Artificial intelligence

Artificial intelligence (also known as machine intelligence and often abbreviated as AI) is intelligence exhibited by any manufactured (i.e. ) system. The term is often applied to general purpose computers and also in the field of scientific investigation into the theory and practical application of AI. "AI" the term is often used in works of science fiction to refer to that which exhibits artificial intelligence as well, as in "the AI" referring to a singular discrete or distributed mechanism.

History

Prehistory of AI

Humans have always speculated about the nature of mind, thought, and language, and searched for discrete representations of their knowledge. Aristotle tried to formalize this speculation by means of syllogistic logic, which remains one of the key strategies of AI. The first is-a hierarchy was created in 260 by Porphyry of Tyros. Classical and medieval grammarians explored more subtle features of language that Aristotle shortchanged, and mathematician Bernard Bolzano made the first modern attempt to formalize semantics in 1837.

Related Topics:
Aristotle - Syllogistic logic - Is-a hierarchy - 260 - Porphyry of Tyros - Grammarians - Bernard Bolzano - Semantics - 1837

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Early computer design was driven mainly by the complex mathematics needed to target weapons accurately, with analog feedback devices inspiring an ideal of cybernetics. The expression "artificial intelligence" was introduced as a 'digital' replacement for the analog 'cybernetics'.

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Development of AI theory

Much 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).

Related Topics:
Psychology - Turing test

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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.

Related Topics:
Robotics - Collective intelligence - Consensus decision making - Biology - Political science

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AI 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. Some researchers argue that animals, which are apparently simpler than humans, ought to be considerably easier to mimic. But satisfactory computational models for animal intelligence are not available.

Related Topics:
Artificial life - Ape

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Seminal papers advancing AI 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.

Related Topics:
1943 - Warren McCulloch - Walter Pitts - On Computing Machinery and Intelligence - 1950 - Alan Turing - Man-Computer Symbiosis - Cybernetics - Turing test

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There were also early papers which denied the possibility of machine intelligence on logical or philosophical grounds such as Minds, Machines and Gödel (1961) by John Lucas http://users.ox.ac.uk/~jrlucas/Godel/mmg.html.

Related Topics:
Logic - Philosophical - Minds, Machines and Gödel - 1961 - John Lucas

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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. Douglas Hofstadter, in Gödel, Escher, Bach, pointed out that this moving of the goalposts effectively defines "intelligence" as "whatever humans can do that machines cannot".

Related Topics:
Computer chess - Speech recognition - Douglas Hofstadter - Gödel, Escher, Bach

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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.

Related Topics:
John von Neumann - E.T. Jaynes - 1948 - Church-Turing thesis

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In 1969 McCarthy and Hayes started the discussion about the frame problem with their essay, "Some Philosophical Problems from the Standpoint of Artificial Intelligence".

Related Topics:
1969 - Frame problem

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Experimental AI research

Artificial 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 John 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.

Related Topics:
1950s - Allen Newell - Herbert Simon - Carnegie Mellon University - John McCarthy - Marvin Minsky - MIT AI Lab - 1959 - Dartmouth College - Summer AI conference - 1956 - Nathan Rochester - IBM - Claude Shannon

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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 artificial 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.

Related Topics:
Neats - Scruffies - Symbolic - Artificial neural network - 1960s - 1970s - 1980s

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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.

Related Topics:
1980s - Defense Advanced Research Projects Agency - United States - Fifth generation computer systems project - Japan - AI winter - Machine learning - Robotics - Computer vision

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