitably run into upper limits on computing power: "in the end there are limits to how big and fast computers can run. We would end up in the same place; we'd just get there a bit faster. There would be no singularity."

无论对于人类智能还是人工智能，硬件改进都会提高未来硬件改进的速度。简单地说，Moore's Law认为，如果第一次速度翻倍需要18个月，第二次则需要18个主观月；或者9个外部月，之后，4个月、2个月，以此类推，走向速度奇点 速度的上限最终可能会达到，尽管还不清楚这会有多高。杰夫·霍金斯（Jeff Hawkins）曾表示，一个自我完善的计算机系统不可避免地会遇到计算能力的上限：“最终，计算机的运行速度和速度都是有限的。我们最终会在同一个地方；我们只会更快到达那里。不会有奇点。”

It is difficult to directly compare silicon-based hardware with neurons. But Berglas (2008) notes that computer speech recognition is approaching human capabilities, and that this capability seems to require 0.01% of the volume of the brain. This analogy suggests that modern computer hardware is within a few orders of magnitude of being as powerful as the human brain.

很难直接将基于硅的硬件与神经元相比较。但是{Harvtxt| Berglas|2008}指出计算机语音识别正在接近人类的能力，而且这种能力似乎需要0.01%的脑容量。这个类比表明，现代计算机硬件与人脑一样强大，只差几个数量级。

Some critics, like philosopher Hubert Dreyfus, assert that computers or machines cannot achieve human intelligence, while others, like physicist Stephen Hawking, hold that the definition of intelligence is irrelevant if the net result is the same.}}

一些批评家，比如哲学家休伯特 · 德雷福斯，断言计算机或机器不能实现人类智能，而另一些人，比如物理学家斯蒂芬 · 霍金，则认为如果最终结果是相同的，那么智能的定义就无关紧要

Exponential growth指数增长

Martin Ford in The Lights in the Tunnel: Automation, Accelerating Technology and the Economy of the Future

马丁 · 福特在《隧道中的灯光: 自动化，加速技术和未来经济》一书中

Ray Kurzweil writes that, due to paradigm shifts, a trend of exponential growth extends Moore's law from integrated circuits to earlier transistors, vacuum tubes, relays, and electromechanical computers. He predicts that the exponential growth will continue, and that in a few decades the computing power of all computers will exceed that of ("unenhanced") human brains, with superhuman artificial intelligence appearing around the same time.

[[图片：PPTMooresLawai.jpg|thumb | Ray Kurzweil写道，由于范式转换s，指数增长的趋势将摩尔定律从集成电路扩展到早期的晶体管、真空管、继电器和机电机械计算机。他预测，这种指数增长将继续下去，在几十年内，所有计算机的计算能力将超过（“未增强的”）人脑，同时出现超人人工智能

An updated version of Moore's law over 120 Years (based on Kurzweil's graph). The 7 most recent data points are all NVIDIA GPUs.

[[资料图：摩尔超过120的定律年.png|拇指|左|摩尔定律120年的更新版本（基于 Kurzweil's图形）。最近的7个数据点都是 Nvidia GPU.]]

In a 2007 paper, Schmidhuber stated that the frequency of subjectively "notable events" appears to be approaching a 21st-century singularity, but cautioned readers to take such plots of subjective events with a grain of salt: perhaps differences in memory of recent and distant events could create an illusion of accelerating change where none exists.

在2007年的一篇论文中，施密德胡贝尔Schmidhuber指出主观上“显著事件”的频率似乎正在接近21世纪的奇点，但提醒读者，对这些主观事件的情节要持保留态度：也许对最近和遥远的事件记忆上的差异，可能会造成一种在根本不存在的情况下加速变化的错觉。

The exponential growth in computing technology suggested by Moore's law is commonly cited as a reason to expect a singularity in the relatively near future, and a number of authors have proposed generalizations of Moore's law. Computer scientist and futurist Hans Moravec proposed in a 1998 book that the exponential growth curve could be extended back through earlier computing technologies prior to the integrated circuit.

