1960s - 1970s: Expert Systems Gain Attendance The concept of expert systems dates back to the 1960s but first gained prominence in the 1970s. Conclusive for this development were the insights of the Stanford University professor Edward Feigenbaum, who in 1977 demonstrated that the problem-solving capacity of a computer program rather is a result of the knowledge it posses, than of the applied programming techniques and formalisms. Expert systems were designed to mimic the knowledge and reasoning capabilities of a human specialist in a given domain by using (top down) artificial intelligence techniques. Made possible by the large storage capacity of the computers at the time, expert systems had the potential to interpret statistics and formulate rules. An initial use of expert systems was to diagnose and treat human physical disorders, but as its applications in the market place were extensive over the course of the following years they were also employed in fields such as stock market forecast, taxation, chemistry, and geology.

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Artificial intelligence approaches Looking for ways to create intelligent machines, the field of artificial intelligence (AI) has split into several different approaches based on the opinions about the most promising methods and theories. The two basic AI approaches are: bottom-up and top-down. The bottom-up theory suggests that the best way to achieve artificial intelligence is to build electronic replicas of the human brain's complex network of neurons (through neural networks and parallel computing) while the top-down approach attempts to mimic the brain's behavior with computer programs (for example expert systems).

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