The Great 1980s AI Bubble: A Review of The Brain Makers

From a gleam in Alan Turing’s eye in the 1940s, machine intelligence took root as an academic discipline in the 1950s; was cultivated in the 1960s; matured in the 1970s; and spread seed in the 1980s, which took root and bore fruit in a myriad companies in the 1990s. Newquist is a business reporter who covered the field during the 1980s when academic researchers went commercial in one of the 1980’s smaller speculative bubbles. His book begins with a history spanning Babbage to Turing to Minsky, McCarthy, Newell, Simon, Samuel, and others at the 1956 Dartmouth meeting and moves on to the 1980s, where the real story begins. Good, if glib, descriptions of people, places, and events are punctuated by technical explanations ranging from poor to inane. Because I am a little slow, it took me a quarter of the book to recognize a journalist with an attitude. Only laboratory geeks (variations on the term pepper the book) waste time on confusing technical minutiae; real people skim executive summaries, then spend quality time on the financials. The book never misses an opportunity to sneer at academics, who always fail to grasp this simple fact. By contrast, it fawns over Ateam executives from the best business schools—masters of management, finance, and marketing, the core of America’s greatness. Because most of the characters encountered in the first wave of AI businesses were researchers, sneering dominates over fawning. The author’s aversion to places away from the executive suite distorts the book’s coverage. Information about Stanford University comes by way of companies founded by Ed Feigenbaum. In this perspective, John McCarthy’s AI lab in the Stanford hills in the 1970s is a nonentity, despite the fact that it sparked a robot boom that foreshadowed the AI boom by five years, because pioneering companies making small assembly robots and industrial vision systems failed just as the robots became essential to manufacturing worldwide. Alan Newell’s world-leading but unmarketed reasoning program research at Carnegie Mellon University, conducted vigorously through the 1980s, is dismissed. Companies and products oriented toward technical users, for example, Stephen Wolfram’s Mathematica, are completely overlooked. Research labs, engineering offices, and factory floors are simply not on Harvey Newquist’s beat. Within its one-sided scope, however, the book is pretty interesting. The book’s subtitle lists genius, ego, and greed, but naivete and dilettantism deserve equal billing because most of the book’s antiheroes were as unprepared for business as Newquist is for technical comprehension. The first AI companies rushed to market academically interesting but underdeveloped techniques with few applications or customers. In the speculative hysteria of the 1980s, most found enough backing to support lavish facilities and bloated staffs, lured from academia with inflated salaries and promises. Symbolics, maker of Lisp machines invented by Richard Greenblatt, was the major player. It had several successful years in the mid-1980s selling machines to research groups and secondary AI companies and their immediate clients. The secondary companies sold application-oriented or generic expert systems at exorbitant prices. The bubble developed fatal leaks by the end of the decade when rapidly evolving, cheap UNIX workstations started to run Lisp code as well as the Lisp machines could, followed a few years later by personal computers. In addition, when expert systems began to be written in C and other non-Lisp languages, client companies found they could implement their applications in house at a lower cost. Symbolics, Palladin, IntelliCorp, Teknowledge, Gold Hill, and other smaller companies, as well as the decade-long Japanese Fifth-Generation Project, evaporated with the 1980s, leaving behind a few viable puddles. Symbolics became Macsyma, Inc., marketing a symbolic mathematics program that was once a minor product. Teknowledge was reduced to a small division. Hundreds of traditional companies now use AI techniques in house for such things as geological exploration, financial decision making, medical advice, factory scheduling, and mechanical troubleshooting. The expert system hype made its successes look like failures in comparison, giving young turks with a competing approach an opportunity to overreact. Biologically inspired neural nets, which learn input-output relationships from examples, lost to AI in 1965 when Minsky and Papert’s Perceptrons proved fundamental limitations in two-layer nets. In 1983, John Hopfield showed how to train threelayer nets, and soon enthusiasts were claiming neural nets would deliver AI’s failed promises. Nets were better in data-intensive applications, such as detecting fraud or classifying sensor data, but were unsuitable for long chains of inference, and the restrained economy of the 1990s has moderated attempts to imitate the