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{{Redirect|AI|other uses|AI (disambiguation)|and|Artificial intelligence (disambiguation)}}
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{{short description|Intelligence demonstrated by machines}}
{{Artificial intelligence}}

'''Artificial intelligence''' ('''AI'''), sometimes called '''[[machine]] intelligence''', is [[intelligence]] demonstrated by [[machine]]s, in contrast to the '''[[natural]] intelligence''' displayed by humans and other animals. In [[computer science]] AI research is defined as the study of "[[intelligent agent]]s": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.<ref name="Definition of AI"/> Colloquially, the term "artificial intelligence" is applied when a machine mimics "cognitive" functions that humans associate with other [[human mind]]s, such as "learning" and "problem solving".{{sfn|Russell|Norvig|2009|p=2}}

The scope of AI is disputed: as machines become increasingly capable, tasks considered as requiring "intelligence" are often removed from the definition, a phenomenon known as the [[AI effect]], leading to the quip in Tesler's Theorem, "AI is whatever hasn't been done yet."<ref>{{Cite web|url=http://people.cs.georgetown.edu/~maloof/cosc270.f17/cosc270-intro-handout.pdf|title=Artificial Intelligence: An Introduction, p. 37|last=Maloof|first=Mark|date=|website=georgetown.edu|archive-url=|archive-date=|dead-url=|access-date=}}</ref> For instance, [[optical character recognition]] is frequently excluded from "artificial intelligence", having become a routine technology.<ref>{{cite magazine |last=Schank |first=Roger C. |title=Where's the AI |magazine=AI magazine |volume=12 |issue=4 |year=1991|p=38}}</ref> Modern machine capabilities generally classified as AI include successfully [[natural language understanding|understanding human speech]],{{sfn|Russell|Norvig|2009}} competing at the highest level in [[strategic game]] systems (such as [[chess]] and [[Go (game)|Go]]),<ref name="bbc-alphago"/> [[autonomous car|autonomously operating car]]s, and intelligent routing in [[content delivery network]]s and [[military simulations]].


Artificial intelligence was founded as an academic discipline in 1956, and in the years since has experienced several waves of optimism,<ref name="Optimism of early AI"/><ref name="AI in the 80s"/> followed by disappointment and the loss of funding (known as an "[[AI winter]]"),<ref name="First AI winter"/><ref name="Second AI winter"/> followed by new approaches, success and renewed funding.<ref name="AI in the 80s"/><ref name="AI in 2000s"/> For most of its history, AI research has been divided into subfields that often fail to communicate with each other.<ref name="Fragmentation of AI"/> These sub-fields are based on technical considerations, such as particular goals (e.g. "[[robotics]]" or "machine learning"),<ref name="Problems of AI"/> the use of particular tools ("logic" or [[artificial neural network]]s), or deep philosophical differences.<ref name="Biological intelligence vs. intelligence in general"/><ref name="Neats vs. scruffies"/><ref name="Symbolic vs. sub-symbolic"/> Subfields have also been based on social factors (particular institutions or the work of particular researchers).<ref name="Fragmentation of AI"/>


The traditional problems (or goals) of AI research include [[automated reasoning|reasoning]], [[knowledge representation]], [[Automated planning and scheduling|planning]], [[machine learning|learning]], [[natural language processing]], [[machine perception|perception]] and the ability to move and manipulate objects.<ref name="Problems of AI"/> [[artificial general intelligence|General intelligence]] is among the field's long-term goals.<ref name="General intelligence"/> Approaches include [[#Statistical|statistical methods]], [[#Sub-symbolic|computational intelligence]], and [[#Symbolic|traditional symbolic AI]]. Many tools are used in AI, including versions of [[#Search and optimization|search and mathematical optimization]], [[artificial neural network]]s, and [[#Probabilistic methods for uncertain reasoning|methods based on statistics, probability and economics]]. The AI field draws upon [[computer science]], [[Information engineering (field)|information engineering]], [[mathematics]], [[psychology]], [[linguistics]], [[philosophy]], and many others.


The field was founded on the claim that [[human intelligence]] "can be so precisely described that a machine can be made to simulate it".<ref>See the [[Dartmouth Workshop|Dartmouth proposal]], under [[#Philosophy|Philosophy]], below.</ref> This raises philosophical arguments about the nature of the [[mind]] and the ethics of creating artificial beings endowed with human-like intelligence which are issues that have been explored by [[History of AI#AI in myth, fiction and speculation|myth]], [[artificial intelligence in fiction|fiction]] and [[philosophy of AI|philosophy]] since [[ancient history|antiquity]].<ref name="McCorduck's thesis"/> Some people also consider AI to be [[Technological singularity|a danger to humanity]] if it progresses unabated.<ref>{{cite web|url=https://betanews.com/2016/10/21/artificial-intelligence-stephen-hawking/|title=Stephen Hawking believes AI could be mankind's last accomplishment|date=21 October 2016|website=BetaNews|deadurl=no|archiveurl=https://web.archive.org/web/20170828183930/https://betanews.com/2016/10/21/artificial-intelligence-stephen-hawking/|archivedate=28 August 2017|df=dmy-all}}</ref> Others believe that AI, unlike previous technological revolutions, will create a [[Technological unemployment#21st century|risk of mass unemployment]].<ref name="guardian jobs debate"/>


In the twenty-first century, AI techniques have experienced a resurgence following concurrent advances in [[Computer performance|computer power]], large amounts of [[big data|data]], and theoretical understanding; and AI techniques have become an essential part of the [[technology industry]], helping to solve many challenging problems in computer science, [[software engineering]] and [[operations research]].<ref name="AI widely used"/><ref name="AI in 2000s"/>
{{toclimit|3}}

== History ==

{{Main|History of artificial intelligence|Timeline of artificial intelligence}}

[[File:Medeia and Talus.png|thumb|[[Talos]], an ancient mythical [[automaton]] with artificial intelligence]]


Thought-capable [[artificial being]]s appeared as [[storytelling device]]s in antiquity,<ref name="AI in myth"/> and have been common in fiction, as in [[Mary Shelley]]'s ''[[Frankenstein]]'' or [[Karel Čapek]]'s ''[[R.U.R. (Rossum's Universal Robots)]]''.<ref name="AI in early science fiction"/> These characters and their fates raised many of the same issues now discussed in the [[ethics of artificial intelligence]].<ref name="McCorduck's thesis"/>


The study of mechanical or [[formal reasoning|"formal" reasoning]] began with [[philosopher]]s and mathematicians in antiquity. The study of mathematical logic led directly to [[Alan Turing]]'s [[theory of computation]], which suggested that a machine, by shuffling symbols as simple as "0" and "1", could simulate any conceivable act of mathematical deduction. This insight, that digital computers can simulate any process of formal reasoning, is known as the [[Church–Turing thesis]].<ref name="Formal reasoning"/> Along with concurrent discoveries in [[Neuroscience|neurobiology]], [[information theory]] and [[cybernetic]]s, this led researchers to consider the possibility of building an electronic brain. Turing proposed that "if a human could not distinguish between responses from a machine and a human, the machine could be considered “intelligent".<ref>"Artificial Intelligence." Encyclopedia of Emerging Industries, edited by Lynn M. Pearce, 6th ed., Gale, 2011, pp. 73–80. Gale Virtual Reference Library, http://link.galegroup.com/apps/doc/CX1930200017/GVRL?u=mcc_pv&xid=cd5adac2. Accessed 31 Mar. 2018.</ref> The first work that is now generally recognized as AI was [[Warren McCullouch|McCullouch]] and [[Walter Pitts|Pitts]]' 1943 formal design for [[Turing-complete]] "artificial neurons".{{sfn|Russell|Norvig|2009|p=16}}


The field of AI research was born at [[Dartmouth workshop|a workshop]] at [[Dartmouth College]] in 1956.<ref name="Dartmouth conference"/> Attendees [[Allen Newell]] ([[Carnegie Mellon University|CMU]]), [[Herbert A. Simon|Herbert Simon]] ([[Carnegie Mellon University|CMU]]), [[John McCarthy (computer scientist)|John McCarthy]] ([[Massachusetts Institute of Technology|MIT]]), [[Marvin Minsky]] ([[Massachusetts Institute of Technology|MIT]]) and [[Arthur Samuel]] ([[IBM]]) became the founders and leaders of AI research.<ref name="Hegemony of the Dartmouth conference attendees"/> They and their students produced programs that the press described as "astonishing":{{sfn|Russell|Norvig|2003|p=18|quote=it was astonishing whenever a computer did anything kind of smartish}} computers were learning [[draughts|checkers]] strategies (c. 1954)<ref>Schaeffer J. (2009) Didn’t Samuel Solve That Game?. In: One Jump Ahead. Springer, Boston, MA</ref> (and by 1959 were reportedly playing better than the average human),<ref>{{cite journal|last1=Samuel|first1=A. L.|title=Some Studies in Machine Learning Using the Game of Checkers|journal=IBM Journal of Research and Development|date=July 1959|volume=3|issue=3|pages=210–229|doi=10.1147/rd.33.0210}}</ref> solving word problems in algebra, proving logical theorems ([[Logic Theorist]], first run c. 1956) and speaking English.<ref name="Golden years of AI"/> By the middle of the 1960s, research in the U.S. was heavily funded by the [[DARPA|Department of Defense]]<ref name="AI funding in the 60s"/> and laboratories had been established around the world.<ref name="AI in England"/> AI's founders were optimistic about the future: [[Herbert A. Simon|Herbert Simon]] predicted, "machines will be capable, within twenty years, of doing any work a man can do". [[Marvin Minsky]] agreed, writing, "within a generation ... the problem of creating 'artificial intelligence' will substantially be solved".<ref name="Optimism of early AI"/>


They failed to recognize the difficulty of some of the remaining tasks. Progress slowed and in 1974, in response to the criticism of [[Sir James Lighthill]]{{sfn|Lighthill|1973}} and ongoing pressure from the US Congress to fund more productive projects, both the U.S. and British governments cut off exploratory research in AI. The next few years would later be called an "[[AI winter]]",<ref name="First AI winter"/> a period when obtaining funding for AI projects was difficult.


In the early 1980s, AI research was revived by the commercial success of [[expert system]]s,<ref name="Expert systems"/> a form of AI program that simulated the knowledge and analytical skills of human experts. By 1985, the market for AI had reached over a billion dollars. At the same time, Japan's [[fifth generation computer]] project inspired the U.S and British governments to restore funding for academic research.<ref name="AI in the 80s"/> However, beginning with the collapse of the [[Lisp Machine]] market in 1987, AI once again fell into disrepute, and a second, longer-lasting hiatus began.<ref name="Second AI winter"/>


In the late 1990s and early 21st century, AI began to be used for logistics, [[data mining]], [[medical diagnosis]] and other areas.<ref name="AI widely used"/> The success was due to increasing computational power (see [[Moore's law]]), greater emphasis on solving specific problems, new ties between AI and other fields (such as [[statistics]], [[economics]] and [[mathematical optimization|mathematics]]), and a commitment by researchers to mathematical methods and scientific standards.<ref name="Formal methods in AI"/> [[IBM Deep Blue|Deep Blue]] became the first computer chess-playing system to beat a reigning world chess champion, [[Garry Kasparov]], on 11 May 1997.{{sfn|McCorduck|2004|pp=480–483}}


In 2011, a ''[[Jeopardy!]]'' [[quiz show]] exhibition match, [[IBM]]'s [[question answering system]], [[Watson (artificial intelligence software)|Watson]], defeated the two greatest ''Jeopardy!'' champions, [[Brad Rutter]] and [[Ken Jennings]], by a significant margin.{{sfn|Markoff|2011}} [[Moore's law|Faster computers]], algorithmic improvements, and access to [[big data|large amounts of data]] enabled advances in [[machine learning]] and perception; data-hungry [[deep learning]] methods started to dominate accuracy benchmarks [[Deep learning#Deep learning revolution|around 2012]].<ref>{{cite web|title=Ask the AI experts: What’s driving today’s progress in AI?|url=https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/ask-the-ai-experts-whats-driving-todays-progress-in-ai|website=McKinsey & Company|accessdate=13 April 2018|language=en}}</ref> The [[Kinect]], which provides a 3D body–motion interface for the [[Xbox 360]] and the [[Xbox One]], uses algorithms that emerged from lengthy AI research<ref>{{cite web|url=http://www.i-programmer.info/news/105-artificial-intelligence/2176-kinects-ai-breakthrough-explained.html|title=Kinect's AI breakthrough explained|author=Administrator|work=i-programmer.info|deadurl=no|archiveurl=https://web.archive.org/web/20160201031242/http://www.i-programmer.info/news/105-artificial-intelligence/2176-kinects-ai-breakthrough-explained.html|archivedate=1 February 2016|df=dmy-all}}</ref> as do [[intelligent personal assistant]]s in [[smartphone]]s.<ref>{{cite web|url=http://readwrite.com/2013/01/15/virtual-personal-assistants-the-future-of-your-smartphone-infographic|title=Virtual Personal Assistants & The Future Of Your Smartphone [Infographic]|date=15 January 2013|author=Rowinski, Dan|work=ReadWrite|deadurl=no|archiveurl=https://web.archive.org/web/20151222083034/http://readwrite.com/2013/01/15/virtual-personal-assistants-the-future-of-your-smartphone-infographic|archivedate=22 December 2015|df=dmy-all}}</ref> In March 2016, [[AlphaGo]] won 4 out of 5 games of [[Go (game)|Go]] in a match with Go champion [[Lee Sedol]], becoming the first [[Computer Go|computer Go-playing system]] to beat a professional Go player without [[Go handicaps|handicaps]].<ref name="bbc-alphago">{{cite web|url=https://deepmind.com/alpha-go.html|title=AlphaGo – Google DeepMind|publisher=|deadurl=no|archiveurl=https://web.archive.org/web/20160310191926/https://www.deepmind.com/alpha-go.html|archivedate=10 March 2016|df=dmy-all}}</ref><ref>{{cite news|title=Artificial intelligence: Google's AlphaGo beats Go master Lee Se-dol|url=https://www.bbc.com/news/technology-35785875|accessdate=1 October 2016|work=BBC News|date=12 March 2016|deadurl=no|archiveurl=https://web.archive.org/web/20160826103910/http://www.bbc.com/news/technology-35785875|archivedate=26 August 2016|df=dmy-all}}</ref> In the 2017 [[Future of Go Summit]], [[AlphaGo]] won a [[AlphaGo versus Ke Jie|three-game match]] with [[Ke Jie]],<ref>{{cite web|url=https://www.wired.com/2017/05/win-china-alphagos-designers-explore-new-ai/|title=After Win in China, AlphaGo’s Designers Explore New AI|date=27 May 2017|deadurl=no|archiveurl=https://web.archive.org/web/20170602234726/https://www.wired.com/2017/05/win-china-alphagos-designers-explore-new-ai/|archivedate=2 June 2017|df=dmy-all}}</ref> who at the time continuously held the world No. 1 ranking for two years.<ref>{{cite web|url=http://www.goratings.org/|title=World's Go Player Ratings|date=May 2017|deadurl=no|archiveurl=https://web.archive.org/web/20170401123616/https://www.goratings.org/|archivedate=1 April 2017|df=dmy-all}}</ref><ref>{{cite web|title=柯洁迎19岁生日雄踞人类世界排名第一已两年|url=http://sports.sina.com.cn/go/2016-08-02/doc-ifxunyya3020238.shtml|language=Chinese|date=May 2017|deadurl=no|archiveurl=https://web.archive.org/web/20170811222849/http://sports.sina.com.cn/go/2016-08-02/doc-ifxunyya3020238.shtml|archivedate=11 August 2017|df=dmy-all}}</ref> This marked the completion of a significant milestone in the development of Artificial Intelligence as Go is an extremely complex game, more so than Chess.

According to [[Bloomberg News|Bloomberg's]] Jack Clark, 2015 was a landmark year for artificial intelligence, with the number of software projects that use AI within Google increased from a "sporadic usage" in 2012 to more than 2,700 projects. Clark also presents factual data indicating that error rates in image processing tasks have fallen significantly since 2011.<ref name=":0">{{cite web
|url = https://www.bloomberg.com/news/articles/2015-12-08/why-2015-was-a-breakthrough-year-in-artificial-intelligence
|title = Why 2015 Was a Breakthrough Year in Artificial Intelligence
|last = Clark
|first = Jack
|website = Bloomberg News
|date = 8 December 2015
|access-date = 23 November 2016
|quote = After a half-decade of quiet breakthroughs in artificial intelligence, 2015 has been a landmark year. Computers are smarter and learning faster than ever.
|deadurl = no
|archiveurl = https://web.archive.org/web/20161123053855/https://www.bloomberg.com/news/articles/2015-12-08/why-2015-was-a-breakthrough-year-in-artificial-intelligence
|archivedate = 23 November 2016
|df = dmy-all
}}</ref> He attributes this to an increase in affordable [[Artificial neural network|neural networks]], due to a rise in cloud computing infrastructure and to an increase in research tools and datasets.<ref name="AI in 2000s"/> Other cited examples include Microsoft's development of a Skype system that can automatically translate from one language to another and Facebook's system that can describe images to blind people.<ref name=":0"/> In a 2017 survey, one in five companies reported they had "incorporated AI in some offerings or processes".<ref>{{cite web|title=Reshaping Business With Artificial Intelligence|url=https://sloanreview.mit.edu/projects/reshaping-business-with-artificial-intelligence/|website=MIT Sloan Management Review|accessdate=2 May 2018|language=en}}</ref><ref>{{cite web|last1=Lorica|first1=Ben|title=The state of AI adoption|url=https://www.oreilly.com/ideas/the-state-of-ai-adoption|website=O'Reilly Media|accessdate=2 May 2018|language=en|date=18 December 2017}}</ref> Around 2016, [[China]] greatly accelerated its government funding; given its large supply of data and its rapidly increasing research output, some observers believe it may be on track to becoming an "AI superpower".<ref>{{cite news |title=Who's afraid of artificial intelligence in China? {{!}} DW {{!}} 18.09.2018 |url=https://www.dw.com/en/whos-afraid-of-artificial-intelligence-in-china/a-45546972 |accessdate=4 November 2018 |work=DW.COM |date=September 2018 |language=en}}</ref><ref>{{cite news |title=Review {{!}} How two AI superpowers — the U.S. and China — battle for supremacy in the field |url=https://www.washingtonpost.com/outlook/in-the-race-for-supremacy-in-artificial-intelligence-its-us-innovation-vs-chinese-ambition/2018/11/02/013e0030-b08c-11e8-aed9-001309990777_story.html |accessdate=4 November 2018 |work=Washington Post |date=2 November 2018 |language=en}}</ref>

== Basics ==


A typical AI perceives its environment and takes actions that maximize its chance of successfully achieving its goals.<ref name="Definition of AI"/> An AI's intended [[utility function|goal function]] can be simple ("1 if the AI wins a game of [[Go (game)|Go]], 0 otherwise") or complex ("Do actions mathematically similar to the actions that got you rewards in the past"). Goals can be explicitly defined, or can be induced. If the AI is programmed for "[[reinforcement learning]]", goals can be implicitly induced by rewarding some types of behavior and punishing others.{{efn|The act of doling out rewards can itself be formalized or automated into a "[[reward function]]".}} Alternatively, an evolutionary system can induce goals by using a "[[fitness function]]" to mutate and preferentially replicate high-scoring AI systems; this is similar to how animals evolved to innately desire certain goals such as finding food, or how dogs can be bred via [[artificial selection]] to possess desired traits.{{sfn|Domingos|2015|loc=Chapter 5}} Some AI systems, such as nearest-neighbor, instead reason by analogy; these systems are not generally given goals, except to the degree that goals are somehow implicit in their training data.{{sfn|Domingos|2015|loc=Chapter 7}} Such systems can still be benchmarked if the non-goal system is framed as a system whose "goal" is to successfully accomplish its narrow classification task.<ref>Lindenbaum, M., Markovitch, S., & Rusakov, D. (2004). Selective sampling for nearest neighbor classifiers. Machine learning, 54(2), 125–152.</ref>

AI often revolves around the use of [[algorithms]]. An algorithm is a set of unambiguous instructions that a mechanical computer can execute.{{efn|Terminology varies; see [[algorithm characterizations]].}} A complex algorithm is often built on top of other, simpler, algorithms. A simple example of an algorithm is the following recipe for optimal play at [[tic-tac-toe]]:{{sfn|Domingos|2015|loc=Chapter 1}}

# If someone has a "threat" (that is, two in a row), take the remaining square. Otherwise,
# if a move "forks" to create two threats at once, play that move. Otherwise,
# take the center square if it is free. Otherwise,
# if your opponent has played in a corner, take the opposite corner. Otherwise,
# take an empty corner if one exists. Otherwise,
# take any empty square.

Many AI algorithms are capable of learning from data; they can enhance themselves by learning new [[heuristic (computer science)|heuristics]] (strategies, or "rules of thumb", that have worked well in the past), or can themselves write other algorithms. Some of the "learners" described below, including Bayesian networks, decision trees, and nearest-neighbor, could theoretically, if given infinite data, time, and memory, learn to approximate any [[function (mathematics)|function]], including whatever combination of mathematical functions would best describe the entire world. These learners could therefore, in theory, derive all possible knowledge, by considering every possible hypothesis and matching it against the data. In practice, it is almost never possible to consider every possibility, because of the phenomenon of "[[combinatorial explosion]]", where the amount of time needed to solve a problem grows exponentially. Much of AI research involves figuring out how to identify and avoid considering broad swaths of possibilities that are unlikely to be fruitful.<ref name="Intractability"/>{{sfn|Domingos|2015|loc=Chapter 2, Chapter 3}} For example, when viewing a map and looking for the shortest driving route from [[Denver]] to [[New York City|New York]] in the East, one can in most cases skip looking at any path through [[San Francisco]] or other areas far to the West; thus, an AI wielding an pathfinding algorithm like [[A*]] can avoid the combinatorial explosion that would ensue if every possible route had to be ponderously considered in turn.<ref>{{cite journal
| first = P. E.
| last = Hart
|author2= Nilsson, N. J.|author3= Raphael, B.
| title = Correction to "A Formal Basis for the Heuristic Determination of Minimum Cost Paths"
| journal = [[Association for Computing Machinery|SIGART]] Newsletter
| issue = 37
| pages = 28–29
| year = 1972
| doi=10.1145/1056777.1056779
}}</ref>

The earliest (and easiest to understand) approach to AI was symbolism (such as formal logic): "If an otherwise healthy adult has a fever, then they may have [[influenza]]". A second, more general, approach is [[Bayesian inference]]: "If the current patient has a fever, adjust the probability they have influenza in such-and-such way". The third major approach, extremely popular in routine business AI applications, are analogizers such as [[Support vector machine|SVM]] and [[K-nearest neighbor algorithm|nearest-neighbor]]: "After examining the records of known past patients whose temperature, symptoms, age, and other factors mostly match the current patient, X% of those patients turned out to have influenza". A fourth approach is harder to intuitively understand, but is inspired by how the brain's machinery works: the [[artificial neural network]] approach uses artificial "[[neurons]]" that can learn by comparing itself to the desired output and altering the strengths of the connections between its internal neurons to "reinforce" connections that seemed to be useful. These four main approaches can overlap with each other and with evolutionary systems; for example, neural nets can learn to make inferences, to generalize, and to make analogies. Some systems implicitly or explicitly use multiple of these approaches, alongside many other AI and non-AI algorithms;<ref>{{cite web | url=https://artificial-future.com/artificial-intelligence | title= Algorithm in Artificial Intelligence }}</ref> the best approach is often different depending on the problem.{{sfn|Domingos|2015|loc=Chapter 2, Chapter 4, Chapter 6}}<ref>{{cite news|title=Can neural network computers learn from experience, and if so, could they ever become what we would call 'smart'?|url=https://www.scientificamerican.com/article/can-neural-network-comput/|accessdate=24 March 2018|work=Scientific American|date=2018|language=en}}</ref>

[[File:Overfitted Data.png|thumb|The blue line could be an example of [[overfitting]] a linear function due to random noise.]]
Learning algorithms work on the basis that strategies, algorithms, and inferences that worked well in the past are likely to continue working well in the future. These inferences can be obvious, such as "since the sun rose every morning for the last 10,000 days, it will probably rise tomorrow morning as well". They can be nuanced, such as "X% of [[Family (biology)|families]] have geographically separate species with color variants, so there is an Y% chance that undiscovered [[black swans]] exist". Learners also work on the basis of "[[Occam's razor#Probability theory and statistics|Occam's razor]]": The simplest theory that explains the data is the likeliest. Therefore, to be successful, a learner must be designed such that it prefers simpler theories to complex theories, except in cases where the complex theory is proven substantially better. Settling on a bad, overly complex theory gerrymandered to fit all the past training data is known as [[overfitting]]. Many systems attempt to reduce overfitting by rewarding a theory in accordance with how well it fits the data, but penalizing the theory in accordance with how complex the theory is.{{sfn|Domingos|2015|loc=Chapter 6, Chapter 7}} Besides classic overfitting, learners can also disappoint by "learning the wrong lesson". A toy example is that an image classifier trained only on pictures of brown horses and black cats might conclude that all brown patches are likely to be horses.{{sfn|Domingos|2015|p=286}} A real-world example is that, unlike humans, current image classifiers don't determine the spatial relationship between components of the picture; instead, they learn abstract patterns of pixels that humans are oblivious to, but that linearly correlate with images of certain types of real objects. Faintly superimposing such a pattern on a legitimate image results in an "adversarial" image that the system misclassifies.{{efn|Adversarial vulnerabilities can also result in nonlinear systems, or from non-pattern perturbations. Some systems are so brittle that changing a single adversarial pixel predictably induces misclassification.}}<ref>{{cite news|title=Single pixel change fools AI programs|url=https://www.bbc.com/news/technology-41845878|accessdate=12 March 2018|work=BBC News|date=3 November 2017}}</ref><ref>{{cite news|title=AI Has a Hallucination Problem That's Proving Tough to Fix|url=https://www.wired.com/story/ai-has-a-hallucination-problem-thats-proving-tough-to-fix/|accessdate=12 March 2018|work=WIRED|date=2018}}</ref><ref>Goodfellow, Ian J., Jonathon Shlens, and Christian Szegedy. "Explaining and harnessing adversarial examples." arXiv preprint arXiv:1412.6572 (2014).</ref>

