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[[Image:p11 kasparov breakout.jpg|thumb|right|280px|[[Garry Kasparov]] playing against [[IBM Deep Blue|Deep Blue]], the first machine to win a chess match against a reigning world champion.]]
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[[Image:HONDA ASIMO.jpg|200px|thumb|right|Honda's intelligent humanoid robot]]
 
{{Redirect|AI}}
 
'''Artificial intelligence''' ('''AI''') is defined as [[intelligence (trait)|intelligence]] exhibited by an [[artificial]] entity. Such a system is generally assumed to be a [[computer]].
 
   
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The modern definition of '''artificial intelligence''' (or '''AI''') is "the study and design of [[intelligent agents]]" where an intelligent agent is a system that perceives its environment and takes actions which maximizes its chances of success.<ref>Textbooks that define AI this way include {{Harvnb|Poole|Mackworth|Goebel|1998|loc=[http://www.cs.ubc.ca/spider/poole/ci/ch1.pdf p. 1]}} and {{Harvnb|Russell|Norvig|2003|loc=[http://aima.cs.berkeley.edu/preface.html preface]}} (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}}</ref>
Although AI has a strong [[science fiction]] connotation, it forms a vital branch of [[computer science]], dealing with intelligent [[behavior]], [[learn]]ing and [[adaptation]] in [[machine]]s. [[Research]] in AI is concerned with producing machines to automate tasks requiring intelligent behavior. Examples include [[control system|control]], [[Automated planning and scheduling|planning and scheduling]], the ability to answer diagnostic and consumer questions, [[handwriting recognition|handwriting]], [[speech recognition|speech]], and [[facial recognition system|facial recognition]]. As such, it has become a [[scientific]] discipline, focused on providing solutions to real life problems. AI systems are now in routine use in [[economics]], [[medicine]], [[engineering]] and the [[military]], as well as being built into many common home computer [[Computer software|software]] applications, traditional strategy games like [[computer chess]] and other [[video games]].
 
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[[John McCarthy (computer scientist)|John McCarthy]], who coined the term in 1956,<ref>Although there is some controversy on this point (see {{Harvnb|Crevier|1993|p=50}}), [[John McCarthy|McCarthy]] states unequivocally "I came up with the term" in a c|net interview. (See [http://news.com.com/Getting+machines+to+think+like+us/2008-11394_3-6090207.html Getting Machines to Think Like Us].)</ref>
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defines it as "the science and engineering of making intelligent machines."<ref>See [http://www-formal.stanford.edu/jmc/whatisai/whatisai.html WHAT IS ARTIFICIAL INTELLIGENCE? by John McCarthy]</ref>
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Other names for the field have been proposed, such as [[computational intelligence]],<ref name="P1">{{Harvnb|Poole|Mackworth|Goebel|1998|loc=[http://www.cs.ubc.ca/spider/poole/ci/ch1.pdf p. 1]}}</ref>
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[[synthetic intelligence]]<ref name="P1"/><ref>{{Harvnb|Law|1994}}</ref>
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or computational rationality.<ref>{{Harvnb|Russell|Norvig|2003|p=17}}</ref>
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The term '''artificial intelligence''' is also used to describe a ''property'' of machines or programs: the [[intelligence (trait)|intelligence]] that the system demonstrates.
   
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AI research uses tools and insights from many fields, including [[computer science]], [[psychology]], [[philosophy]], [[neuroscience]], [[cognitive science]], [[computational linguistics|linguistics]], [[operations research]], [[computational economics|economics]], [[control theory]], [[probability]], [[optimization (mathematics)|optimization]] and [[logic]].<ref>{{Harvnb|Russell|Norvig|2003|pp=5–16}}</ref>
==Schools of thought==
 
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AI research also overlaps with tasks such as [[robotics]], [[control system]]s, [[automated planning and scheduling|scheduling]], [[data mining]], [[logistics]], [[speech recognition]], [[facial recognition system|facial recognition]] and many others.<ref>See [http://www.aaai.org/AITopics/html/applications.html AI Topics: applications]</ref>
   
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==Perspectives on AI==
AI divides roughly into two schools of thought: Conventional AI and [[Computational Intelligence]] (CI).
 
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===The rise and fall of AI in public perception===
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{{Main|History of artificial intelligence|Timeline of artificial intelligence}}
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{{See also|AI Winter}}
   
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The field was born at a [[Dartmouth Conferences|conference]] on the campus of [[Dartmouth College]] in the summer of 1956.<ref>{{Harvnb|Crevier|1993|pp=47–49}} and {{Harvnb|Russell|Norvig|2003|p=17}}</ref>
Conventional AI mostly involves methods now classified as [[machine learning]], characterized by [[formalism]] and [[statistical analysis]]. This is also known as [[symbolic]] AI, [[logical]] AI, [[Neats|neat AI]] and [[GOFAI|Good Old Fashioned Artificial Intelligence (GOFAI)]]. (Also see [[semantics]].) Methods include:
 
