Douglas Lenat

Douglas Bruce Lenat (born 1950) is the CEO of Cycorp, Inc. of Austin, Texas, and has been a prominent researcher in artificial intelligence; he was awarded the biannual IJCAI Computers and Thought Award in 1976 for creating the landmark machine learning program, AM. He has worked on (symbolic, not statistical) machine learning (with his AM and Eurisko programs), knowledge representation, "cognitive economy", blackboard systems, and what he dubbed in 1984 "ontological engineering" (with his Cyc program at MCC and, since 1994, at Cycorp). He has also worked in military simulations, and numerous projects for US government, military, intelligence, and scientific organizations. In 1980, he published a critique of conventional random-mutation Darwinism based on his experience with Eurisko, proposing a system very much like the subsequently-discovered epigenetics, namely that long before Darwinian random generate and test would evolve complex organisms and systems, nature would have stumbled onto the much simpler and more powerful idea of, in effect, the scientific method: experiment and learn from the results. He authored a series of articles in the Journal of Artificial Intelligence exploring the nature of heuristic rules and trying to lay out a science for studying those qua phenomenon.

Lenat was one of the original Fellows of the AAAI, and is the only individual to have served on the Scientific Advisory Boards of both Microsoft and Apple. He is a Fellow of the AAAS, AAAI, and Cognitive Science Society, and an editor of the J. Automated Reasoning, J. Learning Sciences, and J. Applied Ontology. He was one of the founders of TTI/Vanguard in 1991 and remains a member of its advisory board still in 2017. He was named one of the Wired 25.Lenat's quest, in the long-running Cyc project begun in 1984, is to build the basis of a general artificial intelligence by manually representing knowledge as contextualized logical axioms in the formal language, CycL, based on extensions to first-order predicate calculus, and then use that enormous ontology, inference engine (tasked with efficiently finding hundreds-of-step arguments in that sea of tens of millions of axioms), and contextualized knowledge base as an inductive bias to increasingly automate and accelerate the continuing education of Cyc itself, via (symbolic, not statistical) machine learning and (symbolic, not statistical) natural language understanding. Since about 2010, this multi-thousand-person-year enterprise has entered into that last phase, with his team's efforts on Cyc-powered machine learning and Cyc-powered natural language understanding supplementing and overtaking the still-ongoing manual creation of Cyc knowledge base content.