This article is about modelling human thought with computers.
For other uses of the term AI, see Ai.Artificial intelligence, also known as machine intelligence, is defined as intelligence exhibited by anything manufactured (i.e. artificial) by humans or other sentient beings or systems (should such things ever exist on Earth or elsewhere).
It is usually hypothetically applied to general-purpose computers.
The term is also used to refer to the field of scientific investigation into the plausibility of and approaches to creating such systems.
1 Overview 2 Strong AI and weak AI2.1 Strong artificial intelligence 2.2 Weak artificial intelligence 2.3 Philosophical criticism and support of strong AI 3 History3.1
Development of AI theory 3.2 Experimental AI research 4 Practical applications of AI techniques 5 Hypothetical consequences of AI 6 Sub-fields of AI research 7 Famous figures7.1 Machines displaying some degree of "intelligence" 7.2 AI researchers 8 Resources8.1
Further reading8.1.1 Non-fiction 8.1.2
Fiction8.2 AI related organizations 8.3 Sources 8.4 See also8.4.1 Philosophy 8.4.2 Logic 8.4.3 Science 8.4.4 Applications 8.4.5 Uncategorised8.5 External linksOverviewThe question of what artificial intelligence is can be reduced to two parts: "what is the nature of artifice" and "what is intelligence"?
The first question is fairly easy to answer, though it does point to the question of what it is possible to manufacture (within the constraints of certain types of system, e.g. classical computational systems, of available processes of manufacturing and of possible limits on human intellect, for instance).
The second is much harder, raising questions of consciousness and self, mind (including the unconscious mind) and the question of what components are involved in the only type of intelligence it is universally agreed we have available to study: that of human beings.
Intelligent behavior in humans is complex and difficult to study or understand.
Study of animals and artificial systems that are not just models of what exists already are also considered widely pertinent.
Several distinct types of artificial intelligence have been elucidated below.
Also, the subject divisions , history, proponents and opponents and applications of research in the subject are described .
Finally, references to fictional and non-fictional descriptions of AI are provided.
Strong AI and weak AIOne popular and early definition of artificial intelligence research, put forth by John McCarthy at the Dartmouth Conference in 1956, is "making a machine behave in ways that would be called intelligent if a human were so behaving."
However this definition seems to ignore the possibility of strong AI (see below).
Another definition of artificial intelligence is intelligence arising from an artificial device.
Most definitions could be categorized as concerning either systems that think like humans, systems that act like humans, systems that think rationally or systems that act rationally.
Strong artificial intelligenceStrong artificial intelligence research deals with the creation of some form of computer-based artificial intelligence that can truly reason and solve problems ; a strong form of AI is said to be sentient, or self-aware.
In theory, there are two types of strong AI:Human-like AI, in which the computer program thinks and reasons much like a human mind.
Non-human-like AI, in which the computer program develops a totally non-human sentience, and a non-human way of thinking and reasoning.
Weak artificial intelligenceWeak artificial intelligence research deals with the creation of some form of computer-based artificial intelligence that cannot truly reason and solve problems ; such a machine would, in some ways, act as if it were intelligent, but it would not possess true intelligence or sentience.
There are several fields of weak AI, one of which is natural language.
Many weak AI fields have specialised software or programming languages created for them.
For example, the 'most-human' natural language chatterbot A.L.I.C.E. uses a programming language AIML that is specific to its program.
To date, much of the work in this field has been done with computer simulations of intelligence based on predefined sets of rules.
Very little progress has been made in strong AI.
Depending on how one defines one's goals, a moderate amount of progress has been made in weak AI.Philosophical criticism and support of strong AIThe term "Strong AI" was originally coined by John Searle and was applied to digital computers and other information processing machines.
Searle defined strong AI:"according to strong AI, the computer is not merely a tool in the study of the mind; rather, the appropriately programmed computer really is a mind" (J Searle in Minds Brains and Programs.
The Behavioral and Brain Sciences, vol.
3, 1980).
