University of Alberta Dictionary of Cognitive Science: Artificial Intelligence

Artificial intelligence is concerned with the attempt to develop computer programs that will be capable of performing difficult cognitive tasks. Some of those who work in artificial intelligence are relatively unconcerned as to whether the programs they devise mimic human cognitive functioning, while others have the explicit goal of simulating human cognition on the computer.

The artificial intelligence approach has been applied to several different areas within cognitive science, including perception, memory, imagery, thinking, and problem solving. Historically important overviews of this approach can be found in Newell and Simon (1972) and Nillson (1980).

There are a number of advantages of the artificial intelligence approach to cognition. Computer programming requires that every process be specified in detail, unlike cognitive psychology which often relies on vague descriptions. AI also tends to be highly theoretical, which leads to general theoretical orientations having wide applicability. The main disadvantage of AI is that there is a lot of controversy about the ultimate similarity between human cognitive functioning and computer functioning.

Artificial intelligence is usually associated with the so-called classical or symbolic approach to cognition, which has been argued to be far removed from brain-like processing. Some of the major differences between brains and computers were spelled out in the following terms by Churchland (1989, p.100): "The brain seems to be a computer with a radically different style. For example, the brain changes as it learns, it appears to store and process information in the same places...Most obviously, the brain is a parallel machine, in which many interactions occur at the same time in many different channels." This contrasts with most computer functions which involves serial processing and relatively few interactions. However, many expert systems have been developed in the framework of artificial neural networks, and would indeed count as examples of artificial intelligence.

References:

Churchland, P.S. (1989). From Descartes to neural networks. Scientific American , July, 100.
Newell, A., & Simon, H. A. (1972). Human Problem Solving. Englewood Cliffs, NJ: Prentice-Hall
Nilsson, N. J. (1980). Principles Of Artificial Intelligence. Los Altos, CA: Morgan Kaufman.