[[File:Détection de personne - exemple 3.jpg|thumb|A self-driving car system may use a neural network to determine which parts of the picture seem to match previous training images of pedestrians, and then model those areas as slow-moving but somewhat unpredictable rectangular prisms that must be avoided.<ref>{{cite journal|last1=Matti|first1=D.|last2=Ekenel|first2=H. K.|last3=Thiran|first3=J. P.|title=Combining LiDAR space clustering and convolutional neural networks for pedestrian detection|journal=2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)|date=2017|pages=1–6|doi=10.1109/AVSS.2017.8078512|isbn=978-1-5386-2939-0}}</ref><ref>{{cite journal|last1=Ferguson|first1=Sarah|last2=Luders|first2=Brandon|last3=Grande|first3=Robert C.|last4=How|first4=Jonathan P.|title=Real-Time Predictive Modeling and Robust Avoidance of Pedestrians with Uncertain, Changing Intentions|journal=Algorithmic Foundations of Robotics XI|volume=107|date=2015|pages=161–177|doi=10.1007/978-3-319-16595-0_10|publisher=Springer, Cham|language=en|series=Springer Tracts in Advanced Robotics|isbn=978-3-319-16594-3|arxiv=1405.5581}}</ref>]]
Compared with humans, existing AI lacks several features of human "[[commonsense reasoning]]"; most notably, humans have powerful mechanisms for reasoning about "[[naïve physics]]" such as space, time, and physical interactions. This enables even young children to easily make inferences like "If I roll this pen off a table, it will fall on the floor". Humans also have a powerful mechanism of "[[folk psychology]]" that helps them to interpret natural-language sentences such as "The city councilmen refused the demonstrators a permit because they advocated violence". (A generic AI has difficulty inferring whether the councilmen or the demonstrators are the ones alleged to be advocating violence.)<ref>{{cite news|title=Cultivating Common Sense {{!}} DiscoverMagazine.com|url=http://discovermagazine.com/2017/april-2017/cultivating-common-sense|accessdate=24 March 2018|work=Discover Magazine|date=2017}}</ref><ref>{{cite journal|last1=Davis|first1=Ernest|last2=Marcus|first2=Gary|title=Commonsense reasoning and commonsense knowledge in artificial intelligence|journal=Communications of the ACM|date=24 August 2015|volume=58|issue=9|pages=92–103|doi=10.1145/2701413|url=https://m.cacm.acm.org/magazines/2015/9/191169-commonsense-reasoning-and-commonsense-knowledge-in-artificial-intelligence/}}</ref><ref>{{cite journal|last1=Winograd|first1=Terry|title=Understanding natural language|journal=Cognitive Psychology|date=January 1972|volume=3|issue=1|pages=1–191|doi=10.1016/0010-0285(72)90002-3}}</ref> This lack of "common knowledge" means that AI often makes different mistakes than humans make, in ways that can seem incomprehensible. For example, existing self-driving cars cannot reason about the location nor the intentions of pedestrians in the exact way that humans do, and instead must use non-human modes of reasoning to avoid accidents.<ref>{{cite news|title=Don't worry: Autonomous cars aren't coming tomorrow (or next year)|url=http://autoweek.com/article/technology/fully-autonomous-vehicles-are-more-decade-down-road|accessdate=24 March 2018|work=Autoweek|date=2016}}</ref><ref>{{cite news|last1=Knight|first1=Will|title=Boston may be famous for bad drivers, but it’s the testing ground for a smarter self-driving car|url=https://www.technologyreview.com/s/608871/finally-a-driverless-car-with-some-common-sense/|accessdate=27 March 2018|work=MIT Technology Review|date=2017|language=en}}</ref><ref>{{cite journal|last1=Prakken|first1=Henry|title=On the problem of making autonomous vehicles conform to traffic law|journal=Artificial Intelligence and Law|date=31 August 2017|volume=25|issue=3|pages=341–363|doi=10.1007/s10506-017-9210-0}}</ref>

== Problems ==


The overall research goal of artificial intelligence is to create technology that allows computers and machines to function in an intelligent manner. The general problem of simulating (or creating) intelligence has been broken down into sub-problems. These consist of particular traits or capabilities that researchers expect an intelligent system to display. The traits described below have received the most attention.<ref name="Problems of AI"/>

=== Reasoning, problem solving ===

Early researchers developed algorithms that imitated step-by-step reasoning that humans use when they solve puzzles or make logical deductions.<ref name="Reasoning"/> By the late 1980s and 1990s, AI research had developed methods for dealing with [[uncertainty|uncertain]] or incomplete information, employing concepts from [[probability]] and [[economics]].<ref name="Uncertain reasoning"/>

These algorithms proved to be insufficient for solving large reasoning problems, because they experienced a "combinatorial explosion": they became exponentially slower as the problems grew larger.<ref name="Intractability"/> In fact, even humans rarely use the step-by-step deduction that early AI research was able to model. They solve most of their problems using fast, intuitive judgements.<ref name="Psychological evidence of sub-symbolic reasoning"/>

=== Knowledge representation ===

[[File:GFO taxonomy tree.png|right|thumb|An ontology represents knowledge as a set of concepts within a domain and the relationships between those concepts.]]
{{Main|Knowledge representation|Commonsense knowledge}}

[[Knowledge representation]]<ref name="Knowledge representation"/> and [[knowledge engineering]]<ref name="Knowledge engineering"/> are central to classical AI research. Some "expert systems" attempt to gather together explicit knowledge possessed by experts in some narrow domain. In addition, some projects attempt to gather the "commonsense knowledge" known to the average person into a database containing extensive knowledge about the world. Among the things a comprehensive commonsense knowledge base would contain are: objects, properties, categories and relations between objects;<ref name="Representing categories and relations"/> situations, events, states and time;<ref name="Representing time"/> causes and effects;<ref name="Representing causation"/> knowledge about knowledge (what we know about what other people know);<ref name="Representing knowledge about knowledge"/> and many other, less well researched domains. A representation of "what exists" is an [[ontology (computer science)|ontology]]: the set of objects, relations, concepts, and properties formally described so that software agents can interpret them. The [[semantics]] of these are captured as [[description logic]] concepts, roles, and individuals, and typically implemented as classes, properties, and individuals in the [[Web Ontology Language]].<ref>{{cite book |last=Sikos |first=Leslie F. |date=June 2017 |title=Description Logics in Multimedia Reasoning |url=https://www.springer.com/us/book/9783319540658 |location=Cham |publisher=Springer |isbn=978-3-319-54066-5 |doi=10.1007/978-3-319-54066-5 |deadurl=no |archiveurl=https://web.archive.org/web/20170829120912/https://www.springer.com/us/book/9783319540658 |archivedate=29 August 2017 |df=dmy-all }}</ref> The most general ontologies are called [[upper ontology|upper ontologies]], which attempt to provide a foundation for all other knowledge<ref name="Ontology"/> by acting as mediators between [[Domain ontology|domain ontologies]] that cover specific knowledge about a particular knowledge domain (field of interest or area of concern). Such formal knowledge representations can be used in content-based indexing and retrieval,<ref>{{cite journal|last1=Smoliar|first1=Stephen W.|last2=Zhang|first2=HongJiang|title=Content based video indexing and retrieval|journal=IEEE multimedia|date=1994|volume=1.2|pages=62–72}}</ref> scene interpretation,<ref>{{cite journal|last1=Neumann|first1=Bernd|last2=Möller|first2=Ralf|title=On scene interpretation with description logics|journal=Image and Vision Computing|date=January 2008|volume=26|issue=1|pages=82–101|doi=10.1016/j.imavis.2007.08.013}}</ref> clinical decision support,<ref>{{cite journal|last1=Kuperman|first1=G. J.|last2=Reichley|first2=R. M.|last3=Bailey|first3=T. C.|title=Using Commercial Knowledge Bases for Clinical Decision Support: Opportunities, Hurdles, and Recommendations|journal=Journal of the American Medical Informatics Association|date=1 July 2006|volume=13|issue=4|pages=369–371|doi=10.1197/jamia.M2055|pmc=1513681}}</ref> knowledge discovery (mining "interesting" and actionable inferences from large databases),<ref>{{cite journal|last1=MCGARRY|first1=KEN|title=A survey of interestingness measures for knowledge discovery|journal=The Knowledge Engineering Review|date=1 December 2005|volume=20|issue=1|page=39|doi=10.1017/S0269888905000408}}</ref> and other areas.<ref>{{cite conference |url= |title=Automatic annotation and semantic retrieval of video sequences using multimedia ontologies |last1=Bertini |first1=M |last2=Del Bimbo |first2=A |last3=Torniai |first3=C |date=2006 |publisher=ACM |book-title=MM ‘06 Proceedings of the 14th ACM international conference on Multimedia |pages=679–682 |location=Santa Barbara |conference=14th ACM international conference on Multimedia}}</ref>

Among the most difficult problems in knowledge representation are:
;[[Default reasoning]] and the [[qualification problem]]: Many of the things people know take the form of "working assumptions". For example, if a bird comes up in conversation, people typically picture an animal that is fist sized, sings, and flies. None of these things are true about all birds. [[John McCarthy (computer scientist)|John McCarthy]] identified this problem in 1969<ref name="Qualification problem"/> as the qualification problem: for any commonsense rule that AI researchers care to represent, there tend to be a huge number of exceptions. Almost nothing is simply true or false in the way that abstract logic requires. AI research has explored a number of solutions to this problem.<ref name="Default reasoning and non-monotonic logic"/>
;The breadth of commonsense knowledge: The number of atomic facts that the average person knows is very large. Research projects that attempt to build a complete knowledge base of [[commonsense knowledge]] (e.g., [[Cyc]]) require enormous amounts of laborious [[ontology engineering|ontological engineering]]—they must be built, by hand, one complicated concept at a time.<ref name="Breadth of commonsense knowledge"/>
;The subsymbolic form of some commonsense knowledge: Much of what people know is not represented as "facts" or "statements" that they could express verbally. For example, a chess master will avoid a particular chess position because it "feels too exposed"{{sfn|Dreyfus|Dreyfus|1986}} or an art critic can take one look at a statue and realize that it is a fake.{{sfn|Gladwell|2005}} These are non-conscious and sub-symbolic intuitions or tendencies in the human brain.<ref name="Intuition"/> Knowledge like this informs, supports and provides a context for symbolic, conscious knowledge. As with the related problem of sub-symbolic reasoning, it is hoped that [[situated artificial intelligence|situated AI]], [[computational intelligence]], or [[#Statistical|statistical AI]] will provide ways to represent this kind of knowledge.<ref name="Intuition"/>

=== Planning ===

[[File:Hierarchical-control-system.svg|thumb| A [[hierarchical control system]] is a form of [[control system]] in which a set of devices and governing software is arranged in a hierarchy.]]

{{Main|Automated planning and scheduling}}

Intelligent agents must be able to set goals and achieve them.<ref name="Planning"/> They need a way to visualize the future—a representation of the state of the world and be able to make predictions about how their actions will change it—and be able to make choices that maximize the [[utility]] (or "value") of available choices.<ref name="Information value theory"/>

In classical planning problems, the agent can assume that it is the only system acting in the world, allowing the agent to be certain of the consequences of its actions.<ref name="Classical planning"/> However, if the agent is not the only actor, then it requires that the agent can reason under uncertainty. This calls for an agent that can not only assess its environment and make predictions, but also evaluate its predictions and adapt based on its assessment.<ref name="Non-deterministic planning"/>

[[Multi-agent planning]] uses the [[cooperation]] and competition of many agents to achieve a given goal. [[Emergent behavior]] such as this is used by [[evolutionary algorithms]] and [[swarm intelligence]].<ref name="Multi-agent planning"/>

=== Learning ===

{{Main|Machine learning}}

Machine learning, a fundamental concept of AI research since the field's inception,<ref>[[Alan Turing]] discussed the centrality of learning as early as 1950, in his classic paper "[[Computing Machinery and Intelligence]]".{{Harv|Turing|1950}} In 1956, at the original Dartmouth AI summer conference, [[Ray Solomonoff]] wrote a report on unsupervised probabilistic machine learning: "An Inductive Inference Machine".{{Harv|Solomonoff|1956}}</ref> is the study of computer algorithms that improve automatically through experience.<ref>This is a form of [[Tom M. Mitchell|Tom Mitchell]]'s widely quoted definition of machine learning: "A computer program is set to learn from an experience ''E'' with respect to some task ''T'' and some performance measure ''P'' if its performance on ''T'' as measured by ''P'' improves with experience ''E''."</ref><ref name="Machine learning"/>

[[Unsupervised learning]] is the ability to find patterns in a stream of input, without requiring a human to label the inputs first.<ref>{{Cite web|url=https://deepai.org/machine-learning-glossary-and-terms/unsupervised-learning|title=What is Unsupervised Learning?|last=|first=|date=|website=deepai.org|archive-url=|archive-date=|dead-url=|access-date=}}</ref> [[Supervised learning]] includes both [[statistical classification|classification]] and numerical [[Regression analysis|regression]], which requires a human to label the input data first. Classification is used to determine what category something belongs in, after seeing a number of examples of things from several categories. Regression is the attempt to produce a function that describes the relationship between inputs and outputs and predicts how the outputs should change as the inputs change.<ref name="Machine learning"/> Both classifiers and regression learners can be viewed as "function approximators" trying to learn an unknown (possibly implicit) function; for example, a spam classifier can be viewed as learning a function that maps from the text of an email to one of two categories, "spam" or "not spam". [[Computational learning theory]] can assess learners by [[computational complexity]], by [[sample complexity]] (how much data is required), or by other notions of [[optimization theory|optimization]].<ref>{{cite journal|last1=Jordan|first1=M. I.|last2=Mitchell|first2=T. M.|title=Machine learning: Trends, perspectives, and prospects|journal=Science|date=16 July 2015|volume=349|issue=6245|pages=255–260|doi=10.1126/science.aaa8415|bibcode=2015Sci...349..255J}}</ref> In [[reinforcement learning]]<ref name="Reinforcement learning"/> the agent is rewarded for good responses and punished for bad ones. The agent uses this sequence of rewards and punishments to form a strategy for operating in its problem space.

=== Natural language processing ===

[[File:ParseTree.svg|thumb| A [[parse tree]] represents the [[syntax|syntactic]] structure of a sentence according to some [[formal grammar]].]]
{{Main|Natural language processing}}

[[Natural language processing]]<ref name="Natural language processing"/> (NLP) gives machines the ability to read and [[natural language understanding|understand]] human language. A sufficiently powerful natural language processing system would enable [[natural-language user interface]]s and the acquisition of knowledge directly from human-written sources, such as newswire texts. Some straightforward applications of natural language processing include [[information retrieval]], [[text mining]], [[question answering]]<ref>[https://www.academia.edu/2475776/Versatile_question_answering_systems_seeing_in_synthesis "Versatile question answering systems: seeing in synthesis"] {{webarchive|url=https://web.archive.org/web/20160201125047/http://www.academia.edu/2475776/Versatile_question_answering_systems_seeing_in_synthesis |date=1 February 2016 }}, Mittal et al., IJIIDS, 5(2), 119–142, 2011
</ref> and [[machine translation]].<ref name="Applications of natural language processing"/> Many current approaches use word co-occurrence frequencies to construct syntactic representations of text. "Keyword spotting" strategies for search are popular and scalable but dumb; a search query for "dog" might only match documents with the literal word "dog" and miss a document with the word "poodle". "Lexical affinity" strategies use the occurrence of words such as "accident" to [[sentiment analysis|assess the sentiment]] of a document. Modern statistical NLP approaches can combine all these strategies as well as others, and often achieve acceptable accuracy at the page or paragraph level, but continue to lack the semantic understanding required to classify isolated sentences well. Besides the usual difficulties with encoding semantic commonsense knowledge, existing semantic NLP sometimes scales too poorly to be viable in business applications. Beyond semantic NLP, the ultimate goal of "narrative" NLP is to embody a full understanding of commonsense reasoning.<ref>{{cite journal|last1=Cambria|first1=Erik|last2=White|first2=Bebo|title=Jumping NLP Curves: A Review of Natural Language Processing Research [Review Article]|journal=IEEE Computational Intelligence Magazine|date=May 2014|volume=9|issue=2|pages=48–57|doi=10.1109/MCI.2014.2307227}}</ref>

=== Perception ===

{{Main|Machine perception|Computer vision|Speech recognition}}

[[File:Ääretuvastuse näide.png|thumb|[[Feature detection (computer vision)|Feature detection]] (pictured: [[edge detection]]) helps AI compose informative abstract structures out of raw data.]]

[[Machine perception]]<ref name="Machine perception"/> is the ability to use input from sensors (such as cameras (visible spectrum or infrared), microphones, wireless signals, and active [[lidar]], sonar, radar, and [[tactile sensor]]s) to deduce aspects of the world. Applications include [[speech recognition]],<ref name="Speech recognition"/> [[facial recognition system|facial recognition]], and [[object recognition]].<ref name="Object recognition"/> [[Computer vision]] is the ability to analyze visual input. Such input is usually ambiguous; a giant, fifty-meter-tall pedestrian far away may produce exactly the same pixels as a nearby normal-sized pedestrian, requiring the AI to judge the relative likelihood and reasonableness of different interpretations, for example by using its "object model" to assess that fifty-meter pedestrians do not exist.<ref name="Computer vision"/>

=== Motion and manipulation ===

{{Main|Robotics}}

AI is heavily used in [[robotics]].<ref name="Robotics"/> Advanced [[robotic arm]]s and other [[industrial robot]]s, widely used in modern factories, can learn from experience how to move efficiently despite the presence of friction and gear slippage.<ref name="Configuration space"/> A modern mobile robot, when given a small, static, and visible environment, can easily determine its location and [[robotic mapping|map]] its environment; however, dynamic environments, such as (in [[endoscopy]]) the interior of a patient's breathing body, pose a greater challenge. [[Motion planning]] is the process of breaking down a movement task into "primitives" such as individual joint movements. Such movement often involves compliant motion, a process where movement requires maintaining physical contact with an object.{{sfn|Tecuci|2012}}<ref name="Robotic mapping"/><ref>{{cite journal|last1=Cadena|first1=Cesar|last2=Carlone|first2=Luca|last3=Carrillo|first3=Henry|last4=Latif|first4=Yasir|last5=Scaramuzza|first5=Davide|last6=Neira|first6=Jose|last7=Reid|first7=Ian|last8=Leonard|first8=John J.|title=Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age|journal=IEEE Transactions on Robotics|date=December 2016|volume=32|issue=6|pages=1309–1332|doi=10.1109/TRO.2016.2624754}}</ref> [[Moravec's paradox]] generalizes that low-level sensorimotor skills that humans take for granted are, counterintuitively, difficult to program into a robot; the paradox is named after [[Hans Moravec]], who stated in 1988 that "it is comparatively easy to make computers exhibit adult level performance on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one-year-old when it comes to perception and mobility".<ref>{{Cite book| first = Hans | last = Moravec | year = 1988 | title = Mind Children | publisher = Harvard University Press | author-link =Hans Moravec| p=15}}</ref><ref>{{cite news|last1=Chan|first1=Szu Ping|title=This is what will happen when robots take over the world|url=https://www.telegraph.co.uk/finance/economics/11994694/Heres-what-will-happen-when-robots-take-over-the-world.html|accessdate=23 April 2018|date=15 November 2015}}</ref> This is attributed to the fact that, unlike checkers, physical dexterity has been a direct target of [[natural selection]] for millions of years.<ref name="The Economist">{{cite news|title=IKEA furniture and the limits of AI|url=https://www.economist.com/news/leaders/21740735-humans-have-had-good-run-most-recent-breakthrough-robotics-it-clear|accessdate=24 April 2018|work=The Economist|date=2018|language=en}}</ref>

=== Social intelligence ===

{{Main|Affective computing}}
[[File:Kismet robot at MIT Museum.jpg|thumb|[[Kismet (robot)|Kismet]], a robot with rudimentary social skills{{sfn|''Kismet''}}]]

Moravec's paradox can be extended to many forms of social intelligence.<ref>{{cite news|last1=Thompson|first1=Derek|title=What Jobs Will the Robots Take?|url=https://www.theatlantic.com/business/archive/2014/01/what-jobs-will-the-robots-take/283239/|accessdate=24 April 2018|work=The Atlantic|date=2018}}</ref><ref>{{cite journal|last1=Scassellati|first1=Brian|title=Theory of mind for a humanoid robot|journal=Autonomous Robots|volume=12|issue=1|year=2002|pages=13–24}}</ref> Distributed multi-agent coordination of autonomous vehicles remains a difficult problem.<ref>{{cite journal|last1=Cao|first1=Yongcan|last2=Yu|first2=Wenwu|last3=Ren|first3=Wei|last4=Chen|first4=Guanrong|title=An Overview of Recent Progress in the Study of Distributed Multi-Agent Coordination|journal=IEEE Transactions on Industrial Informatics|date=February 2013|volume=9|issue=1|pages=427–438|doi=10.1109/TII.2012.2219061|arxiv=1207.3231}}</ref> [[Affective computing]] is an interdisciplinary umbrella that comprises systems which recognize, interpret, process, or simulate human [[Affect (psychology)|affects]].{{sfn|Thro|1993}}{{sfn|Edelson|1991}}{{sfn|Tao|Tan|2005}} Moderate successes related to affective computing include textual [[sentiment analysis]] and, more recently, multimodal affect analysis (see [[multimodal sentiment analysis]]), wherein AI classifies the affects displayed by a videotaped subject.<ref>{{cite journal|last1=Poria|first1=Soujanya|last2=Cambria|first2=Erik|last3=Bajpai|first3=Rajiv|last4=Hussain|first4=Amir|title=A review of affective computing: From unimodal analysis to multimodal fusion|journal=Information Fusion|date=September 2017|volume=37|pages=98–125|doi=10.1016/j.inffus.2017.02.003}}</ref>

In the long run, social skills and an understanding of human emotion and [[game theory]] would be valuable to a social agent. Being able to predict the actions of others by understanding their motives and emotional states would allow an agent to make better decisions. Some computer systems mimic human emotion and expressions to appear more sensitive to the emotional dynamics of human interaction, or to otherwise facilitate [[human–computer interaction]].<ref name="Emotion and affective computing"/> Similarly, some [[virtual assistant]]s are programmed to speak conversationally or even to banter humorously; this tends to give naïve users an unrealistic conception of how intelligent existing computer agents actually are.<ref>{{cite news|last1=Waddell|first1=Kaveh|title=Chatbots Have Entered the Uncanny Valley|url=https://www.theatlantic.com/technology/archive/2017/04/uncanny-valley-digital-assistants/523806/|accessdate=24 April 2018|work=The Atlantic|date=2018}}</ref>

=== General intelligence ===

{{Main|Artificial general intelligence|AI-complete}}

Historically, projects such as the Cyc knowledge base (1984–) and the massive Japanese [[Fifth generation computer|Fifth Generation Computer Systems]] initiative (1982–1992) attempted to cover the breadth of human cognition. These early projects failed to escape the limitations of non-quantitative symbolic logic models and, in retrospect, greatly underestimated the difficulty of cross-domain AI. Nowadays, the vast majority of current AI researchers work instead on tractable "narrow AI" applications (such as medical diagnosis or automobile navigation).<ref name="contemporary agi">{{cite journal|last1=Pennachin|first1=C.|last2=Goertzel|first2=B.|title=Contemporary Approaches to Artificial General Intelligence|journal=Artificial General Intelligence. Cognitive Technologies|date=2007|doi=10.1007/978-3-540-68677-4_1|publisher=Springer|location=Berlin, Heidelberg|series=Cognitive Technologies|isbn=978-3-540-23733-4}}</ref> Many researchers predict that such "narrow AI" work in different individual domains will eventually be incorporated into a machine with [[artificial general intelligence]] (AGI), combining most of the narrow skills mentioned in this article and at some point even exceeding human ability in most or all these areas.<ref name="General intelligence"/><ref name="Roberts">{{cite journal|last1=Roberts|first1=Jacob|title=Thinking Machines: The Search for Artificial Intelligence|journal=Distillations|date=2016|volume=2|issue=2|pages=14–23|url=https://www.sciencehistory.org/distillations/magazine/thinking-machines-the-search-for-artificial-intelligence|accessdate=20 March 2018}}</ref> Many advances have general, cross-domain significance. One high-profile example is that [[DeepMind]] in the 2010s developed a "generalized artificial intelligence" that could learn many diverse [[Atari 2600|Atari]] games on its own, and later developed a variant of the system which succeeds at [[Catastrophic interference#The Sequential Learning Problem: McCloskey and Cohen (1989)|sequential learning]].<ref>{{cite news|title=The superhero of artificial intelligence: can this genius keep it in check?|url=https://www.theguardian.com/technology/2016/feb/16/demis-hassabis-artificial-intelligence-deepmind-alphago|accessdate=26 April 2018|work=the Guardian|date=16 February 2016|language=en}}</ref><ref>{{cite journal|last1=Mnih|first1=Volodymyr|last2=Kavukcuoglu|first2=Koray|last3=Silver|first3=David|last4=Rusu|first4=Andrei A.|last5=Veness|first5=Joel|last6=Bellemare|first6=Marc G.|last7=Graves|first7=Alex|last8=Riedmiller|first8=Martin|last9=Fidjeland|first9=Andreas K.|last10=Ostrovski|first10=Georg|last11=Petersen|first11=Stig|last12=Beattie|first12=Charles|last13=Sadik|first13=Amir|last14=Antonoglou|first14=Ioannis|last15=King|first15=Helen|last16=Kumaran|first16=Dharshan|last17=Wierstra|first17=Daan|last18=Legg|first18=Shane|last19=Hassabis|first19=Demis|title=Human-level control through deep reinforcement learning|journal=Nature|date=26 February 2015|volume=518|issue=7540|pages=529–533|doi=10.1038/nature14236|bibcode=2015Natur.518..529M}}</ref><ref>{{cite news|last1=Sample|first1=Ian|title=Google’s DeepMind makes AI program that can learn like a human|url=https://www.theguardian.com/global/2017/mar/14/googles-deepmind-makes-ai-program-that-can-learn-like-a-human|accessdate=26 April 2018|work=the Guardian|date=14 March 2017|language=en}}</ref> Besides [[transfer learning]],<ref>{{cite news|title=From not working to neural networking|url=https://www.economist.com/news/special-report/21700756-artificial-intelligence-boom-based-old-idea-modern-twist-not|accessdate=26 April 2018|work=The Economist|date=2016|language=en}}</ref> hypothetical AGI breakthroughs could include the development of reflective architectures that can engage in decision-theoretic metareasoning, and figuring out how to "slurp up" a comprehensive knowledge base from the entire unstructured [[World Wide Web|Web]].{{sfn|Russell|Norvig|2009|chapter=27. AI: The Present and Future}} Some argue that some kind of (currently-undiscovered) conceptually straightforward, but mathematically difficult, "Master Algorithm" could lead to AGI.{{sfn|Domingos|2015|chapter=9. The Pieces of the Puzzle Fall into Place}} Finally, a few "emergent" approaches look to simulating human intelligence extremely closely, and believe that [[anthropomorphism|anthropomorphic]] features like an [[artificial brain]] or simulated [[developmental robotics|child development]] may someday reach a critical point where general intelligence emerges.<ref name="Brain simulation"/><ref>{{cite journal|last1=Goertzel|first1=Ben|last2=Lian|first2=Ruiting|last3=Arel|first3=Itamar|last4=de Garis|first4=Hugo|last5=Chen|first5=Shuo|title=A world survey of artificial brain projects, Part II: Biologically inspired cognitive architectures|journal=Neurocomputing|date=December 2010|volume=74|issue=1–3|pages=30–49|doi=10.1016/j.neucom.2010.08.012}}</ref>

Many of the problems in this article may also require general intelligence, if machines are to solve the problems as well as people do. For example, even specific straightforward tasks, like [[machine translation]], require that a machine read and write in both languages ([[#Natural language processing|NLP]]), follow the author's argument ([[#Deduction, reasoning, problem solving|reason]]), know what is being talked about ([[#Knowledge representation|knowledge]]), and faithfully reproduce the author's original intent ([[#Social intelligence|social intelligence]]). A problem like machine translation is considered "[[AI-complete]]", because all of these problems need to be solved simultaneously in order to reach human-level machine performance.

== Approaches ==
There is no established unifying theory or [[paradigm]] that guides AI research. Researchers disagree about many issues.<ref>[[Nils Nilsson (researcher)|Nils Nilsson]] writes: "Simply put, there is wide disagreement in the field about what AI is all about" {{Harv|Nilsson|1983|p=10}}.</ref> A few of the most long standing questions that have remained unanswered are these: should artificial intelligence simulate natural intelligence by studying [[psychology]] or [[Neuroscience|neurobiology]]? Or is [[human biology]] as irrelevant to AI research as bird biology is to [[aeronautical engineering]]?<ref name="Biological intelligence vs. intelligence in general"/>
Can intelligent behavior be described using simple, elegant principles (such as [[logic]] or [[optimization (mathematics)|optimization]])? Or does it necessarily require solving a large number of completely unrelated problems?<ref name="Neats vs. scruffies"/>

=== Cybernetics and brain simulation ===
{{Main|Cybernetics|Computational neuroscience}}
In the 1940s and 1950s, a number of researchers explored the connection between [[neurobiology]], [[information theory]], and [[cybernetics]]. Some of them built machines that used electronic networks to exhibit rudimentary intelligence, such as [[W. Grey Walter]]'s [[turtle (robot)|turtles]] and the [[Johns Hopkins Beast]]. Many of these researchers gathered for meetings of the Teleological Society at [[Princeton University]] and the [[Ratio Club]] in England.<ref name="AI's immediate precursors"/> By 1960, this approach was largely abandoned, although elements of it would be revived in the 1980s.