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Those who attended would become the leaders of AI research for many decades, especially [[John McCarthy (computer scientist)|John McCarthy]], [[Marvin Minsky]], [[Allen Newell]] and [[Herbert Simon]], who founded AI laboratories at [[MIT]], [[CMU]] and [[Stanford]]. They and their students wrote programs that were, to most people, simply astonishing:<ref>Russell and Norvig write "it was astonishing whenever a computer did anything kind of smartish." {{Harvnb|Russell|Norvig|2003|p=18}}</ref>
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computers were solving word problems in algebra, proving logical theorems and speaking English.<ref>{{Harvnb|Crevier|1993|pp=52–107}}, {{Harvnb|Moravec|1988|p=9}} and {{Harvnb|Russell|Norvig|2003|pp=18–21}}. The programs described are [[Daniel Bobrow]]'s [[STUDENT (computer program)|STUDENT]], [[Allen Newell|Newell]] and [[Herbert Simon|Simon]]'s [[Logic Theorist]] and [[Terry Winograd]]'s [[SHRDLU]].</ref>
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By the middle 60s their research was heavily funded by [[DARPA]],<ref>{{Harvnb|Crevier|1993|pp=64–65}}</ref>
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and they were optimistic about the future of the new field:
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*1965, [[H. A. Simon]]: "machines will be capable, within twenty years, of doing any work a man can do"<ref>{{Harvnb|Simon|1965|p=96}} quoted in {{Harvnb|Crevier|1993|p=109}}</ref>
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*1967, [[Marvin Minsky]]: "Within a generation ... the problem of creating 'artificial intelligence' will substantially be solved."<ref>{{Harvnb|Minsky|1967|p=2}} quoted in {{Harvnb|Crevier|1993|p=109}}</ref>
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These predictions, and many like them, would not come not true. They had failed to recognize the difficulty of some of the problems they faced: the lack of raw computer power,<ref>{{Harvnb|Crevier|1993|pp=146–148}}, {{Harvnb|Buchanan|2005|p=56}}, {{Harvnb|Moravec|1976}}</ref>
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the [[intractability|intractable]] [[combinatorial explosion]] of their algorithms,<ref>{{Harvnb|Russell|Norvig|2003|pp=9,21–22}}</ref>
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the difficulty of representing commonsense knowledge and doing [[commonsense reasoning]],<ref>{{Harvnb|Crevier|1993|pp=113–114}}, {{Harvnb|Moravec|1988|p=13}}, {{Harvnb|Lenat|1989}} (Introduction) and {{Harvnb|Russell|Norvig|2003|p=21}}</ref>
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the incredible difficulty of perception and motion<ref>{{Harvnb|Moravec|1988|pp=15–16}} and see [[Moravec's paradox]]</ref>
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and the failings of logic.<ref>{{Harvnb|McCarthy|Hayes|1969}}, {{Harvnb|Crevier|1993|pp=117–119}} and see the [[frame problem]], [[qualification problem]] and [[ramification problem]].</ref>
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In 1974, in response to the criticism of England's [[Sir James Lighthill]] and ongoing pressure from congress to fund more productive projects, [[DARPA]] cut off all undirected, exploratory research in AI. This was the first [[AI Winter]].<ref>{{Harvnb|Crevier|1993|pp=115–117}}, {{Harvnb|Russell|Norvig|2003|p=22}}, {{Harvnb|NRC|1999}} under "Shift to Applied Research Increases Investment." and also see Howe, J. [http://www.dai.ed.ac.uk/AI_at_Edinburgh_perspective.html ''"Artificial Intelligence at Edinburgh University : a Perspective"'']</ref>
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In the early 80s, the field was revived by the commercial success of [[expert systems]] and by 1985 the market for AI had reached more than a billion dollars.<ref>{{harvnb|Crevier|1993|pp=161–162,197–203}} and and {{Harvnb|Russell|Norvig|2003|p=24}}</ref>
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[[Marvin Minsky|Minsky]] and others warned the community that enthusiasm for AI had spiraled out of control and that disappointment was sure to follow.<ref>{{Harvnb|Crevier|1993|p=203}}</ref>
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[[Marvin Minsky|Minsky]] was right. Beginning with the collapse of the [[Lisp Machine]] market in 1987, AI once again fell into disrepute, and a second, more lasting [[AI Winter]] began.<ref>{{Harvnb|Crevier|1993|pp=209–210}}</ref>
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In the 90s AI achieved its greatest successes, albeit somewhat behind the scenes. Artificial intelligence was adopted throughout the technology industry, providing the heavy lifting for [[logistics]], [[data mining]], [[medical diagnosis]] and many other areas.<ref>{{Harvnb|Russell|Norvig|p=28}}, {{Harvnb|NRC|1999}} under "Artificial Intelligence in the 90s"</ref>
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The success was due to several factors: the incredible power of computers today (see [[Moore's law]]), a greater emphasis on solving specific subproblems, the creation of new ties between AI and other fields working on similar problems, and above all a new commitment by researchers to solid mathematical methods and rigorous scientific standards.<ref>{{Harvnb|Russell|Norvig|pp=25–26}}</ref>
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'''1961-65''' -- A.L.Samuel Developed a program which learned to play checkers at Masters level.
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'''1965''' -- J.A.Robinson introduced [[Resolution (logic)|resolution]] as an inference method in logic.
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'''1965''' -- Work on DENDRAL was begun at Stanford University by J.Lederberg, Edward Feigenbaum and Carl Djerassi. DENDRAL is an expert system which discovers molecule structure given only information of the constituents of the compound and mass spectra data. DENDRAL was the first knowledge-based expert system to be developed.
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'''1968''' -- Work on MACSYMA was initiated at MIT by Carl Engleman, William Martin and Joel Moses. MACSYMA is a large interactive program which solves numerous types of mathematical problems. Written in LISP, MACSYMA was a continuation of earlier work on SIN, an indefinite integration solving problem
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References on early work in AI include Pamela McCorduck's Machines Who Think (1979) and Newell and Simon's Human Problem Solving (1972).
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===The philosophy of AI===
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{{main|Philosophy of artificial intelligence|Ethics of artificial intelligence}}
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The [[philosophy of artificial intelligence|strong AI vs. weak AI]] debate ("can a man-made artifact be conscious?") is still a hot topic amongst AI [[philosopher]]s. This involves [[philosophy of mind]] and the [[mind-body problem]]. Most notably [[Roger Penrose]] in his book ''[[The Emperor's New Mind]]'' and [[John Searle]] with his "[[Chinese room]]" [[thought experiment]] argue that true [[consciousness]] cannot be achieved by [[formal logic]] systems, while [[Douglas Hofstadter]] in ''[[Gödel, Escher, Bach]]'' and [[Daniel Dennett]] in ''[[Consciousness Explained]]'' argue in favour of [[Functionalism (philosophy of mind)|functionalism]]. In many strong AI supporters' opinions, [[artificial consciousness]] is considered the [[holy grail]] of artificial intelligence. [[Edsger Dijkstra]] famously opined that the debate had little importance: "The question of whether a computer can think is no more interesting than the question of whether a submarine can swim."
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[[Epistemology]], the study of knowledge, also makes contact with AI, as engineers find themselves debating similar questions to philosophers about how, best to represent and use knowledge and information (e.g., [[semantic networks]]).
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This area is the focus of [[Philosophy of Artificial Intelligence and Cognitive Science]] Department at [[Sussex University]]
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===The future of AI===
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{{Main|Strong AI}}
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==AI research==
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===Problems of AI research===
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{{expert-subject|Computers}}
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===Approaches to AI research===
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{{expert-subject|Computers}}
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Conventional AI mostly involves methods now classified as [[machine learning]], characterized by [[formalism]] and [[statistical analysis]]. This is also known as symbolic AI, logical AI, [[Neats|neat AI]] and [[GOFAI|Good Old Fashioned Artificial Intelligence (GOFAI)]]. (Also see [[semantics]].) Methods include:
 
*[[Expert system]]s: apply reasoning capabilities to reach a conclusion. An expert system can process large amounts of known information and provide conclusions based on them.
 
*[[Expert system]]s: apply reasoning capabilities to reach a conclusion. An expert system can process large amounts of known information and provide conclusions based on them.
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*[[Case based reasoning]]: stores a set of problems and answers in an organized data structure called cases. A case based reasoning system upon being presented with a problem finds a case in its knowledge base that is most closely related to the new problem and presents its solutions as an output with suitable modifications.<ref>Hammond J, Kristian. ''Case-based planning: viewing planning as a memory task''. Academic Press Perspectives In Artificial Intelligence; Vol 1. Pages: 277. 1989. ISBN 0-12-322060-2</ref>
*[[Case based reasoning]]
 
 
*[[Bayesian network]]s
 
*[[Bayesian network]]s
*[[Behavior based AI]]: a modular method of building AI systems by hand.
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*[[Behavior based AI]]: a modular method of building AI systems by hand.
   
Computational Intelligence involves [[iterative]] development or learning (e.g. parameter tuning e.g. in [[connectionist]] systems). Learning is based on [[empirical]] data and is associated with non-symbolic AI, [[Scruffies|scruffy AI]] and [[soft computing]]. Methods mainly include:
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Computational intelligence involves [[iterative]] development or learning (e.g., parameter tuning in [[connectionist]] systems). Learning is based on [[empirical]] data and is associated with non-symbolic AI, [[Scruffies|scruffy AI]] and [[soft computing]]. Subjects in computational intelligence as defined by [http://www.ieee-cis.org/ IEEE Computational Intelligence Society] mainly include:
*[[Neural network]]s: systems with very strong [[pattern recognition]] capabilities.
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*[[Artificial neural network|Neural network]]s: trainable systems with very strong [[pattern recognition]] capabilities.
*[[Fuzzy system]]s: techniques for [[reasoning under uncertainty]], has been widely used in modern industrial and consumer product control systems.
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*[[Fuzzy system]]s: techniques for [[reasoning under uncertainty]], have been widely used in modern industrial and consumer product control systems; capable of working with concepts such as 'hot', 'cold', 'warm' and 'boiling'.
*[[Evolutionary computation]]: applies biologically inspired concepts such as [[population]]s, [[mutation]] and [[survival of the fittest]] to generate increasingly better solutions to the problem. These methods most notably divide into [[evolutionary algorithm]]s (e.g. [[genetic algorithm]]s) and [[swarm intelligence]] (e.g. [[ant colony optimization|ant algorithm]]s).
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*[[Evolutionary computation]]: applies biologically inspired concepts such as [[population]]s, [[mutation]] and [[survival of the fittest]] to generate increasingly better solutions to the problem. These methods most notably divide into [[evolutionary algorithm]]s (e.g., [[genetic algorithm]]s) and [[swarm intelligence]] (e.g., [[ant colony optimization|ant algorithm]]s).
   