摩尔定律所建议的计算技术的指数增长通常被认为是在相对不远的将来出现奇点的一个理由，许多作者已经提出了摩尔定律的推广。计算机科学家和未来学家Hans Moravec在1998年的一本书中提出指数增长曲线可以通过集成电路之前的早期计算技术进行延伸。

Paul Allen argued the opposite of accelerating returns, the complexity brake; He goes on to assert: "The reason to believe in human agency over technological determinism is that you can then have an economy where people earn their own way and invent their own lives. If you structure a society on not emphasizing individual human agency, it's the same thing operationally as denying people clout, dignity, and self-determination ... to embrace [the idea of the Singularity] would be a celebration of bad data and bad politics."

保罗 · 艾伦认为加速回报的反面是复杂性制动器; 他继续断言: “相信人类的能动性而非技术决定论的原因在于，你可以拥有一种经济，在这种经济中，人们可以自食其力，创造自己的生活。如果你建立一个不强调个人能动性的社会，那么在操作层面上，这与否定人们的影响力、尊严和自决是一回事... ... 接受[奇点理念]将是对错误数据和错误政治的颂扬。”

Ray Kurzweil postulates a law of accelerating returns in which the speed of technological change (and more generally, all evolutionary processes) increases exponentially, generalizing Moore's law in the same manner as Moravec's proposal, and also including material technology (especially as applied to nanotechnology), medical technology and others. Between 1986 and 2007, machines' application-specific capacity to compute information per capita roughly doubled every 14 months; the per capita capacity of the world's general-purpose computers has doubled every 18 months; the global telecommunication capacity per capita doubled every 34 months; and the world's storage capacity per capita doubled every 40 months. On the other hand, it has been argued that the global acceleration pattern having the 21st century singularity as its parameter should be characterized as hyperbolic rather than exponential.

Ray Kurzweil假设了一个加速回报定律，其中技术变革的速度（更广泛地说，所有进化过程）急剧增长。从1986年到2007年，摩尔定律以与莫拉维克提案相同的方式呈指数增长，并包括材料技术（尤其是应用于纳米技术）、医疗技术和其他技术，计算机计算人均信息的特定应用能力大约每14个月翻一番；世界通用计算机的人均容量每18个月翻一番；全球人均电信容量每34个月翻一番；世界人均存储容量每40个月翻一番。 另一方面，有人认为，以21世纪奇点为参数的全球加速度模式应该被描述为双曲而不是指数型

In addition to general criticisms of the singularity concept, several critics have raised issues with Kurzweil's iconic chart. One line of criticism is that a log-log chart of this nature is inherently biased toward a straight-line result. Others identify selection bias in the points that Kurzweil chooses to use. For example, biologist PZ Myers points out that many of the early evolutionary "events" were picked arbitrarily.

除了对奇点概念的普遍批评外，一些评论家还对库兹韦尔的标志性图表提出了质疑。有一种批评意见认为，这种性质的对数图表天生偏向于直线结果。其他人则认为库兹韦尔选择使用的观点存在选择偏差。例如，生物学家 pzmyers 指出，许多早期的进化“事件”是随意挑选的。

Kurzweil reserves the term "singularity" for a rapid increase in artificial intelligence (as opposed to other technologies), writing for example that "The Singularity will allow us to transcend these limitations of our biological bodies and brains ... There will be no distinction, post-Singularity, between human and machine". He also defines his predicted date of the singularity (2045) in terms of when he expects computer-based intelligences to significantly exceed the sum total of human brainpower, writing that advances in computing before that date "will not represent the Singularity" because they do "not yet correspond to a profound expansion of our intelligence."

Kurzweil将“奇点”一词保留为人工智能的快速增长（与其他技术相反），他举例写道，“奇点将使我们超越我们生物身体和大脑的这些限制。。。在奇点之后，人类和机器之间将没有区别。他还将他预测的奇点日期（2045年）定义为，他预计基于计算机的智能将大大超过人类脑力总和，在那之前写下计算技术的进步“不会代表奇点”，因为它们“还不符合我们智力的深刻扩展”。

Accelerating change加速变革

The term "technological singularity" reflects the idea that such change may happen suddenly, and that it is difficult to predict how the resulting new world would operate.

