Artificial Intelligence Whil Hentzen Greater Cincinnati IBM PC User's Group The first problem of Artificial Intelligence (AI) is that everyone has his own definition. Herb Simon and John McCarthy from the East Coast, Edward Feighenbaum and Nils Nilsson on the West Coast, we in America's heartland - no two definitions are alike, and all have been misunderstood by the media as they try to capture the latest buzzwords for the 80's. Instead of trying to set a definition in concrete, lets use this: Artificial Intelligence is a process by which a device is made able to perform tasks which, when they are performed by humans, are said to require some thought. The reason for the hedging in that statement is that it usually happens that as soon as a machine can do some task, that task is declared not to have needed intelligence to be performed in the first place. Let's look at some of the applications of AI. GAMES Computers that play chess (and win!) use AI techniques. The first idea is to create a tree that branches out into all the possible moves that the two players can make. However, as the game develops, the number of branches of a chess tree become so p#large that even a Cray works overtime. The next step is to assign values to moves - a high value to a move that takes the other queen, a somewhat lower value to a move that opens your king to mate in two moves. Thus, whole sets of branches can be eliminated because of the low value calculated. The final step is to create strategies, plans, counterplans and all the other things that humans do. THEOREM PROVING This is basically checking up on the work of mathematicians and other folks of that breed. As mathematics get more complicated (e.g. Fermat's Last Theorem or the Four Color Problem), a computer with a human-like mind is handy to double check your work. The English language analogy would be a spelling checker that made sure you used "your/you're" and "effect/affect" in the right places. PREDICATE CALCULUS This is applying Boolean Logic to ideas instead of what BL usually is applied to. For example, imagine the diagnostic procedures doctors go through to determine a patients' illness. Now imagine if you had some symptoms that were related to a rare disease...a computer with the ability to sort through all these Rules wouldn't care, because it had a perfect memory and rather fast search time. A doctor would probably have a tougher pçtime. These Rules could be hooked up with programs and called Expert Systems. There are useful expert systems around, however - several of the famous ones are Mycin (a medical diagnostic expert system), and Prospector, a mineral deposits locator that recently found a massive Molybdenum strike in Montana. PATTERN RECOGNITION How to give ears and eyes to a machine. Let's talk about vision. A camera takes a picture and breaks it down into PIXELS. Sufficient resolution for an 8.5 x 11 drawing might be 250 dots per inch. This means, for black and white only, that picture has 11.5 million pixels to process. That takes a while. You and I (and your 12 year old son) can look at a piece of paper and determine if it is an insurance form or a centerfold rather quickly. The computer, however, must analyze each and every pizel. One way of speeding it up is to break the picture up into regions of light and dark, of edges and places, of shapes and objects. Then it compares these to images it already has stored in memory, and makes "guess" if they are pretty close. Naturally, if the picture is in color, the number of pixels that must be processed increases tremendously. Try doing that on an abacus! ============================