But at a more aggregate level, symbols can be represented by neuronal patterns, and, in a manner that is wholly different physically, symbols can also be represented by patterns of electromagnetism in computers. It is at this symbolic level that, despite the radical difference in implementation, the symbols stored and processed in a computer can simulate the symbols stored and processed in the brain. A symbol is a pattern, any pattern that denotes or points to some other pattern. The physical symbol system hypothesis asserts that possessing these processes is the necessary and sufficient condition for a system to be capable of thinking. As a matter of fact, numbers seldom enter at all into the programs that have been written to simulate human thinking.
Artificial intelligence embraces both endeavors.
In fact, the most successful programs at present substitute a great deal of brute force search for human cunning. Deep Thought, the current champion among computer programs, may look at 50 million branches of the game, or more, before choosing its next move. Instead, we try to incorporate a rich body of chess knowledge in the program, and imitate the human capability of choosing good moves after searching very selectively. Computer programs, quite unlike Deep Thought, that play along these lines have performed at perhaps the Expert level, but not the Grandmaster level. This means that, while we have managed to simulate the principal mechanisms of human chess play, we have not yet managed to provide a single program with the full array of knowledge that a grandmaster possesses.