=== Symbolic ===
{{Main|Symbolic AI}}
When access to digital computers became possible in the middle 1950s, AI research began to explore the possibility that human intelligence could be reduced to symbol manipulation. The research was centered in three institutions: [[Carnegie Mellon University]], [[Stanford]] and [[MIT]], and as described below, each one developed its own style of research. [[John Haugeland]] named these symbolic approaches to AI "good old fashioned AI" or "[[GOFAI]]".<ref name="GOFAI"/> During the 1960s, symbolic approaches had achieved great success at simulating high-level thinking in small demonstration programs. Approaches based on [[cybernetics]] or [[artificial neural network]]s were abandoned or pushed into the background.<ref>The most dramatic case of sub-symbolic AI being pushed into the background was the devastating critique of [[perceptron]]s by [[Marvin Minsky]] and [[Seymour Papert]] in 1969. See [[History of AI]], [[AI winter]], or [[Frank Rosenblatt]].</ref>
Researchers in the 1960s and the 1970s were convinced that symbolic approaches would eventually succeed in creating a machine with [[artificial general intelligence]] and considered this the goal of their field.

==== Cognitive simulation ====
Economist [[Herbert A. Simon|Herbert Simon]] and [[Allen Newell]] studied human problem-solving skills and attempted to formalize them, and their work laid the foundations of the field of artificial intelligence, as well as [[cognitive science]], [[operations research]] and [[management science]]. Their research team used the results of [[psychology|psychological]] experiments to develop programs that simulated the techniques that people used to solve problems. This tradition, centered at [[Carnegie Mellon University]] would eventually culminate in the development of the [[Soar (cognitive architecture)|Soar]] architecture in the middle 1980s.<ref name="AI at CMU in the 60s"/><ref name="Soar"/>

==== Logic-based ====
Unlike Simon and Newell, [[John McCarthy (computer scientist)|John McCarthy]] felt that machines did not need to simulate human thought, but should instead try to find the essence of abstract reasoning and problem-solving, regardless of whether people used the same algorithms.<ref name="Biological intelligence vs. intelligence in general"/> His laboratory at [[Stanford University|Stanford]] ([[Stanford Artificial Intelligence Laboratory|SAIL]]) focused on using formal [[logic]] to solve a wide variety of problems, including [[knowledge representation]], [[automated planning and scheduling|planning]] and [[machine learning|learning]].<ref name="AI at Stanford in the 60s"/> Logic was also the focus of the work at the [[University of Edinburgh]] and elsewhere in Europe which led to the development of the programming language [[Prolog]] and the science of [[logic programming]].<ref name="AI at Edinburgh and France in the 60s"/>

==== Anti-logic or scruffy ====
Researchers at [[MIT]] (such as [[Marvin Minsky]] and [[Seymour Papert]])<ref name="AI at MIT in the 60s"/> found that solving difficult problems in [[computer vision|vision]] and [[natural language processing]] required ad-hoc solutions – they argued that there was no simple and general principle (like [[logic]]) that would capture all the aspects of intelligent behavior. [[Roger Schank]] described their "anti-logic" approaches as "[[Neats vs. scruffies|scruffy]]" (as opposed to the "[[neats vs. scruffies|neat]]" paradigms at [[Carnegie Mellon University|CMU]] and Stanford).<ref name="Neats vs. scruffies"/> [[Commonsense knowledge bases]] (such as [[Doug Lenat]]'s [[Cyc]]) are an example of "scruffy" AI, since they must be built by hand, one complicated concept at a time.<ref name="Cyc"/>

====Knowledge-based====
When computers with large memories became available around 1970, researchers from all three traditions began to build [[knowledge representation|knowledge]] into AI applications.<ref name="Knowledge revolution"/> This "knowledge revolution" led to the development and deployment of [[expert system]]s (introduced by [[Edward Feigenbaum]]), the first truly successful form of AI software.<ref name="Expert systems"/> Key component on system arhitecute for all expert systems is Knowledge base, which stores facts and rules that illustrates AI.<ref>{{Cite journal |last=Frederick |first=Hayes-Roth |last2=William |first2=Murray |last3=Leonard |first3=Adelman |title=Expert systems |language=en |doi=10.1036/1097-8542.248550}}</ref> The knowledge revolution was also driven by the realization that enormous amounts of knowledge would be required by many simple AI applications.

=== Sub-symbolic ===
By the 1980s, progress in symbolic AI seemed to stall and many believed that symbolic systems would never be able to imitate all the processes of human cognition, especially [[machine perception|perception]], [[robotics]], [[machine learning|learning]] and [[pattern recognition]]. A number of researchers began to look into "sub-symbolic" approaches to specific AI problems.<ref name="Symbolic vs. sub-symbolic"/> Sub-symbolic methods manage to approach intelligence without specific representations of knowledge.

==== Embodied intelligence ====
This includes [[embodied agent|embodied]], [[situated]], [[behavior-based AI|behavior-based]], and [[nouvelle AI]]. Researchers from the related field of [[robotics]], such as [[Rodney Brooks]], rejected symbolic AI and focused on the basic engineering problems that would allow robots to move and survive.<ref name="Embodied AI"/> Their work revived the non-symbolic viewpoint of the early [[cybernetic]]s researchers of the 1950s and reintroduced the use of [[control theory]] in AI. This coincided with the development of the [[embodied mind thesis]] in the related field of [[cognitive science]]: the idea that aspects of the body (such as movement, perception and visualization) are required for higher intelligence.

Within [[developmental robotics]], developmental learning approaches are elaborated upon to allow robots to accumulate repertoires of novel skills through autonomous self-exploration, social interaction with human teachers, and the use of guidance mechanisms (active learning, maturation, motor synergies, etc.).{{sfn|Weng|McClelland|Pentland|Sporns|2001}}{{sfn|Lungarella|Metta|Pfeifer|Sandini|2003}}{{sfn|Asada|Hosoda|Kuniyoshi|Ishiguro|2009}}{{sfn|Oudeyer|2010}}

====Computational intelligence and soft computing====
Interest in [[Artificial neural network|neural networks]] and "[[connectionism]]" was revived by [[David Rumelhart]] and others in the middle of the 1980s.<ref name="Revival of connectionism"/> [[Artificial neural network]]s are an example of [[soft computing]] --- they are solutions to problems which cannot be solved with complete logical certainty, and where an approximate solution is often sufficient. Other [[soft computing]] approaches to AI include [[fuzzy system]]s, [[evolutionary computation]] and many statistical tools. The application of soft computing to AI is studied collectively by the emerging discipline of [[computational intelligence]].<ref name="Computational intelligence"/>

=== Statistical learning ===
Much of traditional GOFAI got bogged down on ''ad hoc'' patches to symbolic computation that worked on their own toy models but failed to generalize to real-world results. However, around the 1990s, AI researchers adopted sophisticated mathematical tools, such as [[hidden Markov model]]s (HMM), [[information theory]], and normative Bayesian [[decision theory]] to compare or to unify competing architectures. The shared mathematical language permitted a high level of collaboration with more established fields (like [[mathematics]], economics or [[operations research]]).{{efn|While such a "victory of the neats" may be a consequence of the field becoming more mature, [[Artificial Intelligence: A Modern Approach|AIMA]] states that in practice both [[neats and scruffies|neat and scruffy]] approaches continue to be necessary in AI research.}} Compared with GOFAI, new "statistical learning" techniques such as HMM and neural networks were gaining higher levels of accuracy in many practical domains such as [[data mining]], without necessarily acquiring semantic understanding of the datasets. The increased successes with real-world data led to increasing emphasis on comparing different approaches against shared test data to see which approach performed best in a broader context than that provided by idiosyncratic toy models; AI research was becoming more [[scientific method|scientific]]. Nowadays results of experiments are often rigorously measurable, and are sometimes (with difficulty) reproducible.<ref name="Formal methods in AI"/><ref>{{cite news|last1=Hutson|first1=Matthew|title=Artificial intelligence faces reproducibility crisis|url=http://science.sciencemag.org/content/359/6377/725|accessdate=28 April 2018|work=[[Science Magazine|Science]]|date=16 February 2018|pages=725–726|language=en|doi=10.1126/science.359.6377.725|bibcode=2018Sci...359..725H}}</ref> Different statistical learning techniques have different limitations; for example, basic HMM cannot model the infinite possible combinations of natural language.{{sfn|Norvig|2012}} Critics note that the shift from GOFAI to statistical learning is often also a shift away from [[Explainable AI]]. In AGI research, some scholars caution against over-reliance on statistical learning, and argue that continuing research into GOFAI will still be necessary to attain general intelligence.{{sfn|Langley|2011}}{{sfn|Katz|2012}}

=== Integrating the approaches ===
;Intelligent agent paradigm: An [[intelligent agent]] is a system that perceives its environment and takes actions which maximize its chances of success. The simplest intelligent agents are programs that solve specific problems. More complicated agents include human beings and organizations of human beings (such as [[firm]]s). The paradigm allows researchers to directly compare or even combine different approaches to isolated problems, by asking which agent is best at maximizing a given "goal function". An agent that solves a specific problem can use any approach that works – some agents are symbolic and logical, some are sub-symbolic [[artificial neural network]]s and others may use new approaches. The paradigm also gives researchers a common language to communicate with other fields—such as [[decision theory]] and economics—that also use concepts of abstract agents. Building a complete agent requires researchers to address realistic problems of integration; for example, because sensory systems give uncertain information about the environment, planning systems must be able to function in the presence of uncertainty. The intelligent agent paradigm became widely accepted during the 1990s.<ref name="Intelligent agents"/>

;[[Agent architecture]]s and [[cognitive architecture]]s: Researchers have designed systems to build intelligent systems out of interacting [[intelligent agents]] in a [[multi-agent system]].<ref name="Agent architectures"/> A [[hierarchical control system]] provides a bridge between sub-symbolic AI at its lowest, reactive levels and traditional symbolic AI at its highest levels, where relaxed time constraints permit planning and world modelling.<ref name="Hierarchical control system"/> Some cognitive architectures are custom-built to solve a narrow problem; others, such as [[Soar (cognitive architecture)|Soar]], are designed to mimic human cognition and to provide insight into general intelligence. Modern extensions of Soar are [[hybrid intelligent system]]s that include both symbolic and sub-symbolic components.<ref>{{cite journal|last1=Laird|first1=John|title=Extending the Soar cognitive architecture|journal=Frontiers in Artificial Intelligence and Applications|date=2008|volume=171|page=224|url=http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.77.2473&rep=rep1&type=pdf}}</ref><ref>{{cite journal|last1=Lieto|first1=Antonio|last2=Bhatt|first2=Mehul|last3=Oltramari|first3=Alessandro|last4=Vernon|first4=David|title=The role of cognitive architectures in general artificial intelligence|journal=Cognitive Systems Research|date=May 2018|volume=48|pages=1–3|doi=10.1016/j.cogsys.2017.08.003}}</ref>

== Tools ==
AI has developed a large number of tools to solve the most difficult problems in [[computer science]]. A few of the most general of these methods are discussed below.

=== Search and optimization ===

{{Main|Search algorithm|Mathematical optimization|Evolutionary computation}}

Many problems in AI can be solved in theory by intelligently searching through many possible solutions:<ref name="Search"/> [[#Deduction, reasoning, problem solving|Reasoning]] can be reduced to performing a search. For example, logical proof can be viewed as searching for a path that leads from [[premise]]s to [[Logical consequence|conclusions]], where each step is the application of an [[inference rule]].<ref name="Logic as search"/> [[Automated planning and scheduling|Planning]] algorithms search through trees of goals and subgoals, attempting to find a path to a target goal, a process called [[means-ends analysis]].<ref name="Planning as search"/> [[Robotics]] algorithms for moving limbs and grasping objects use [[local search (optimization)|local searches]] in [[Configuration space (physics)|configuration space]].<ref name="Configuration space"/> Many [[machine learning|learning]] algorithms use search algorithms based on [[optimization (mathematics)|optimization]].

Simple exhaustive searches<ref name="Uninformed search"/> are rarely sufficient for most real-world problems: the [[search algorithm|search space]] (the number of places to search) quickly grows to [[Astronomically large|astronomical numbers]]. The result is a search that is [[Computation time|too slow]] or never completes. The solution, for many problems, is to use "[[heuristics]]" or "rules of thumb" that prioritize choices in favor of those that are more likely to reach a goal and to do so in a shorter number of steps. In some search methodologies heuristics can also serve to entirely eliminate some choices that are unlikely to lead to a goal (called "[[pruning (algorithm)|pruning]] the [[search tree]]"). [[Heuristics]] supply the program with a "best guess" for the path on which the solution lies.<ref name="Informed search"/> Heuristics limit the search for solutions into a smaller sample size.{{sfn|Tecuci|2012}}

A very different kind of search came to prominence in the 1990s, based on the mathematical theory of [[optimization (mathematics)|optimization]]. For many problems, it is possible to begin the search with some form of a guess and then refine the guess incrementally until no more refinements can be made. These algorithms can be visualized as blind [[hill climbing]]: we begin the search at a random point on the landscape, and then, by jumps or steps, we keep moving our guess uphill, until we reach the top. Other optimization algorithms are [[simulated annealing]], [[beam search]] and [[random optimization]].<ref name="Optimization search"/>

[[File:ParticleSwarmArrowsAnimation.gif|thumb|A [[particle swarm optimization|particle swarm]] seeking the [[global minimum]]]]
[[Evolutionary computation]] uses a form of optimization search. For example, they may begin with a population of organisms (the guesses) and then allow them to mutate and recombine, [[artificial selection|selecting]] only the fittest to survive each generation (refining the guesses). Classic [[evolutionary algorithms]] include [[genetic algorithms]], [[gene expression programming]], and [[genetic programming]].<ref name="Genetic programming"/> Alternatively, distributed search processes can coordinate via [[swarm intelligence]] algorithms. Two popular swarm algorithms used in search are [[particle swarm optimization]] (inspired by bird [[flocking (behavior)|flocking]]) and [[ant colony optimization]] (inspired by [[ant trail]]s).<ref name="Society based learning"/><ref>{{cite book|author1=Daniel Merkle|author2=Martin Middendorf|editor1-last=Burke|editor1-first=Edmund K.|editor2-last=Kendall|editor2-first=Graham|title=Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques|date=2013|publisher=Springer Science & Business Media|isbn=9781461469407|language=en|chapter=Swarm Intelligence}}</ref>

=== Logic ===

{{Main|Logic programming|Automated reasoning}}

[[Logic]]<ref name="Logic"/> is used for knowledge representation and problem solving, but it can be applied to other problems as well. For example, the [[satplan]] algorithm uses logic for [[automated planning and scheduling|planning]]<ref name="Satplan"/> and [[inductive logic programming]] is a method for [[machine learning|learning]].<ref name="Symbolic learning techniques"/>

Several different forms of logic are used in AI research. [[Propositional logic]]<ref name="Propositional logic"/> involves [[truth function]]s such as "or" and "not". [[First-order logic]]<ref name="First-order logic"/> adds [[quantifier (logic)|quantifiers]] and [[predicate (mathematical logic)|predicates]], and can express facts about objects, their properties, and their relations with each other. [[Fuzzy set theory]] assigns a "degree of truth" (between 0 and 1) to vague statements such as "Alice is old" (or rich, or tall, or hungry) that are too linguistically imprecise to be completely true or false. [[Fuzzy logic]] is successfully used in [[control system]]s to allow experts to contribute vague rules such as "if you are close to the destination station and moving fast, increase the train's brake pressure"; these vague rules can then be numerically refined within the system. Fuzzy logic fails to scale well in knowledge bases; many AI researchers question the validity of chaining fuzzy-logic inferences.{{efn|"There exist many different types of uncertainty, vagueness, and ignorance... [We] independently confirm the inadequacy of systems for reasoning about uncertainty that propagates numerical factors according to only to which connectives appear in assertions."<ref>{{cite journal|last1=Elkin|first1=Charles|title=The paradoxical success of fuzzy logic|journal=IEEE expert|date=1994|volume=9|issue=4|pages=3–49}}</ref>}}<ref name="Fuzzy logic"/><ref>{{cite news|title=What is 'fuzzy logic'? Are there computers that are inherently fuzzy and do not apply the usual binary logic?|url=https://www.scientificamerican.com/article/what-is-fuzzy-logic-are-t/|accessdate=5 May 2018|work=Scientific American|language=en}}</ref>

[[Default logic]]s, [[non-monotonic logic]]s and [[circumscription (logic)|circumscription]]<ref name="Default reasoning and non-monotonic logic"/> are forms of logic designed to help with default reasoning and the [[qualification problem]]. Several extensions of logic have been designed to handle specific domains of [[knowledge representation|knowledge]], such as: [[description logic]]s;<ref name="Representing categories and relations"/> [[situation calculus]], [[event calculus]] and [[fluent calculus]] (for representing events and time);<ref name="Representing time"/> [[Causality#Causal calculus|causal calculus]];<ref name="Representing causation"/> belief calculus;<ref>"The Belief Calculus and Uncertain Reasoning", Yen-Teh Hsia</ref> and [[modal logic]]s.<ref name="Representing knowledge about knowledge"/>

Overall, qualitiative symbolic logic is brittle and scales poorly in the presence of [[noise (signal processing)|noise]] or other uncertainty. Exceptions to rules are numerous, and it is difficult for logical systems to function in the presence of contradictory rules.{{sfn|Domingos|2015|loc=chapter 6}}<ref>{{cite web|title=Logic and Probability|url=https://plato.stanford.edu/archives/sum2017/entries/logic-probability/|website=[[Stanford Encyclopedia of Philosophy]]|accessdate=5 May 2018}}</ref>

=== Probabilistic methods for uncertain reasoning ===

{{Main|Bayesian network|Hidden Markov model|Kalman filter|Particle filter|Decision theory|Utility theory}}
[[File:EM Clustering of Old Faithful data.gif|right|frame|[[Expectation-maximization]] clustering of [[Old Faithful]] eruption data starts from a random guess but then successfully converges on an accurate clustering of the two physically distinct modes of eruption.]]

Many problems in AI (in reasoning, planning, learning, perception, and robotics) require the agent to operate with incomplete or uncertain information. AI researchers have devised a number of powerful tools to solve these problems using methods from [[probability]] theory and economics.<ref name="Stochastic methods for uncertain reasoning"/>

[[Bayesian network]]s<ref name="Bayesian networks"/> are a very general tool that can be used for a large number of problems: reasoning (using the [[Bayesian inference]] algorithm),<ref name="Bayesian inference"/> [[Machine learning|learning]] (using the [[expectation-maximization algorithm]]),{{efn|Expectation-maximization, one of the most popular algorithms in machine learning, allows clustering in the presence of unknown [[latent variables]]{{sfn|Domingos|2015|p=210}}}}<ref name="Bayesian learning"/> [[Automated planning and scheduling|planning]] (using [[decision network]]s)<ref name="Bayesian decision networks"/> and [[machine perception|perception]] (using [[dynamic Bayesian network]]s).<ref name="Stochastic temporal models"/> Probabilistic algorithms can also be used for filtering, prediction, smoothing and finding explanations for streams of data, helping [[machine perception|perception]] systems to analyze processes that occur over time (e.g., [[hidden Markov model]]s or [[Kalman filter]]s).<ref name="Stochastic temporal models"/> Compared with symbolic logic, formal Bayesian inference is computationally expensive. For inference to be tractable, most observations must be [[conditionally independent]] of one another. Complicated graphs with diamonds or other "loops" (undirected [[cycle (graph theory)|cycle]]s) can require a sophisticated method such as [[Markov Chain Monte Carlo]], which spreads an ensemble of [[random walk]]ers throughout the Bayesian network and attempts to converge to an assessment of the conditional probabilities. Bayesian networks are used on [[Xbox Live]] to rate and match players; wins and losses are "evidence" of how good a player is. [[AdSense]] uses a Bayesian network with over 300 million edges to learn which ads to serve.{{sfn|Domingos|2015|loc=chapter 6}}

A key concept from the science of economics is "[[utility]]": a measure of how valuable something is to an intelligent agent. Precise mathematical tools have been developed that analyze how an agent can make choices and plan, using [[decision theory]], [[decision analysis]],<ref name="Decisions theory and analysis"/> and [[applied information economics|information value theory]].<ref name="Information value theory"/> These tools include models such as [[Markov decision process]]es,<ref name="Markov decision process"/> dynamic [[decision network]]s,<ref name="Stochastic temporal models"/> [[game theory]] and [[mechanism design]].<ref name="Game theory and mechanism design"/>

=== Classifiers and statistical learning methods ===

{{Main|Classifier (mathematics)|Statistical classification|Machine learning}}

The simplest AI applications can be divided into two types: classifiers ("if shiny then diamond") and controllers ("if shiny then pick up"). Controllers do, however, also classify conditions before inferring actions, and therefore classification forms a central part of many AI systems. [[Classifier (mathematics)|Classifiers]] are functions that use [[pattern matching]] to determine a closest match. They can be tuned according to examples, making them very attractive for use in AI. These examples are known as observations or patterns. In supervised learning, each pattern belongs to a certain predefined class. A class can be seen as a decision that has to be made. All the observations combined with their class labels are known as a data set. When a new observation is received, that observation is classified based on previous experience.<ref name="Classifiers"/>

A classifier can be trained in various ways; there are many statistical and [[machine learning]] approaches. The [[decision tree learning|decision tree]]<ref name="Decision tree"/> is perhaps the most widely used machine learning algorithm.{{sfn|Domingos|2015|p=88}} Other widely used classifiers are the [[Artificial neural network|neural network]],<ref name="Neural networks"/>
[[k-nearest neighbor algorithm]],{{efn|The most widely used analogical AI until the mid-1990s{{sfn|Domingos|2015|p=187}}}}<ref name="K-nearest neighbor algorithm"/>
[[kernel methods]] such as the [[support vector machine]] (SVM),{{efn|SVM displaced k-nearest neighbor in the 1990s{{sfn|Domingos|2015|p=188}}}}<ref name="Kernel methods"/>
[[Gaussian mixture model]],<ref name="Gaussian mixture model"/> and the extremely popular [[naive Bayes classifier]].{{efn|Naive Bayes is reportedly the "most widely used learner" at Google, due in part to its scalability.{{sfn|Domingos|2015|p=152}}}}<ref name="Naive Bayes classifier"/> Classifier performance depends greatly on the characteristics of the data to be classified, such as the dataset size, the dimensionality, and the level of noise. Model-based classifiers perform well if the assumed model is an extremely good fit for the actual data. Otherwise, if no matching model is available, and if accuracy (rather than speed or scalability) is the sole concern, conventional wisdom is that discriminative classifiers (especially SVM) tend to be more accurate than model-based classifiers such as "naive Bayes" on most practical data sets.<ref name="Classifier performance"/>{{sfn|Russell|Norvig|2009|loc=18.12: Learning from Examples: Summary}}

=== Artificial neural networks ===

{{Main|Artificial neural network|Connectionism}}
[[File:Artificial neural network.svg|thumb|A neural network is an interconnected group of nodes, akin to the vast network of [[neuron]]s in the [[human brain]].]]

Neural networks, or neural nets, were inspired by the architecture of neurons in the human brain. A simple "neuron" ''N'' accepts input from multiple other neurons, each of which, when activated (or "fired"), cast a weighted "vote" for or against whether neuron ''N'' should itself activate. Learning requires an algorithm to adjust these weights based on the training data; one simple algorithm (dubbed "[[Hebbian learning|fire together, wire together]]") is to increase the weight between two connected neurons when the activation of one triggers the successful activation of another. The net forms "concepts" that are distributed among a subnetwork of shared{{efn|Each individual neuron is likely to participate in more than one concept.}} neurons that tend to fire together; a concept meaning "leg" might be coupled with a subnetwork meaning "foot" that includes the sound for "foot". Neurons have a continuous spectrum of activation; in addition, neurons can process inputs in a nonlinear way rather than weighing straightforward votes. Modern neural nets can learn both continuous functions and, surprisingly, digital logical operations. Neural networks' early successes included predicting the stock market and (in 1995) a mostly self-driving car.{{efn|Steering for the 1995 "[[History of autonomous cars#1990s|No Hands Across America]]" required "only a few human assists".}}{{sfn|Domingos|2015|loc=Chapter 4}} In the 2010s, advances in neural networks using deep learning thrust AI into widespread public consciousness and contributed to an enormous upshift in corporate AI spending; for example, AI-related [[mergers and acquisitions|M&A]] in 2017 was over 25 times as large as in 2015.<ref>{{cite news|title=Why Deep Learning Is Suddenly Changing Your Life|url=http://fortune.com/ai-artificial-intelligence-deep-machine-learning/|accessdate=12 March 2018|work=Fortune|date=2016}}</ref><ref>{{cite news|title=Google leads in the race to dominate artificial intelligence|url=https://www.economist.com/news/business/21732125-tech-giants-are-investing-billions-transformative-technology-google-leads-race|accessdate=12 March 2018|work=The Economist|date=2017|language=en}}</ref>

The study of non-learning [[artificial neural network]]s<ref name="Neural networks"/> began in the decade before the field of AI research was founded, in the work of [[Walter Pitts]] and [[Warren McCullouch]]. [[Frank Rosenblatt]] invented the [[perceptron]], a learning network with a single layer, similar to the old concept of [[linear regression]]. Early pioneers also include [[Alexey Grigorevich Ivakhnenko]], [[Teuvo Kohonen]], [[Stephen Grossberg]], [[Kunihiko Fukushima]], Christoph von der Malsburg, David Willshaw, [[Shun-Ichi Amari]], [[Bernard Widrow]], [[John Hopfield]], [[Eduardo R. Caianiello]], and others.

