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The technological lineage of RIC and almost every other second-wave system can be traced back to Mycin, an expert system written at Stanford in the mid- 1970s. Named for a group of antibiotics, Mycin was the brainchild of a Ph.D. candidate named Edward Shortliffe, who designed it to help physicians diagnose certain infectious diseases and choose appropriate remedies. After painstakingly interviewing doctors about the process of diagnosis and treatment, Shortliffe and company programmed Mycin with some 500 rules to guide its decisions.
Unlike the basic unit of conventional computer programming -- the algorithm, which details a precise series of steps that will yield a precise result -- those rules (referred to in computerese as heuristics) state a relationship that is likely, but not guaranteed, to yield an outcome. Heuristics allow computers to deal with situations that cannot be reduced to mathematical formulas and may involve many exceptions. It is the kind of reasoning that governs countless everyday decisions, ranging from the mundane, such as choosing the appropriate clothes for a job interview, to the apocalyptic, such as deciding whether a Soviet missile launch is a routine test or an all-out attack.
Mycin took some 20 man-years to complete. It turned out to be more accurate than the humans against whom it was tested: in one trial, the system prescribed the correct treatment 65% of the time, in contrast to human specialists, who were right in 42.5% to 62.5% of cases. Still, Mycin did not have a clue that it was diagnosing a human being, nor did it have any idea what a human is. In fact, it was perfectly capable of trying to prescribe penicillin to fix a broken window. All it could do was rigidly test the applicability of various rules to pieces of data. This led critics like Joseph Weizenbaum, a professor of computer science at M.I.T., to dismiss expert systems like Mycin as "Potemkin villages. You move a little to the left, and you see it's all a facade."
While Weizenbaum and other critics insisted on measuring Mycin against human intelligence and knowledge, others looked at the system and saw a computer- handling expertise that had previously resisted automation. No one, however, was going to build expert systems if they took several years to construct. Solution: create a Mycin without medical knowledge -- in effect, construct an empty shell into which programmers could pour all kinds of different expertise. In 1977 a team of Stanford researchers under Feigenbaum dubbed the new shell Emycin (for Empty Mycin) and used it to build several more expert systems. Emycin spurred a number of start-up companies, led by AI entrepreneurs like Feigenbaum, to build knowledge shells for the commercial market.
The second wave had a rocky start. Too often, enthusiastic young computer nerds babbling in technospeak would sell flashy systems to computer-dazzled counterparts in the research divisions of Fortune 500 companies. In turn, the corporate techies built glitzy prototypes that ran on exotic hardware. By the mid-1980s it became clear that both groups had missed the point: big companies did not want sexy technology for its own sake; they wanted solutions to business problems. Consequently, a number of once gung-ho companies began to sour on artificial-intelligence technology as expensive and impractical.