With [[hybrid intelligent system]]s attempts are made to combine these two groups. Expert inference rules can be generated through neural network or [[production rule]]s from statistical learning such as in [[ACT-R]].
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With [[hybrid intelligent system]]s, attempts are made to combine these two groups. Expert inference rules can be generated through neural network or [[production rule]]s from statistical learning such as in [[ACT-R]] or [[CLARION]] (see References below). It is thought that the human brain uses multiple techniques to both formulate and cross-check results. Thus, [[Artificial intelligence systems integration|systems integration]] is seen as promising and perhaps necessary for true AI,
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especially the integration of symbolic and connectionist models (e.g., as advocated by
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[[Ron Sun]]).
   
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Conventional AI research focuses on attempts to mimic human intelligence through symbol manipulation and symbolically structured knowledge bases. This approach limits the situations to which conventional AI can be applied. Lotfi Zadeh stated that "we are also in possession of computational tools which are far more effective in the conception and design of intelligent systems than the predicate-logic-based methods which form the core of traditional AI." These techniques, which include [[fuzzy logic]], have become known as soft computing. These often biologically inspired methods stand in contrast to conventional AI and compensate for the shortcomings of symbolicism.<ref>J.-S. R. Jang, C.-T. Sun, E. Mizutani, (foreword L. Zadeh) "Neuro-Fuzzy and Soft Computing," Prentice Hall, 1997</ref> These two methodologies have also been labeled as [[neats vs. scruffies]], with neats emphasizing the use of logic and formal representation of knowledge while scruffies take an application-oriented heuristic bottom-up approach.<ref>G.F. Luger, W.A. Stubblefield "Artificial Intelligence and the Design of Expert Systems"</ref>
==History==
 
{{main|History of artificial intelligence}}
 
   
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===Tools of AI research===
Early in the 17th century, [[René Descartes]] proposed that bodies of animals are nothing more than complex machines. [[Blaise Pascal]] created the first mechanical digital calculating machine in [[1642]]. [[Charles Babbage]] and [[Ada Lovelace]] worked on programmable mechanical calculating machines.
 
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{{expert-subject|computers}}
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==== Production systems ====
[[Bertrand Russell]] and [[Alfred North Whitehead]] published ''[[Principia Mathematica]]'', which revolutionized formal logic. [[Warren McCulloch]] and [[Walter Pitts]] published "A Logical Calculus of the Ideas Immanent in Nervous Activity" in [[1943]] laying foundations for [[neural network]]s.
 
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Many expert systems are organized collections of if-then such statements, called [[AI production|productions]]. These can include [[stochastic]] elements, producing intrinsic variation, or rely on variation produced in response to a dynamic environment.
   
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They are built around automated [[inference]] engines including [[forward reasoning]] and [[backwards reasoning]]. Based on certain conditions ("if") the system infers certain consequences ("then").
The 1950s were a period of active efforts in AI. The first working AI programs were written in 1951 to run on the Ferranti Mark I machine of the University of Manchester (UK): a draughts-playing program written by Christopher Strachey and a chess-playing program written by Dietrich Prinz. [[John McCarthy (computer scientist)|John McCarthy]] coined the term "artificial intelligence" in the first conference devoted to the subject, in 1956. He also invented the [[Lisp programming language]]. [[Alan Turing]] introduced the "[[Turing test]]" as a way of operationalizing a test of intelligent behavior. [[Joseph Weizenbaum]] built [[ELIZA]], a [[chatterbot]] implementing [[Rogerian psychotherapy]].
 
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==== Classifiers ====
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In the simplest case, 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 most AI systems.
   
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[[Classifier (mathematics)|Classifiers]] make use of [[pattern recognition]] for condition matching. In many cases this does not imply absolute, but rather the closest match.
During the 1960s and 1970s, [[Joel Moses]] demonstrated the power of symbolic reasoning for integration problems in the Macsyma program, the first successful knowledge-based program in mathematics. [[Marvin Minsky]] and [[Seymour Papert]] publish ''Perceptrons'', demonstrating limits of simple neural nets and [[Alain Colmerauer]] developed the [[Prolog]] computer language. [[Ted Shortliffe]] demonstrated the power of rule-based systems for [[knowledge representation]] and inference in medical diagnosis and therapy in what is sometimes called the first expert system. [[Hans Moravec]] developed the first computer-controlled vehicle to [[autonomous vehicle|autonomously]] negotiate cluttered obstacle courses.
 
   
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Classifiers are functions that 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.
In the 1980s, neural networks became widely used with the [[backpropagation]] algorithm, first described by [[Paul John Werbos]] in [[1974]]. The 1990s marked major achievements in many areas of AI and demonstrations of various applications. Most notably [[Deep Blue]], a chess-playing computer, beat [[Garry Kasparov]] in a famous six-game match in 1997. [[Defense Advanced Research Projects Agency|DARPA]] stated that the costs saved by implementing AI methods for scheduling units in the first [[Gulf War]] have repaid the US government's entire investment in AI research since the 1950s.
 
   
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When a new observation is received, that observation is classified based on previous experience. A classifier can be trained in various ways; there are mainly statistical and machine learning approaches.
==Philosophy==
 
{{portalpar|Mind and Brain}}
 
''Main article: [[Philosophy of artificial intelligence]]''
 
   
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A wide range of classifiers are available, each with its strengths and weaknesses. Classifier performance depends greatly on the characteristics of the data to be classified. There is no single classifier that works best on all given problems; this is also referred to as the "no free lunch" theorem. Various empirical tests have been performed to compare classifier performance and to find the characteristics of data that determine classifier performance. Determining a suitable classifier for a given problem is however still more an art than science.
The [[strong AI]] vs. weak AI debate is still a hot topic amongst AI [[philosopher]]s. This involves [[philosophy of mind]] and the [[mind-body problem]]. Most notably [[Roger Penrose]] in his book ''[[The Emperor's New Mind]]'' and [[John Searle]] with his "[[Chinese room]]" [[thought experiment]] argue that true [[consciousness]] can not be achieved by [[formal logic]] systems, while [[Douglas Hofstadter]] in ''[[Gödel, Escher, Bach]]'' and [[Daniel Dennett]] in ''[[Consciousness Explained]]'' argue in favour of [[Functionalism (philosophy of mind)|Functionalism]]. In many strong AI supporters’ opinion, [[artificial consciousness]] is considered as the [[list of holy grails|holy grail]] of artificial intelligence.
 
   
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The most widely used classifiers are the [[Artificial neural network|neural network]], [[support vector machine]], [[k-nearest neighbor algorithm]], [[Gaussian mixture model]], [[naive Bayes classifier]], and [[decision tree]]. The performance of these classifiers have been compared over a wide range of classification tasks <ref>http://www.patternrecognition.co.za/publications/cvdwalt_data_characteristics_classifiers.pdf</ref>
==Science fiction==
 
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in order to find data characteristics that determine classifier performance; to fully describe the relationship between data characateristics and classifier performance, however, remains an intriguing task.
In [[science fiction]] AI is commonly portrayed as an upcoming power trying to overthrow human authority as in [[HAL 9000]], [[Skynet]], [[Colossus: The Forbin Project|Colossus]] and [[The Matrix]] or as service [[humanoid]]s like [[C-3PO]], [[Data (Star Trek)|Data]], the [[Bicentennial Man]], the ''Mechas'' in [[A.I. (film)|A.I.]] or Sonny in [[I, Robot (film)|I, Robot]].
 
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==== Neural networks ====
<!--this is not a list of your favorite sci-fi AI, keep it short and use only famous and clear examples-->
 
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{{main|Neural networks}}
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[[Image:Artificial neural network.svg|thumb|180px|A neural network is an interconnected group of nodes, akin to the vast network of [[neuron]]s in the [[human brain]].]]
   