The main categories of networks are acyclic or [[feedforward neural network]]s (where the signal passes in only one direction) and [[recurrent neural network]]s (which allow feedback and short-term memories of previous input events). Among the most popular feedforward networks are [[perceptron]]s, [[multi-layer perceptron]]s and [[radial basis network]]s.<ref name="Feedforward neural networks"/> Neural networks can be applied to the problem of [[intelligent control]] (for robotics) or [[machine learning|learning]], using such techniques as [[Hebbian learning]] ("fire together, wire together"), [[GMDH]] or [[competitive learning]].<ref name="Learning in neural networks"/>

Today, neural networks are often trained by the [[backpropagation]] algorithm, which had been around since 1970 as the reverse mode of [[automatic differentiation]] published by [[Seppo Linnainmaa]],<ref name="lin1970">[[Seppo Linnainmaa]] (1970). The representation of the cumulative rounding error of an algorithm as a Taylor expansion of the local rounding errors. Master's Thesis (in Finnish), Univ. Helsinki, 6–7.</ref><ref name="grie2012">Griewank, Andreas (2012). Who Invented the Reverse Mode of Differentiation?. Optimization Stories, Documenta Matematica, Extra Volume ISMP (2012), 389–400.</ref> and was introduced to neural networks by [[Paul Werbos]].<ref name="WERBOS1974">[[Paul Werbos]], "Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences", ''PhD thesis, Harvard University'', 1974.</ref><ref name="werbos1982">[[Paul Werbos]] (1982). Applications of advances in nonlinear sensitivity analysis. In System modeling and optimization (pp. 762–770). Springer Berlin Heidelberg. [http://werbos.com/Neural/SensitivityIFIPSeptember1981.pdf Online] {{webarchive|url=https://web.archive.org/web/20160414055503/http://werbos.com/Neural/SensitivityIFIPSeptember1981.pdf |date=14 April 2016 }}</ref><ref name="Backpropagation"/>

[[Hierarchical temporal memory]] is an approach that models some of the structural and algorithmic properties of the [[neocortex]].<ref name="Hierarchical temporal memory"/>

In short, most neural networks use some form of [[gradient descent]] on a hand-created neural topology. However, some research groups, such as [[Uber]], argue that simple [[neuroevolution]] to mutate new neural network topologies and weights may be competitive with sophisticated gradient descent approaches. One advantage of neuroevolution is that it may be less prone to get caught in "dead ends".<ref>{{cite news|title=Artificial intelligence can 'evolve' to solve problems|url=http://www.sciencemag.org/news/2018/01/artificial-intelligence-can-evolve-solve-problems|accessdate=7 February 2018|work=Science {{!}} AAAS|date=10 January 2018|language=en}}</ref>

==== Deep feedforward neural networks ====

{{Main|Deep learning}}

[[Deep learning]] is any [[artificial neural network]] that can learn a long chain of causal links. For example, a feedforward network with six hidden layers can learn a seven-link causal chain (six hidden layers + output layer) and has a [[deep learning#Credit assignment|"credit assignment path"]] (CAP) depth of seven. Many deep learning systems need to be able to learn chains ten or more causal links in length.<ref name="schmidhuber2015"/> Deep learning has transformed many important subfields of artificial intelligence, including [[computer vision]], [[speech recognition]], [[natural language processing]] and others.<ref name="goodfellow2016">Ian Goodfellow, Yoshua Bengio, and Aaron Courville (2016). Deep Learning. MIT Press. [http://www.deeplearningbook.org Online] {{webarchive|url=https://web.archive.org/web/20160416111010/http://www.deeplearningbook.org/ |date=16 April 2016 }}</ref><ref name="HintonDengYu2012">{{cite journal | last1 = Hinton | first1 = G. | last2 = Deng | first2 = L. | last3 = Yu | first3 = D. | last4 = Dahl | first4 = G. | last5 = Mohamed | first5 = A. | last6 = Jaitly | first6 = N. | last7 = Senior | first7 = A. | last8 = Vanhoucke | first8 = V. | last9 = Nguyen | first9 = P. | last10 = Sainath | first10 = T. | last11 = Kingsbury | first11 = B. | year = 2012 | title = Deep Neural Networks for Acoustic Modeling in Speech Recognition --- The shared views of four research groups | url = | journal = IEEE Signal Processing Magazine | volume = 29 | issue = 6| pages = 82–97 | doi=10.1109/msp.2012.2205597}}</ref><ref name="schmidhuber2015">{{cite journal |last=Schmidhuber |first=J. |year=2015 |title=Deep Learning in Neural Networks: An Overview |journal=Neural Networks |volume=61 |pages=85–117 |arxiv=1404.7828 |doi=10.1016/j.neunet.2014.09.003|pmid=25462637 }}</ref>

According to one overview,<ref name="scholarpedia">{{cite journal | last1 = Schmidhuber | first1 = Jürgen | authorlink = Jürgen Schmidhuber | year = 2015 | title = Deep Learning | url = http://www.scholarpedia.org/article/Deep_Learning | journal = Scholarpedia | volume = 10 | issue = 11 | page = 32832 | doi = 10.4249/scholarpedia.32832 | deadurl = no | archiveurl = https://web.archive.org/web/20160419024349/http://www.scholarpedia.org/article/Deep_Learning | archivedate = 19 April 2016 | df = dmy-all | bibcode = 2015SchpJ..1032832S }}</ref> the expression "Deep Learning" was introduced to the [[Machine Learning]] community by [[Rina Dechter]] in 1986<ref name="dechter1986">[[Rina Dechter]] (1986). Learning while searching in constraint-satisfaction problems. University of California, Computer Science Department, Cognitive Systems Laboratory.[https://www.researchgate.net/publication/221605378_Learning_While_Searching_in_Constraint-Satisfaction-Problems Online] {{webarchive|url=https://web.archive.org/web/20160419054654/https://www.researchgate.net/publication/221605378_Learning_While_Searching_in_Constraint-Satisfaction-Problems |date=19 April 2016 }}</ref> and gained traction after
Igor Aizenberg and colleagues introduced it to [[Artificial Neural Networks]] in 2000.<ref name="aizenberg2000">Igor Aizenberg, Naum N. Aizenberg, Joos P.L. Vandewalle (2000). Multi-Valued and Universal Binary Neurons: Theory, Learning and Applications. Springer Science & Business Media.</ref> The first functional Deep Learning networks were published by [[Alexey Grigorevich Ivakhnenko]] and V. G. Lapa in 1965.<ref>{{Cite book|title=Cybernetic Predicting Devices|last=Ivakhnenko|first=Alexey|publisher=Naukova Dumka|year=1965|isbn=|location=Kiev|pages=}}</ref>{{page needed|date=December 2016}} These networks are trained one layer at a time. Ivakhnenko's 1971 paper<ref name="ivak1971">{{Cite journal |last=Ivakhnenko|first=Alexey|date=1971|title=Polynomial theory of complex systems|url=|journal=IEEE Transactions on Systems, Man and Cybernetics (4)|pages=364–378|doi=|pmid=|access-date=}}</ref> describes the learning of a deep feedforward multilayer perceptron with eight layers, already much deeper than many later networks. In 2006, a publication by [[Geoffrey Hinton]] and Ruslan Salakhutdinov introduced another way of pre-training many-layered [[feedforward neural network]]s (FNNs) one layer at a time, treating each layer in turn as an [[unsupervised learning|unsupervised]] [[restricted Boltzmann machine]], then using [[supervised learning|supervised]] [[backpropagation]] for fine-tuning.{{sfn|Hinton|2007}} Similar to shallow artificial neural networks, deep neural networks can model complex non-linear relationships. Over the last few years, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks that contain many layers of non-linear hidden units and a very large output layer.<ref>{{cite web|last1=Research|first1=AI|title=Deep Neural Networks for Acoustic Modeling in Speech Recognition|url=http://airesearch.com/ai-research-papers/deep-neural-networks-for-acoustic-modeling-in-speech-recognition/|website=airesearch.com|accessdate=23 October 2015|date=23 October 2015}}</ref>

Deep learning often uses [[convolutional neural network]]s (CNNs), whose origins can be traced back to the [[Neocognitron]] introduced by [[Kunihiko Fukushima]] in 1980.<ref name="FUKU1980">{{cite journal | last1 = Fukushima | first1 = K. | year = 1980 | title = Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position | url = | journal = Biological Cybernetics | volume = 36 | issue = 4| pages = 193–202 | doi=10.1007/bf00344251 | pmid=7370364}}</ref> In 1989, [[Yann LeCun]] and colleagues applied [[backpropagation]] to such an architecture. In the early 2000s, in an industrial application CNNs already processed an estimated 10% to 20% of all the checks written in the US.<ref name="lecun2016slides">[[Yann LeCun]] (2016). Slides on Deep Learning [https://indico.cern.ch/event/510372/ Online] {{webarchive|url=https://web.archive.org/web/20160423021403/https://indico.cern.ch/event/510372/ |date=23 April 2016 }}</ref>
Since 2011, fast implementations of CNNs on GPUs have
won many visual pattern recognition competitions.<ref name="schmidhuber2015"/>

CNNs with 12 convolutional layers were used in conjunction with [[reinforcement learning]] by Deepmind's "[[AlphaGo]] Lee", the program that beat a top [[Go (game)|Go]] champion in 2016.<ref name="Nature2017">{{cite journal |first1=David |last1=Silver|author-link1=David Silver (programmer)|first2= Julian|last2= Schrittwieser|first3= Karen|last3= Simonyan|first4= Ioannis|last4= Antonoglou|first5= Aja|last5= Huang|author-link5=Aja Huang|first6=Arthur|last6= Guez|first7= Thomas|last7= Hubert|first8= Lucas|last8= Baker|first9= Matthew|last9= Lai|first10= Adrian|last10= Bolton|first11= Yutian|last11= Chen|author-link11=Chen Yutian|first12= Timothy|last12= Lillicrap|first13=Hui|last13= Fan|author-link13=Fan Hui|first14= Laurent|last14= Sifre|first15= George van den|last15= Driessche|first16= Thore|last16= Graepel|first17= Demis|last17= Hassabis |author-link17=Demis Hassabis|url=https://www.nature.com/nature/journal/v550/n7676/full/nature24270.html|title=Mastering the game of Go without human knowledge|journal=[[Nature (journal)|Nature]]|issn= 0028-0836|pages=354–359|volume =550|issue =7676|doi =10.1038/nature24270|date=19 October 2017|quote=AlphaGo Lee... 12 convolutional layers|bibcode=2017Natur.550..354S}}{{closed access}}</ref>

==== Deep recurrent neural networks ====

{{Main|Recurrent neural networks}}

Early on, deep learning was also applied to sequence learning with [[recurrent neural networks]] (RNNs)<ref name="Recurrent neural networks"/> which are in theory Turing complete<ref>{{cite journal|last1=Hyötyniemi|first1=Heikki|title=Turing machines are recurrent neural networks|journal=Proceedings of STeP '96/Publications of the Finnish Artificial Intelligence Society|pages=13–24|date=1996}}</ref> and can run arbitrary programs to process arbitrary sequences of inputs. The depth of an RNN is unlimited and depends on the length of its input sequence; thus, an RNN is an example of deep learning.<ref name="schmidhuber2015"/> RNNs can be trained by [[gradient descent]]<ref>P. J. Werbos. Generalization of backpropagation with application to a recurrent gas market model" ''Neural Networks'' 1, 1988.</ref><ref>A. J. Robinson and F. Fallside. The utility driven dynamic error propagation network. Technical Report CUED/F-INFENG/TR.1, Cambridge University Engineering Department, 1987.</ref><ref>R. J. Williams and D. Zipser. Gradient-based learning algorithms for recurrent networks and their computational complexity. In Back-propagation: Theory, Architectures and Applications. Hillsdale, NJ: Erlbaum, 1994.</ref> but suffer from the [[vanishing gradient problem]].<ref name="goodfellow2016"/><ref name="hochreiter1991">[[Sepp Hochreiter]] (1991), [http://people.idsia.ch/~juergen/SeppHochreiter1991ThesisAdvisorSchmidhuber.pdf Untersuchungen zu dynamischen neuronalen Netzen] {{webarchive|url=https://web.archive.org/web/20150306075401/http://people.idsia.ch/~juergen/SeppHochreiter1991ThesisAdvisorSchmidhuber.pdf |date=6 March 2015 }}, Diploma thesis. Institut f. Informatik, Technische Univ. Munich. Advisor: J. Schmidhuber.</ref> In 1992, it was shown that unsupervised pre-training of a stack of [[recurrent neural network]]s can speed up subsequent supervised learning of deep sequential problems.<ref name="SCHMID1992">{{cite journal | last1 = Schmidhuber | first1 = J. | year = 1992 | title = Learning complex, extended sequences using the principle of history compression | url = | journal = Neural Computation | volume = 4 | issue = 2| pages = 234–242 | doi=10.1162/neco.1992.4.2.234| citeseerx = 10.1.1.49.3934}}</ref>

Numerous researchers now use variants of a deep learning recurrent NN called the [[long short-term memory]] (LSTM) network published by Hochreiter & Schmidhuber in 1997.<ref name=lstm>[[Sepp Hochreiter|Hochreiter, Sepp]]; and [[Jürgen Schmidhuber|Schmidhuber, Jürgen]]; ''Long Short-Term Memory'', Neural Computation, 9(8):1735–1780, 1997</ref> LSTM is often trained by Connectionist Temporal Classification (CTC).<ref name="graves2006">Alex Graves, Santiago Fernandez, Faustino Gomez, and [[Jürgen Schmidhuber]] (2006). Connectionist temporal classification: Labelling unsegmented sequence data with recurrent neural nets. Proceedings of ICML’06, pp. 369–376.</ref> At Google, Microsoft and Baidu this approach has revolutionised [[speech recognition]].<ref name="hannun2014">{{cite arXiv
|last1=Hannun |first1=Awni
|last2=Case |first2=Carl
|last3=Casper |first3=Jared
|last4=Catanzaro |first4=Bryan
|last5=Diamos |first5=Greg
|last6=Elsen |first6=Erich
|last7=Prenger |first7=Ryan
|last8=Satheesh |first8=Sanjeev
|last9=Sengupta |first9=Shubho
|last10=Coates |first10=Adam
|last11=Ng |first11=Andrew Y. |author11-link=Andrew Ng
|year=2014
|title=Deep Speech: Scaling up end-to-end speech recognition
|eprint=1412.5567
|class=cs.CL
}}</ref><ref name="sak2014">Hasim Sak and Andrew Senior and Francoise Beaufays (2014). Long Short-Term Memory recurrent neural network architectures for large scale acoustic modeling. Proceedings of Interspeech 2014.</ref><ref name="liwu2015">{{cite arXiv
|last1=Li |first1=Xiangang
|last2=Wu |first2=Xihong
|year=2015
|title=Constructing Long Short-Term Memory based Deep Recurrent Neural Networks for Large Vocabulary Speech Recognition
|eprint=1410.4281
|class=cs.CL
}}</ref> For example, in 2015, Google's speech recognition experienced a dramatic performance jump of 49% through CTC-trained LSTM, which is now available through [[Google Voice]] to billions of smartphone users.<ref name="sak2015">Haşim Sak, Andrew Senior, Kanishka Rao, Françoise Beaufays and Johan Schalkwyk (September 2015): [http://googleresearch.blogspot.ch/2015/09/google-voice-search-faster-and-more.html Google voice search: faster and more accurate.] {{webarchive|url=https://web.archive.org/web/20160309191532/http://googleresearch.blogspot.ch/2015/09/google-voice-search-faster-and-more.html |date=9 March 2016 }}</ref> Google also used LSTM to improve machine translation,<ref name="sutskever2014">{{cite arXiv
|last1=Sutskever |first1=Ilya
|last2=Vinyals |first2=Oriol
|last3=Le |first3=Quoc V.
|year=2014
|title=Sequence to Sequence Learning with Neural Networks
|eprint=1409.3215
|class=cs.CL
}}</ref> Language Modeling<ref name="vinyals2016">{{cite arXiv
|last1=Jozefowicz |first1=Rafal
|last2=Vinyals |first2=Oriol
|last3=Schuster |first3=Mike
|last4=Shazeer |first4=Noam
|last5=Wu |first5=Yonghui
|year=2016
|title=Exploring the Limits of Language Modeling
|eprint=1602.02410
|class=cs.CL
}}</ref> and Multilingual Language Processing.<ref name="gillick2015">{{cite arXiv
|last1=Gillick |first1=Dan
|last2=Brunk |first2=Cliff
|last3=Vinyals |first3=Oriol
|last4=Subramanya |first4=Amarnag
|year=2015
|title=Multilingual Language Processing From Bytes
|eprint=1512.00103
|class=cs.CL
}}</ref> LSTM combined with CNNs also improved automatic image captioning<ref name="vinyals2015">{{cite arXiv
|last1=Vinyals |first1=Oriol
|last2=Toshev |first2=Alexander
|last3=Bengio |first3=Samy
|last4=Erhan |first4=Dumitru
|year=2015
|title=Show and Tell: A Neural Image Caption Generator
|eprint=1411.4555
|class=cs.CV
}}</ref> and a plethora of other applications.

=== Evaluating progress ===
{{Further|Progress in artificial intelligence|Competitions and prizes in artificial intelligence}}
AI, like electricity or the steam engine, is a general purpose technology. There is no consensus on how to characterize which tasks AI tends to excel at.<ref>{{cite news|last1=Brynjolfsson|first1=Erik|last2=Mitchell|first2=Tom|title=What can machine learning do? Workforce implications|url=http://science.sciencemag.org/content/358/6370/1530|accessdate=7 May 2018|work=Science|date=22 December 2017|pages=1530–1534|language=en|doi=10.1126/science.aap8062|bibcode=2017Sci...358.1530B}}</ref> While projects such as [[AlphaZero]] have succeeded in generating their own knowledge from scratch, many other machine learning projects require large training datasets.<ref>{{cite news|last1=Sample|first1=Ian|title='It's able to create knowledge itself': Google unveils AI that learns on its own|url=https://www.theguardian.com/science/2017/oct/18/its-able-to-create-knowledge-itself-google-unveils-ai-learns-all-on-its-own|accessdate=7 May 2018|work=the Guardian|date=18 October 2017|language=en}}</ref><ref>{{cite news|title=The AI revolution in science|url=http://www.sciencemag.org/news/2017/07/ai-revolution-science|accessdate=7 May 2018|work=Science {{!}} AAAS|date=5 July 2017|language=en}}</ref> Researcher [[Andrew Ng]] has suggested, as a "highly imperfect rule of thumb", that "almost anything a typical human can do with less than one second of mental thought, we can probably now or in the near future automate using AI."<ref>{{cite news|title=Will your job still exist in 10 years when the robots arrive?|url=http://www.scmp.com/tech/innovation/article/2098164/robots-are-coming-here-are-some-jobs-wont-exist-10-years|accessdate=7 May 2018|work=[[South China Morning Post]]|date=2017|language=en}}</ref> [[Moravec's paradox]] suggests that AI lags humans at many tasks that the human brain has specifically evolved to perform well.<ref name="The Economist"/>

Games provide a well-publicized benchmark for assessing rates of progress. [[AlphaGo]] around 2016 brought the era of classical board-game benchmarks to a close. Games of [[perfect knowledge|imperfect knowledge]] provide new challenges to AI in the area of [[game theory]].<ref>{{cite news|last1=Borowiec|first1=Tracey Lien, Steven|title=AlphaGo beats human Go champ in milestone for artificial intelligence|url=http://www.latimes.com/world/asia/la-fg-korea-alphago-20160312-story.html|accessdate=7 May 2018|work=latimes.com|date=2016}}</ref><ref>{{cite news|last1=Brown|first1=Noam|last2=Sandholm|first2=Tuomas|title=Superhuman AI for heads-up no-limit poker: Libratus beats top professionals|url=http://science.sciencemag.org/content/359/6374/418|accessdate=7 May 2018|work=Science|date=26 January 2018|pages=418–424|language=en|doi=10.1126/science.aao1733}}</ref> [[E-sports]] such as [[StarCraft]] continue to provide additional public benchmarks.<ref>{{cite journal|last1=Ontanon|first1=Santiago|last2=Synnaeve|first2=Gabriel|last3=Uriarte|first3=Alberto|last4=Richoux|first4=Florian|last5=Churchill|first5=David|last6=Preuss|first6=Mike|title=A Survey of Real-Time Strategy Game AI Research and Competition in StarCraft|journal=IEEE Transactions on Computational Intelligence and AI in Games|date=December 2013|volume=5|issue=4|pages=293–311|doi=10.1109/TCIAIG.2013.2286295}}</ref><ref>{{cite news|title=Facebook Quietly Enters StarCraft War for AI Bots, and Loses|url=https://www.wired.com/story/facebook-quietly-enters-starcraft-war-for-ai-bots-and-loses/|accessdate=7 May 2018|work=WIRED|date=2017}}</ref> There are many competitions and prizes, such as the [[ImageNet challenge|Imagenet Challenge]], to promote research in artificial intelligence. The most common areas of competition include general machine intelligence, conversational behavior, data-mining, [[autonomous car|robotic cars]], and robot soccer as well as conventional games.<ref>{{Cite web|url=http://image-net.org/challenges/LSVRC/2017/|title=ILSVRC2017|website=image-net.org|language=en|access-date=2018-11-06}}</ref>

The "imitation game" (an interpretation of the 1950 [[Turing test]] that assesses whether a computer can imitate a human) is nowadays considered too exploitable to be a meaningful benchmark.<ref>{{cite journal|last1=Schoenick|first1=Carissa|last2=Clark|first2=Peter|last3=Tafjord|first3=Oyvind|last4=Turney|first4=Peter|last5=Etzioni|first5=Oren|title=Moving beyond the Turing Test with the Allen AI Science Challenge|journal=Communications of the ACM|date=23 August 2017|volume=60|issue=9|pages=60–64|doi=10.1145/3122814}}</ref> A derivative of the Turing test is the Completely Automated Public Turing test to tell Computers and Humans Apart ([[CAPTCHA]]). As the name implies, this helps to determine that a user is an actual person and not a computer posing as a human. In contrast to the standard Turing test, CAPTCHA is administered by a machine and targeted to a human as opposed to being administered by a human and targeted to a machine. A computer asks a user to complete a simple test then generates a grade for that test. Computers are unable to solve the problem, so correct solutions are deemed to be the result of a person taking the test. A common type of CAPTCHA is the test that requires the typing of distorted letters, numbers or symbols that appear in an image undecipherable by a computer.{{sfn|O'Brien|Marakas|2011}}

Proposed "universal intelligence" tests aim to compare how well machines, humans, and even non-human animals perform on problem sets that are generic as possible. At an extreme, the test suite can contain every possible problem, weighted by [[Kolmogorov complexity]]; unfortunately, these problem sets tend to be dominated by impoverished pattern-matching exercises where a tuned AI can easily exceed human performance levels.<ref name="Mathematical definitions of intelligence"/><ref>{{cite journal|last1=Hernández-Orallo|first1=José|last2=Dowe|first2=David L.|last3=Hernández-Lloreda|first3=M.Victoria|title=Universal psychometrics: Measuring cognitive abilities in the machine kingdom|journal=Cognitive Systems Research|date=March 2014|volume=27|pages=50–74|doi=10.1016/j.cogsys.2013.06.001}}</ref>

== Applications{{anchor|Goals}} ==
[[File:Automated online assistant.png|thumb|An [[automated online assistant]] providing customer service on a web page – one of many very primitive applications of artificial intelligence]]
{{Main|Applications of artificial intelligence}}

AI is relevant to any intellectual task.{{sfn|Russell|Norvig|2009|p=1}} Modern artificial intelligence techniques are pervasive and are too numerous to list here. Frequently, when a technique reaches mainstream use, it is no longer considered artificial intelligence; this phenomenon is described as the [[AI effect]].{{sfn|''CNN''|2006}}

High-profile examples of AI include autonomous vehicles (such as [[Unmanned aerial vehicle|drones]] and [[self-driving cars]]), medical diagnosis, creating art (such as poetry), proving mathematical theorems, playing games (such as Chess or Go), search engines (such as [[Google search]]), online assistants (such as [[Siri]]), image recognition in photographs, spam filtering, predicting flight delays<ref>[https://ishti.org/2018/11/19/using-artificial-intelligence-to-predict-flight-delays/ Using AI to predict flight delays], Ishti.org.</ref>, prediction of judicial decisions<ref name="ecthr2016">{{cite journal |author1=N. Aletras |author2=D. Tsarapatsanis |author3=D. Preotiuc-Pietro |author4=V. Lampos |title=Predicting judicial decisions of the European Court of Human Rights: a Natural Language Processing perspective |journal=PeerJ Computer Science |year=2016 |url=https://peerj.com/articles/cs-93/ |deadurl=no |archiveurl=https://web.archive.org/web/20161029084624/https://peerj.com/articles/cs-93/ |archivedate=29 October 2016 |df=dmy-all }}</ref> and targeting online advertisements.{{sfn|Russell|Norvig|2009|p=1}}<ref>{{cite news|title=The Economist Explains: Why firms are piling into artificial intelligence|url=https://www.economist.com/blogs/economist-explains/2016/04/economist-explains|accessdate=19 May 2016|work=[[The Economist]]|date=31 March 2016|deadurl=no|archiveurl=https://web.archive.org/web/20160508010311/http://www.economist.com/blogs/economist-explains/2016/04/economist-explains|archivedate=8 May 2016|df=dmy-all}}</ref><ref>{{cite news|url=https://www.nytimes.com/2016/02/29/technology/the-promise-of-artificial-intelligence-unfolds-in-small-steps.html|title=The Promise of Artificial Intelligence Unfolds in Small Steps|last=Lohr|first=Steve|newspaper=[[The New York Times]]|date=28 February 2016|accessdate=29 February 2016|deadurl=no|archiveurl=https://web.archive.org/web/20160229171843/http://www.nytimes.com/2016/02/29/technology/the-promise-of-artificial-intelligence-unfolds-in-small-steps.html|archivedate=29 February 2016|df=dmy-all}}</ref>

With social media sites overtaking TV as a source for news for young people and news organisations increasingly reliant on social media platforms for generating distribution,<ref>{{cite web|url=https://www.bbc.co.uk/news/uk-36528256|title=Social media 'outstrips TV' as news source for young people|date=15 June 2016|author=Wakefield, Jane|work=BBC News|deadurl=no|archiveurl=https://web.archive.org/web/20160624000744/http://www.bbc.co.uk/news/uk-36528256|archivedate=24 June 2016|df=dmy-all}}</ref> major publishers now use artificial intelligence (AI) technology to post stories more effectively and generate higher volumes of traffic.<ref>{{cite web|url=https://www.bbc.co.uk/news/business-36837824|title=So you think you chose to read this article?|date=22 July 2016|author=Smith, Mark|work=BBC News|deadurl=no|archiveurl=https://web.archive.org/web/20160725205007/http://www.bbc.co.uk/news/business-36837824|archivedate=25 July 2016|df=dmy-all}}</ref>

=== Healthcare ===
{{Main|Artificial intelligence in healthcare}}
[[File:Laproscopic Surgery Robot.jpg|thumb| A patient-side surgical arm of [[Da Vinci Surgical System]]]]AI is being applied to the high cost problem of dosage issues—where findings suggested that AI could save $16 billion. In 2016, a ground breaking study in California found that a mathematical formula developed with the help of AI correctly determined the accurate dose of immunosuppressant drugs to give to organ patients.<ref>{{Cite news|url=https://hbr.org/2018/05/10-promising-ai-applications-in-health-care|title=10 Promising AI Applications in Health Care|date=2018-05-10|work=Harvard Business Review|access-date=2018-08-28}}</ref> [[File:X-ray of a hand with automatic bone age calculation.jpg|thumb|[[Projectional radiography|X-ray]] of a hand, with automatic calculation of [[bone age]] by computer software]]
Artificial intelligence is breaking into the healthcare industry by assisting doctors. According to Bloomberg Technology, Microsoft has developed AI to help doctors find the right treatments for cancer.<ref>{{cite web | author=Dina Bass | title=Microsoft Develops AI to Help Cancer Doctors Find the Right Treatments | url=https://www.bloomberg.com/news/articles/2016-09-20/microsoft-develops-ai-to-help-cancer-doctors-find-the-right-treatments | date=20 September 2016 | publisher=Bloomberg | deadurl=no | archiveurl=https://web.archive.org/web/20170511103625/https://www.bloomberg.com/news/articles/2016-09-20/microsoft-develops-ai-to-help-cancer-doctors-find-the-right-treatments | archivedate=11 May 2017 | df=dmy-all }}</ref> There is a great amount of research and drugs developed relating to cancer. In detail, there are more than 800 medicines and vaccines to treat cancer. This negatively affects the doctors, because there are too many options to choose from, making it more difficult to choose the right drugs for the patients. Microsoft is working on a project to develop a machine called "Hanover". Its goal is to memorize all the papers necessary to cancer and help predict which combinations of drugs will be most effective for each patient. One project that is being worked on at the moment is fighting [[acute myeloid leukemia|myeloid leukemia]], a fatal cancer where the treatment has not improved in decades. Another study was reported to have found that artificial intelligence was as good as trained doctors in identifying skin cancers.<ref>{{Cite news|url=https://www.bbc.co.uk/news/health-38717928|title=Artificial intelligence 'as good as cancer doctors'|last=Gallagher|first=James|date=26 January 2017|newspaper=BBC News|language=en-GB|access-date=26 January 2017|deadurl=no|archiveurl=https://web.archive.org/web/20170126133849/http://www.bbc.co.uk/news/health-38717928|archivedate=26 January 2017|df=dmy-all}}</ref> Another study is using artificial intelligence to try and monitor multiple high-risk patients, and this is done by asking each patient numerous questions based on data acquired from live doctor to patient interactions.<ref>{{Citation|title=Remote monitoring of high-risk patients using artificial intelligence|date=18 Oct 1994|url=https://www.google.com/patents/US5357427|editor-last=Langen|editor2-last=Katz|editor3-last=Dempsey|editor-first=Pauline A.|editor2-first=Jeffrey S.|editor3-first=Gayle|issue=US5357427 A|accessdate=27 February 2017|deadurl=no|archiveurl=https://web.archive.org/web/20170228090520/https://www.google.com/patents/US5357427|archivedate=28 February 2017|df=dmy-all}}</ref>