Searle and most others involved in this debate are addressing the problem of whether a machine that works solely through the transformation of encoded data could be a mind, not the wider issue of Monism versus Dualism (ie: whether a biological machine could contain a mind).
Searle points out in his Chinese Room Argument that information processors carry encoded data which describe other things.
The encoded data itself is meaningless without a cross reference to the things it describes .
This leads Searle to point out that there is no meaning or understanding in an information processor itself.
As a result Searle claims to demonstrate that even a machine that passed the Turing test would not necessarily be conscious in the human sense.
Other philosophers hold opposing views.
Daniel C. Dennett argues in Consciousness Explained that if there is no magic spark or soul, then Man is just a machine, and he asks why the Man-machine should have a privileged position over all other possible machines when it comes to intelligence.
Dennett goes further than this support for weak AI, and also proposes that information processors could become minds.
Some philosophers hold that if Weak AI is accepted as possible then so must Strong AI.
The Weak AI position, that intelligence might be apparent but would not be a 'mind', is countered in many ways, but one accessible example can be found in Simon Blackburn introduction to philosophy, Think .
Blackburn points out that you might appear intelligent but there is no way of telling if that intelligence is real (ie: a 'mind'): We have to take it on trust or faith.
Strong AI seems to involve the following assumptions about the mind:the mind is software, a finite state machine so the Church-Turing thesis applies to itpresentism describes the mindthe mind exists exclusively within the brainAnd the following assumption about the brain:the brain is purely hardware (i.e. only follows the rules of a classical computer)Some (including Roger Penrose) attack the applicability of the Church-Turing thesis.
Others say the mind is not completely physical.
Roger Penrose's argument rests on the conception of hypercomputation being possible in our universe.
Quantum mechanics and newtonian mechanics do not allow hypercomputation but it is thought that some strange space times would.
However there seems to be agreement that our universe is not sufficiently convoluted to allow such hypercomputation.
Ultimately the truth of Strong AI depends upon whether information processing machines can include all the properties of minds such as Consciousness.
However, Weak AI is independent of the Strong AI problem and there can be no doubt that many of the features of modern computers such as multiplication or database searching might have been considered 'intelligent' only a century ago.
HistoryDevelopment of AI theoryMuch of the (original) focus of artificial intelligence research draws from an experimental approach to psychology, and emphasizes what may be called linguistic intelligence (best exemplified in the Turing test).
Approaches to artificial intelligence that do not focus on linguistic intelligence include robotics and collective intelligence approaches, which focus on active manipulation of an environment, or consensus decision making, and draw from biology and political science when seeking models of how "intelligent" behavior is organized.
Artificial intelligence theory also draws from animal studies, in particular with insects, which are easier to emulate as robots (see artificial life), as well as animals with more complex cognition, including apes, who resemble humans in many ways but have less developed capacities for planning and cognition.
AI researchers argue that animals, which are simpler than humans, ought to be considerably easier to mimic.
But satisfactory computational models for animal intelligence are not available.
Seminal papers advancing the concept of machine intelligence include A Logical Calculus of the Ideas Immanent in Nervous Activity (1943), by Warren McCulloch and Walter Pitts, and On Computing Machinery and Intelligence (1950), by Alan Turing, and Man-Computer Symbiosis by J.C.R. Licklider.
See cybernetics and Turing test for further discussion.
There were also early papers which denied the possibility of machine intelligence on logical or philosophical grounds such as Minds, Machines and Godel (1961) by John Lucas [1] (http://users.ox.ac.uk/~jrlucas/Godel/mmg.html).
With the development of practical techniques based on AI research, advocates of AI have argued that opponents of AI have repeatedly changed their position on tasks such as computer chess or speech recognition that were previously regarded as "intelligent" in order to deny the accomplishments of AI.
They point out that this moving of the goalposts effectively defines "intelligence" as "whatever humans can do that machines cannot".
John von Neumann ( quoted by E.T. Jaynes) anticipated this in 1948 by saying , in response to a comment at a lecture that it was impossible for a machine to think : "You insist that there is something a machine cannot do.
