It had all the high-tech razzle-dazzle of a consumer-electronics trade show. But most of the computer systems on display started at $50,000 and did a good deal more than play video games. At the booth of a company called Intellicorp, engineers from Ford Aerospace were showing off a program for troubleshooting balky satellites. At the Apollo Computer display, a firm called Visual Intelligence had a system to help nuclear-plant operators quickly interpret the kind of instrument readings that confused technicians at Three Mile Island. On a Digital Equipment computer, newspaper specialists from Composition Systems exhibited a program that lets editors accommodate late- breaking news by reducing from hours to minutes the time it takes to lay out and print a new edition.
The event, held last week at the University of California, Los Angeles, was the Ninth International Joint Conference on Artificial Intelligence, the premier showcase of the most esoteric of computer sciences. Five years ago, a similar meeting at Stanford University was attended by 900 earnest academics. The UCLA gathering drew a crowd of 10,000 that included military brass, investment bankers and scores of corporate headhunters.
Why this sudden interest in artificial intelligence? In two words: expert systems. An expert system is an AI program that captures in computer code the knowledge and informal rules of thumb used by a particular human expert to solve a particular problem. "It's the closest thing to cloning a human mind," says Randall Davis, a professor at M.I.T.'s Artificial Intelligence Lab. Expert systems for use in nuclear power plants, for example, are programmed with the relevant knowledge of the handful of top engineers who know just what to do when a dozen different alarms and signals go off all at once. Once transferred into the computer through a painstaking debriefing of the expert by a team of programmers--a process known as knowledge engineering --this expertise can be made available for consultation round the clock. Similar systems have cloned insurance underwriters, geologists, military field commanders and scores of other human specialists.
The technique behind all these systems can be traced to MYCIN, a computer program written in the mid-1970s by a Stanford physician and computer scientist named Edward Shortliffe. Using tools developed for AI research, Shortliffe boiled down everything he knew about diagnosing infectious blood diseases and meningitis into about 500 "if-then"rules. Rule 27, for example, said that if an organism found in a patient's blood is rod shaped, gram- negative and able to survive in the absence of oxygen, then there is a strong likelihood that the organism is a type of bacteria called Bacteroides. In tests that applied these rules to cases reported in the medical literature, MYCIN was eventually able to diagnose as well as or better than most medical experts.