According to [[CNN]], a recent study by surgeons at the Children's National Medical Center in Washington successfully demonstrated surgery with an autonomous robot. The team supervised the robot while it performed soft-tissue surgery, stitching together a pig's bowel during open surgery, and doing so better than a human surgeon, the team claimed.<ref>{{cite news|author=Senthilingam, Meera|title=Are Autonomous Robots Your next Surgeons?|work=CNN|publisher=Cable News Network|date=12 May 2016|accessdate=4 December 2016|url=http://www.cnn.com/2016/05/12/health/robot-surgeon-bowel-operation/|deadurl=no|archiveurl=https://web.archive.org/web/20161203154119/http://www.cnn.com/2016/05/12/health/robot-surgeon-bowel-operation|archivedate=3 December 2016|df=dmy-all}}</ref> IBM has created its own artificial intelligence computer, the [[IBM Watson]], which has beaten human intelligence (at some levels). Watson not only won at the game show ''Jeopardy!'' against former champions,<ref>{{cite news|last1=Markoff|first1=John|title=On ‘Jeopardy!’ Watson Win Is All but Trivial|url=https://www.nytimes.com/2011/02/17/science/17jeopardy-watson.html|work=The New York Times|date=16 February 2011|deadurl=no|archiveurl=https://web.archive.org/web/20170922050941/http://www.nytimes.com/2011/02/17/science/17jeopardy-watson.html|archivedate=22 September 2017|df=dmy-all}}</ref> but was declared a hero after successfully diagnosing a woman who was suffering from leukemia.<ref>{{cite news|last1=Ng|first1=Alfred|title=IBM’s Watson gives proper diagnosis after doctors were stumped|url=http://www.nydailynews.com/news/world/ibm-watson-proper-diagnosis-doctors-stumped-article-1.2741857|work=NY Daily News|date=7 August 2016|language=en|deadurl=no|archiveurl=https://web.archive.org/web/20170922101344/http://www.nydailynews.com/news/world/ibm-watson-proper-diagnosis-doctors-stumped-article-1.2741857|archivedate=22 September 2017|df=dmy-all}}</ref>

=== Automotive ===
{{Main|driverless cars}}
Advancements in AI have contributed to the growth of the automotive industry through the creation and evolution of self-driving vehicles. {{as of|2016}}, there are over 30 companies utilizing AI into the creation of [[driverless cars]]. A few companies involved with AI include [[Tesla Motors|Tesla]], [[Google]], and [[Apple Inc.|Apple]].<ref>"33 Corporations Working On Autonomous Vehicles". CB Insights. N.p., 11 August 2016. 12 November 2016.</ref>

Many components contribute to the functioning of self-driving cars. These vehicles incorporate systems such as braking, lane changing, collision prevention, navigation and mapping. Together, these systems, as well as high performance computers, are integrated into one complex vehicle.<ref>West, Darrell M. "Moving forward: Self-driving vehicles in China, Europe, Japan, Korea, and the United States". Center for Technology Innovation at Brookings. N.p., September 2016. 12 November 2016.</ref>

Recent developments in autonomous automobiles have made the innovation of self-driving trucks possible, though they are still in the testing phase. The UK government has passed legislation to begin testing of self-driving truck platoons in 2018.<ref>{{cite web|last1=Burgess|first1=Matt|title=The UK is about to Start Testing Self-Driving Truck Platoons|url=https://www.wired.co.uk/article/uk-trial-self-driving-trucks-platoons-roads|website=WIRED|accessdate=20 September 2017|deadurl=no|archiveurl=https://web.archive.org/web/20170922055917/http://www.wired.co.uk/article/uk-trial-self-driving-trucks-platoons-roads|archivedate=22 September 2017|df=dmy-all}}</ref> Self-driving truck platoons are a fleet of self-driving trucks following the lead of one non-self-driving truck, so the truck platoons aren't entirely autonomous yet. Meanwhile, the Daimler, a German automobile corporation, is testing the Freightliner Inspiration which is a semi-autonomous truck that will only be used on the highway.<ref>{{cite web|last1=Davies|first1=Alex|title=World's First Self-Driving Semi-Truck Hits the Road|url=https://www.wired.com/2015/05/worlds-first-self-driving-semi-truck-hits-road/|website=WIRED|accessdate=20 September 2017|deadurl=no|archiveurl=https://web.archive.org/web/20171028222802/https://www.wired.com/2015/05/worlds-first-self-driving-semi-truck-hits-road/|archivedate=28 October 2017|df=dmy-all}}</ref>

One main factor that influences the ability for a driver-less automobile to function is mapping. In general, the vehicle would be pre-programmed with a map of the area being driven. This map would include data on the approximations of street light and curb heights in order for the vehicle to be aware of its surroundings. However, Google has been working on an algorithm with the purpose of eliminating the need for pre-programmed maps and instead, creating a device that would be able to adjust to a variety of new surroundings.<ref>McFarland, Matt. "Google's artificial intelligence breakthrough may have a huge impact on self-driving cars and much more". ''The Washington Post'' 25 February 2015. Infotrac Newsstand. 24 October 2016</ref> Some self-driving cars are not equipped with steering wheels or brake pedals, so there has also been research focused on creating an algorithm that is capable of maintaining a safe environment for the passengers in the vehicle through awareness of speed and driving conditions.<ref>"Programming safety into self-driving cars". National Science Foundation. N.p., 2 February 2015. 24 October 2016.</ref>

Another factor that is influencing the ability for a driver-less automobile is the safety of the passenger. To make a driver-less automobile, engineers must program it to handle high-risk situations. These situations could include a head-on collision with pedestrians. The car's main goal should be to make a decision that would avoid hitting the pedestrians and saving the passengers in the car. But there is a possibility the car would need to make a decision that would put someone in danger. In other words, the car would need to decide to save the pedestrians or the passengers.<ref>ArXiv, E. T. (26 October 2015). Why Self-Driving Cars Must Be Programmed to Kill. Retrieved 17 November 2017, from https://www.technologyreview.com/s/542626/why-self-driving-cars-must-be-programmed-to-kill/</ref> The programming of the car in these situations is crucial to a successful driver-less automobile.

=== Finance and economics ===
[[Financial institution]]s have long used [[artificial neural network]] systems to detect charges or claims outside of the norm, flagging these for human investigation. The use of AI in [[banking]] can be traced back to 1987 when [[Security Pacific National Bank]] in US set-up a Fraud Prevention Task force to counter the unauthorised use of debit cards. Programs like Kasisto and Moneystream are using AI in financial services.

Banks use artificial intelligence systems today to organize operations, maintain book-keeping, invest in stocks, and manage properties. AI can react to changes overnight or when business is not taking place.<ref name="Eleanor">{{cite web|url=https://www.icas.com/ca-today-news/how-accountancy-and-finance-are-using-artificial-intelligence|title=Accounting, automation and AI|first=Eleanor|last=O'Neill,|website=www.icas.com|language=English|date=31 July 2016|access-date=18 November 2016|deadurl=no|archiveurl=https://web.archive.org/web/20161118165901/https://www.icas.com/ca-today-news/how-accountancy-and-finance-are-using-artificial-intelligence|archivedate=18 November 2016|df=dmy-all}}</ref> In August 2001, robots beat humans in a simulated [[stock trader|financial trading]] competition.<ref>[http://news.bbc.co.uk/2/hi/business/1481339.stm Robots Beat Humans in Trading Battle.] {{webarchive|url=https://web.archive.org/web/20090909001249/http://news.bbc.co.uk/2/hi/business/1481339.stm |date=9 September 2009 }} BBC.com (8 August 2001)</ref> AI has also reduced fraud and financial crimes by monitoring [[behavioral pattern]]s of users for any abnormal changes or anomalies.<ref name="fsroundtable.org">{{Cite news|url=http://fsroundtable.org/cto-corner-artificial-intelligence-use-in-financial-services/|title=CTO Corner: Artificial Intelligence Use in Financial Services – Financial Services Roundtable|date=2 April 2015|newspaper=Financial Services Roundtable|language=en-US|access-date=18 November 2016|deadurl=no|archiveurl=https://web.archive.org/web/20161118165842/http://fsroundtable.org/cto-corner-artificial-intelligence-use-in-financial-services/|archivedate=18 November 2016|df=dmy-all}}</ref>

The use of AI machines in the market in applications such as online trading and decision making has changed major economic theories.<ref>{{cite book |last1=Marwala |first1= Tshilidzi| last2=Hurwitz |first2= Evan |title=Artificial Intelligence and Economic Theory: Skynet in the Market |year=2017 |publisher=[[Springer Science+Business Media|Springer]] |location=London |isbn=978-3-319-66104-9}}</ref> For example, AI based buying and selling platforms have changed the law of [[supply and demand]] in that it is now possible to easily estimate individualized demand and supply curves and thus individualized pricing. Furthermore, AI machines reduce [[information asymmetry]] in the market and thus making markets more efficient while reducing the volume of trades. Furthermore, AI in the markets limits the consequences of behavior in the markets again making markets more efficient. Other theories where AI has had impact include in [[rational choice]], [[rational expectations]], [[game theory]], [[Lewis turning point]], [[portfolio optimization]] and [[counterfactual thinking]].

=== Video games ===
{{Main|Artificial intelligence (video games)}}
In video games, artificial intelligence is routinely used to generate dynamic purposeful behavior in [[non-player character]]s (NPCs). In addition, well-understood AI techniques are routinely used for [[pathfinding]]. Some researchers consider NPC AI in games to be a "solved problem" for most production tasks. Games with more atypical AI include the AI director of ''[[Left 4 Dead]]'' (2008) and the neuroevolutionary training of platoons in ''[[Supreme Commander 2]]'' (2010).<ref>{{cite news|url=https://www.economist.com/news/science-and-technology/21721890-games-help-them-understand-reality-why-ai-researchers-video-games|title=Why AI researchers like video games|website=The Economist|deadurl=no|archiveurl=https://web.archive.org/web/20171005051028/https://www.economist.com/news/science-and-technology/21721890-games-help-them-understand-reality-why-ai-researchers-video-games|archivedate=5 October 2017|df=dmy-all}}</ref><ref>Yannakakis, G. N. (2012, May). Game AI revisited. In Proceedings of the 9th conference on Computing Frontiers (pp. 285–292). ACM.</ref>

=== Military ===
{{Further|Artificial intelligence arms race|Lethal autonomous weapon|Unmanned combat aerial vehicle}}
Worldwide annual military spending on robotics rose from 5.1 billion USD in 2010 to 7.5 billion USD in 2015.<ref>{{cite news|title=Getting to grips with military robotics|url=https://www.economist.com/news/special-report/21735478-autonomous-robots-and-swarms-will-change-nature-warfare-getting-grips|accessdate=7 February 2018|work=The Economist|date=25 January 2018|language=en}}</ref><ref>{{cite web|title=Autonomous Systems: Infographic|url=https://www.siemens.com/innovation/en/home/pictures-of-the-future/digitalization-and-software/autonomous-systems-infographic.html|website=www.siemens.com|accessdate=7 February 2018|language=en}}</ref> Military drones capable of autonomous action are widely considered a useful asset. In 2017, [[Vladimir Putin]] stated that "Whoever becomes the leader in (artificial intelligence) will become the ruler of the world".<ref>{{cite news|title=Artificial Intelligence Fuels New Global Arms Race|url=https://www.wired.com/story/for-superpowers-artificial-intelligence-fuels-new-global-arms-race/|accessdate=24 December 2017|work=WIRED}}</ref><ref>{{cite news|last1=Clifford|first1=Catherine|title=In the same way there was a nuclear arms race, there will be a race to build A.I., says tech exec|url=https://www.cnbc.com/2017/09/28/hootsuite-ceo-next-version-of-arms-race-will-be-a-race-to-build-ai.html|accessdate=24 December 2017|work=CNBC|date=29 September 2017}}</ref> Many artificial intelligence researchers seek to distance themselves from military applications of AI.<ref>{{cite news|last1=Metz|first1=Cade|title=Pentagon Wants Silicon Valley’s Help on A.I.|url=https://www.nytimes.com/2018/03/15/technology/military-artificial-intelligence.html|accessdate=19 March 2018|work=The New York Times|date=15 March 2018}}</ref>

=== Audit ===
For financial statements audit, AI makes continuous audit possible. AI tools could analyze many sets of different information immediately. The potential benefit would be the overall audit risk will be reduced, the level of assurance will be increased and the time duration of audit will be reduced.<ref>{{cite journal|last1=Chang|first1=Hsihui|last2=Kao|first2=Yi-Ching|last3=Mashruwala|first3=Raj|last4=Sorensen|first4=Susan M.|title=Technical Inefficiency, Allocative Inefficiency, and Audit Pricing|journal=Journal of Accounting, Auditing & Finance|date=10 April 2017|pages=0148558X1769676|doi=10.1177/0148558X17696760}}</ref>

=== Advertising ===
It is possible to use AI to predict or generalize the behavior of customers from their [[digital footprints]] in order to target them with personalized promotions or build customer personas automatically<ref name="Matz et al 2017">Matz, S. C., et al. "Psychological targeting as an effective approach to digital mass persuasion." Proceedings of the National Academy of Sciences (2017): 201710966.</ref> . A documented case reports that online gambling companies were using AI to improve customer targeting<ref>{{cite web |last1=Busby |first1=Mattha |title=Revealed: how bookies use AI to keep gamblers hooked |url=https://www.theguardian.com/technology/2018/apr/30/bookies-using-ai-to-keep-gamblers-hooked-insiders-say |website=the Guardian |language=en |date=30 April 2018}}</ref>.

Moreover, the application of [[Personality computing]] AI models can help reducing the cost of advertising campaigns by adding psychological targeting to more traditional sociodemographic or behavioral tagrting<ref name="Celli et al. 2017">Celli, Fabio, Pietro Zani Massani, and Bruno Lepri. "Profilio: Psychometric Profiling to Boost Social Media Advertising." Proceedings of the 2017 ACM on Multimedia Conference. ACM, 2017 [https://www.researchgate.net/publication/320542489_Profilio_Psychometric_Profiling_to_Boost_Social_Media_Advertising]</ref>.

=== Art ===

Artificial Intelligence has inspired numerous creative applications including its usage to produce visual art. The exhibition "Thinking Machines: Art and Design in the Computer Age, 1959-1989" at MoMA <ref name = moma >https://www.moma.org/calendar/exhibitions/3863 Retrieved July 29, 2018</ref> provides a good overview of the historical applications of AI for art, architecture, and design. Recent exhibitions showcasing the usage of AI to produce art include the Google-sponsored benefit and auction at the Gray Area Foundation in San Francisco, where artists experimented with the deepdream algorithm <ref name = wp1>https://www.washingtonpost.com/news/innovations/wp/2016/03/10/googles-psychedelic-paint-brush-raises-the-oldest-question-in-art/ Retrieved July 29</ref> and the exhibition "Unhuman: Art in the Age of AI," which took place in Los Angeles and Frankfurt in the fall of 2017.<ref name = sf>{{cite web|url=https://www.statefestival.org/program/2017/unhuman-art-in-the-age-of-ai |title=Unhuman: Art in the Age of AI — STATE Festival |publisher=Statefestival.org |date= |accessdate=2018-09-13}}</ref><ref name = artsy>https://www.artsy.net/article/artsy-editorial-hard-painting-made-computer-human. Retrieved July 29</ref> In the spring of 2018, the Association of Computing Machinery dedicated a special magazine issue to the subject of computers and art highlighting the role of machine learning in the arts.<ref name = acm>https://dl.acm.org/citation.cfm?id=3204480.3186697 Retrieved July 29</ref>

== Philosophy and ethics ==
{{Main|Philosophy of artificial intelligence|Ethics of artificial intelligence}}
There are three philosophical questions related to AI:
# Is [[artificial general intelligence]] possible? Can a machine solve any problem that a human being can solve using intelligence? Or are there hard limits to what a machine can accomplish?
# Are intelligent machines dangerous? How can we ensure that machines behave ethically and that they are used ethically?
# Can a machine have a [[mind]], [[consciousness]] and [[philosophy of mind|mental states]] in exactly the same sense that human beings do? Can a machine be [[Sentience|sentient]], and thus deserve certain rights? Can a machine [[intention]]ally cause harm?

=== The limits of artificial general intelligence ===
{{Main|Philosophy of AI|Turing test|Physical symbol systems hypothesis|Dreyfus' critique of AI|The Emperor's New Mind|AI effect}}

Can a machine be intelligent? Can it "think"?

;''[[Computing Machinery and Intelligence|Alan Turing's "polite convention"]]'': We need not decide if a machine can "think"; we need only decide if a machine can act as intelligently as a human being. This approach to the philosophical problems associated with artificial intelligence forms the basis of the [[Turing test]].<ref name="Turing test"/>

;''The [[Dartmouth Workshop|Dartmouth proposal]]'': "Every aspect of learning or any other feature of intelligence can be so precisely described that a machine can be made to simulate it." This conjecture was printed in the proposal for the Dartmouth Conference of 1956, and represents the position of most working AI researchers.<ref name="Dartmouth proposal"/>

;''[[Physical symbol system|Newell and Simon's physical symbol system hypothesis]]'': "A physical symbol system has the necessary and sufficient means of general intelligent action." Newell and Simon argue that intelligence consists of formal operations on symbols.<ref name="Physical symbol system hypothesis"/> [[Hubert Dreyfus]] argued that, on the contrary, human expertise depends on unconscious instinct rather than conscious symbol manipulation and on having a "feel" for the situation rather than explicit symbolic knowledge. (See [[Dreyfus' critique of AI]].)<ref>
Dreyfus criticized the [[necessary and sufficient|necessary]] condition of the [[physical symbol system]] hypothesis, which he called the "psychological assumption": "The mind can be viewed as a device operating on bits of information according to formal rules." {{Harv|Dreyfus|1992|p=156}}</ref><ref name="Dreyfus' critique"/>

;''Gödelian arguments'': [[Gödel]] himself,<ref name="Gödel himself"/> [[John Lucas (philosopher)|John Lucas]] (in 1961) and [[Roger Penrose]] (in a more detailed argument from 1989 onwards) made highly technical arguments that human mathematicians can consistently see the truth of their own "Gödel statements" and therefore have computational abilities beyond that of mechanical Turing machines.<ref name="The mathematical objection"/> However, the modern consensus in the scientific and mathematical community is that these "Gödelian arguments" fail.<ref>{{cite web|author1=[[Graham Oppy]]|title=Gödel's Incompleteness Theorems|url=http://plato.stanford.edu/entries/goedel-incompleteness/#GdeArgAgaMec|website=[[Stanford Encyclopedia of Philosophy]]|accessdate=27 April 2016|date=20 January 2015|quote=These Gödelian anti-mechanist arguments are, however, problematic, and there is wide consensus that they fail.}}</ref><ref>{{cite book|author1=[[Stuart J. Russell]]|author2=[[Peter Norvig]]|title=[[Artificial Intelligence: A Modern Approach]]|date=2010|publisher=[[Prentice Hall]]|location=Upper Saddle River, NJ|isbn=0-13-604259-7|edition=3rd|chapter=26.1.2: Philosophical Foundations/Weak AI: Can Machines Act Intelligently?/The mathematical objection|quote=...even if we grant that computers have limitations on what they can prove, there is no evidence that humans are immune from those limitations.}}</ref><ref>Mark Colyvan. An introduction to the philosophy of mathematics. [[Cambridge University Press]], 2012. From 2.2.2, 'Philosophical significance of Gödel's incompleteness results': "The accepted wisdom (with which I concur) is that the Lucas-Penrose arguments fail."</ref>

;''The [[artificial brain]] argument'': The brain can be simulated by machines and because brains are intelligent, simulated brains must also be intelligent; thus machines can be intelligent. [[Hans Moravec]], [[Ray Kurzweil]] and others have argued that it is technologically feasible to copy the brain directly into hardware and software and that such a simulation will be essentially identical to the original.<ref name="Brain simulation"/>

;''The [[AI effect]]'': Machines are ''already'' intelligent, but observers have failed to recognize it. When [[Deep Blue (chess computer)|Deep Blue]] beat [[Garry Kasparov]] in chess, the machine was acting intelligently. However, onlookers commonly discount the behavior of an artificial intelligence program by arguing that it is not "real" intelligence after all; thus "real" intelligence is whatever intelligent behavior people can do that machines still cannot. This is known as the AI Effect: "AI is whatever hasn't been done yet."

=== Potential harm ===
Widespread use of artificial intelligence could have [[unintended consequences]] that are dangerous or undesirable. Scientists from the [[Future of Life Institute]], among others, described some short-term research goals to see how AI influences the economy, the laws and ethics that are involved with AI and how to minimize AI security risks. In the long-term, the scientists have proposed to continue optimizing function while minimizing possible security risks that come along with new technologies.<ref>Russel, Stuart., Daniel Dewey, and Max Tegmark. Research Priorities for Robust and Beneficial Artificial Intelligence. AI Magazine 36:4 (2015). 8 December 2016.</ref>

==== Existential risk ====
{{Main|Existential risk from artificial general intelligence}}

Physicist [[Stephen Hawking]], [[Microsoft]] founder [[Bill Gates]], and [[SpaceX]] founder [[Elon Musk]] have expressed concerns about the possibility that AI could evolve to the point that humans could not control it, with Hawking theorizing that this could "[[Global catastrophic risk|spell the end of the human race]]".
<ref>{{Cite news|last1=Rawlinson|first1=Kevin|title=Microsoft's Bill Gates insists AI is a threat|url=https://www.bbc.co.uk/news/31047780|publisher=BBC News|accessdate=30 January 2015|deadurl=no|archiveurl=https://web.archive.org/web/20150129183607/http://www.bbc.co.uk/news/31047780|archivedate=29 January 2015|df=dmy-all|date=2015-01-29}}</ref>
<ref name="Holley">{{Cite news|title = Bill Gates on dangers of artificial intelligence: 'I don't understand why some people are not concerned'|url = https://www.washingtonpost.com/news/the-switch/wp/2015/01/28/bill-gates-on-dangers-of-artificial-intelligence-dont-understand-why-some-people-are-not-concerned/|newspaper = The Washington Post|date = 28 January 2015|access-date = 30 October 2015|issn = 0190-8286|first = Peter|last = Holley|deadurl = no|archiveurl = https://web.archive.org/web/20151030054330/https://www.washingtonpost.com/news/the-switch/wp/2015/01/28/bill-gates-on-dangers-of-artificial-intelligence-dont-understand-why-some-people-are-not-concerned/|archivedate = 30 October 2015|df = dmy-all}}</ref>
<ref>{{Cite news|title = Elon Musk: artificial intelligence is our biggest existential threat|url = https://www.theguardian.com/technology/2014/oct/27/elon-musk-artificial-intelligence-ai-biggest-existential-threat|newspaper = The Guardian|accessdate = 30 October 2015|first = Samuel|last = Gibbs|deadurl = no|archiveurl = https://web.archive.org/web/20151030054330/http://www.theguardian.com/technology/2014/oct/27/elon-musk-artificial-intelligence-ai-biggest-existential-threat|archivedate = 30 October 2015|df = dmy-all|date = 2014-10-27}}</ref>

{{quote|text=The development of full artificial intelligence could spell the end of the human race. Once humans develop artificial intelligence, it will take off on its own and redesign itself at an ever-increasing rate. Humans, who are limited by slow biological evolution, couldn't compete and would be superseded.|author=[[Stephen Hawking]]<ref>{{Cite news|title = Stephen Hawking warns artificial intelligence could end mankind|url = https://www.bbc.com/news/technology-30290540|accessdate = 30 October 2015|deadurl = no|archiveurl = https://web.archive.org/web/20151030054329/http://www.bbc.com/news/technology-30290540|archivedate = 30 October 2015|df = dmy-all|work = [[BBC News]]|date = 2014-12-02|last1 = Cellan-Jones|first1 = Rory}}</ref>}}

In his book ''[[Superintelligence: Paths, Dangers, Strategies|Superintelligence]]'', [[Nick Bostrom]] provides an argument that artificial intelligence will pose a threat to mankind. He argues that sufficiently intelligent AI, if it chooses actions based on achieving some goal, will exhibit [[Instrumental convergence|convergent]] behavior such as acquiring resources or protecting itself from being shut down. If this AI's goals do not reflect humanity's – one example is an AI told to compute as many digits of pi as possible – it might harm humanity in order to acquire more resources or prevent itself from being shut down, ultimately to better achieve its goal.

