The Keeper of the Power of Knowledge

"There are three important things that go into building a knowledge-based system: knowledge, knowledge, knowledge. The competence of a system is primarily a function of what the system knows as opposed to how well it reasons."

- Edward Feigenbaum

Edward A. Feigenbaum's story begins with a tragedy. He was born in Weehawken, NJ in 1936, and just before his first birthday, his father tragically passed away. Feigenbaum's stepfather, an accountant of a small local bakery, was in turn charged with the job to ignite his interest in science and technology, and so he did by taking young Feigenbaum to the Hayden Planetarium in New York City once a month to all the new exhibits. In 1952, Feigenbaum started his college career at the Carnegie Institute of Technology (now known as Carnegie Mellon University) majoring in electrical engineering, per his parents' request. Computer science did not yet exist for the average undergraduate at Carnegie, so Feigenbaum "began taking courses at Carnegie's then new Graduate School of Industrial Administration" (210). These courses, via professor James March, were the first to introduce him to the ideas of game theory and a lot of other work done by a Hungarian mathematician John von Neumann. Feigenbaum also had a rare opportunity to attend a course read by Herbert Simon, a professor at Carnegie in the fields of political science, sociology, and economics, as well as a former federal  administrator for the Marshall Plan, on mathematical models in social sciences. One day,
Simon, and his co-lecturer Allen Newell, announced that they had invented "a thinking machine" called "the Logic Theorist" and handed out user manuals for the IBM 701 (211). Feigenbaum took the manual home, read it, and finally realized what he wanted to do. The idea of the Logic Theorists was interesting: the "...program attempted to discover proofs in propositional logic" based on some other logic that is already known to the program using educated guessing problem-solving technique formally called a heuristic by a Hungarian mathematician George Polya (212). Fascinated by these ideas, Feigenbaum stayed at Carnegie with the School of Industrial Administration until 1956 when he graduated with his PhD in electrical engineering. His doctoral thesis involved more work with the Logic Theorist as he attempted to further model human problem-solving abilities, such that he could draw some conclusions about human problem solving. It turned out to be a very hard problem, but it was completed under the name Elementary Perceiver and Memorizer (EPAM), and it is still used today at Carnegie Mellon. More specifically, the program modeled how humans are able to memorize pairs of unrelated, nonsense words in a stimulus-response setting. The process included a training portion and a testing portion, and from a psychology standpoint, provided a lot of insights into the working and abilities of short-term memory. This research lead Feigenbaum first to the University of California at Berkeley and then, eventually, to Stanford where John McCarthy was doing his work with artificial intelligence in 1965. At Stanford, Feigenbaum began to formulate his thoughts about expert systems. In a collection of papers Computers and Thoughts that he co-edited with a colleague Julian Feldman, he first began advocating for further exploration of computer-based processes of induction. In 1964, Feigenbaum, Joshua Lederberg, the chairman of the Stanford genetics department, and a Stanford chemist Carl Djerassi, began their work on a joint project Dendral which attempted to develop a "Mars probe that would land on the surface of the red planet and explore for life or precursor molecules" (216). The project, which a year later had been declared successful, is considered to be the the world's first true expert system capable of determining chemical structure of molecules even better than most humans could. This project also laid out the framework for expert systems in general: "a set of data, a set of hypotheses, and a set of rules to choose among the hypotheses" (218). Soon, the company developed all kinds of other expert systems including Mycin, which was meant to help doctors diagnose infectious diseases and recommend treatment to numerous patients, and airline management systems, which supported airport traffic controllers. In the end, the idea of standardized knowledge turned out to be key to the expert system structure. The more knowledge exists in the system, the better, more efficient, and simply smarter the expert system can be. Despite the extensive work that Feigenbaum did in this area, expert systems are in their developing stages in the world of computer science, but even Feigenbaum himself believes that "the expert system will gain its rightful place as an intelligent agent that can cooperate with people to solve some of the world's more challenging problems" (222).