“技术奇点”一词反映了这种变化可能会突然发生的想法，而且很难预测由此产生的新世界将如何运作。

Main article: Accelerating change

Main article: 加速变革

文件:ParadigmShiftsFrr15Events.svg

According to Kurzweil, his logarithmic graph of 15 lists of paradigm shifts for key historic events shows an exponential trend

[[图片：ParadigmShiftsFrr15Events.svg|thumb |根据Kurzweil的说法，他对关键的历史事件的15个范式转移列表的对数图显示了指数趋势]]

claims that there is no direct evolutionary motivation for an AI to be friendly to humans. Evolution has no inherent tendency to produce outcomes valued by humans, and there is little reason to expect an arbitrary optimisation process to promote an outcome desired by mankind, rather than inadvertently leading to an AI behaving in a way not intended by its creators. Anders Sandberg has also elaborated on this scenario, addressing various common counter-arguments. AI researcher Hugo de Garis suggests that artificial intelligences may simply eliminate the human race for access to scarce resources, and humans would be powerless to stop them. Alternatively, AIs developed under evolutionary pressure to promote their own survival could outcompete humanity.  proposes an AI design that avoids several dangers including self-delusion, unintended instrumental actions, and corruption of the reward generator. and testing AI. His 2001 book Super-Intelligent Machines advocates the need for public education about AI and public control over AI. It also proposed a simple design that was vulnerable to corruption of the reward generator.

声称人工智能对人类友好并没有直接的进化动机。进化并没有产生人类所重视的结果的内在倾向，也没有理由期望一个任意的优化过程来促进人类所期望的结果，而不是无意中导致人工智能以一种非其创造者意图的方式行为。anderssandberg也详细阐述了这个场景，讨论了各种常见的反论点。Hugai认为，如果研究人员排除了人类稀缺的智力资源，那么他们可能就无能为力了。另一方面，人工智能是在进化的压力下发展起来的，以促进自身的生存，这一点可以超越人类。提出了一个人工智能设计，避免了一些危险，包括自欺欺人，无意识的工具行为，和奖励生成器的腐败。测试人工智能。他在2001年出版的《超级智能机器》（Super Intelligent Machines）一书倡导公众对人工智能的教育和公众对人工智能的控制。它还提出了一个简单的设计，容易腐败的奖励生成器。

Some singularity proponents argue its inevitability through extrapolation of past trends, especially those pertaining to shortening gaps between improvements to technology. In one of the first uses of the term "singularity" in the context of technological progress, Stanislaw Ulam tells of a conversation with John von Neumann about accelerating change: /* Styling for Template:Quote */ .templatequote { overflow: hidden; margin: 1em 0; padding: 0 40px; } .templatequote .templatequotecite {

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一些奇点的支持者通过对过去趋势的推断，特别是那些与缩短技术进步之间差距的趋势，来论证它的必然性。在技术进步的背景下，第一次使用“奇点”一词时，Stanislaw Ulam讲述了与John von Neumann关于加速变革的谈话：{{引用}一次围绕不断加速的技术进步和人类生活方式变化的对话，这使得人类历史上出现了一些基本的奇点，超过了这些奇点，人类的事务，如我们所知，将无法继续下去。}}

Kurzweil claims that technological progress follows a pattern of exponential growth, following what he calls the "law of accelerating returns". Whenever technology approaches a barrier, Kurzweil writes, new technologies will surmount it. He predicts paradigm shifts will become increasingly common, leading to "technological change so rapid and profound it represents a rupture in the fabric of human history". Kurzweil believes that the singularity will occur by approximately 2045. His predictions differ from Vinge's in that he predicts a gradual ascent to the singularity, rather than Vinge's rapidly self-improving superhuman intelligence.

J. Storrs Hall believes that "many of the more commonly seen scenarios for overnight hard takeoff are circular – they seem to assume hyperhuman capabilities at the starting point of the self-improvement process" in order for an AI to be able to make the dramatic, domain-general improvements required for takeoff. Hall suggests that rather than recursively self-improving its hardware, software, and infrastructure all on its own, a fledgling AI would be better off specializing in one area where it was most effective and then buying the remaining components on the marketplace, because the quality of products on the marketplace continually improves, and the AI would have a hard time keeping up with the cutting-edge technology used by the rest of the world.