If you will tell me precisely what it is that a machine cannot do, then I can always make a machine which will do just that!". Von Neumann was presumably alluding to the Church-Turing thesis which states that any effective procedure can be simulated by a (generalized) computer.
In 1969 McCarthy and Hayes started the discussion about the frame problem with their essay, "Some Philosophical Problems from the Standpoint of Artificial Intelligence".
Experimental AI researchArtificial intelligence began as an experimental field in the 1950s with such pioneers as Allen Newell and Herbert Simon, who founded the first artificial intelligence laboratory at Carnegie-Mellon University, and McCarthy and Marvin Minsky, who founded the MIT AI Lab in 1959.
They all attended the aforementioned Dartmouth College summer AI conference in 1956, which was organized by McCarthy, Minsky, Nathan Rochester of IBM and Claude Shannon.
Historically, there are two broad styles of AI research - the "neats" and "scruffies".
"Neat", classical or symbolic AI research, in general, involves symbolic manipulation of abstract concepts, and is the methodology used in most expert systems.
Parallel to this are the "scruffy", or "connectionist", approaches, of which neural networks are the best-known example, which try to "evolve" intelligence through building systems and then improving them through some automatic process rather than systematically designing something to complete the task.
Both approaches appeared very early in AI history.
Throughout the 1960s and 1970s scruffy approaches were pushed to the background, but interest was regained in the 1980s when the limitations of the "neat" approaches of the time became clearer.
However, it has become clear that contemporary methods using both broad approaches have severe limitations .
Artificial intelligence research was very heavily funded in the 1980s by the Defense Advanced Research Projects Agency in the United States and by the fifth generation computer systems project in Japan.
The failure of the work funded at the time to produce immediate results, despite the grandiose promises of some AI practitioners, led to correspondingly large cutbacks in funding by government agencies in the late 1980s, leading to a general downturn in activity in the field known as AI winter.
Over the following decade, many AI researchers moved into related areas with more modest goals such as machine learning, robotics, and computer vision, though research in pure AI continued at reduced levels.
Practical applications of AI techniquesWhilst progress towards the ultimate goal of human-like intelligence has been slow, many spinoffs have come in the process.
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 McCarthy, Minsky, Seymour Papert (who developed Logo there), Terry Winograd (who abandoned AI after developing SHRDLU).
Many other useful systems have been built using technologies that at least once were active areas of AI research.
Some examples include:Chinook was declared the Man-Machine World Champion in checkers (draughts) in 1994.
Deep Blue, a chess-playing computer, beat Garry Kasparov in a famous match in 1997.
Fuzzy logic, a technique for reasoning under uncertainty, has been widely used in industrial control systems.
Expert systems are being used to some extent industrially.
Machine translation systems such as SYSTRAN are widely used, although results are not yet comparable with human translators.
Neural networks have been used for a wide variety of tasks, from intrusion detection systems to computer games.
Optical character recognition systems can translate arbitrary typewritten European script into text.
Handwriting recognition is used in millions of personal digital assistants.
Speech recognition is commercially available and is widely deployed.
Computer algebra systems, such as Mathematica and Macsyma, are commonplace.
Machine vision systems are used in many industrial applications ranging from hardware verification to security systems.
The vision of artificial intelligence replacing human professional judgment has arisen many times in the history of the field, in science fiction and today in some specialized areas where "expert systems" are used to augment or to replace professional judgment in some areas of engineering and of medicine.
Hypothetical consequences of AISome observers foresee the development of systems that are far more intelligent and complex than anything currently known.
One name for these hypothetical systems is artilects.
With the introduction of artificially intelligent non-deterministic systems, many ethical issues will arise.
Many of these issues have never been encountered by humanity.
Over time, debates have tended to focus less and less on "possibility" and more on "desirability", as emphasized in the "Cosmist" (versus "Terran") debates initiated by Hugo de Garis and Kevin Warwick.
A Cosmist, according to de Garis, is actually seeking to build more intelligent successors to the human species.