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Techniques and technologies in AI which have been directly derived from [[neuroscience]] include neural networks, [[Hebbian learning]] and the relatively new field of [[Hierarchical Temporal Memory]] which simulates the architecture of the [[neocortex]].
The inevitability of AI world domination, sometimes called "[[Technological singularity|the Singularity]]", is also argued by some science writers like [[Isaac Asimov]], [[Vernor Vinge]] and [[Kevin Warwick]]. In works such as the Japanese [[manga]] ''[[Ghost in the Shell (manga)|Ghost in the Shell]]'', the existence of intelligent machines questions the definition of life as organisms rather than a broader category of autonomous entities, establishing a notional concept of systemic intelligence.
 
''See [[list of fictional computers]] and [[list of fictional robots and androids]].''
 
   
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==== Specialized languages ====
==See also==
 
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AI research has led to many advances in programming languages including the first list processing language by [[Allen Newell]] ''et al.'', [[Lisp programming language|Lisp]] dialects, [[Planner programming language|Planner]], [[Actor model|Actors]], the [[Scientific Community Metaphor]], [[Rete algorithm|production systems]], and [[Rule engine|rule-based]] languages.
{{wikibookspar||Artificial Intelligence}}
 
   
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[[GOFAI]] research is often done in [[programming languages]] such as [[Prolog]] or [[Lisp programming language|Lisp]]. [[Matlab]] and [[Lush programming language|Lush]] (a numerical dialect of Lisp) include many specialist probabilistic libraries for Bayesian systems. AI research often emphasises rapid development and prototyping, using such [[interpreted language]]s to empower rapid command-line testing and experimentation. Real-time systems are however likely to require dedicated optimized software.
*[[Philosophy of artificial intelligence]]
 
*[[Functionalism]] - a philosophical theory of mind which allows for artificial intelligence
 
   
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Notable examples include the languages [[Lisp programming language|LISP]] and [[Prolog]], which were invented for AI research but are now used for non-AI tasks. [[Hacker]] culture first sprang from AI laboratories, in particular the [[MIT AI Lab]], home at various times to such luminaries as [[John McCarthy (computer scientist)|John McCarthy]], [[Marvin Minsky]], [[Seymour Papert]] (who developed [[Logo programming language|Logo]] there) and [[Terry Winograd]] (who abandoned AI after developing [[SHRDLU]]).
Typical problems to which AI methods are applied:
 
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===Research challenges===
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[[Image:Robocup.legged.leauge.2004.nk.jpg|thumb|right|200px|A legged league game from RoboCup 2004 in Lisbon, Portugal.]]
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The 800 million-Euro [[EUREKA Prometheus Project]] on [[driverless car]]s (1987-1995) showed that fast [[autonomous]] vehicles, notably those of [[Ernst Dickmanns]] and his team, can drive long distances (over 100 miles) in traffic, automatically recognizing and [[tracking]] other cars through [[computer vision]], passing slower cars in the left lane. But the challenge of safe door-to-door autonomous driving in arbitrary environments will require additional research.
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The [[DARPA Grand Challenge]] was a race for a $2 million prize where cars had to drive themselves over a hundred miles of challenging desert terrain without any communication with humans, using [[GPS]], computers and a sophisticated array of sensors. In 2005, the winning vehicles completed all {{convert|132|mi|km|0}} of the course in just under seven hours. This was the first in a series of challenges aimed at a congressional mandate stating that by 2015 one-third of the operational ground combat vehicles of the US Armed Forces should be unmanned.<ref>[http://www.darpa.mil/grandchallenge04/sponsor_toolkit/congress_lang.pdf Congressional Mandate] DARPA</ref> For November 2007, DARPA introduced the [[DARPA Urban Challenge]]. The course will involve a sixty-mile urban area course. Darpa has secured the prize money for the challenge as $2 million for first place, $1 million for second and $500,000 for third.
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A popular challenge amongst AI research groups is the [[RoboCup]] and [[Federation of International Robot-soccer Association|FIRA]] annual international robot soccer competitions. Hiroaki Kitano has formulated the International RoboCup Federation challenge: "In 2050 a team of fully autonomous humanoid robot soccer players shall win the soccer game, comply with the official rule of the FIFA, against the winner of the most recent World Cup."<ref>[http://www.robocup.org/Press/pr/RoboCup2003_020603eng.pdf The RoboCup2003 Presents: Humanoid Robots playing Soccer] PRESS RELEASE: 2 June 2003</ref>
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A lesser known challenge to promote AI research is the annual [[Arimaa]] challenge match. The challenge offers a $10,000 prize until the year 2020 to develop a program that plays the board game [[Arimaa]] and defeats a group of selected human opponents.
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In the post-dot-com boom era, some search engine websites use a simple form of AI to provide answers to questions entered by the visitor.
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Questions such as ''What is the tallest building?'' can be entered into the search engine's input form, and a list of answers will be returned. [[AskWiki]] is an example this sort of search engine.
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==Applications of artificial intelligence==
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===Business===
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Banks use artificial intelligence systems to organize operations, invest in stocks, and manage properties. In August 2001, robots beat humans in a simulated [[stock trader|financial trading]] competition ([[BBC News]], 2001).<ref name="xppr">{{cite web
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|url=http://news.bbc.co.uk/2/hi/business/1481339.stm
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|title=Robots beat humans in trading battle
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|year=[[August 8]] [[2001]]
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|accessdate=2006-11-02
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|work=BBC News, Business
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|publisher=The British Broadcasting Corporation
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}}</ref> A medical clinic can use artificial intelligence systems to organize bed schedules, make a staff rotation, and provide medical information. Many practical applications are dependent on [[artificial neural networks]], networks that pattern their organization in mimicry of a brain's neurons, which have been found to excel in pattern recognition. [[Financial institution]]s have long used such systems to detect charges or claims outside of the norm, flagging these for human investigation. Neural networks are also being widely deployed in [[homeland security]], speech and text recognition, [[medical diagnosis]] (such as in [[Concept Processing]] technology in [[EMR]] software), [[data mining]], and [[e-mail spam]] filtering.
  +
  +
[[Robot]]s have become common in many industries. They are often given jobs that are considered dangerous to humans. Robots have proven effective in jobs that are very repetitive which may lead to mistakes or accidents due to a lapse in concentration and other jobs which humans may find degrading. General Motors uses around 16,000 robots for tasks such as painting, welding, and assembly. [[Japan]] is the leader in using and producing robots in the world. In 1995, 700,000 robots were in use worldwide; over 500,000 of which were from Japan.<ref>[http://encarta.msn.com/encyclopedia_761564255/Robot.html "Robot," Microsoft® Encarta® Online Encyclopedia 2006 ]</ref>
  +
  +
===Toys and games===
  +
The 1990s saw some of the first attempts to mass-produce domestically aimed types of basic Artificial Intelligence for education, or leisure. This prospered greatly with the [[Digital Revolution]], and helped introduce people, especially children, to a life of dealing with various types of AI, specifically in the form of [[Tamagotchi]]s and [[Giga Pet]]s, the [[Internet]] (example: basic search engine interfaces are one simple form), and the first widely released robot, [[Furby]]. A mere year later an improved type of [[domestic robot]] was released in the form of [[Aibo]], a robotic dog with intelligent features and [[autonomy]].
  +
  +
===List of applications===
  +
;Typical problems to which AI methods are applied:
  +
{{MultiCol}}
 
*[[Pattern recognition]]
 
*[[Pattern recognition]]
 
**[[Optical character recognition]]
 
**[[Optical character recognition]]
Line 64: Line 159:
 
**[[Speech recognition]]
 
**[[Speech recognition]]
 
**[[Facial recognition system|Face recognition]]
 
**[[Facial recognition system|Face recognition]]
  +
*[[Artificial Creativity]]
 
  +
{{ColBreak}}
*[[Natural language processing]], [[Translation]] and [[Chatterbot]]s
 
*[[Non-linear control]] and [[Robotics]]
 
 
*[[Computer vision]], [[Virtual reality]] and [[Image processing]]
 
