Curriculum Descant: Interdisciplinary artificial intelligence

Abstract

s a course offered within computer science programs, artificial intelligence should be an interdisciplinary course. Stated more carefully, an undergraduate artificial intelligence course for a computer science department, correctly designed, should be able to be taken by any student with good analytic skills but lacking programming skills. Making a well-designed artificial intelligence course interdisciplinary is not itself a goal of the preferred course design but rather a consequence of it. Many computer science students are primarily and sometimes exclusively interested in programming and related technical matters. Their focus is implementation. Most computer science instructors, myself included, talk, sometimes a good deal, about the idea that we aim primarily to teach students problem solving , but in fact we mostly end up focusing on implementation, too. (Perhaps a " proper " computer science degree should, after all, à la Dijkstra, ban actual programming for the first two years or so.) We as instructors contribute to this unfortunate state of affairs by, sometimes unwittingly, overdesigning our class projects. In our attempts to make sure that the students get the top-level design " right, " we give it to them up front, often giving detailed descriptions of the suite of functions and so on that must be implemented. The task that falls to the student is often little more than to implement our design. It is more difficult to correct the situation than those who have not taught might imagine. Such is the case much of the time in typical computer science courses , mine included. In an artificial intelligence course, problem solving fares even worse because the problems tackled by artificial intelligence are so much more difficult. The problems tackled by artificial intelligence are not only complex, they also require a good deal of background theory in order to be properly grasped. The amount of background varies, but it is always considerable. Computer science programs are not the ideal training grounds for artificial intelligence. There are of course exceptions, but in general, computer science students lack, for example, an understanding of philosophical issues, which bears on KR, or a detailed understanding of natural languages, which bears on NLP. But most of all, they are not strong mathematically: many struggle through calculus, statistics, logic, and discrete math. As a result, the theoretical content and mathematical sophistication of discussions in artificial intelligence courses are all too often quite weak or, at any rate, weaker …