The emergence of this debate suggests that desirability questions may also have influenced some of the early thinkers "against".
Some issues that bring up interesting ethical questions are:Determining the sentience of a system we create.
Turing testCognitionWhy do we have a need to categorize these systems at all?
Can AI be defined in a graded sense?
Freedoms and rights for these systemsCan AIs be "smarter" than humans in the same way that we are "smarter" than other animals?
Designing systems that are far more intelligent than any one humanDeciding how many safe-guards to design into these systemsSeeing how much learning capability a system needs to replicate human thought, or how well it could do tasks without it (e.g. expert systems)The SingularityEffect on careers and jobs.
The problems may resemble problems seen under free trade.
Sub-fields of AI researchCombinatorial searchComputer visionExpert systemGenetic programmingGenetic algorithmKnowledge representationMachine learningMachine planningNeural networkNatural language processingProgram synthesisRoboticsArtificial lifeArtificial beingDistributed artificial intelligenceSwarm IntelligenceLogic programming was sometimes considered a field of artificial intelligence, but this is no longer the case.
Famous figuresMachines displaying some degree of "intelligence"There are many examples of programs displaying some degree of intelligence.
Some of these are:The Start Project (http://www.ai.mit.edu/projects/infolab/)
- a web-based system which answers questions in English.
Cyc, a knowledge base with vast collection of facts about the real world and logical reasoning ability.
ALICE, a chatterbotAlan (http://www.a-i.com/alan1),
another chatterbotELIZA, a program which pretends to be a psychotherapist, developed in 1966PAM (Plan Applier Mechanism) - a story understanding system developed by John Wilensky in 1978.
SAM (Script applier mechanism) - a story understanding system, developed in 1975.
SHRDLU - an early natural language understanding computer program developed in 1968-1970.
Creatures, a computer game with breeding, evolving creatures coded from the genetic level upwards using a sophisticated biochemistry and neural network brains.
BBC news story (http://news.bbc.co.uk/1/hi/wales/3521852.stm)
on the creator of Creatures latest creation.
Steve Grand's Lucy.
Eurisko - a language for solving problems which consists of heuristics, including heuristics for how to use and change its heuristics.
Developed in 1978 by Douglas Lenat.
X-Ray Vision for Surgeons (http://www.ai.mit.edu/projects/medical-vision/)
- a group in MIT which researches medical vision.
Neural networks-based programs for backgammon and go (http://www.jellyfish-ai.com).
AI researchersThere are many thousands of AI researchers around the world at hundreds of research institutions and companies.
Among the many who have made significant contributions are:Maggie BodenRodney BrooksBoris KatzDoug LenatJohn McCarthyMarvin MinskyRaj ReddyRoger SchankAlan TuringWolfgang WahlsterTerry WinogradTo some computer scientists, the phrase artificial intelligence has acquired somewhat of a bad name due to the large discrepancy between what has been achieved so far in the field and some more usual notions of intelligence.
This problem has been aggravated by various popular science writers and media personalities such as Kevin Warwick whose work has raised the expectations of AI research far beyond its current capabilities.
For this reason, some researchers working on topics related to artificial intelligence say they work in cognitive science, informatics, statistical inference or information engineering.
However, progress has in fact been made, and AI is today routinely employed in thousands of industrial systems around the world.
See Raj Reddy's AAAI paper for a huge review of real-world AI systems in deployment today.
ResourcesFurther readingNon-fictionArtificial Intelligence: A Modern Approach by Stuart J. Russell and Peter NorvigGodel, Escher, Bach: An Eternal Golden Braid by Douglas R. HofstadterShadows of the Mind and The Emperor's New Mind by Roger PenroseConsciousness Explained by Dennett.
