| Ah, the early days of AI. If a book or movie is ever made about the history of AI, the script would include this period of AI history and would probably go something like this… (Some dramatic license here, sure. But not much more than your average "based on true events" script.) In 1957, Frank Rosenblatt built a physical neural network machine called the Perceptron. It used variable resistors and reconfigurable wiring to simulate brain-like learning. Each resistor had a motor to adjust weights, allowing the system to "learn" from input data. Hook it up to a fridge-sized video camera (20x20 resolution), train it overnight, and it could recognize objects. Pretty wild for the time. Rosenblatt was a showman—loud, charismatic, and convinced intelligent machines were just around the corner. Marvin Minsky, a jealous academic peer of Frank, was in favor of a different approach to AI: Expert Systems. He published a book (Perceptrons, 1969) which all but killed research into neural nets. Marvin pointed out that no neural net with a depth of one layer could solve the "XOR" problem. While the book's findings and mathematical proof were correct, they were based on incorrect assumptions (that the Perceptron only used one layer and that algorithms like backpropagation did not exist). As a result, a lot of academic AI funding was directed towards Expert Systems.
The flagship of this was the MYCIN project. Essentially, it was a system to find the correct antibiotic based on the exact bacteria a patient was infected with. The system thus had knowledge about thousands and thousands of different diseases with their associated symptoms. At the time, many different antibiotics existed, and using the wrong one for a given disease could be fatal to the patient. When the system was finally ready for use... after six years (!), the pharmaceutical industry had developed “broad-spectrum antibiotics,” which did not require any of the detailed analysis MYCIN was developed for. The period of suppressing Neural Net research is now referred to as (one of) the winter(s) of AI. -------- As said, that is the fictional treatment. In reality, the facts, motivations, and behavior of the characters are a lot more nuanced. |
I went through Stanford CS when those guys were in charge. It was starting to become clear that the emperor had no clothes, but most of the CS faculty was unwilling to admit it. It was really discouraging. Peak hype was in "The fifth generation: artificial intelligence and Japan's computer challenge to the world" (1983), by Feigenbaum. (Japan at one point in the 1980s had an AI program which attempted to build hardware to run Prolog fast.)
Trying to use expert systems for medicine lent an appearance of importance to something that might work for auto repair manuals. It's mostly a mechanization of trouble-shooting charts. It's not totally useless, but you get out pretty much what you carefully put in.