Concern over risk from artificial intelligence has led to some high-profile donations and investments. A group of prominent tech titans including [[Peter Thiel]], Amazon Web Services and Musk have committed $1billion to [[OpenAI]], a nonprofit company aimed at championing responsible AI development.<ref>{{cite web|url=http://www.chicagotribune.com/bluesky/technology/ct-tech-titans-against-terminators-20151214-story.html|title=Tech titans like Elon Musk are spending $1 billion to save you from terminators|first=Washington|last=Post|publisher=|deadurl=no|archiveurl=https://web.archive.org/web/20160607121118/http://www.chicagotribune.com/bluesky/technology/ct-tech-titans-against-terminators-20151214-story.html|archivedate=7 June 2016|df=dmy-all}}</ref> The opinion of experts within the field of artificial intelligence is mixed, with sizable fractions both concerned and unconcerned by risk from eventual superhumanly-capable AI.<ref>{{cite journal
|last1 = Müller
|first1 = Vincent C.
|last2 = Bostrom
|first2 = Nick
|year = 2014
|title = Future Progress in Artificial Intelligence: A Poll Among Experts
|journal = AI Matters
|volume = 1
|issue = 1
|pages = 9–11
|doi = 10.1145/2639475.2639478
|url = http://www.sophia.de/pdf/2014_PT-AI_polls.pdf
|deadurl = no
|archiveurl = https://web.archive.org/web/20160115114604/http://www.sophia.de/pdf/2014_PT-AI_polls.pdf
|archivedate = 15 January 2016
|df = dmy-all
}}</ref> In January 2015, [[Elon Musk]] donated ten million dollars to the [[Future of Life Institute]] to fund research on understanding AI decision making. The goal of the institute is to "grow wisdom with which we manage" the growing power of technology. Musk also funds companies developing artificial intelligence such as [[Google DeepMind]] and [[Vicarious (company)|Vicarious]] to "just keep an eye on what's going on with artificial intelligence.<ref>{{cite web|title = The mysterious artificial intelligence company Elon Musk invested in is developing game-changing smart computers|url = http://www.techinsider.io/mysterious-artificial-intelligence-company-elon-musk-investment-2015-10|website = Tech Insider|accessdate = 30 October 2015|deadurl = no|archiveurl = https://web.archive.org/web/20151030165333/http://www.techinsider.io/mysterious-artificial-intelligence-company-elon-musk-investment-2015-10|archivedate = 30 October 2015|df = dmy-all}}</ref> I think there is potentially a dangerous outcome there."<ref>{{cite web|title = Musk-Backed Group Probes Risks Behind Artificial Intelligence|url = https://www.bloomberg.com/news/articles/2015-07-01/musk-backed-group-probes-risks-behind-artificial-intelligence|website = Bloomberg.com|accessdate = 30 October 2015|first = Jack|last = Clark|deadurl = no|archiveurl = https://web.archive.org/web/20151030202356/http://www.bloomberg.com/news/articles/2015-07-01/musk-backed-group-probes-risks-behind-artificial-intelligence|archivedate = 30 October 2015|df = dmy-all}}</ref><ref>{{cite web|title = Elon Musk Is Donating $10M Of His Own Money To Artificial Intelligence Research|url = http://www.fastcompany.com/3041007/fast-feed/elon-musk-is-donating-10m-of-his-own-money-to-artificial-intelligence-research|website = Fast Company|accessdate = 30 October 2015|deadurl = no|archiveurl = https://web.archive.org/web/20151030202356/http://www.fastcompany.com/3041007/fast-feed/elon-musk-is-donating-10m-of-his-own-money-to-artificial-intelligence-research|archivedate = 30 October 2015|df = dmy-all|date = 2015-01-15}}</ref>

For this danger to be realized, the hypothetical AI would have to overpower or out-think all of humanity, which a minority of experts argue is a possibility far enough in the future to not be worth researching.<ref>{{cite web|title = Is artificial intelligence really an existential threat to humanity?|url = http://thebulletin.org/artificial-intelligence-really-existential-threat-humanity8577|website = Bulletin of the Atomic Scientists|accessdate = 30 October 2015|deadurl = no|archiveurl = https://web.archive.org/web/20151030054330/http://thebulletin.org/artificial-intelligence-really-existential-threat-humanity8577|archivedate = 30 October 2015|df = dmy-all|date = 2015-08-09}}</ref><ref>{{cite web|title = The case against killer robots, from a guy actually working on artificial intelligence|url = http://fusion.net/story/54583/the-case-against-killer-robots-from-a-guy-actually-building-ai/|website = Fusion.net|accessdate = 31 January 2016|deadurl = no|archiveurl = https://web.archive.org/web/20160204175716/http://fusion.net/story/54583/the-case-against-killer-robots-from-a-guy-actually-building-ai/|archivedate = 4 February 2016|df = dmy-all}}</ref> Other counterarguments revolve around humans being either intrinsically or convergently valuable from the perspective of an artificial intelligence.<ref>{{cite web|title = Will artificial intelligence destroy humanity? Here are 5 reasons not to worry.|url = https://www.vox.com/2014/8/22/6043635/5-reasons-we-shouldnt-worry-about-super-intelligent-computers-taking|website = Vox|accessdate = 30 October 2015|deadurl = no|archiveurl = https://web.archive.org/web/20151030092203/http://www.vox.com/2014/8/22/6043635/5-reasons-we-shouldnt-worry-about-super-intelligent-computers-taking|archivedate = 30 October 2015|df = dmy-all|date = 2014-08-22}}</ref>

==== Devaluation of humanity ====
{{Main|Computer Power and Human Reason}}
[[Joseph Weizenbaum]] wrote that AI applications cannot, by definition, successfully simulate genuine human empathy and that the use of AI technology in fields such as [[customer service]] or [[psychotherapy]]<ref>In the early 1970s, [[Kenneth Colby]] presented a version of Weizenbaum's [[ELIZA]] known as DOCTOR which he promoted as a serious therapeutic tool. {{Harv|Crevier|1993|pp=132–144}}</ref> was deeply misguided. Weizenbaum was also bothered that AI researchers (and some philosophers) were willing to view the human mind as nothing more than a computer program (a position is now known as [[computationalism]]). To Weizenbaum these points suggest that AI research devalues human life.<ref name="Weizenbaum's critique"/>

==== Decrease in demand for human labor ====
{{Further|Technological unemployment#21st century}}
The relationship between automation and employment is complicated. While automation eliminates old jobs, it also creates new jobs through micro-economic and macro-economic effects.<ref>E McGaughey, 'Will Robots Automate Your Job Away? Full Employment, Basic Income, and Economic Democracy' (2018) [https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3044448 SSRN, part 2(3)]</ref> Unlike previous waves of automation, many middle-class jobs may be eliminated by artificial intelligence; ''[[The Economist]]'' states that "the worry that AI could do to white-collar jobs what steam power did to blue-collar ones during the Industrial Revolution" is "worth taking seriously".<ref>{{cite news|title=Automation and anxiety|url=https://www.economist.com/news/special-report/21700758-will-smarter-machines-cause-mass-unemployment-automation-and-anxiety|accessdate=13 January 2018|work=The Economist|date=9 May 2015}}</ref> Subjective estimates of the risk vary widely; for example, Michael Osborne and [[Carl Benedikt Frey]] estimate 47% of U.S. jobs are at "high risk" of potential automation, while an OECD report classifies only 9% of U.S. jobs as "high risk".<ref>{{cite news|last1=Lohr|first1=Steve|title=Robots Will Take Jobs, but Not as Fast as Some Fear, New Report Says|url=https://www.nytimes.com/2017/01/12/technology/robots-will-take-jobs-but-not-as-fast-as-some-fear-new-report-says.html|accessdate=13 January 2018|work=The New York Times|date=2017}}</ref><ref>{{Cite journal|date=1 January 2017|title=The future of employment: How susceptible are jobs to computerisation?|url=https://www.sciencedirect.com/science/article/pii/S0040162516302244|journal=Technological Forecasting and Social Change|volume=114|pages=254–280|doi=10.1016/j.techfore.2016.08.019|issn=0040-1625|last1=Frey|first1=Carl Benedikt|last2=Osborne|first2=Michael A|citeseerx=10.1.1.395.416}}</ref><ref>Arntz, Melanie, Terry Gregory, and Ulrich Zierahn. "The risk of automation for jobs in OECD countries: A comparative analysis." OECD Social, Employment, and Migration Working Papers 189 (2016). p. 33.</ref> Jobs at extreme risk range from paralegals to fast food cooks, while job demand is likely to increase for care-related professions ranging from personal healthcare to the clergy.<ref>{{cite news|last1=Mahdawi|first1=Arwa|title=What jobs will still be around in 20 years? Read this to prepare your future|url=https://www.theguardian.com/us-news/2017/jun/26/jobs-future-automation-robots-skills-creative-health|accessdate=13 January 2018|work=The Guardian|date=26 June 2017}}</ref> Author [[Martin Ford (author)|Martin Ford]] and others go further and argue that a large number of jobs are routine, repetitive and (to an AI) predictable; Ford warns that these jobs may be automated in the next couple of decades, and that many of the new jobs may not be "accessible to people with average capability", even with retraining. Economists point out that in the past technology has tended to increase rather than reduce total employment, but acknowledge that "we're in uncharted territory" with AI.<ref name="guardian jobs debate">{{cite news|last1=Ford|first1=Martin|last2=Colvin|first2=Geoff|title=Will robots create more jobs than they destroy?|url=https://www.theguardian.com/technology/2015/sep/06/will-robots-create-destroy-jobs|accessdate=13 January 2018|work=The Guardian|date=6 September 2015}}</ref>

==== Autonomous weapons ====
{{See also|Lethal autonomous weapon}}
Currently, 50+ countries are researching battlefield robots, including the United States, China, Russia, and the United Kingdom. Many people concerned about risk from superintelligent AI also want to limit the use of artificial soldiers and drones.<ref>{{cite web|title = Stephen Hawking, Elon Musk, and Bill Gates Warn About Artificial Intelligence|url = http://observer.com/2015/08/stephen-hawking-elon-musk-and-bill-gates-warn-about-artificial-intelligence/|website = Observer|accessdate = 30 October 2015|deadurl = no|archiveurl = https://web.archive.org/web/20151030053323/http://observer.com/2015/08/stephen-hawking-elon-musk-and-bill-gates-warn-about-artificial-intelligence/|archivedate = 30 October 2015|df = dmy-all|date = 2015-08-19}}</ref>

=== Ethical machines ===
Machines with intelligence have the potential to use their intelligence to prevent harm and minimize the risks; they may have the ability to use [[ethics|ethical reasoning]] to better choose their actions in the world. Research in this area includes [[machine ethics]], [[artificial moral agents]], and [[friendly AI]].

==== Artificial moral agents ====
Wendell Wallach introduced the concept of [[artificial moral agents]] (AMA) in his book ''Moral Machines''<ref>Wendell Wallach (2010). ''Moral Machines'', Oxford University Press.</ref> For Wallach, AMAs have become a part of the research landscape of artificial intelligence as guided by its two central questions which he identifies as "Does Humanity Want Computers Making Moral Decisions"<ref>Wallach, pp 37–54.</ref> and "Can (Ro)bots Really Be Moral".<ref>Wallach, pp 55–73.</ref> For Wallach the question is not centered on the issue of ''whether'' machines can demonstrate the equivalent of moral behavior in contrast to the ''constraints'' which society may place on the development of AMAs.<ref>Wallach, Introduction chapter.</ref>

==== Machine ethics ====
{{Main|Machine ethics}}
The field of machine ethics is concerned with giving machines ethical principles, or a procedure for discovering a way to resolve the ethical dilemmas they might encounter, enabling them to function in an ethically responsible manner through their own ethical decision making.<ref name="autogenerated1">Michael Anderson and Susan Leigh Anderson (2011), Machine Ethics, Cambridge University Press.</ref> The field was delineated in the AAAI Fall 2005 Symposium on Machine Ethics: "Past research concerning the relationship between technology and ethics has largely focused on responsible and irresponsible use of technology by human beings, with a few people being interested in how human beings ought to treat machines. In all cases, only human beings have engaged in ethical reasoning. The time has come for adding an ethical dimension to at least some machines. Recognition of the ethical ramifications of behavior involving machines, as well as recent and potential developments in machine autonomy, necessitate this. In contrast to computer hacking, software property issues, privacy issues and other topics normally ascribed to computer ethics, machine ethics is concerned with the behavior of machines towards human users and other machines. Research in machine ethics is key to alleviating concerns with autonomous systems—it could be argued that the notion of autonomous machines without such a dimension is at the root of all fear concerning machine intelligence. Further, investigation of machine ethics could enable the discovery of problems with current ethical theories, advancing our thinking about Ethics."<ref name="autogenerated2">{{cite web|url=http://www.aaai.org/Library/Symposia/Fall/fs05-06 |title=Machine Ethics |work=aaai.org |deadurl=yes |archiveurl=https://web.archive.org/web/20141129044821/http://www.aaai.org/Library/Symposia/Fall/fs05-06 |archivedate=29 November 2014 }}</ref> Machine ethics is sometimes referred to as machine morality, computational ethics or computational morality. A variety of perspectives of this nascent field can be found in the collected edition "Machine Ethics"<ref name="autogenerated1"/> that stems from the AAAI Fall 2005 Symposium on Machine Ethics.<ref name="autogenerated2"/>

==== Malevolent and friendly AI ====
{{Main|Friendly AI}}
Political scientist [[Charles T. Rubin]] believes that AI can be neither designed nor guaranteed to be benevolent.<ref>{{cite journal|last=Rubin |first=Charles |authorlink=Charles T. Rubin |date=Spring 2003 |title=Artificial Intelligence and Human Nature |`The New Atlantis |volume=1 |pages=88–100 |url=http://www.thenewatlantis.com/publications/artificial-intelligence-and-human-nature |deadurl=yes |archiveurl=https://web.archive.org/web/20120611115223/http://www.thenewatlantis.com/publications/artificial-intelligence-and-human-nature |archivedate=11 June 2012 |df=dmy}}</ref> He argues that "any sufficiently advanced benevolence may be indistinguishable from malevolence." Humans should not assume machines or robots would treat us favorably because there is no ''a priori'' reason to believe that they would be sympathetic to our system of morality, which has evolved along with our particular biology (which AIs would not share). Hyper-intelligent software may not necessarily decide to support the continued existence of humanity and would be extremely difficult to stop. This topic has also recently begun to be discussed in academic publications as a real source of risks to civilization, humans, and planet Earth.

One proposal to deal with this is to ensure that the first generally intelligent AI is '[[Friendly AI]]', and will then be able to control subsequently developed AIs. Some question whether this kind of check could really remain in place.

Leading AI researcher [[Rodney Brooks]] writes, "I think it is a mistake to be worrying about us developing malevolent AI anytime in the next few hundred years. I think the worry stems from a fundamental error in not distinguishing the difference between the very real recent advances in a particular aspect of AI, and the enormity and complexity of building sentient volitional intelligence."<ref>{{cite web|last=Brooks|first=Rodney|title=artificial intelligence is a tool, not a threat|date=10 November 2014|url=http://www.rethinkrobotics.com/artificial-intelligence-tool-threat/|deadurl=yes|archiveurl=https://web.archive.org/web/20141112130954/http://www.rethinkrobotics.com/artificial-intelligence-tool-threat/|archivedate=12 November 2014|df=dmy-all}}</ref>

=== Machine consciousness, sentience and mind ===
{{Main|Artificial consciousness}}
If an AI system replicates all key aspects of human intelligence, will that system also be [[Sentience|sentient]] – will it have a [[mind]] which has [[consciousness|conscious experiences]]? This question is closely related to the philosophical problem as to the nature of human consciousness, generally referred to as the [[hard problem of consciousness]].

==== Consciousness ====
{{Main|Hard problem of consciousness|Theory of mind}}
[[David Chalmers]] identified two problems in understanding the mind, which he named the "hard" and "easy" problems of consciousness.<ref name=Chalmers>{{cite journal |url=http://www.imprint.co.uk/chalmers.html |title=Facing up to the problem of consciousness |last=Chalmers |first=David |authorlink=David Chalmers |journal=[[Journal of Consciousness Studies]] |volume= 2 |issue=3 |year=1995 |pages=200–219}} See also [http://consc.net/papers/facing.html this link]
</ref> The easy problem is understanding how the brain processes signals, makes plans and controls behavior. The hard problem is explaining how this ''feels'' or why it should feel like anything at all. Human [[information processing]] is easy to explain, however human [[subjective experience]] is difficult to explain.

For example, consider what happens when a person is shown a color swatch and identifies it, saying "it's red". The easy problem only requires understanding the machinery in the brain that makes it possible for a person to know that the color swatch is red. The hard problem is that people also know something else—they also know ''what red looks like''. (Consider that a person born blind can know that something is red without knowing what red looks like.){{efn|This is based on [[Mary's Room]], a thought experiment first proposed by [[Frank Cameron Jackson|Frank Jackson]] in 1982}} Everyone knows subjective experience exists, because they do it every day (e.g., all sighted people know what red looks like). The hard problem is explaining how the brain creates it, why it exists, and how it is different than knowledge and other aspects of the brain.

==== Computationalism and functionalism ====
{{Main|Computationalism|Functionalism (philosophy of mind)}}
Computationalism is the position in the [[philosophy of mind]] that the human mind or the human brain (or both) is an information processing system and that thinking is a form of computing.<ref>[[Steven Horst|Horst, Steven]], (2005) [http://plato.stanford.edu/entries/computational-mind/ "The Computational Theory of Mind"] in ''The Stanford Encyclopedia of Philosophy''</ref> Computationalism argues that the relationship between mind and body is similar or identical to the relationship between software and hardware and thus may be a solution to the [[mind-body problem]]. This philosophical position was inspired by the work of AI researchers and cognitive scientists in the 1960s and was originally proposed by philosophers [[Jerry Fodor]] and [[Hilary Putnam]].

==== Strong AI hypothesis ====
{{Main|Chinese room}}
The philosophical position that John Searle has named [[strong AI hypothesis|"strong AI"]] states: "The appropriately programmed computer with the right inputs and outputs would thereby have a mind in exactly the same sense human beings have minds."<ref name="Searle's strong AI"/> Searle counters this assertion with his [[Chinese room]] argument, which asks us to look ''inside'' the computer and try to find where the "mind" might be.<ref name="Chinese room"/>

==== Robot rights ====
{{Main|Robot rights}}
If a machine can be created that has intelligence, could it also ''[[sentience|feel]]''? If it can feel, does it have the same rights as a human? This issue, now known as "[[robot rights]]", is currently being considered by, for example, California's [[Institute for the Future]], although many critics believe that the discussion is premature.<ref name="Robot rights"/> Some critics of [[transhumanism]] argue that any hypothetical robot rights would lie on a spectrum with [[animal rights]] and human rights.<ref Name="Evans 2015">{{cite web | last = Evans | first = Woody | authorlink = Woody Evans | title = Posthuman Rights: Dimensions of Transhuman Worlds | work = Teknokultura | publisher = Universidad Complutense, Madrid | date = 2015 | url = http://revistas.ucm.es/index.php/TEKN/article/view/49072/46310 | accessdate = 5 December 2016 | deadurl = no | archiveurl = https://web.archive.org/web/20161228094440/http://revistas.ucm.es/index.php/TEKN/article/view/49072/46310 | archivedate = 28 December 2016 | df = dmy-all }}</ref> The subject is profoundly discussed in the 2010 documentary film ''[[Plug & Pray]]''.<ref>{{cite web|url=http://www.plugandpray-film.de/en/content.html|title=Content: Plug & Pray Film – Artificial Intelligence – Robots -|author=maschafilm|work=plugandpray-film.de|deadurl=no|archiveurl=https://web.archive.org/web/20160212040134/http://www.plugandpray-film.de/en/content.html|archivedate=12 February 2016|df=dmy-all}}</ref>

=== Superintelligence ===
{{Main|Superintelligence}}
Are there limits to how intelligent machines – or human-machine hybrids – can be? A superintelligence, hyperintelligence, or superhuman intelligence is a hypothetical agent that would possess intelligence far surpassing that of the brightest and most gifted human mind. ‘’Superintelligence’’ may also refer to the form or degree of intelligence possessed by such an agent.<ref name="Roberts"/>

==== Technological singularity ====
{{Main|Technological singularity|Moore's law}}
If research into [[artificial general intelligence|Strong AI]] produced sufficiently intelligent software, it might be able to reprogram and improve itself. The improved software would be even better at improving itself, leading to [[Intelligence explosion|recursive self-improvement]].<ref name="recurse"/> The new intelligence could thus increase exponentially and dramatically surpass humans. Science fiction writer [[Vernor Vinge]] named this scenario "[[technological singularity|singularity]]".<ref name=Singularity/> Technological singularity is when accelerating progress in technologies will cause a runaway effect wherein artificial intelligence will exceed human intellectual capacity and control, thus radically changing or even ending civilization. Because the capabilities of such an intelligence may be impossible to comprehend, the technological singularity is an occurrence beyond which events are unpredictable or even unfathomable.<ref name=Singularity/><ref name="Roberts"/>

[[Ray Kurzweil]] has used [[Moore's law]] (which describes the relentless exponential improvement in digital technology) to calculate that [[desktop computer]]s will have the same processing power as human brains by the year 2029, and predicts that the singularity will occur in 2045.<ref name=Singularity/>

==== Transhumanism ====
{{Main|Transhumanism}}
{{quote|You awake one morning to find your brain has another lobe functioning. Invisible, this auxiliary lobe answers your questions with information beyond the realm of your own memory, suggests plausible courses of action, and asks questions that help bring out relevant facts. You quickly come to rely on the new lobe so much that you stop wondering how it works. You just use it. This is the dream of artificial intelligence.|''[[Byte (magazine)|Byte]]'', April 1985<ref name="lemmon198504">{{cite news | url=https://archive.org/stream/byte-magazine-1985-04/1985_04_BYTE_10-04_Artificial_Intelligence#page/n125/mode/2up | title=Artificial Intelligence | work=BYTE | date=April 1985 | accessdate=14 February 2015 | author=Lemmons, Phil | page=125 | deadurl=no | archiveurl=https://web.archive.org/web/20150420115129/https://archive.org/stream/byte-magazine-1985-04/1985_04_BYTE_10-04_Artificial_Intelligence#page/n125/mode/2up | archivedate=20 April 2015 | df=dmy-all }}</ref>}}
Robot designer [[Hans Moravec]], cyberneticist [[Kevin Warwick]] and inventor [[Ray Kurzweil]] have predicted that humans and machines will merge in the future into [[cyborg]]s that are more capable and powerful than either.<ref name="Transhumanism"/> This idea, called [[transhumanism]], which has roots in [[Aldous Huxley]] and [[Robert Ettinger]].

[[Edward Fredkin]] argues that "artificial intelligence is the next stage in evolution", an idea first proposed by [[Samuel Butler (novelist)|Samuel Butler]]'s "[[Darwin among the Machines]]" (1863), and expanded upon by [[George Dyson (science historian)|George Dyson]] in his book of the same name in 1998.<ref name="AI as evolution"/>

== In fiction ==
{{Main|Artificial intelligence in fiction}}
[[File:Capek play.jpg|thumb|The word "robot" itself was coined by [[Karel Čapek]] in his 1921 play ''[[R.U.R.]]'', the title standing for "[[Rossum's Universal Robots]]"]]

Thought-capable artificial beings appeared as storytelling devices since antiquity,<ref name="AI in myth"/>
and have been a persistent theme in [[science fiction]].

A common [[Trope (literature)|trope]] in these works began with [[Mary Shelley]]'s ''[[Frankenstein]]'', where a human creation becomes a threat to its masters. This includes such works as [[2001: A Space Odyssey (novel)|Arthur C. Clarke's]] and [[2001: A Space Odyssey (film)|Stanley Kubrick's]] ''[[2001: A Space Odyssey]]'' (both 1968), with [[HAL 9000]], the murderous computer in charge of the ''[[Discovery One]]'' spaceship, as well as ''[[The Terminator]]'' (1984) and ''[[The Matrix]]'' (1999). In contrast, the rare loyal robots such as Gort from ''[[The Day the Earth Stood Still]]'' (1951) and Bishop from ''[[Aliens (film)|Aliens]]'' (1986) are less prominent in popular culture.<ref>{{cite journal|last1=Buttazzo|first1=G.|title=Artificial consciousness: Utopia or real possibility?|url=http://ieeexplore.ieee.org/document/933500/?reload=true|journal=[[Computer (magazine)|Computer (IEEE)]]|date=July 2001|volume=34|issue=7|pages=24–30|doi=10.1109/2.933500|accessdate=29 December 2016|deadurl=no|archiveurl=https://web.archive.org/web/20161230092217/http://ieeexplore.ieee.org/document/933500/?reload=true|archivedate=30 December 2016|df=dmy-all}}</ref>

[[Isaac Asimov]] introduce the [[Three Laws of Robotics]] in many books and stories, most notably the "Multivac" series about a super-intelligent computer of the same name. Asimov's laws are often brought up during layman discussions of machine ethics;<ref>Anderson, Susan Leigh. "Asimov's "three laws of robotics" and machine metaethics." AI & Society 22.4 (2008): 477–493.</ref> while almost all artificial intelligence researchers are familiar with Asimov's laws through popular culture, they generally consider the laws useless for many reasons, one of which is their ambiguity.<ref>{{cite journal | last1 = McCauley | first1 = Lee | year = 2007 | title = AI armageddon and the three laws of robotics | url = | journal = Ethics and Information Technology | volume = 9 | issue = 2| pages = 153–164 | doi=10.1007/s10676-007-9138-2| citeseerx = 10.1.1.85.8904}}</ref>

[[Transhumanism]] (the merging of humans and machines) is explored in the [[manga]] ''[[Ghost in the Shell]]'' and the science-fiction series ''[[Dune (novel)|Dune]]''. In the 1980s, artist [[Hajime Sorayama]]'s Sexy Robots series were painted and published in Japan depicting the actual organic human form with lifelike muscular metallic skins and later "the Gynoids" book followed that was used by or influenced movie makers including [[George Lucas]] and other creatives. Sorayama never considered these organic robots to be real part of nature but always unnatural product of the human mind, a fantasy existing in the mind even when realized in actual form.