J. Storrs Hall 认为，“很多常见的一夜之间硬起飞的场景都是循环的——它们似乎是在自我提升过程的起点上假设了超人类的能力” ，以便人工智能能够进行起飞所需的戏剧性的、领域一般性的改进。霍尔认为，一个初出茅庐的人工智能最好专注于它最有效的一个领域，然后在市场上购买剩余的部件，而不是自己不断地自我改进硬件、软件和基础设施，因为市场上的产品质量不断提高，人工智能很难跟上世界其他地方使用的尖端技术。

Oft-cited dangers include those commonly associated with molecular nanotechnology and genetic engineering. These threats are major issues for both singularity advocates and critics, and were the subject of Bill Joy's Wired magazine article "Why the future doesn't need us".

Ben Goertzel agrees with Hall's suggestion that a new human-level AI would do well to use its intelligence to accumulate wealth. The AI's talents might inspire companies and governments to disperse its software throughout society. Goertzel is skeptical of a hard five minute takeoff but speculates that a takeoff from human to superhuman level on the order of five years is reasonable. Goerzel refers to this scenario as a "semihard takeoff".

Ben Goertzel 同意 Hall 的建议，即一个新的人类级别的人工智能将会很好地利用它的智能来积累财富。人工智能的天赋可能会激励公司和政府将其软件推广到整个社会。戈特泽尔对五分钟的起飞持怀疑态度，但他推测五年从人类到超人级别的起飞是合理的。Goerzel 将这种情况称为“半硬起飞”。

Algorithm improvements算法改进

Some intelligence technologies, like "seed AI", may also have the potential to not just make themselves faster, but also more efficient, by modifying their source code. These improvements would make further improvements possible, which would make further improvements possible, and so on. 一些智能技术，比如“种子人工智能”， 通过修改它们的源代码，也可能不仅使自己更快，而且更高效。这些改进将使进一步的改进成为可能，从而再次使进一步的改进成为可能，以此类推。

Max More disagrees, arguing that if there were only a few superfast human-level AIs, that they would not radically change the world, as they would still depend on other people to get things done and would still have human cognitive constraints. Even if all superfast AIs worked on intelligence augmentation, it is unclear why they would do better in a discontinuous way than existing human cognitive scientists at producing super-human intelligence, although the rate of progress would increase. More further argues that a superintelligence would not transform the world overnight: a superintelligence would need to engage with existing, slow human systems to accomplish physical impacts on the world. "The need for collaboration, for organization, and for putting ideas into physical changes will ensure that all the old rules are not thrown out overnight or even within years."

马克斯 · 莫尔不同意这种观点，他认为，如果只有少数几个超快的人工智能，它们不会从根本上改变世界，因为它们仍然依赖于其他人来完成事情，仍然会受到人类认知的约束。即使所有超快的人工智能都在增强智力，也不清楚为什么它们会以不连续的方式比现有的人类认知科学家在产生超人类智力方面做得更好，尽管进步的速度会加快。更进一步认为，超级智能不会在一夜之间改变世界: 超级智能需要与现有的、速度缓慢的人类系统接触，以完成对世界的物理影响。“合作、组织以及将想法转化为实际变化的需要，将确保所有旧规则不会在一夜之间甚至几年之内被抛弃。”

The mechanism for a recursively self-improving set of algorithms differs from an increase in raw computation speed in two ways. First, it does not require external influence: machines designing faster hardware would still require humans to create the improved hardware, or to program factories appropriately. An AI rewriting its own source code could do so while contained in an AI box.

递归自改进算法集的机制在两个方面不同于原始计算速度的提高。首先，它不需要外部影响：设计更快硬件的机器仍然需要人类来创建改进的硬件，或者对工厂进行适当的编程。

In his 2005 book, The Singularity is Near, Kurzweil suggests that medical advances would allow people to protect their bodies from the effects of aging, making the life expectancy limitless. Kurzweil argues that the technological advances in medicine would allow us to continuously repair and replace defective components in our bodies, prolonging life to an undetermined age. Kurzweil further buttresses his argument by discussing current bio-engineering advances. Kurzweil suggests somatic gene therapy; after synthetic viruses with specific genetic information, the next step would be to apply this technology to gene therapy, replacing human DNA with synthesized genes.