*[[Computer vision]], [[Virtual reality]] and [[Image processing]]
  +
*[[Diagnosis (artificial intelligence)]]
 
*[[Game theory]] and [[Strategic planning]]
 
*[[Game theory]] and [[Strategic planning]]
*[[Game AI]] and [[Computer game bot]]
+
*[[Game artificial intelligence]] and [[Computer game bot]]
  +
*[[Natural language processing]], [[Translation]] and [[Chatterbot]]s
*[[Artificial Creativity]]
 
  +
*[[Non-linear control]] and [[Robot]]ics
  +
{{EndMultiCol}}
   
Other fields in which AI methods are implemented:
+
;Other fields in which AI methods are implemented:
  +
{{MultiCol}}
  +
*[[Artificial life]]
  +
*[[Automated reasoning]]
 
*[[Automation]]
 
*[[Automation]]
*[[Bio-inspired computing]]
+
*[[Biologically-inspired computing]]
*[[Cybernetics]]
+
*[[Colloquis]]
  +
*[[Concept mining]]
  +
*[[Data mining]]
  +
*[[Knowledge representation]]
  +
*[[Semantic Web]]
  +
*[[E-mail spam]] filtering
  +
{{ColBreak}}
  +
* [[Robot]]ics
  +
**[[Behavior-based robotics]]
  +
**[[Cognitive]]
  +
**[[Cybernetics]]
  +
**[[Developmental robotics]]
  +
**[[Epigenetic robotics]]
  +
**[[Evolutionary robotics]]
 
*[[Hybrid intelligent system]]
 
*[[Hybrid intelligent system]]
 
*[[Intelligent agent]]
 
*[[Intelligent agent]]
 
*[[Intelligent control]]
 
*[[Intelligent control]]
*[[Automated reasoning]]
+
*[[Litigation]]
  +
{{EndMultiCol}}
*[[Data mining]]
 
*[[Behavior-based robotics]]
 
*[[Cognitive robotics]]
 
*[[Developmental robotics]]
 
*[[Evolutionary robotics]]
 
*[[Chatbot]]
 
*[[Knowledge Representation]]
 
   
== Links to researchers, projects & institutions ==
+
;Lists of researchers, projects & publications
 
*[[:Category:Artificial intelligence researchers|List of AI researchers]]
 
*[[:Category:Artificial intelligence researchers|List of AI researchers]]
*[[List of Artificial Intelligence projects|List of AI projects]]
+
*[[List of notable artificial intelligence projects|List of AI projects]]
 
*[[List of important publications in computer science#Artificial intelligence|List of important AI publications]]
 
*[[List of important publications in computer science#Artificial intelligence|List of important AI publications]]
  +
<!--This is not a list of your pet website or article, or favorite AI software & books. please add those to the appropriate links in the see also section. Keep this list short and use only famous and clear examples-->
   
== External links==
+
==See also==
  +
{{portalpar|Robotics|Animation2.gif}}
  +
: ''Main list: [[List of basic artificial intelligence topics]]''
   
  +
{{MultiCol}}
*[http://www.aaai.org/ American Association for Artificial Intelligence]
 
  +
*[[History of artificial intelligence]]
*[http://agiri.org/ AGIRI - Artificial General Intelligence Research Institute]
 
  +
*[[AI effect]]
*[http://www.eccai.org/ European Coordinating Committee for Artificial Intelligence]
 
  +
*[[AI winter]]
*[http://www.dfki.de/ German Research Center for Artificial Intelligence, DFKI]
 
  +
*[[Artificial intelligence systems integration]]
*[http://www.cild.iastate.edu/ Center for Computational Intelligence, Learning, and Discovery @ Iowa State University]
 
*[http://ai-news.elzemozgurce.net/ Artificial Intelligence News]
+
*[[Association for the Advancement of Artificial Intelligence]]
  +
*[[Automation]]
*[http://www.auai.org/ Association for Uncertainty in Artificial Intelligence]
 
  +
*[[Automated speech recognition]]
*[http://www.singinst.org Singularity Institute for Artificial Intelligence]
 
  +
*[[Autonomous foraging]]
*[http://www.aisb.org.uk/ The Society for the Study of AI and Simulation of Behaviour]
 
  +
*[[Cognitive processes]]
*[http://www.cs.berkeley.edu/~russell/ai.html University of California at Berkeley AI Resources] links to 868 AI resource pages
 
  +
*[[Cognitive science]]
*[http://www.loebner.net/Prizef/loebner-prize.html Loebner Prize website].
 
  +
*[[Cybernetics]]
*[http://commonsense.media.mit.edu/cgi-bin/search.cgi/ OpenMind CommonSense]
 
  +
*[[Decision support systems]]
*[http://sourceforge.net/softwaremap/trove_list.php?form_cat=133 SourceForge Open Source AI projects] - 1139 projects
 
  +
*[[Embodied agent]]
*[http://www.aaai.org/AITopics/html/ethics.html Ethical and Social Implications of AI en Computerization]
 
  +
*[[Expert systems]]
*[http://www.geocities.com/fhzeya20042000/lisp.htm A tutorial on AI programming language LISP]
 
  +
*[[Fifth generation computer]]
*[http://web.media.mit.edu/~minsky/ Marvin Minsky's Homepage]
 
  +
* [[Friendly artificial intelligence]]
*[http://www.csail.mit.edu/ MIT's Computer Science and Artificial Intelligence Lab]
 
  +
*[[Generative systems]]
*[http://www.isi.edu/divisions/div3/ AI research group at Information Sciences Institute]
 
*[http://www.alanturing.net/turing_archive/pages/Reference%20Articles/What%20is%20AI.html What is Artificial Intelligence?]
+
*[[German Research Centre for Artificial Intelligence]]
  +
*[[Human machine systems]]
*[http://uk.arxiv.org/abs/cs.AI/0601052 Artificial and biological intelligence]
 
  +
*[[Intelligent system]]
*[http://plato.stanford.edu/entries/logic-ai/ Stanford Encyclopedia of Philosophy entry on Logic and Artificial Intelligence]
 
  +
*[[Intelligent agent]]
*[http://www.ai-junkie.com/ AI-Junkie: Genetic Algorithm and Neural Network tutorials]
 
*[http://www-ai.cs.uni-dortmund.de/ Artificial Intelligence Group] @ University of Dortmund, Germany
+
*[[International Joint Conference on Artificial Intelligence]]
  +
*[[Loebner prize]]
<!--This is not a list of your pet website or article, or favorite AI software & books. please add those to the appropriate links in the see also section. Keep this list short and use only famous and clear examples-->
 