Several works use AI to force us to confront the fundamental of question of what makes us human, showing us artificial beings that have [[sentience|the ability to feel]], and thus to suffer. This appears in [[Karel Čapek]]'s "[[R.U.R.]]", the films "[[A.I. Artificial Intelligence]]" and "[[Ex Machina (film)|Ex Machina]]", as well as the novel ''[[Do Androids Dream of Electric Sheep?]]'', by [[Philip K. Dick]]. Dick considers the idea that our understanding of human subjectivity is altered by technology created with artificial intelligence.<ref>{{Cite journal|last=Galvan|first=Jill|date=1 January 1997|title=Entering the Posthuman Collective in Philip K. Dick's "Do Androids Dream of Electric Sheep?"|journal=Science Fiction Studies|volume=24|issue=3|pages=413–429|jstor=4240644}}</ref>
{{div col end}}

==See also==
{{col div|colwidth=20em}}
* {{Portal inline|size=tiny|Artificial intelligence}}
* [[Abductive reasoning]]
* [[Behavior selection algorithm]]
* [[Business process automation]]
* [[Case-based reasoning]]
* [[Commonsense reasoning]]
* [[Emergent algorithm]]
* [[Evolutionary computation]]
* [[Glossary of artificial intelligence]]
* [[Machine learning]]
* [[Mathematical optimization]]
* [[Multi-agent system]]
* [[Robotic process automation]]
* [[Soft computing]]
* [[Weak AI]]
* [[Personality computing]]
{{colend}}

== Explanatory notes ==
{{notelist}}

== References ==
{{reflist|30em|refs=



<ref name="Definition of AI">
Definition of AI as the study of [[intelligent agents]]:
* {{Harvnb|Poole|Mackworth|Goebel|1998|loc=[http://people.cs.ubc.ca/~poole/ci/ch1.pdf p. 1]}}, which provides the version that is used in this article. Note that they use the term "computational intelligence" as a synonym for artificial intelligence.
* {{Harvtxt|Russell|Norvig|2003}} (who prefer the term "rational agent") and write "The whole-agent view is now widely accepted in the field" {{Harv|Russell|Norvig|2003|p=55}}.
* {{Harvnb|Nilsson|1998}}

* {{Harvnb|Legg|Hutter|2007}}.
</ref>





<ref name="McCorduck's thesis">
This is a central idea of [[Pamela McCorduck]]'s ''Machines Who Think''. She writes: "I like to think of artificial intelligence as the scientific apotheosis of a venerable cultural tradition." {{Harv|McCorduck|2004|p=34}} "Artificial intelligence in one form or another is an idea that has pervaded Western intellectual history, a dream in urgent need of being realized." {{Harv|McCorduck|2004|p=xviii}} "Our history is full of attempts—nutty, eerie, comical, earnest, legendary and real—to make artificial intelligences, to reproduce what is the essential us—bypassing the ordinary means. Back and forth between myth and reality, our imaginations supplying what our workshops couldn't, we have engaged for a long time in this odd form of self-reproduction." {{Harv|McCorduck|2004|p=3}} She traces the desire back to its [[Hellenistic]] roots and calls it the urge to "forge the Gods." {{Harv|McCorduck|2004|pp=340–400}}
</ref>

<ref name="Fragmentation of AI">
Pamela {{Harvtxt|McCorduck|2004|pp=424}} writes of "the rough shattering of AI in subfields—vision, natural language, decision theory, genetic algorithms, robotics ... and these with own sub-subfield—that would hardly have anything to say to each other."
</ref>

<ref name="Problems of AI">
This list of intelligent traits is based on the topics covered by the major AI textbooks, including:
* {{Harvnb|Russell|Norvig|2003}}
* {{Harvnb|Luger|Stubblefield|2004}}
* {{Harvnb|Poole|Mackworth|Goebel|1998}}
* {{Harvnb|Nilsson|1998}}
</ref>

<ref name="General intelligence">
General intelligence ([[artificial general intelligence|strong AI]]) is discussed in popular introductions to AI:
* {{Harvnb|Kurzweil|1999}} and {{Harvnb|Kurzweil|2005}}
</ref>



<ref name="AI in myth">
AI in myth:
* {{Harvnb|McCorduck|2004|pp=4–5}}
* {{Harvnb|Russell|Norvig|2003|p=939}}
</ref>

<ref name="AI in early science fiction">
AI in early science fiction.
* {{Harvnb|McCorduck|2004|pp=17–25}}
</ref>

<ref name="Formal reasoning">
Formal reasoning:
* {{cite book | first = David | last = Berlinski | year = 2000 | title =The Advent of the Algorithm| publisher = Harcourt Books |author-link=David Berlinski | isbn=0-15-601391-6 | oclc = 46890682}}
</ref>{{page needed|date=December 2016}}

<ref name="AI's immediate precursors">
AI's immediate precursors:
* {{Harvnb|McCorduck|2004|pp=51–107}}
* {{Harvnb|Crevier|1993|pp=27–32}}
* {{Harvnb|Russell|Norvig|2003|pp=15, 940}}
* {{Harvnb|Moravec|1988|p=3}}</ref>
See also {{slink|History of artificial intelligence|Cybernetics and early neural networks}}. Among the researchers who laid the foundations of AI were [[Alan Turing]], [[John von Neumann]], [[Norbert Wiener]], [[Claude Shannon]], [[Warren McCullough]], [[Walter Pitts]] and [[Donald Hebb]].<ref name="Dartmouth conference">
[[Dartmouth Workshop|Dartmouth conference]]:
* {{Harvnb|McCorduck|2004|pp=111–136}}
* {{Harvnb|Crevier|1993|pp=47–49}}, who writes "the conference is generally recognized as the official birthdate of the new science."
* {{Harvnb|Russell|Norvig|2003|p=17}}, who call the conference "the birth of artificial intelligence."
* {{Harvnb|NRC|1999|pp=200–201}}
</ref>

<ref name="Hegemony of the Dartmouth conference attendees">
Hegemony of the Dartmouth conference attendees:
* {{Harvnb|Russell|Norvig|2003|p=17}}, who write "for the next 20 years the field would be dominated by these people and their students."
* {{Harvnb|McCorduck|2004|pp=129–130}}
</ref>

<ref name="Golden years of AI">
"[[History of AI#The golden years 1956–1974|Golden years]]" of AI (successful symbolic reasoning programs 1956–1973):
* {{Harvnb|McCorduck|2004|pp=243–252}}
* {{Harvnb|Crevier|1993|pp=52–107}}
* {{Harvnb|Moravec|1988|p=9}}
* {{Harvnb|Russell|Norvig|2003|pp=18–21}}
The programs described are [[Arthur Samuel]]'s checkers program for the [[IBM 701]], [[Daniel Bobrow]]'s [[STUDENT (computer program)|STUDENT]], [[Allen Newell|Newell]] and [[Herbert A. Simon|Simon]]'s [[Logic Theorist]] and [[Terry Winograd]]'s [[SHRDLU]].
</ref>

<ref name="AI funding in the 60s">
[[DARPA]] pours money into undirected pure research into AI during the 1960s:
* {{Harvnb|McCorduck|2004|pp=131}}
* {{Harvnb|Crevier|1993|pp=51, 64–65}}
* {{Harvnb|NRC|1999|pp=204–205}}
</ref>

<ref name="AI in England">
AI in England:
* {{Harvnb|Howe|1994}}
</ref>

<ref name="Optimism of early AI">
Optimism of early AI:
* [[Herbert A. Simon|Herbert Simon]] quote: {{Harvnb|Simon|1965|p=96}} quoted in {{Harvnb|Crevier|1993|p=109}}.
* [[Marvin Minsky]] quote: {{Harvnb|Minsky|1967|p=2}} quoted in {{Harvnb|Crevier|1993|p=109}}.
</ref>

<ref name="First AI winter">
First [[AI Winter]], [[Mansfield Amendment]], [[Lighthill report]]
* {{Harvnb|Crevier|1993|pp=115–117}}
* {{Harvnb|Russell|Norvig|2003|p=22}}
* {{Harvnb|NRC|1999|pp=212–213}}
* {{Harvnb|Howe|1994}}
</ref>

<ref name="Expert systems">
Expert systems:
* {{Harvnb|ACM|1998|loc=I.2.1}}
* {{Harvnb|Russell|Norvig|2003|pp=22–24}}
* {{Harvnb|Luger|Stubblefield|2004|pp=227–331}}
* {{Harvnb|Nilsson|1998|loc=chpt. 17.4}}
* {{Harvnb|McCorduck|2004|pp=327–335, 434–435}}
* {{Harvnb|Crevier|1993|pp=145–62, 197–203}}
</ref>

<ref name="AI in the 80s">
Boom of the 1980s: rise of [[expert systems]], [[Fifth generation computer|Fifth Generation Project]], [[Alvey]], [[Microelectronics and Computer Technology Corporation|MCC]], [[Strategic Computing Initiative|SCI]]:
* {{Harvnb|McCorduck|2004|pp=426–441}}
* {{Harvnb|Crevier|1993|pp=161–162,197–203, 211, 240}}
* {{Harvnb|Russell|Norvig|2003|p=24}}
* {{Harvnb|NRC|1999|pp=210–211}}
</ref>

<ref name="Second AI winter">
Second [[AI winter]]:
* {{Harvnb|McCorduck|2004|pp=430–435}}
* {{Harvnb|Crevier|1993|pp=209–210}}
* {{Harvnb|NRC|1999|pp=214–216}}
</ref>

<ref name="Formal methods in AI">
Formal methods are now preferred ("Victory of the [[neats vs. scruffies|neats]]"):
* {{Harvnb|Russell|Norvig|2003|pp=25–26}}
* {{Harvnb|McCorduck|2004|pp=486–487}}
</ref>

<ref name="AI widely used">
AI applications widely used behind the scenes:
* {{Harvnb|Russell|Norvig|2003|p=28}}
* {{Harvnb|Kurzweil|2005|p=265}}
* {{Harvnb|NRC|1999|pp=216–222}}
</ref>

<ref name="AI in 2000s">
AI becomes hugely successful in the early 21st century
* {{Harvnb|Clark|2015}}
</ref>



<ref name="Reasoning">
Problem solving, puzzle solving, game playing and deduction:
* {{Harvnb|Russell|Norvig|2003|loc=chpt. 3–9}},
* {{Harvnb|Poole|Mackworth|Goebel|1998|loc=chpt. 2,3,7,9}},
* {{Harvnb|Luger|Stubblefield|2004|loc=chpt. 3,4,6,8}},
* {{Harvnb|Nilsson|1998|loc=chpt. 7–12}}
</ref>

<ref name="Uncertain reasoning">
Uncertain reasoning:
* {{Harvnb|Russell|Norvig|2003|pp=452–644}},
* {{Harvnb|Poole|Mackworth|Goebel|1998|pp=345–395}},
* {{Harvnb|Luger|Stubblefield|2004|pp=333–381}},
* {{Harvnb|Nilsson|1998|loc=chpt. 19}}
</ref>

<ref name="Intractability">
[[Intractably|Intractability and efficiency]] and the [[combinatorial explosion]]:
* {{Harvnb|Russell|Norvig|2003|pp=9, 21–22}}
</ref>

<ref name="Psychological evidence of sub-symbolic reasoning">
Psychological evidence of sub-symbolic reasoning:
* {{Harvtxt|Wason|Shapiro|1966}} showed that people do poorly on completely abstract problems, but if the problem is restated to allow the use of intuitive [[social intelligence]], performance dramatically improves. (See [[Wason selection task]])
* {{Harvtxt|Kahneman|Slovic|Tversky|1982}} have shown that people are terrible at elementary problems that involve uncertain reasoning. (See [[list of cognitive biases]] for several examples).
* {{Harvtxt|Lakoff|Núñez|2000}} have controversially argued that even our skills at mathematics depend on knowledge and skills that come from "the body", i.e. sensorimotor and perceptual skills. (See [[Where Mathematics Comes From]])
</ref>

<ref name="Knowledge representation">
[[Knowledge representation]]:
* {{Harvnb|ACM|1998|loc=I.2.4}},
* {{Harvnb|Russell|Norvig|2003|pp=320–363}},
* {{Harvnb|Poole|Mackworth|Goebel|1998|pp=23–46, 69–81, 169–196, 235–277, 281–298, 319–345}},
* {{Harvnb|Luger|Stubblefield|2004|pp=227–243}},
* {{Harvnb|Nilsson|1998|loc=chpt. 18}}
</ref>

<ref name="Knowledge engineering">
[[Knowledge engineering]]:
* {{Harvnb|Russell|Norvig|2003|pp=260–266}},
* {{Harvnb|Poole|Mackworth|Goebel|1998|pp=199–233}},
* {{Harvnb|Nilsson|1998|loc=chpt. ≈17.1–17.4}}
</ref>

<ref name="Representing categories and relations">
Representing categories and relations: [[Semantic network]]s, [[description logic]]s, [[inheritance (computer science)|inheritance]] (including [[frame (artificial intelligence)|frames]] and [[scripts (artificial intelligence)|scripts]]):
* {{Harvnb|Russell|Norvig|2003|pp=349–354}},
* {{Harvnb|Poole|Mackworth|Goebel|1998|pp=174–177}},
* {{Harvnb|Luger|Stubblefield|2004|pp=248–258}},
* {{Harvnb|Nilsson|1998|loc=chpt. 18.3}}
</ref>

<ref name="Representing time">
Representing events and time:[[Situation calculus]], [[event calculus]], [[fluent calculus]] (including solving the [[frame problem]]):
* {{Harvnb|Russell|Norvig|2003|pp=328–341}},
* {{Harvnb|Poole|Mackworth|Goebel|1998|pp=281–298}},
* {{Harvnb|Nilsson|1998|loc=chpt. 18.2}}
</ref>

<ref name="Representing causation">
[[Causality#Causal calculus|Causal calculus]]:
* {{Harvnb|Poole|Mackworth|Goebel|1998|pp=335–337}}
</ref>

<ref name="Representing knowledge about knowledge">
Representing knowledge about knowledge: [[Belief calculus]], [[modal logic]]s:
* {{Harvnb|Russell|Norvig|2003|pp=341–344}},
* {{Harvnb|Poole|Mackworth|Goebel|1998|pp=275–277}}
</ref>

<ref name="Ontology">
[[Ontology (computer science)|Ontology]]:
* {{Harvnb|Russell|Norvig|2003|pp=320–328}}
</ref>

<ref name="Qualification problem">
[[Qualification problem]]:
* {{Harvnb|McCarthy|Hayes|1969}}
* {{Harvnb|Russell|Norvig|2003}}{{Page needed|date=February 2011}}
While McCarthy was primarily concerned with issues in the logical representation of actions, {{Harvnb|Russell|Norvig|2003}} apply the term to the more general issue of default reasoning in the vast network of assumptions underlying all our commonsense knowledge.
</ref>

<ref name="Default reasoning and non-monotonic logic">
Default reasoning and [[default logic]], [[non-monotonic logic]]s, [[circumscription (logic)|circumscription]], [[closed world assumption]], [[abductive reasoning|abduction]] (Poole ''et al.'' places abduction under "default reasoning". Luger ''et al.'' places this under "uncertain reasoning"):
* {{Harvnb|Russell|Norvig|2003|pp=354–360}},
* {{Harvnb|Poole|Mackworth|Goebel|1998|pp=248–256, 323–335}},
* {{Harvnb|Luger|Stubblefield|2004|pp=335–363}},
* {{Harvnb|Nilsson|1998|loc=~18.3.3}}
</ref>

<ref name="Breadth of commonsense knowledge">
Breadth of commonsense knowledge:
* {{Harvnb|Russell|Norvig|2003|p=21}},
* {{Harvnb|Crevier|1993|pp=113–114}},
* {{Harvnb|Moravec|1988|p=13}},
* {{Harvnb|Lenat|Guha|1989}} (Introduction)
</ref>

<ref name="Intuition">
Expert knowledge as [[embodied cognition|embodied]] intuition:
* {{Harvnb|Dreyfus|Dreyfus|1986}} ([[Hubert Dreyfus]] is a philosopher and critic of AI who was among the first to argue that most useful human knowledge was encoded sub-symbolically. See [[Dreyfus' critique of AI]])
* {{Harvnb|Gladwell|2005}} (Gladwell's ''[[Blink (book)|Blink]]'' is a popular introduction to sub-symbolic reasoning and knowledge.)
* {{Harvnb|Hawkins|Blakeslee|2005}} (Hawkins argues that sub-symbolic knowledge should be the primary focus of AI research.)
</ref>

<ref name="Planning">
[[automated planning and scheduling|Planning]]:
* {{Harvnb|ACM|1998|loc=~I.2.8}},
* {{Harvnb|Russell|Norvig|2003|pp= 375–459}},
* {{Harvnb|Poole|Mackworth|Goebel|1998|pp=281–316}},
* {{Harvnb|Luger|Stubblefield|2004|pp=314–329}},
* {{Harvnb|Nilsson|1998|loc=chpt. 10.1–2, 22}}
</ref>

<ref name="Information value theory">
[[Applied information economics|Information value theory]]:
* {{Harvnb|Russell|Norvig|2003|pp=600–604}}
</ref>

<ref name="Classical planning">
Classical planning:
* {{Harvnb|Russell|Norvig|2003|pp=375–430}},
* {{Harvnb|Poole|Mackworth|Goebel|1998|pp=281–315}},
* {{Harvnb|Luger|Stubblefield|2004|pp=314–329}},
* {{Harvnb|Nilsson|1998|loc=chpt. 10.1–2, 22}}
</ref>

<ref name="Non-deterministic planning">
Planning and acting in non-deterministic domains: conditional planning, execution monitoring, replanning and continuous planning:
* {{Harvnb|Russell|Norvig|2003|pp=430–449}}
</ref>

<ref name="Multi-agent planning">
Multi-agent planning and emergent behavior:
* {{Harvnb|Russell|Norvig|2003|pp=449–455}}
</ref>

<ref name="Machine learning">
[[machine learning|Learning]]:
* {{Harvnb|ACM|1998|loc=I.2.6}},
* {{Harvnb|Russell|Norvig|2003|pp=649–788}},
* {{Harvnb|Poole|Mackworth|Goebel|1998|pp=397–438}},
* {{Harvnb|Luger|Stubblefield|2004|pp=385–542}},
* {{Harvnb|Nilsson|1998|loc=chpt. 3.3, 10.3, 17.5, 20}}
</ref>

<ref name="Reinforcement learning">
[[Reinforcement learning]]:
* {{Harvnb|Russell|Norvig|2003|pp=763–788}}
* {{Harvnb|Luger|Stubblefield|2004|pp=442–449}}
</ref>

<ref name="Natural language processing">
[[Natural language processing]]:
* {{Harvnb|ACM|1998|loc=I.2.7}}
* {{Harvnb|Russell|Norvig|2003|pp=790–831}}
* {{Harvnb|Poole|Mackworth|Goebel|1998|pp=91–104}}
* {{Harvnb|Luger|Stubblefield|2004|pp=591–632}}
</ref>

<ref name="Applications of natural language processing">
Applications of natural language processing, including [[information retrieval]] (i.e. [[text mining]]) and [[machine translation]]:
* {{Harvnb|Russell|Norvig|2003|pp=840–857}},
* {{Harvnb|Luger|Stubblefield|2004|pp=623–630}}
</ref>

<ref name="Robotics">
[[Robotic]]s:
* {{Harvnb|ACM|1998|loc=I.2.9}},
* {{Harvnb|Russell|Norvig|2003|pp=901–942}},
* {{Harvnb|Poole|Mackworth|Goebel|1998|pp=443–460}}
</ref>

<ref name="Configuration space">
Moving and [[Configuration space (physics)|configuration space]]:
* {{Harvnb|Russell|Norvig|2003|pp=916–932}}
</ref>

<ref name="Robotic mapping">
[[Robotic mapping]] (localization, etc):
* {{Harvnb|Russell|Norvig|2003|pp=908–915}}
</ref>

<ref name="Machine perception">
[[Machine perception]]:
* {{Harvnb|Russell|Norvig|2003|pp=537–581, 863–898}}
* {{Harvnb|Nilsson|1998|loc=~chpt. 6}}
</ref>

<ref name="Computer vision">
[[Computer vision]]:
* {{Harvnb|ACM|1998|loc=I.2.10}}
* {{Harvnb|Russell|Norvig|2003|pp=863–898}}
* {{Harvnb|Nilsson|1998|loc=chpt. 6}}
</ref>

<ref name="Speech recognition">
[[Speech recognition]]:
* {{Harvnb|ACM|1998|loc=~I.2.7}}
* {{Harvnb|Russell|Norvig|2003|pp=568–578}}
</ref>

<ref name="Object recognition">
[[Object recognition]]:
* {{Harvnb|Russell|Norvig|2003|pp=885–892}}
</ref>

<ref name="Emotion and affective computing">
Emotion and [[affective computing]]:
* {{Harvnb|Minsky|2006}}
</ref>

<ref name="Brain simulation">
[[Artificial brain]] arguments: AI requires a simulation of the operation of the human brain
* {{Harvnb|Russell|Norvig|2003|p=957}}
* {{Harvnb|Crevier|1993|pp=271 and 279}}
A few of the people who make some form of the argument:
* {{Harvnb|Moravec|1988}}
* {{Harvnb|Kurzweil|2005|p=262}}
* {{Harvnb|Hawkins|Blakeslee|2005}}
The most extreme form of this argument (the brain replacement scenario) was put forward by [[Clark Glymour]] in the mid-1970s and was touched on by [[Zenon Pylyshyn]] and [[John Searle]] in 1980.
</ref>





<ref name="Biological intelligence vs. intelligence in general">
Biological intelligence vs. intelligence in general:
* {{Harvnb|Russell|Norvig|2003|pp=2–3}}, who make the analogy with [[aeronautical engineering]].
* {{Harvnb|McCorduck|2004|pp=100–101}}, who writes that there are "two major branches of artificial intelligence: one aimed at producing intelligent behavior regardless of how it was accomplished, and the other aimed at modeling intelligent processes found in nature, particularly human ones."
* {{Harvnb|Kolata|1982}}, a paper in ''[[Science (journal)|Science]]'', which describes [[John McCarthy (computer scientist)|McCarthy's]] indifference to biological models. Kolata quotes McCarthy as writing: "This is AI, so we don't care if it's psychologically real"{{cite web |url=https://books.google.com/books?id=PEkqAAAAMAAJ}}. McCarthy recently reiterated his position at the [[AI@50]] conference where he said "Artificial intelligence is not, by definition, simulation of human intelligence" {{Harv|Maker|2006}}.
</ref>

<ref name="Neats vs. scruffies">
[[Neats vs. scruffies]]:
* {{Harvnb|McCorduck|2004|pp=421–424, 486–489}}
* {{Harvnb|Crevier|1993|pp=168}}
* {{Harvnb|Nilsson|1983|pp=10–11}}
</ref>

<ref name="Symbolic vs. sub-symbolic">
Symbolic vs. sub-symbolic AI:
* {{Harvtxt|Nilsson|1998|p=7}}, who uses the term "sub-symbolic".
</ref>

<ref name="GOFAI">
{{Harvnb|Haugeland|1985|pp=112–117}}
</ref>

<ref name="AI at CMU in the 60s">
Cognitive simulation, [[Allen Newell|Newell]] and [[Herbert A. Simon|Simon]], AI at [[Carnegie Mellon University|CMU]] (then called [[Carnegie Tech]]):
* {{Harvnb|McCorduck|2004|pp=139–179, 245–250, 322–323 (EPAM)}}
* {{Harvnb|Crevier|1993|pp=145–149}}
</ref>

<ref name="Soar">
[[Soar (cognitive architecture)|Soar]] (history):
* {{Harvnb|McCorduck|2004|pp=450–451}}
* {{Harvnb|Crevier|1993|pp=258–263}}
</ref>

<ref name="AI at Stanford in the 60s">
[[John McCarthy (computer scientist)|McCarthy]] and AI research at [[Stanford Artificial Intelligence Laboratory|SAIL]] and [[SRI International]]:
* {{Harvnb|McCorduck|2004|pp=251–259}}
* {{Harvnb|Crevier|1993}}
</ref>

<ref name="AI at Edinburgh and France in the 60s">
AI research at [[University of Edinburgh|Edinburgh]] and in France, birth of [[Prolog]]:
* {{Harvnb|Crevier|1993|pp=193–196}}
* {{Harvnb|Howe|1994}}
</ref>

<ref name="AI at MIT in the 60s">
AI at [[MIT]] under [[Marvin Minsky]] in the 1960s :
* {{Harvnb|McCorduck|2004|pp=259–305}}
* {{Harvnb|Crevier|1993|pp=83–102, 163–176}}
* {{Harvnb|Russell|Norvig|2003|p=19}}
</ref>

<ref name="Cyc">
[[Cyc]]:
* {{Harvnb|McCorduck|2004|p=489}}, who calls it "a determinedly scruffy enterprise"
* {{Harvnb|Crevier|1993|pp=239–243}}
* {{Harvnb|Russell|Norvig|2003|p=363−365}}
* {{Harvnb|Lenat|Guha|1989}}
</ref>

<ref name="Knowledge revolution">
Knowledge revolution:
* {{Harvnb|McCorduck|2004|pp=266–276, 298–300, 314, 421}}
* {{Harvnb|Russell|Norvig|2003|pp=22–23}}
</ref>

<ref name="Embodied AI">
[[Embodied agent|Embodied]] approaches to AI:
* {{Harvnb|McCorduck|2004|pp=454–462}}
* {{Harvnb|Brooks|1990}}
* {{Harvnb|Moravec|1988}}
</ref>

<ref name="Revival of connectionism">
Revival of [[connectionism]]:
* {{Harvnb|Crevier|1993|pp=214–215}}
* {{Harvnb|Russell|Norvig|2003|p=25}}
</ref>

<ref name="Computational intelligence">
[[Computational intelligence]]
* [http://www.ieee-cis.org/ IEEE Computational Intelligence Society] {{webarchive|url=https://web.archive.org/web/20080509191840/http://www.ieee-cis.org/ |date=9 May 2008 }}
</ref>

<ref name="Intelligent agents">
The [[intelligent agent]] paradigm:
* {{Harvnb|Russell|Norvig|2003|pp=27, 32–58, 968–972}}
* {{Harvnb|Poole|Mackworth|Goebel|1998|pp=7–21}}
* {{Harvnb|Luger|Stubblefield|2004|pp=235–240}}
* {{Harvnb|Hutter|2005|pp=125–126}}
The definition used in this article, in terms of goals, actions, perception and environment, is due to {{Harvtxt|Russell|Norvig|2003}}. Other definitions also include knowledge and learning as additional criteria.
</ref>

<ref name="Agent architectures">
[[Agent architecture]]s, [[hybrid intelligent system]]s:
* {{Harvtxt|Russell|Norvig|2003|pp=27, 932, 970–972}}
* {{Harvtxt|Nilsson|1998|loc=chpt. 25}}
</ref>

<ref name="Hierarchical control system">
[[Hierarchical control system]]:
* {{Harvnb|Albus|2002}}
</ref>



<ref name="Search">
[[Search algorithm]]s:
* {{Harvnb|Russell|Norvig|2003|pp=59–189}}
* {{Harvnb|Poole|Mackworth|Goebel|1998|pp=113–163}}
* {{Harvnb|Luger|Stubblefield|2004|pp=79–164, 193–219}}
* {{Harvnb|Nilsson|1998|loc=chpt. 7–12}}
</ref>

<ref name="Logic as search">
[[Forward chaining]], [[backward chaining]], [[Horn clause]]s, and logical deduction as search:
* {{Harvnb|Russell|Norvig|2003|pp=217–225, 280–294}}
* {{Harvnb|Poole|Mackworth|Goebel|1998|pp=~46–52}}
* {{Harvnb|Luger|Stubblefield|2004|pp=62–73}}
* {{Harvnb|Nilsson|1998|loc=chpt. 4.2, 7.2}}
</ref>

<ref name="Planning as search">
[[State space search]] and [[automated planning and scheduling|planning]]:
* {{Harvnb|Russell|Norvig|2003|pp=382–387}}
* {{Harvnb|Poole|Mackworth|Goebel|1998|pp=298–305}}
* {{Harvnb|Nilsson|1998|loc=chpt. 10.1–2}}
</ref>

<ref name="Uninformed search">
Uninformed searches ([[breadth first search]], [[depth first search]] and general [[state space search]]):
* {{Harvnb|Russell|Norvig|2003|pp=59–93}}
* {{Harvnb|Poole|Mackworth|Goebel|1998|pp=113–132}}
* {{Harvnb|Luger|Stubblefield|2004|pp=79–121}}
* {{Harvnb|Nilsson|1998|loc=chpt. 8}}
</ref>

<ref name="Informed search">
[[Heuristic]] or informed searches (e.g., greedy [[best-first search|best first]] and [[A*]]):
* {{Harvnb|Russell|Norvig|2003|pp= 94–109}},
* {{Harvnb|Poole|Mackworth|Goebel|1998|pp=pp. 132–147}},
* {{Harvnb|Luger|Stubblefield|2004|pp= 133–150}},
* {{Harvnb|Nilsson|1998|loc=chpt. 9}},
* {{Harvnb|Poole|Mackworth|2017|loc=Section 3.6}}
</ref>

<ref name="Optimization search">
[[optimization (mathematics)|Optimization]] searches:
* {{Harvnb|Russell|Norvig|2003|pp=110–116,120–129}}
* {{Harvnb|Poole|Mackworth|Goebel|1998|pp=56–163}}
* {{Harvnb|Luger|Stubblefield|2004|pp= 127–133}}
</ref>

<ref name="Society based learning">
[[Artificial life]] and society based learning:
* {{Harvnb|Luger|Stubblefield|2004|pp=530–541}}
</ref>

<ref name="Genetic programming">
[[Genetic programming]] and [[genetic algorithms]]:
* {{Harvnb|Luger|Stubblefield|2004|pp=509–530}},
* {{Harvnb|Nilsson|1998|loc=chpt. 4.2}},
* {{Harvnb|Holland|1975}},
* {{Harvnb|Koza|1992}},
* {{Harvnb|Poli|Langdon|McPhee|2008}}.
</ref>

<ref name="Logic">
[[Logic]]:
* {{Harvnb|ACM|1998|loc=~I.2.3}},
* {{Harvnb|Russell|Norvig|2003|pp=194–310}},
* {{Harvnb|Luger|Stubblefield|2004|pp=35–77}},
* {{Harvnb|Nilsson|1998|loc=chpt. 13–16}}
</ref>

<ref name="Satplan">
[[Satplan]]:
* {{Harvnb|Russell|Norvig|2003|pp=402–407}},
* {{Harvnb|Poole|Mackworth|Goebel|1998|pp=300–301}},
* {{Harvnb|Nilsson|1998|loc=chpt. 21}}
</ref>

<ref name="Symbolic learning techniques">
[[Explanation based learning]], [[relevance based learning]], [[inductive logic programming]], [[case based reasoning]]:
* {{Harvnb|Russell|Norvig|2003|pp=678–710}},
* {{Harvnb|Poole|Mackworth|Goebel|1998|pp=414–416}},
* {{Harvnb|Luger|Stubblefield|2004|pp=~422–442}},
* {{Harvnb|Nilsson|1998|loc=chpt. 10.3, 17.5}}
</ref>

<ref name="Propositional logic">
[[Propositional logic]]:
* {{Harvnb|Russell|Norvig|2003|pp=204–233}},
* {{Harvnb|Luger|Stubblefield|2004|pp=45–50}}
* {{Harvnb|Nilsson|1998|loc=chpt. 13}}
</ref>