在他2005年出版的《奇点迫近书中，Kurzweil 提出，医学的进步将使人们能够保护自己的身体免受衰老的影响，从而使人的寿命无限。库兹韦尔认为，医学技术的进步将使我们能够不断地修复和替换身体中有缺陷的组件，从而延长寿命，直到不确定的年龄。库兹韦尔通过讨论当前生物工程的进展进一步支持他的论点。库兹韦尔建议进行体细胞基因治疗; 在合成具有特定基因信息的病毒之后，下一步将把这项技术应用于基因治疗，用合成基因取代人类 DNA。

Second, as with Vernor Vinge’s conception of the singularity, it is much harder to predict the outcome. While speed increases seem to be only a quantitative difference from human intelligence, actual algorithm improvements would be qualitatively different. Eliezer Yudkowsky compares it to the changes that human intelligence brought: humans changed the world thousands of times more rapidly than evolution had done, and in totally different ways. Similarly, the evolution of life was a massive departure and acceleration from the previous geological rates of change, and improved intelligence could cause change to be as different again.

第二，和Vernor Vinge关于奇点的概念一样，预测结果要困难得多。虽然速度的提高似乎与人类的智能只是数量上的区别，但实际的算法改进在质量上是不同的。Eliezer Yudkowsky将其与人类智能带来的变化相比较：人类改变世界的速度比进化速度快数千倍，而且方式完全不同。同样地，生命的进化与以前的地质变化率有着巨大的背离和加速，而智能的提高可能会使变化再次变得不同

K. Eric Drexler, one of the founders of nanotechnology, postulated cell repair devices, including ones operating within cells and utilizing as yet hypothetical biological machines, in his 1986 book Engines of Creation.

K、 埃里克·德雷克斯勒K. Eric Drexler，纳米技术的创始人之一，在他1986年出版的《创造的引擎》一书中提出了假设的细胞修复装置，包括在细胞内运作并利用假设的生物机器的装置。

There are substantial dangers associated with an intelligence explosion singularity originating from a recursively self-improving set of algorithms. First, the goal structure of the AI might not be invariant under self-improvement, potentially causing the AI to optimise for something other than what was originally intended. Secondly, AIs could compete for the same scarce resources mankind uses to survive.

智能爆炸奇点源于一组递归的自我改进算法，这有着巨大的危险。首先，人工智能的目标结构在自我完善的情况下可能不是一成不变的，这可能会导致人工智能对原本计划之外的东西进行优化。第二，人工智能可以竞争人类赖以生存的稀缺资源。

According to Richard Feynman, it was his former graduate student and collaborator Albert Hibbs who originally suggested to him (circa 1959) the idea of a medical use for Feynman's theoretical micromachines. Hibbs suggested that certain repair machines might one day be reduced in size to the point that it would, in theory, be possible to (as Feynman put it) "swallow the doctor". The idea was incorporated into Feynman's 1959 essay There's Plenty of Room at the Bottom.

据理查德 · 费曼说，正是他以前的研究生兼合作者阿尔伯特 · 希布斯(Albert Hibbs)最初(大约在1959年)向他提出了费曼理论微型机器的医学用途的想法。希布斯建议，某些维修机器可能有一天会缩小到理论上可以(如费曼所说)“吞下医生”的程度。这个想法被纳入了费曼1959年的文章有足够的空间在底部。

While not actively malicious, there is no reason to think that AIs would actively promote human goals unless they could be programmed as such, and if not, might use the resources currently used to support mankind to promote its own goals, causing human extinction.

虽然不是恶意的，但没有理由认为人工智能会积极促进人类目标的实现，除非这些目标可以被编程，如果不能，就可能利用目前用于支持人类的资源来促进自己的目标，从而导致人类灭绝。

Beyond merely extending the operational life of the physical body, Jaron Lanier argues for a form of immortality called "Digital Ascension" that involves "people dying in the flesh and being uploaded into a computer and remaining conscious".