The Age of Spiritual Machines by Ray KurzweilUnderstanding Understanding: Essays on Cybernetics and Cognition by Heinz von FoersterIn the Image of the Brain: Breaking the Barrier Between Human Mind and Intelligent Machines by Jim JubakToday's Computers, Intelligent Machines and Our Future by Hans Moravec, Stanford UniversityFictionThe following is a list of influential works See also longer lists at:-List of fictional robots and androids:List of fictional computers:HAL 9000 in 2001 A Space OdysseyHARLIE in When H.A.R.L.I.E. was One by David GerroldA.I.: Artificial IntelligenceArtificial intelligence - mainly its philosophical implications and its impact on Humanity -- is a major theme in David Lodge's campus novel Thinks ... (2001).
Rosie and other robots from The JetsonsMike in The Moon is a Harsh Mistress by Robert A. HeinleinWilliam Gibson's NeuromancerIsaac Asimov's I, Robot series, introducing the famous Three Laws of Robotics, is often considered to be the most accurate fictional depiction of AIAI related organizationsAmerican Association for Artificial Intelligence (http://www.aaai.org/)
European Coordinating Committee for Artificial Intelligence (http://www.eccai.org/)
The Association for Computational Linguistics (http://www1.cs.columbia.edu/~acl/)
Artificial Intelligence Student Union (http://www.dotmotive.com/~aisu/)
German Research Center for Artificial Intelligence, DFKI GmbH (http://www.dfki.de/)
Association for Uncertainty in Artificial Intelligence (http://www.auai.org/)
Singularity Institute for Artificial Intelligence (http://www.singinst.org)
SourcesJohn McCarthy: Proposal for the Dartmouth Summer Research Project On Artificial Intelligence.
[2] (http://www-formal.stanford.edu/jmc/history/dartmouth/dartmouth.html)
See alsoImportant publications in artificial intelligence.
Philosophyfunctionalismsimulated consciousnessSearle's Chinese roomconsciousnessLogicsemanticsSciencecognitive sciencecomputer sciencecyberneticspsychologyApplications artificial intelligence projects artificial intelligence agentbio-inspired computingUncategorisedCollective intelligence - the idea that a relatively large number of people co-operating in one process can lead to reliable action.
Quantum mind - the idea that large-scale quantum coherence is necessary to understand the brain.the Singularity - a time at which technological progress accelerates beyond the ability of current-day human beings to understand it, or the point in time of the emergence of smarter-than-human intelligence.
Mindpixel - A project to collect simple true / false assertions and collaboratively validate them with the aim of using them as a body of human common sense knowledge that can be utilised by a machine.
Game programming AI artificial consciousnesstruth maintenance systems - by Gerald Jay Sussman and Richard StallmanExternal links (see also #AI-Related Organizations)Programming:AI (http://wikibooks.org/wiki/Programming:AI)
@ Wikibooks.org
University of Berkeley AI Resources (http://www.cs.berkeley.edu/~russell/ai.html)
linking to about 869 other WWW pages about AIAI Depot (http://ai-depot.com/) - community discussion, news, and articlesLoebner Prize website (http://www.loebner.net/Prizef/loebner-prize.html)
AIWiki (http://purl.net/net/AIWiki) - a wiki devoted to AI.AIAWiki (http://ai.squeakydolphin.com/) - AI algorithms and research.
AI web category on Open Directory (http://www.dmoz.org/Computers/Artificial_Intelligence/)
Mindpixel (http://www.mindpixel.com/)
"The Planet's Largest Artificial Intelligence Effort"OpenMind CommonSense (http://commonsense.media.mit.edu/cgi-bin/search.cgi/)
"Teaching computers the stuff we all know "Artificially Intelligent Ouija Board (http://www.bitesizeinc.net/index.php/ouija.html)
- creative example of human-like AIArtificial Life (http://www.ifi.unizh.ch/ailab/)
- AI Lab, ZurichHeuristics and AI in finance and investment (http://www.geocities.com/francorbusetti/)
SourceForge Open Source AI projects (http://sourceforge.net/softwaremap/trove_list.php?form_cat=133) - 1139 projectsRetrieved from "http://en.wikipedia.org/wiki/Artificial_intelligence"
Categories: Artificial intelligence | Science