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Artificial intelligence

Artificial intelligence, commonly abbreviated as AI, also known as machine intelligence, may be defined as "making a machine behave in ways that would be called intelligent if a human were so behaving". (This definition was put forth by John McCarthy in his 1955 Proposal for the Dartmouth Summer Research Project On Artificial Intelligence.)

Since that time several distinct types of artificial intelligence have been elucidated:

Strong artificial intelligence 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 intelligence deals with the creation of some form of computer-based artificial intelligence that can not truly reason and solve problems; such a machine would, in some ways, act as if it were intelligent, but it would not posesses true intelligence or sentience.

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.

Table of contents

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).

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), or with 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.

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 Gödel[?] (1961) by John Lucas[?].

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.

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 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 McCarthy and 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, and Nathan Rochester[?] of IBM.

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 aproaches have severe limitations.

Practical applications of AI techniques

Whilst 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:

The vision of artificial intelligence replacing human professional judgment has arisen many times in the history of the field, 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 AI

Some 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.
  • Freedoms and rights for these systems.
  • Designing systems that are far more impressive than any one human
  • Deciding how much safe-guards to design into these systems
  • Seeing how much learning capability a system needs to replicate human thought, or how well it could do tasks without it (eg expert system)
  • The Singularity

Sub-fields of AI research

Computer programs displaying some degree of "intelligence"

Artificial intelligence in literature and movies

Speculative non-fiction books about artificial intelligence

See also

External links

  • AI Depot (http://ai-depot.com/) -- community discussion, news, and articles
  • Loebner Prize website (http://www.loebner.net/Prizef/loebner-prize)
  • AIWiki (http://purl.net/net/AIWiki) - a wiki devoted entirely to Artificial Intelligence.



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