除了延长肉体的运作寿命，Jaron Lanier 还主张一种称为“数字提升”的永生形式，即“人们死于肉体，被上传到计算机中并保持清醒”。

Carl Shulman and Anders Sandberg suggest that algorithm improvements may be the limiting factor for a singularity; while hardware efficiency tends to improve at a steady pace, software innovations are more unpredictable and may be bottlenecked by serial, cumulative research. They suggest that in the case of a software-limited singularity, intelligence explosion would actually become more likely than with a hardware-limited singularity, because in the software-limited case, once human-level AI is developed, it could run serially on very fast hardware, and the abundance of cheap hardware would make AI research less constrained. An abundance of accumulated hardware that can be unleashed once the software figures out how to use it has been called "computing overhang."

Carl Shulman和Anders Sandberg认为，算法改进可能是奇点的限制因素；虽然硬件效率趋于稳步提高，但软件创新更具不可预测性，可能会受到连续、累积研究的限制。他们认为，在软件受限奇点的情况下，智能爆炸实际上比硬件受限奇点更可能发生，因为在软件有限的情况下，一旦开发出人类水平的人工智能，它可以在非常快的硬件上连续运行，廉价硬件的丰富将使人工智能研究不那么受限制。一旦软件知道如何使用，大量积累的硬件可以释放出来，这被称为“计算过剩”

Criticisms危机

A paper by Mahendra Prasad, published in AI Magazine, asserts that the 18th-century mathematician Marquis de Condorcet was the first person to hypothesize and mathematically model an intelligence explosion and its effects on humanity.

发表在《人工智能杂志》上的一篇论文声称，18世纪的数学家马奎斯·孔多塞是第一个假设和数学模拟智能爆炸及其对人类影响的人。

Some critics, like philosopher Hubert Dreyfus, assert that computers or machines cannot achieve human intelligence, while others, like physicist Stephen Hawking, hold that the definition of intelligence is irrelevant if the net result is the same.

一些批评家，如哲学家Hubert Dreyfus断言计算机或机器无法实现人类智能，而其他人，如物理学家Stephen Hawking，则认为如果最终结果相同，那么智力的定义就无关紧要。

An early description of the idea was made in John Wood Campbell Jr.'s 1932 short story "The last evolution".

早在1932年约翰·W·坎贝尔的短篇小说《最后的进化》中就对这个想法进行了描述。

Psychologist Steven Pinker stated in 2008:

心理学家Steven Pinker在2008年指出：

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In his 1958 obituary for John von Neumann, Ulam recalled a conversation with von Neumann about the "ever accelerating progress of technology and changes in the mode of human life, which gives the appearance of approaching some essential singularity in the history of the race beyond which human affairs, as we know them, could not continue." For example, Kurzweil extrapolates current technological trajectories past the arrival of self-improving AI or superhuman intelligence, which Yudkowsky argues represents a tension with both I. J. Good's proposed discontinuous upswing in intelligence and Vinge's thesis on unpredictability.

在他1958年为《约翰·冯·诺伊曼志撰写的讣告中，Ulam 回忆了他与 von Neumann 的一次对话，内容是关于“科技的不断加速进步和人类生活方式的变化，这使得人类历史上出现了一些本质奇点，而我们所知道的人类事务是不可能继续下去的。”例如，库兹韦尔推断当前的技术轨迹超越了自我完善的人工智能或超人类智能的出现，尤德科夫斯基认为这代表了与 i. j。古德提出智力的间断性提升和文奇关于不可预测性的论点。

University of California, Berkeley, philosophy professor John Searle writes:

[[加州大学伯克利分校]，哲学教授John Searle写道：

Former President of the United States Barack Obama spoke about singularity in his interview to Wired in 2016:

美国前总统巴拉克 · 奥巴马在2016年接受《连线》杂志采访时谈到了奇点:

[Computers] have, literally ..., no intelligence, no motivation, no autonomy, and no agency. We design them to behave as if they had certain sorts of psychology, but there is no psychological reality to the corresponding processes or behavior. ... [T]he machinery has no beliefs, desires, [or] motivations.