  +
{{ColBreak}}
  +
*[[Nanotechnology]]
  +
*[[Neuromancer]]
  +
*[[Nouvelle AI]]
  +
*[[PEAS]]
  +
*[[Personhood]]
  +
*[[Predictive analytics]]
  +
*[[Robot]]
  +
*[[Robotics]]
  +
*[[Singularitarianism]]
  +
*[[Three Laws of Robotics]]
  +
*[[Transhuman]]
  +
{{EndMultiCol}}
  +
  +
==Notes==
  +
{{reflist}}
  +
==References==
  +
* {{Citation | first = Rodney | last = Brooks | title = Elephants Don't Play Chess | journal = Robotics and Autonomous Systems | volume=6 | year =1990 | pages = 3–15 | author-link=Rodney Brooks | url=http://people.csail.mit.edu/brooks/papers/elephants.pdf | accessdate=30 August 2007}}
  +
* {{Citation | first = Bruce G. | last = Buchanan | year= 2005 | title = A (Very) Brief History of Artificial Intelligence | magazine = AI Magazine <!-- WINTER -->| pages=53–60 | url=http://www.aaai.org/AITopics/assets/PDF/AIMag26-04-016.pdf | accessdate=30 August 2007 }}
  +
* {{Crevier 1993}}
  +
* {{Citation | first = Douglas | last = Lenat | year = 1989 | title = Building Large Knowledge-Based Systems | publisher = Addison-Wesley| author-link=Douglas Lenat }}
  +
* {{Citation | first = Diane | last = Law | year = 1994 | url = http://nn.cs.utexas.edu/downloads/papers/law.synthetic.pdf | title =Searle, Subsymbolic Functionalism and Synthetic Intelligence }}
  +
* {{Citation | last = Lighthill | first = Professor Sir James | year = 1973 | contribution= Artificial Intelligence: A General Survey | title = Artificial Intelligence: a paper symposium| publisher = Science Research Council|author-link=James Lighthill }}
  +
* {{Citation | last = McCarthy | first = John | last2 = Hayes | first2=P. J.| year = 1969 | url=http://www-formal.stanford.edu/jmc/mcchay69.html | title= Some philosophical problems from the standpoint of artificial intelligence | journal =Machine Intelligence | volume= 4 | pages = 463–502 | author-link = John McCarthy (computer scientist) }}
  +
* {{Citation | first = Marvin | last = Minsky | year = 1967 | title = Computation: Finite and Infinite Machines | publication-place=Englewood Cliffs, N.J. | publisher = Prentice-Hall | author-link=Marvin Minsky }}
  +
* {{Citation | first = Hans | last = Moravec | year = 1976 | url= http://www.frc.ri.cmu.edu/users/hpm/project.archive/general.articles/1975/Raw.Power.html | title = The Role of Raw Power in Intelligence | author-link=Hans Moravec }}
  +
* {{Citation | first = Hans | last = Moravec | year = 1988 | title = Mind Children | publisher = Harvard University Press | author-link =Hans Moravec }}
  +
* {{Citation | last = NRC |chapter=Developments in Artificial Intelligence|chapter-url=http://www.nap.edu/readingroom/books/far/ch9.html|title=Funding a Revolution: Government Support for Computing Research|publisher=National Academy Press|year=1999| author-link=United States National Research Council | accessdate=30 August 2007}}
  +
* {{Citation | last = Newell | first = Allen | last2 = Simon | first2=H. A. | year = 1963 | contribution=GPS: A Program that Simulates Human Thought| title=Computers and Thought | editor-last= Feigenbaum | editor-first= E.A. |editor2-last= Feldman |editor2-first= J. |publisher= McGraw-Hill|publisher-place= New York | author-link=Allen Newell|author2-link=Herbert Simon}}
  +
* {{Citation | first = David | last = Poole | first2 = Alan | last2 = Mackworth | first3 = Randy | last3 = Goebel | publisher = Oxford University Press | publisher-place = New York | year = 1998 | title = Computational Intelligence: A Logical Approach | url = http://www.cs.ubc.ca/spider/poole/ci/ | author-link=David Poole }}
  +
* {{Russell Norvig 2003}}
  +
* {{cite journal | last = Samuel | first = Arthur L. | year = 1959 | title = Some studies in machine learning using the game of checkers | journal = IBM Journal of Research and Development | issn = 0018-8646 | volume = 3 | issue = 3 | pages = 210–219 | month = July | authorlink = Arthur Samuel | url = http://domino.research.ibm.com/tchjr/journalindex.nsf/600cc5649e2871db852568150060213c/39a870213169f45685256bfa00683d74?OpenDocument| accessdate = 2007-08-20}}
  +
* {{Citation | url = http://www.bbsonline.org/documents/a/00/00/04/84/bbs00000484-00/bbs.searle2.html | first= John | last= Searle | title = Minds, Brains and Programs | journal = Behavioral and Brain Sciences | volume = 3| issue = 3| pages= 417–457 | year = 1980 | author-link=John Searle}}
  +
* {{Citation | first = H. A. | last= Simon| year = 1965 | title=The Shape of Automation for Men and Management | publisher =Harper & Row | publication-place = New York | author-link=Herbert Simon}}
  +
* {{cite book| first = Joseph | last = Weizenbaum | title = Computer Power and Human Reason | publisher = W.H. Freeman & Company | location = San Francisco | year = 1976 | authorlink=Joseph Weizenbaum | isbn = 0716704641}}
  +
  +
== Further reading ==
  +
* R. Sun & L. Bookman, (eds.), Computational Architectures Integrating Neural and Symbolic Processes. Kluwer Academic Publishers, Needham, MA. 1994.
  +
  +
==External links==
  +
{{linkfarm}}
  +
{{sisterlinks|Artificial Intelligence}}
  +
*{{dmoz|Computers/Artificial_Intelligence/|AI}}
  +
*[http://www.learnartificialneuralnetworks.com AI with Neural Networks]
  +
*[http://www.ai-tools.org AI-Tools, the Open Source AI community homepage]
  +
*[http://www.ai-directory.com Artificial Intelligence Directory, a directory of Web resources related to artificial intelligence]
  +
*[http://www.aaai.org/AITopics/html/welcome.html The Association for the Advancement of Artificial Intelligence]
  +
*[http://www.vega.org.uk/video/programme/16 Freeview Video 'Machines with Minds' by the Vega Science Trust and the BBC/OU]
  +
*[http://web.archive.org/web/20011202023856/http://www.geocities.com/francorbusetti Heuristics and artificial intelligence in finance and investment]
  +
*[http://www-formal.stanford.edu/jmc/whatisai/whatisai.html John McCarthy's frequently asked questions about AI]
  +
*[http://www.aiai.ed.ac.uk/events/jonathanedwards2007/bbc-r4-jonathan-edwards-2007-03-28.mp3 Jonathan Edwards looks at AI (BBC audio)]
  +
*[http://www.generation5.org/ Generation5 - Large artificial intelligence portal with articles and news].
  +
*[http://www.mindmakers.org Mindmakers.org, an online organization for people building large scale A.I. systems]
  +
*[http://www.kurzweilai.net/ Ray Kurzweil's website dedicated to AI including prediction of future development in AI]
  +
*[http://www.acceleratingfuture.com/michael/blog/?cat=15 AI articles on the Accelerating Future blog]
  +
*[http://aigp.csres.utexas.edu/~aigp/ AI Genealogy Project]
  +
*[http://www.ailibrary.net/ Artificial intelligence library and other useful links]
  +
*[http://www.waset.org/ijci/ International Journal of Computational Intelligence]
  +
*[http://www.waset.org/ijis/ International Journal of Intelligent Technology]
   
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Latest revision as of 02:13, 8 July 2014

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Psychology: Debates · Journals · Psychologists


File:P11 kasparov breakout.jpg

Garry Kasparov playing against Deep Blue, the first machine to win a chess match against a reigning world champion.


The modern definition of artificial intelligence (or AI) is "the study and design of intelligent agents" where an intelligent agent is a system that perceives its environment and takes actions which maximizes its chances of success.[1] John McCarthy, who coined the term in 1956,[2] defines it as "the science and engineering of making intelligent machines."[3] Other names for the field have been proposed, such as computational intelligence,[4] synthetic intelligence[4][5] or computational rationality.[6] The term artificial intelligence is also used to describe a property of machines or programs: the intelligence that the system demonstrates.

