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AI ) is defined as intelligence exhibited by an artificial entity. Such a system is generally assumed to be a computer.

Although AI has a strong science fiction connotation, it forms a vital branch of computer science, dealing with intelligent behavior, learning and adaptation in machines. Research in AI is concerned with producing machines to automate tasks requiring intelligent behavior. Examples include control, planning and scheduling, the ability to answer diagnostic and consumer questions, handwriting, speech, and 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 software applications and video games.

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AI divides roughly into two schools of thought: Conventional AI and Computational Intelligence (CI).

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. Clippy the Microsoft Office paperclip is an example. As the user types, Clippy recognizes certain traits and makes suggestions.

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, scruffy AI and soft computing. Methods mainly include:

Fuzzy systems: techniques for reasoning under uncertainty, has been widely used in modern industrial and consumer product control systems.Evolutionary computation: applies biologically inspired concepts such as populations, mutation and survival of the fittest to generate increasingly better solutions to the problem. These methods most notably divide into evolutionary algorithms (e.g. genetic algorithms) and swarm intelligence (e.g. ant algorithms).

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.

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.

Bertrand Russell and Alfred North Whitehead published i, 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 networks.

The 1950s were a period of active efforts in AI. John McCarthy coined the term "artificial intelligence" in the first conference devoted to the subject. 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.

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 i, 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 autonomously negotiate cluttered obstacle courses.

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. 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.

The strong AI vs. weak AI debate is still a hot topic amongst AI philosophers. This involves philosophy of mind and the 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 i argue in favour of Functionalism. In many strong AI supporters’ opinion, artificial consciousness is considered as the holy grail of artificial intelligence.

In science fiction AI is commonly portrayed as an upcoming power trying to overthrow human authority as in HAL 9000, Skynet, Colossus and The Matrix or as service humanoids like C-3PO, Data, the Bicentennial Man, the i in A.I. or Sonny in I, Robot.

The inevitability of AI world domination, sometimes called "the Singularity", is also argued by some science writers like Isaac Asimov and Kevin Warwick. In works such as the Japanese manga i, the existence of intelligent machines questions the definition of life as organisms rather than a broader category of autonomous entities.

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