[计算机]从字面上讲，没有智能、没有动机、没有自主和代理。我们设计他们的行为，好像他们有某种心理学，但没有心理现实的对应过程或行为。。。[T] 机械没有信仰、欲望或动机。

Martin Ford in The Lights in the Tunnel: Automation, Accelerating Technology and the Economy of the Future postulates a "technology paradox" in that before the singularity could occur most routine jobs in the economy would be automated, since this would require a level of technology inferior to that of the singularity. This would cause massive unemployment and plummeting consumer demand, which in turn would destroy the incentive to invest in the technologies that would be required to bring about the Singularity. Job displacement is increasingly no longer limited to work traditionally considered to be "routine."

Martin Ford在“隧道中的灯光：自动化、加速技术和未来经济”提出了一个“技术悖论”，即在奇点出现之前，经济体中的大多数日常工作都将自动化，因为这需要的技术水平低于奇点。这将导致大规模的失业和消费者需求的骤降，这反过来又会破坏投资于实现奇点所需技术的动力。取代工作越来越不再局限于传统上被认为是“例行公事”的工作。

Theodore Modis and Jonathan Huebner argue that the rate of technological innovation has not only ceased to rise, but is actually now declining. Evidence for this decline is that the rise in computer clock rates is slowing, even while Moore's prediction of exponentially increasing circuit density continues to hold. This is due to excessive heat build-up from the chip, which cannot be dissipated quickly enough to prevent the chip from melting when operating at higher speeds. Advances in speed may be possible in the future by virtue of more power-efficient CPU designs and multi-cell processors. While Kurzweil used Modis' resources, and Modis' work was around accelerating change, Modis distanced himself from Kurzweil's thesis of a "technological singularity", claiming that it lacks scientific rigor.

Theodore Modis和Jonathan Huebner认为技术创新的速度不仅停止上升，而且现在实际上在下降。这种下降的证据是计算机时钟速率的增长正在放缓，尽管摩尔关于电路密度指数增长的预测仍然成立。这是由于芯片产生过多的热量，当以较高的速度运行时，这些热量不能迅速散去，以防止芯片熔化。在未来，由于更节能的CPU设计和多单元处理器，速度的提高可能成为可能。虽然库兹韦尔利用了莫迪斯的资源，而莫迪斯的工作是围绕加速变革展开的，但莫迪斯却与库兹韦尔的“技术奇点”理论保持距离，声称该理论缺乏科学严谨性。

In a detailed empirical accounting, The Progress of Computing, William Nordhaus argued that, prior to 1940, computers followed the much slower growth of a traditional industrial economy, thus rejecting extrapolations of Moore's law to 19th-century computers.

在一份详细的实证会计“计算的进步”中，William Nordhaus认为，在1940年以前，计算机遵循传统工业经济增长缓慢的趋势，因此拒绝了摩尔定律对19世纪计算机的推断。

In a 2007 paper, Schmidhuber stated that the frequency of subjectively "notable events" appears to be approaching a 21st-century singularity, but cautioned readers to take such plots of subjective events with a grain of salt: perhaps differences in memory of recent and distant events could create an illusion of accelerating change where none exists.

在2007年的一篇论文中，Schmidhuber指出主观上“显著事件”的频率似乎正在接近21世纪的奇点，但提醒读者，对这些主观事件的情节要持保留态度：也许对最近和遥远的事件记忆上的差异，可能会造成一种在根本不存在的情况下加速变化的错觉。

Paul Allen argued the opposite of accelerating returns, the complexity brake; the more progress science makes towards understanding intelligence, the more difficult it becomes to make additional progress. A study of the number of patents shows that human creativity does not show accelerating returns, but in fact, as suggested by Joseph Tainter in his The Collapse of Complex Societies, a law of diminishing returns. The number of patents per thousand peaked in the period from 1850 to 1900, and has been declining since. The growth of complexity eventually becomes self-limiting, and leads to a widespread "general systems collapse".

Paul Allen认为，与加速回报相反的是复杂性制动器；科学在理解智力方面取得的进展越多，取得额外进展就越困难。一项对专利数量的研究表明，人类的创造力并没有表现出加速的回报，但事实上，正如Joseph Tainter在他的《复杂社会的崩溃》中所指出的那样，[[收益递减]定律。每千件专利的数量在1850年至1900年期间达到顶峰，此后一直在下降。