<ref name="First-order logic">
[[First-order logic]] and features such as [[equality (mathematics)|equality]]:
* {{Harvnb|ACM|1998|loc=~I.2.4}},
* {{Harvnb|Russell|Norvig|2003|pp=240–310}},
* {{Harvnb|Poole|Mackworth|Goebel|1998|pp=268–275}},
* {{Harvnb|Luger|Stubblefield|2004|pp=50–62}},
* {{Harvnb|Nilsson|1998|loc=chpt. 15}}
</ref>

<ref name="Fuzzy logic">
[[Fuzzy logic]]:
* {{Harvnb|Russell|Norvig|2003|pp=526–527}}
</ref>

<ref name="Stochastic methods for uncertain reasoning">
Stochastic methods for uncertain reasoning:
* {{Harvnb|ACM|1998|loc=~I.2.3}},
* {{Harvnb|Russell|Norvig|2003|pp=462–644}},
* {{Harvnb|Poole|Mackworth|Goebel|1998|pp=345–395}},
* {{Harvnb|Luger|Stubblefield|2004|pp=165–191, 333–381}},
* {{Harvnb|Nilsson|1998|loc=chpt. 19}}
</ref>

<ref name="Bayesian networks">
[[Bayesian network]]s:
* {{Harvnb|Russell|Norvig|2003|pp=492–523}},
* {{Harvnb|Poole|Mackworth|Goebel|1998|pp=361–381}},
* {{Harvnb|Luger|Stubblefield|2004|pp=~182–190, ≈363–379}},
* {{Harvnb|Nilsson|1998|loc=chpt. 19.3–4}}
</ref>

<ref name="Bayesian inference">
[[Bayesian inference]] algorithm:
* {{Harvnb|Russell|Norvig|2003|pp=504–519}},
* {{Harvnb|Poole|Mackworth|Goebel|1998|pp=361–381}},
* {{Harvnb|Luger|Stubblefield|2004|pp=~363–379}},
* {{Harvnb|Nilsson|1998|loc=chpt. 19.4 & 7}}
</ref>

<ref name="Bayesian learning">
[[Bayesian learning]] and the [[expectation-maximization algorithm]]:
* {{Harvnb|Russell|Norvig|2003|pp=712–724}},
* {{Harvnb|Poole|Mackworth|Goebel|1998|pp=424–433}},
* {{Harvnb|Nilsson|1998|loc=chpt. 20}}
</ref>

<ref name="Bayesian decision networks">
[[Bayesian decision theory]] and Bayesian [[decision network]]s:
* {{Harvnb|Russell|Norvig|2003|pp=597–600}}
</ref>

<ref name="Stochastic temporal models">
Stochastic temporal models:
* {{Harvnb|Russell|Norvig|2003|pp=537–581}}
[[Dynamic Bayesian network]]s:
* {{Harvnb|Russell|Norvig|2003|pp=551–557}}
[[Hidden Markov model]]:
* {{Harv|Russell|Norvig|2003|pp=549–551}}
[[Kalman filter]]s:
* {{Harvnb|Russell|Norvig|2003|pp=551–557}}
</ref>

<ref name="Decisions theory and analysis">
[[decision theory]] and [[decision analysis]]:
* {{Harvnb|Russell|Norvig|2003|pp=584–597}},
* {{Harvnb|Poole|Mackworth|Goebel|1998|pp=381–394}}
</ref>

<ref name="Markov decision process" >
[[Markov decision process]]es and dynamic [[decision network]]s:
* {{Harvnb|Russell|Norvig|2003|pp=613–631}}
</ref>

<ref name="Game theory and mechanism design">
[[Game theory]] and [[mechanism design]]:
* {{Harvnb|Russell|Norvig|2003|pp=631–643}}
</ref>

<ref name="Classifiers">
Statistical learning methods and [[classifier (mathematics)|classifiers]]:
* {{Harvnb|Russell|Norvig|2003|pp=712–754}},
* {{Harvnb|Luger|Stubblefield|2004|pp=453–541}}
</ref>

<ref name="Kernel methods">
[[kernel methods]] such as the [[support vector machine]]:
* {{Harvnb|Russell|Norvig|2003|pp=749–752}}
</ref>

<ref name="K-nearest neighbor algorithm">
[[K-nearest neighbor algorithm]]:
* {{Harvnb|Russell|Norvig|2003|pp=733–736}}
</ref>

<ref name="Gaussian mixture model">
[[Gaussian mixture model]]:
* {{Harvnb|Russell|Norvig|2003|pp=725–727}}
</ref>

<ref name="Naive Bayes classifier">
[[Naive Bayes classifier]]:
* {{Harvnb|Russell|Norvig|2003|pp=718}}
</ref>

<ref name="Decision tree">
[[Alternating decision tree|Decision tree]]:
* {{Harvnb|Russell|Norvig|2003|pp=653–664}},
* {{Harvnb|Poole|Mackworth|Goebel|1998|pp=403–408}},
* {{Harvnb|Luger|Stubblefield|2004|pp=408–417}}
</ref>

<ref name="Classifier performance" >
Classifier performance:
* {{Harvnb|van der Walt|Bernard|2006}}
</ref>

<ref name="Neural networks">
Neural networks and connectionism:
* {{Harvnb|Russell|Norvig|2003|pp=736–748}},
* {{Harvnb|Poole|Mackworth|Goebel|1998|pp=408–414}},
* {{Harvnb|Luger|Stubblefield|2004|pp=453–505}},
* {{Harvnb|Nilsson|1998|loc=chpt. 3}}
</ref>

<ref name="Backpropagation">
[[Backpropagation]]:
* {{Harvnb|Russell|Norvig|2003|pp=744–748}},
* {{Harvnb|Luger|Stubblefield|2004|pp=467–474}},
* {{Harvnb|Nilsson|1998|loc=chpt. 3.3}}
</ref>

<ref name="Feedforward neural networks">
[[Feedforward neural network]]s, [[perceptron]]s and [[radial basis network]]s:
* {{Harvnb|Russell|Norvig|2003|pp=739–748, 758}}
* {{Harvnb|Luger|Stubblefield|2004|pp=458–467}}
</ref>

<ref name="Recurrent neural networks">
[[Recurrent neural networks]], [[Hopfield nets]]:
* {{Harvnb|Russell|Norvig|2003|p=758}}
* {{Harvnb|Luger|Stubblefield|2004|pp=474–505}}
</ref>

<ref name="Learning in neural networks">
[[Competitive learning]], [[Hebbian theory|Hebbian]] coincidence learning, [[Hopfield network]]s and attractor networks:
* {{Harvnb|Luger|Stubblefield|2004|pp=474–505}}
</ref>

<ref name="Hierarchical temporal memory">
[[Hierarchical temporal memory]]:
* {{Harvnb|Hawkins|Blakeslee|2005}}
</ref>





<ref name="Turing test">
The [[Turing test]]:<br>
Turing's original publication:
* {{Harvnb|Turing|1950}}
Historical influence and philosophical implications:
* {{Harvnb|Haugeland|1985|pp=6–9}}
* {{Harvnb|Crevier|1993|p=24}}
* {{Harvnb|McCorduck|2004|pp=70–71}}
* {{Harvnb|Russell|Norvig|2003|pp=2–3 and 948}}
</ref>



<ref name="Mathematical definitions of intelligence">
Mathematical definitions of intelligence:
* {{harvnb|Hernandez-Orallo|2000}}
* {{harvnb|Dowe|Hajek|1997}}
* {{harvnb|Hernandez-Orallo|Dowe|2010}}
</ref>





<ref name="Dartmouth proposal">
Dartmouth proposal:
* {{Harvnb|McCarthy|Minsky|Rochester|Shannon|1955}} (the original proposal)
* {{Harvnb|Crevier|1993|p=49}} (historical significance)
</ref>

<ref name="Physical symbol system hypothesis">
The [[physical symbol system]]s hypothesis:
* {{Harvnb|Newell|Simon|1976|p=116}}
* {{Harvnb|McCorduck|2004|p=153}}
* {{Harvnb|Russell|Norvig|2003|p=18}}
</ref>

<ref name="Dreyfus' critique">
[[Dreyfus' critique of artificial intelligence]]:
* {{Harvnb|Dreyfus|1972}}, {{Harvnb|Dreyfus|Dreyfus|1986}}
* {{Harvnb|Crevier|1993|pp=120–132}}
* {{Harvnb|McCorduck|2004|pp=211–239}}
* {{Harvnb|Russell|Norvig|2003|pp=950–952}},
</ref>

<ref name="Gödel himself">
{{Harvnb|Gödel|1951}}: in this lecture, [[Kurt Gödel]] uses the incompleteness theorem to arrive at the following disjunction: (a) the human mind is not a consistent finite machine, or (b) there exist [[Diophantine equations]] for which it cannot decide whether solutions exist. Gödel finds (b) implausible, and thus seems to have believed the human mind was not equivalent to a finite machine, i.e., its power exceeded that of any finite machine. He recognized that this was only a conjecture, since one could never disprove (b). Yet he considered the disjunctive conclusion to be a "certain fact".
</ref>

<ref name="The mathematical objection">
The Mathematical Objection:
* {{Harvnb|Russell|Norvig|2003|p=949}}
* {{Harvnb|McCorduck|2004|pp=448–449}}
Making the Mathematical Objection:
* {{Harvnb|Lucas|1961}}
* {{Harvnb|Penrose|1989}}
Refuting Mathematical Objection:
* {{Harvnb|Turing|1950}} under "(2) The Mathematical Objection"
* {{Harvnb|Hofstadter|1979}}
Background:
* {{Harvnb|Ref=none|Gödel|1931}}, {{Harvnb|Ref=none|Church|1936}}, {{Harvnb|Ref=none|Kleene|1935}}, {{Harvnb|Ref=none|Turing|1937}}
</ref>

<ref name="Searle's strong AI">
This version is from {{Harvtxt|Searle|1999}}, and is also quoted in {{Harvnb|Dennett|1991|p=435}}. Searle's original formulation was "The appropriately programmed computer really is a mind, in the sense that computers given the right programs can be literally said to understand and have other cognitive states." {{Harv|Searle|1980|p=1}}. Strong AI is defined similarly by {{Harvtxt|Russell|Norvig|2003|p=947}}: "The assertion that machines could possibly act intelligently (or, perhaps better, act as if they were intelligent) is called the 'weak AI' hypothesis by philosophers, and the assertion that machines that do so are actually thinking (as opposed to simulating thinking) is called the 'strong AI' hypothesis."
</ref>

<ref name="Chinese room">
Searle's [[Chinese room]] argument:
* {{Harvnb|Searle|1980}}. Searle's original presentation of the thought experiment.
* {{Harvnb|Searle|1999}}.
Discussion:
* {{Harvnb|Russell|Norvig|2003|pp=958–960}}
* {{Harvnb|McCorduck|2004|pp=443–445}}
* {{Harvnb|Crevier|1993|pp=269–271}}
</ref>



<ref name="Robot rights">
[[Robot rights]]:
* {{Harvnb|Russell|Norvig|2003|p=964}}
* {{Harvnb|''BBC News''|2006}}
Prematurity of:
* {{Harvnb|Henderson|2007}}
In fiction:
* {{Harvtxt|McCorduck|2004|p=190-25}} discusses ''[[Frankenstein]]'' and identifies the key ethical issues as scientific hubris and the suffering of the monster, i.e. [[robot rights]].
</ref>



<ref name="Weizenbaum's critique">
[[Joseph Weizenbaum]]'s critique of AI:
* {{Harvnb|Weizenbaum|1976}}
* {{Harvnb|Crevier|1993|pp=132–144}}
* {{Harvnb|McCorduck|2004|pp=356–373}}
* {{Harvnb|Russell|Norvig|2003|p=961}}
Weizenbaum (the AI researcher who developed the first [[chatterbot]] program, [[ELIZA]]) argued in 1976 that the misuse of artificial intelligence has the potential to devalue human life.
</ref>

<ref name=Singularity>
[[Technological singularity]]:
* {{Harvnb|Vinge|1993}}
* {{Harvnb|Kurzweil|2005}}
* {{Harvnb|Russell|Norvig|2003|p=963}}
</ref>

<ref name="recurse">
{{Cite conference | last = Omohundro|first= Steve| author-link= Steve Omohundro | year = 2008| title= The Nature of Self-Improving Artificial Intelligence| publisher= presented and distributed at the 2007 Singularity Summit, San Francisco, CA.}}
</ref>

<ref name="Transhumanism">
[[Transhumanism]]:
* {{Harvnb|Moravec|1988}}
* {{Harvnb|Kurzweil|2005}}
* {{Harvnb|Russell|Norvig|2003|p=963}}
</ref>

<ref name="AI as evolution">
AI as evolution:
* [[Edward Fredkin]] is quoted in {{Harvtxt|McCorduck|2004|p=401}}.
* {{Harvnb|Butler|1863}}
* {{Harvnb|Dyson|1998}}
</ref>
}}

=== AI textbooks ===
{{refbegin}}
* {{cite book |ref=harv
| last=Hutter |first=Marcus |author-link=Marcus Hutter |year=2005
| title=[[AIXI|Universal Artificial Intelligence]]
| isbn=978-3-540-22139-5
| publisher=Springer
| location=Berlin
}}
* {{cite book |ref=harv
| last=Jackson |first=Philip |author-link=Philip C. Jackson, Jr. |year=1985
| title=Introduction to Artificial Intelligence
| isbn=0-486-24864-X
| publisher=Dover |edition=2nd
}}
* {{cite book |ref=harv
| last1=Luger |first1=George |author-link=George Luger
| last2=Stubblefield |first2=William |author2-link=William Stubblefield
| year=2004
| title=Artificial Intelligence: Structures and Strategies for Complex Problem Solving
| publisher=Benjamin/Cummings |edition=5th
| isbn=0-8053-4780-1
| url=http://www.cs.unm.edu/~luger/ai-final/tocfull.html
}}
* {{cite book
| last=Neapolitan |first=Richard |last2=Jiang |first2=Xia |year=2018|authorlink1=Richard Neapolitan
| title=Artificial Intelligence: With an Introduction to Machine Learning
| publisher=Chapman & Hall/CRC
| isbn= 978-1-13850-238-3
| url=https://www.crcpress.com/Contemporary-Artificial-Intelligence-Second-Edition/Neapolitan-Jiang/p/book/9781138502383
}}
* {{cite book |ref=harv
| last=Nilsson |first=Nils |author-link=Nils Nilsson (researcher) |year=1998
| title=Artificial Intelligence: A New Synthesis
| publisher=Morgan Kaufmann
| isbn=978-1-55860-467-4
}}
* {{Russell Norvig 2003}}.
* {{Cite book |ref=harv
| first = Stuart J.
| last = Russell
| first2 = Peter
| last2 = Norvig
| title = [[Artificial Intelligence: A Modern Approach]] 
| year = 2009
| edition = 3rd
| publisher = Prentice Hall
| publication-place = Upper Saddle River, New Jersey
| isbn = 0-13-604259-7
| author-link=Stuart J. Russell
| author2-link=Peter Norvig
| pages=
}}.
* {{cite book |ref=harv
| first1=David |last1=Poole |author-link=David Poole (researcher)
| first2=Alan |last2=Mackworth |author2-link=Alan Mackworth
| first3=Randy |last3=Goebel |author3-link=Randy Goebel
| year=1998
| title=Computational Intelligence: A Logical Approach
| publisher=Oxford University Press |location = New York
| isbn=0-19-510270-3
| url=http://www.cs.ubc.ca/spider/poole/ci.html
}}
* {{cite book
| last=Winston |first=Patrick Henry |author-link=Patrick Winston |year=1984
| title=Artificial Intelligence
| publisher=Addison-Wesley |location=Reading, MA
| isbn=0-201-08259-4
}}
* {{cite book
| last=Rich |first=Elaine |author-link=Elaine Rich |year=1983
| title=Artificial Intelligence
| publisher = McGraw-Hill
| isbn=0-07-052261-8
}}
* {{cite book
| last=Bundy |first=Alan |author-link=Alan Bundy |year=1980
| title=Artificial Intelligence: An Introductory Course
| publisher = Edinburgh University Press|edition=2nd
| isbn=0-85224-410-X
}}
* {{cite book |ref=harv
|first1=David |last1=Poole |author-link=David Poole (researcher)
|first2=Alan |last2=Mackworth |author2-link=Alan Mackworth
|year=2017
|title=Artificial Intelligence: Foundations of Computational Agents
|publisher = Cambridge University Press|edition=2nd
|isbn=9781107195394
|url=http://artint.info/index.html
}}
{{refend}}

=== History of AI ===
{{refbegin}}
* {{Crevier 1993}}.
* {{McCorduck 2004}}.
* {{cite book
| last=Newquist |first=HP |author-link=HP Newquist |year=1994
| title=The Brain Makers: Genius, Ego, And Greed In The Quest For Machines That Think
| publisher=Macmillan/SAMS |location=New York
| isbn= 0-672-30412-0
}}
* {{cite book
| last=Nilsson |first=Nils |author-link=Nils Nilsson (researcher) |year=2009
| title=The Quest for Artificial Intelligence: A History of Ideas and Achievements
| publisher=Cambridge University Press |location=New York
| isbn=978-0-521-12293-1
}}
{{refend}}

=== Other sources ===
{{refbegin|colwidth=60em}}
* {{cite journal|ref=harv |last1=Asada |first1=M. |last2=Hosoda |first2=K. |last3=Kuniyoshi |first3=Y. |last4=Ishiguro |first4=H. |last5=Inui |first5=T. |last6=Yoshikawa |first6=Y. |last7=Ogino |first7=M. |last8=Yoshida |first8=C. |year=2009 |title=Cognitive developmental robotics: a survey |journal=IEEE Transactions on Autonomous Mental Development |volume=1 |issue=1 |pages=12–34 |url=http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=4895715&url=http%3A%2F%2Fieeexplore.ieee.org%2Fiel5%2F4563672%2F5038478%2F04895715.pdf%3Farnumber%3D4895715 |doi=10.1109/tamd.2009.2021702 |deadurl=yes |archiveurl=https://web.archive.org/web/20131004222242/http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=4895715&url=http%3A%2F%2Fieeexplore.ieee.org%2Fiel5%2F4563672%2F5038478%2F04895715.pdf%3Farnumber%3D4895715 |archivedate=4 October 2013}}
* {{cite web
|ref = {{harvid|ACM|1998}}
|publisher = [[Association for Computing Machinery|ACM]]
|year = 1998
|title = ACM Computing Classification System: Artificial intelligence
|url = http://www.acm.org/class/1998/I.2.html
|accessdate = 30 August 2007
|deadurl = yes
|archiveurl = https://web.archive.org/web/20071012025921/http://www.acm.org/class/1998/I.2.html
|archivedate = 12 October 2007
|df = dmy-all
}}
* {{cite book |ref=harv
| last1=Goodman |first1=Joanna |author-link=Joanna Goodman
| year=2016
| title=Robots in Law: How Artificial Intelligence is Transforming Legal Services
| publisher=Ark Group |edition=1st
| isbn=978-1-78358-264-8
| url=https://www.ark-group.com/product/robots-law-how-artificial-intelligence-transforming-legal-services
}}
* {{cite encyclopedia
|ref=harv
|last=Albus
|first=J. S.
|year=2002
|pages=11–20
|title=4-D/RCS: A Reference Model Architecture for Intelligent Unmanned Ground Vehicles
|editor1-last=Gerhart
|editor-first=G.
|editor2-last=Gunderson
|editor2-first=R.
|editor3-last=Shoemaker
|editor3-first=C.
|encyclopedia=Proceedings of the SPIE AeroSense Session on Unmanned Ground Vehicle Technology
|volume=3693
|url=http://www.isd.mel.nist.gov/documents/albus/4DRCS.pdf
|archive-url=https://web.archive.org/web/20040725051856/http://www.isd.mel.nist.gov/documents/albus/4DRCS.pdf
|dead-url=yes
|archive-date=25 July 2004
|df=
}}
* {{Cite book
| last = Aleksander | first= Igor | authorlink = Igor Aleksander
| year=1995
| title= Artificial Neuroconsciousness: An Update
| publisher=IWANN
| url = http://www.ee.ic.ac.uk/research/neural/publications/iwann.html
| archiveurl = https://web.archive.org/web/19970302014628/http://www.ee.ic.ac.uk/research/neural/publications/iwann.html
| archivedate = 2 March 1997
| ref = harv
}} [http://dblp.uni-trier.de/rec/bibtex/conf/iwann/Aleksander95 BibTex] {{webarchive |url=https://web.archive.org/web/19970302014628/http://www.ee.ic.ac.uk/research/neural/publications/iwann.html |date=2 March 1997}}.
* {{cite encyclopedia |ref=harv
| last=Bach |first=Joscha |year=2008 |pages=63–74
| title=Seven Principles of Synthetic Intelligence
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| isbn=0-471-50306-1
}}
* {{cite book |ref=harv
| last=Simon | first = H. A. | author-link=Herbert A. Simon
| year = 1965
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}}
* {{cite web |ref=harv
| last=Skillings | first=Jonathan
| url=http://news.cnet.com/Getting-machines-to-think-like-us/2008-11394_3-6090207.html
| title=Getting Machines to Think Like Us
| work=cnet
| date=3 July 2006
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}}
* {{cite conference |ref=harv
| last=Solomonoff |first=Ray |authorlink=Ray Solomonoff |year=1956
| title=An Inductive Inference Machine
| conference=Dartmouth Summer Research Conference on Artificial Intelligence
| url=http://world.std.com/~rjs/indinf56.pdf |via=std.com, pdf scanned copy of the original
}} Later published as<br/>{{cite book
| last=Solomonoff |first=Ray |year=1957 |pages=56–62
| chapter=An Inductive Inference Machine
| title=IRE Convention Record |volume=Section on Information Theory, part 2
}}
* {{cite conference |ref=harv
| last=Tao |first=Jianhua |first2=Tieniu |last2=Tan |year=2005
| conference=Affective Computing: A Review
| booktitle=Affective Computing and Intelligent Interaction |volume=[[LNCS]] 3784 |pages=981–995
|publisher=Springer |doi=10.1007/11573548
}}
* {{cite journal |ref=harv
| last=Tecuci |first=Gheorghe |date=March–April 2012
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* {{cite book |ref=harv
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* {{Turing 1950}}.
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}}
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{{refend}}

== Further reading ==
* DH Autor, ‘Why Are There Still So Many Jobs? The History and Future of Workplace Automation’ (2015) 29(3) Journal of Economic Perspectives 3.
* TechCast Article Series, John Sagi, [https://web.archive.org/web/20150713184128/https://www.techcastglobal.com/documents/10193/34869/Consciousness-Sagifinalversion "Framing Consciousness"]
* [[Margaret Boden|Boden, Margaret]], ''Mind As Machine'', [[Oxford University Press]], 2006
* [[Pedro Domingos|Domingos, Pedro]], "Our Digital Doubles: AI will serve our species, not control it", ''[[Scientific American]]'', vol. 319, no. 3 (September 2018), pp. 88–93.
* [[Alison Gopnik|Gopnik, Alison]], "Making AI More Human: Artificial intelligence has staged a revival by starting to incorporate what we know about how children learn", ''[[Scientific American]]'', vol. 316, no. 6 (June 2017), pp. 60–65.
* Johnston, John (2008) ''The Allure of Machinic Life: Cybernetics, Artificial Life, and the New AI'', MIT Press
* [[Gary Marcus|Marcus, Gary]], "Am I Human?: Researchers need new ways to distinguish artificial intelligence from the natural kind", ''[[Scientific American]]'', vol. 316, no. 3 (March 2017), pp. 58–63. ''Multiple'' tests of artificial-intelligence efficacy are needed because, "just as there is no single test of [[Athletics (physical culture)|athletic]] prowess, there cannot be one ultimate test of [[intelligence]]." One such test, a "Construction Challenge", would test perception and physical action—"two important elements of intelligent behavior that were entirely absent from the original [[Turing test]]." Another proposal has been to give machines the same standardized tests of science and other disciplines that schoolchildren take. A so far insuperable stumbling block to artificial intelligence is an incapacity for reliable [[disambiguation]]. "[V]irtually every sentence [that people generate] is [[ambiguity|ambiguous]], often in multiple ways." A prominent example is known as the "pronoun disambiguation problem": a machine has no way of determining to whom or what a [[pronoun]] in a sentence—such as "he", "she" or "it"—refers.
* E McGaughey, 'Will Robots Automate Your Job Away? Full Employment, Basic Income, and Economic Democracy' (2018) [https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3044448 SSRN, part 2(3)].
* Myers, Courtney Boyd ed. (2009). [https://www.forbes.com/2009/06/22/singularity-robots-computers-opinions-contributors-artificial-intelligence-09_land.html "The AI Report"]. ''Forbes'' June 2009
* {{cite book
| last=Raphael |first=Bertram |author-link=Bertram Raphael |year=1976
| title=The Thinking Computer
| publisher = W.H.Freeman and Company
| isbn=0-7167-0723-3
}}
* {{cite journal | last1 = Serenko | first1 = Alexander | year = 2010 | title = The development of an AI journal ranking based on the revealed preference approach | url = http://www.aserenko.com/papers/JOI_Serenko_AI_Journal_Ranking_Published.pdf | format = PDF | journal = Journal of Informetrics | volume = 4 | issue = 4| pages = 447–459 | doi = 10.1016/j.joi.2010.04.001}}
* {{cite journal | last1 = Serenko | first1 = Alexander | author2=Michael Dohan | year = 2011 | title = Comparing the expert survey and citation impact journal ranking methods: Example from the field of Artificial Intelligence | url = http://www.aserenko.com/papers/JOI_AI_Journal_Ranking_Serenko.pdf | format = PDF | journal = Journal of Informetrics | volume = 5 | issue = 4| pages = 629–649 | doi = 10.1016/j.joi.2011.06.002}}
* Sun, R. & Bookman, L. (eds.), ''Computational Architectures: Integrating Neural and Symbolic Processes''. Kluwer Academic Publishers, Needham, MA. 1994.
* {{cite web
|url=http://www.technologyreview.com/news/533686/2014-in-computing-breakthroughs-in-artificial-intelligence/
|title=2014 in Computing: Breakthroughs in Artificial Intelligence
|author=Tom Simonite
|date=29 December 2014
|work=MIT Technology Review
|publisher=
|accessdate=
}}

== External links ==
{{Sister project links|voy=no|Artificial Intelligence}}
* [https://web.archive.org/web/20151118212402/http://www-formal.stanford.edu/jmc/whatisai/whatisai.html What Is AI?] – An introduction to artificial intelligence by [[John McCarthy (computer scientist)|John McCarthy]]—a co-founder of the field, and the person who coined the term.
* [https://archive.org/details/handbookofartific01barr/ The Handbook of Artificial Intelligence Volume Ⅰ by Avron Barr and Edward A. Feigenbaum (Stanford University)]
* {{IEP|art-inte|Artificial Intelligence}}
* {{cite SEP |url-id=logic-ai |title=Logic and Artificial Intelligence |last=Thomason |first=Richmond}}
* {{dmoz|Computers/Artificial_Intelligence/|AI}}
* [http://aitopics.org/ AITopics] – A large directory of links and other resources maintained by the [[Association for the Advancement of Artificial Intelligence]], the leading organization of academic AI researchers.
* [https://blog.standuply.com/200-ai-ml-conferences-in-2018-eec7d0a50bcf/ List of AI Conferences] – A list of 225 AI conferences taking place all over the world.
* [https://www.bbc.co.uk/programmes/p003k9fc Artificial Intelligence], BBC Radio 4 discussion with John Agar, Alison Adam & Igor Aleksander (''In Our Time'', Dec. 8, 2005)
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