AI research uses tools and insights from many fields, including computer science, psychology, philosophy, neuroscience, cognitive science, linguistics, operations research, economics, control theory, probability, optimization and logic.[7] AI research also overlaps with tasks such as robotics, control systems, scheduling, data mining, logistics, speech recognition, facial recognition and many others.[8]

Perspectives on AI[]

The rise and fall of AI in public perception[]

Main article: History of artificial intelligence
See also: AI Winter

The field was born at a conference on the campus of Dartmouth College in the summer of 1956.[9] Those who attended would become the leaders of AI research for many decades, especially John McCarthy, Marvin Minsky, Allen Newell and Herbert Simon, who founded AI laboratories at MIT, CMU and Stanford. They and their students wrote programs that were, to most people, simply astonishing:[10] computers were solving word problems in algebra, proving logical theorems and speaking English.[11] By the middle 60s their research was heavily funded by DARPA,[12] and they were optimistic about the future of the new field:

  • 1965, H. A. Simon: "machines will be capable, within twenty years, of doing any work a man can do"[13]
  • 1967, Marvin Minsky: "Within a generation ... the problem of creating 'artificial intelligence' will substantially be solved."[14]

These predictions, and many like them, would not come not true. They had failed to recognize the difficulty of some of the problems they faced: the lack of raw computer power,[15] the intractable combinatorial explosion of their algorithms,[16] the difficulty of representing commonsense knowledge and doing commonsense reasoning,[17] the incredible difficulty of perception and motion[18] and the failings of logic.[19] In 1974, in response to the criticism of England's Sir James Lighthill and ongoing pressure from congress to fund more productive projects, DARPA cut off all undirected, exploratory research in AI. This was the first AI Winter.[20]

In the early 80s, the field was revived by the commercial success of expert systems and by 1985 the market for AI had reached more than a billion dollars.[21] Minsky and others warned the community that enthusiasm for AI had spiraled out of control and that disappointment was sure to follow.[22] Minsky was right. Beginning with the collapse of the Lisp Machine market in 1987, AI once again fell into disrepute, and a second, more lasting AI Winter began.[23]

In the 90s AI achieved its greatest successes, albeit somewhat behind the scenes. Artificial intelligence was adopted throughout the technology industry, providing the heavy lifting for logistics, data mining, medical diagnosis and many other areas.[24] The success was due to several factors: the incredible power of computers today (see Moore's law), a greater emphasis on solving specific subproblems, the creation of new ties between AI and other fields working on similar problems, and above all a new commitment by researchers to solid mathematical methods and rigorous scientific standards.[25]

1961-65 -- A.L.Samuel Developed a program which learned to play checkers at Masters level.

1965 -- J.A.Robinson introduced resolution as an inference method in logic.

1965 -- Work on DENDRAL was begun at Stanford University by J.Lederberg, Edward Feigenbaum and Carl Djerassi. DENDRAL is an expert system which discovers molecule structure given only information of the constituents of the compound and mass spectra data. DENDRAL was the first knowledge-based expert system to be developed.

1968 -- Work on MACSYMA was initiated at MIT by Carl Engleman, William Martin and Joel Moses. MACSYMA is a large interactive program which solves numerous types of mathematical problems. Written in LISP, MACSYMA was a continuation of earlier work on SIN, an indefinite integration solving problem

References on early work in AI include Pamela McCorduck's Machines Who Think (1979) and Newell and Simon's Human Problem Solving (1972).

The philosophy of AI[]

Main article: Philosophy of artificial intelligence

The strong AI vs. weak AI debate ("can a man-made artifact be conscious?") is still a hot topic amongst AI philosophers. This involves philosophy of mind and the mind-body problem. Most notably Roger Penrose in his book The Emperor's New Mind and John Searle with his "Chinese room" thought experiment argue that true consciousness cannot be achieved by formal logic systems, while Douglas Hofstadter in Gödel, Escher, Bach and Daniel Dennett in Consciousness Explained argue in favour of functionalism. In many strong AI supporters' opinions, artificial consciousness is considered the holy grail of artificial intelligence. Edsger Dijkstra famously opined that the debate had little importance: "The question of whether a computer can think is no more interesting than the question of whether a submarine can swim."

Epistemology, the study of knowledge, also makes contact with AI, as engineers find themselves debating similar questions to philosophers about how, best to represent and use knowledge and information (e.g., semantic networks).

This area is the focus of Philosophy of Artificial Intelligence and Cognitive Science Department at Sussex University

The future of AI[]

Main article: Strong AI

AI research[]

Problems of AI research[]

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Approaches to AI research[]

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Conventional AI mostly involves methods now classified as machine learning, characterized by formalism and statistical analysis. This is also known as symbolic AI, logical AI, neat AI and Good Old Fashioned Artificial Intelligence (GOFAI). (Also see semantics.) Methods include:

  • Expert systems: apply reasoning capabilities to reach a conclusion. An expert system can process large amounts of known information and provide conclusions based on them.
  • Case based reasoning: stores a set of problems and answers in an organized data structure called cases. A case based reasoning system upon being presented with a problem finds a case in its knowledge base that is most closely related to the new problem and presents its solutions as an output with suitable modifications.[26]
  • Bayesian networks
  • Behavior based AI: a modular method of building AI systems by hand.

Computational intelligence involves iterative development or learning (e.g., parameter tuning in connectionist systems). Learning is based on empirical data and is associated with non-symbolic AI, scruffy AI and soft computing. Subjects in computational intelligence as defined by IEEE Computational Intelligence Society mainly include:

With hybrid intelligent systems, attempts are made to combine these two groups. Expert inference rules can be generated through neural network or production rules from statistical learning such as in ACT-R or CLARION (see References below). It is thought that the human brain uses multiple techniques to both formulate and cross-check results. Thus, systems integration is seen as promising and perhaps necessary for true AI, especially the integration of symbolic and connectionist models (e.g., as advocated by Ron Sun).

Conventional AI research focuses on attempts to mimic human intelligence through symbol manipulation and symbolically structured knowledge bases. This approach limits the situations to which conventional AI can be applied. Lotfi Zadeh stated that "we are also in possession of computational tools which are far more effective in the conception and design of intelligent systems than the predicate-logic-based methods which form the core of traditional AI." These techniques, which include fuzzy logic, have become known as soft computing. These often biologically inspired methods stand in contrast to conventional AI and compensate for the shortcomings of symbolicism.[27] These two methodologies have also been labeled as neats vs. scruffies, with neats emphasizing the use of logic and formal representation of knowledge while scruffies take an application-oriented heuristic bottom-up approach.[28]

Tools of AI research[]

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Production systems[]

Many expert systems are organized collections of if-then such statements, called productions. These can include stochastic elements, producing intrinsic variation, or rely on variation produced in response to a dynamic environment.

They are built around automated inference engines including forward reasoning and backwards reasoning. Based on certain conditions ("if") the system infers certain consequences ("then").

Classifiers[]

In the simplest case, 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 most AI systems.

Classifiers make use of pattern recognition for condition matching. In many cases this does not imply absolute, but rather the closest match.

Classifiers are functions that 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. A classifier can be trained in various ways; there are mainly statistical and machine learning approaches.

A wide range of classifiers are available, each with its strengths and weaknesses. Classifier performance depends greatly on the characteristics of the data to be classified. There is no single classifier that works best on all given problems; this is also referred to as the "no free lunch" theorem. Various empirical tests have been performed to compare classifier performance and to find the characteristics of data that determine classifier performance. Determining a suitable classifier for a given problem is however still more an art than science.

The most widely used classifiers are the neural network, support vector machine, k-nearest neighbor algorithm, Gaussian mixture model, naive Bayes classifier, and decision tree. The performance of these classifiers have been compared over a wide range of classification tasks [29] in order to find data characteristics that determine classifier performance; to fully describe the relationship between data characateristics and classifier performance, however, remains an intriguing task.

Neural networks[]

Main article: Neural networks
File:Artificial neural network.svg

A neural network is an interconnected group of nodes, akin to the vast network of neurons in the human brain.

Techniques and technologies in AI which have been directly derived from neuroscience include neural networks, Hebbian learning and the relatively new field of Hierarchical Temporal Memory which simulates the architecture of the neocortex.

Specialized languages[]

AI research has led to many advances in programming languages including the first list processing language by Allen Newell et al., Lisp dialects, Planner, Actors, the Scientific Community Metaphor, production systems, and rule-based languages.

GOFAI research is often done in programming languages such as Prolog or Lisp. Matlab and Lush (a numerical dialect of Lisp) include many specialist probabilistic libraries for Bayesian systems. AI research often emphasises rapid development and prototyping, using such interpreted languages to empower rapid command-line testing and experimentation. Real-time systems are however likely to require dedicated optimized software.

