On the other hand, AI programs that simulate human vision are typically theoretical attempts to understand the actual processes of human beings in perceiving the external world. Just to have labels, let us distinguish between "AI as practical application" (AIPA) and "AI as cognitive science" (AICS). A great deal of the debate about AI confuses the two views, so that sometimes success in AI's practical application is supposed to provide theoretical insights in cognitive science. Chess-playing programs are a good example. Early chess playing programs tried to mimic the thought processes of actual chess players, but they were not very successful. .
More recent successes have been achieved by ignoring the thoughts of chess masters and simply using the much-greater computational power of contemporary hardware. This approach, called "brute force," exploits the fact that specially designed computers can calculate hundreds of thousands or even millions of moves, something no human chess player can do (Matthys, 1995). The best current programs can thus beat all but the very best chess players, but it would be a mistake to think of them as contributions to AICS (Ptacek, 1994). They tell us nothing about human cognition, except that an electrical machine working on different principles can defeat human beings in playing chess, as it can defeat human beings in doing arithmetic.
For the sake of the discussion, let us assume that AIPA is completely successful and that we will soon have programs whose performance can equal or beat that of any human in any comprehension task at all. Assume we had machines that could not only play better chess but display equal or better comprehension of natural languages, write equal or better novels and poems, and prove equal or better mathematical theorems. In short, let us fantasize any success of AIPA that we care to imagine. What should we make of these results? What would be the implications for AICS of such successes in AIPA? .
