What is Artificial Intelligence?

By Jack Copeland

© Copyright B.J. Copeland, May 2000

Top-Down AI vs Bottom-Up AI

Turing's manifesto of 1948 distinguished two different approaches to AI, which may be termed "top down" and "bottom up". The work described so far in this article belongs to the top-down approach. In top-down AI, cognition is treated as a high-level phenomenon that is independent of the low-level details of the implementing mechanism--a brain in the case of a human being, and one or another design of electronic digital computer in the artificial case. Researchers in bottom-up AI, or connectionism, take an opposite approach and simulate networks of artificial neurons that are similar to the neurons in the human brain. They then investigate what aspects of cognition can be recreated in these artificial networks.

The difference between the two approaches may be illustrated by considering the task of building a system to discriminate between W, say, and other letters. A bottom-up approach could involve presenting letters one by one to a neural network that is configured somewhat like a retina, and reinforcing neurons that happen to respond more vigorously to the presence of W than to the presence of aany other letter. A top-down approach could involve writing a computer program that checks inputs of letters against a description of W that is couched in terms of the angles and relative lengths of intersecting line segments. Simply put, the currency of the bottom-up approach is neural activity and of the top-down approach descriptions of relevant features of the task.

The descriptions employed in the top-down approach are stored in the computer's memory as structures of symbols (e.g. lists). In the case of a chess or checkers program, for example, the descriptions involved are of board positions, moves, and so forth. The reliance of top-down AI on symbolically encoded descriptions has earned it the name "symbolic AI". In the 1970s Newell and Simon--vigorous advocates of symbolic AI--summed up the approach in what they called the Physical Symbol System Hypothesis, which says that the processing of structures of symbols by a digital computer is sufficient to produce artificial intelligence, and that, moreover, the processing of structures of symbols by the human brain is the basis of human intelligence. While it remains an open question whether the Physical Symbol System Hypothesis is true or false, recent successes in bottom-up AI have resulted in symbolic AI being to some extent eclipsed by the neural approach, and the Physical Symbol System Hypothesis has fallen out of fashion.

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