Notable examples include the languages LISP and Prolog, which were invented for AI research but are now used for non-AI tasks. Hacker culture first sprang from AI laboratories, in particular the MIT AI Lab, home at various times to such luminaries as John McCarthy, Marvin Minsky, Seymour Papert (who developed Logo there) and Terry Winograd (who abandoned AI after developing SHRDLU).

Research challenges[]

File:Robocup.legged.leauge.2004.nk.jpg

A legged league game from RoboCup 2004 in Lisbon, Portugal.

The 800 million-Euro EUREKA Prometheus Project on driverless cars (1987-1995) showed that fast autonomous vehicles, notably those of Ernst Dickmanns and his team, can drive long distances (over 100 miles) in traffic, automatically recognizing and tracking other cars through computer vision, passing slower cars in the left lane. But the challenge of safe door-to-door autonomous driving in arbitrary environments will require additional research.

The DARPA Grand Challenge was a race for a $2 million prize where cars had to drive themselves over a hundred miles of challenging desert terrain without any communication with humans, using GPS, computers and a sophisticated array of sensors. In 2005, the winning vehicles completed all Template:Convert/miTemplate:Convert/test/A of the course in just under seven hours. This was the first in a series of challenges aimed at a congressional mandate stating that by 2015 one-third of the operational ground combat vehicles of the US Armed Forces should be unmanned.[30] For November 2007, DARPA introduced the DARPA Urban Challenge. The course will involve a sixty-mile urban area course. Darpa has secured the prize money for the challenge as $2 million for first place, $1 million for second and $500,000 for third.

A popular challenge amongst AI research groups is the RoboCup and FIRA annual international robot soccer competitions. Hiroaki Kitano has formulated the International RoboCup Federation challenge: "In 2050 a team of fully autonomous humanoid robot soccer players shall win the soccer game, comply with the official rule of the FIFA, against the winner of the most recent World Cup."[31]

A lesser known challenge to promote AI research is the annual Arimaa challenge match. The challenge offers a $10,000 prize until the year 2020 to develop a program that plays the board game Arimaa and defeats a group of selected human opponents.

In the post-dot-com boom era, some search engine websites use a simple form of AI to provide answers to questions entered by the visitor. Questions such as What is the tallest building? can be entered into the search engine's input form, and a list of answers will be returned. AskWiki is an example this sort of search engine.

Applications of artificial intelligence[]

Business[]

Banks use artificial intelligence systems to organize operations, invest in stocks, and manage properties. In August 2001, robots beat humans in a simulated financial trading competition (BBC News, 2001).[32] A medical clinic can use artificial intelligence systems to organize bed schedules, make a staff rotation, and provide medical information. Many practical applications are dependent on artificial neural networks, networks that pattern their organization in mimicry of a brain's neurons, which have been found to excel in pattern recognition. Financial institutions have long used such systems to detect charges or claims outside of the norm, flagging these for human investigation. Neural networks are also being widely deployed in homeland security, speech and text recognition, medical diagnosis (such as in Concept Processing technology in EMR software), data mining, and e-mail spam filtering.

Robots have become common in many industries. They are often given jobs that are considered dangerous to humans. Robots have proven effective in jobs that are very repetitive which may lead to mistakes or accidents due to a lapse in concentration and other jobs which humans may find degrading. General Motors uses around 16,000 robots for tasks such as painting, welding, and assembly. Japan is the leader in using and producing robots in the world. In 1995, 700,000 robots were in use worldwide; over 500,000 of which were from Japan.[33]

Toys and games[]

The 1990s saw some of the first attempts to mass-produce domestically aimed types of basic Artificial Intelligence for education, or leisure. This prospered greatly with the Digital Revolution, and helped introduce people, especially children, to a life of dealing with various types of AI, specifically in the form of Tamagotchis and Giga Pets, the Internet (example: basic search engine interfaces are one simple form), and the first widely released robot, Furby. A mere year later an improved type of domestic robot was released in the form of Aibo, a robotic dog with intelligent features and autonomy.

List of applications[]

Typical problems to which AI methods are applied
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Other fields in which AI methods are implemented
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Lists of researchers, projects & publications

See also[]

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Main list: List of basic artificial intelligence topics
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Notes[]

  1. Textbooks that define AI this way include Poole, Mackworth & Goebel 1998, p. 1 and Russell & Norvig 2003, preface (who prefer the term "rational agent") and write "The whole-agent view is now widely accepted in the field" (Russell & Norvig 2003, p. 55)
  2. Although there is some controversy on this point (see Crevier 1993, p. 50), McCarthy states unequivocally "I came up with the term" in a c|net interview. (See Getting Machines to Think Like Us.)
  3. See WHAT IS ARTIFICIAL INTELLIGENCE? by John McCarthy
  4. 4.0 4.1 Poole, Mackworth & Goebel 1998, p. 1
  5. Law 1994
  6. Russell & Norvig 2003, p. 17
  7. Russell & Norvig 2003, pp. 5–16
  8. See AI Topics: applications
  9. Crevier 1993, pp. 47–49 and Russell & Norvig 2003, p. 17
  10. Russell and Norvig write "it was astonishing whenever a computer did anything kind of smartish." Russell & Norvig 2003, p. 18
  11. Crevier 1993, pp. 52–107, Moravec 1988, p. 9 and Russell & Norvig 2003, pp. 18–21. The programs described are Daniel Bobrow's STUDENT, Newell and Simon's Logic Theorist and Terry Winograd's SHRDLU.
  12. Crevier 1993, pp. 64–65
  13. Simon 1965, p. 96 quoted in Crevier 1993, p. 109
  14. Minsky 1967, p. 2 quoted in Crevier 1993, p. 109
  15. Crevier 1993, pp. 146–148, Buchanan 2005, p. 56, Moravec 1976
  16. Russell & Norvig 2003, pp. 9,21–22
  17. Crevier 1993, pp. 113–114, Moravec 1988, p. 13, Lenat 1989 (Introduction) and Russell & Norvig 2003, p. 21
  18. Moravec 1988, pp. 15–16 and see Moravec's paradox
  19. McCarthy & Hayes 1969, Crevier 1993, pp. 117–119 and see the frame problem, qualification problem and ramification problem.
  20. Crevier 1993, pp. 115–117, Russell & Norvig 2003, p. 22, NRC 1999 under "Shift to Applied Research Increases Investment." and also see Howe, J. "Artificial Intelligence at Edinburgh University : a Perspective"
  21. Crevier 1993, pp. 161–162,197–203 and and Russell & Norvig 2003, p. 24
  22. Crevier 1993, p. 203
  23. Crevier 1993, pp. 209–210
  24. Russell Norvig, p. 28, NRC 1999 under "Artificial Intelligence in the 90s"
  25. Russell Norvig, pp. 25–26
  26. Hammond J, Kristian. Case-based planning: viewing planning as a memory task. Academic Press Perspectives In Artificial Intelligence; Vol 1. Pages: 277. 1989. ISBN 0-12-322060-2
  27. J.-S. R. Jang, C.-T. Sun, E. Mizutani, (foreword L. Zadeh) "Neuro-Fuzzy and Soft Computing," Prentice Hall, 1997
  28. G.F. Luger, W.A. Stubblefield "Artificial Intelligence and the Design of Expert Systems"
  29. http://www.patternrecognition.co.za/publications/cvdwalt_data_characteristics_classifiers.pdf
  30. Congressional Mandate DARPA
  31. The RoboCup2003 Presents: Humanoid Robots playing Soccer PRESS RELEASE: 2 June 2003
  32. (August 8 2001). Robots beat humans in trading battle. BBC News, Business. The British Broadcasting Corporation. URL accessed on 2006-11-02.
  33. "Robot," Microsoft® Encarta® Online Encyclopedia 2006

References[]

Further reading[]

  • R. Sun & L. Bookman, (eds.), Computational Architectures Integrating Neural and Symbolic Processes. Kluwer Academic Publishers, Needham, MA. 1994.

External links[]

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Computer applications

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