Computers versus Common Sense
November 21, 2022 2:30 PM - 3:15 PM EST
Doug Lenat
CEO, Cycorp
Cycorp
Website: https://cyc.com/leadership-team/
Abstract: Almost everyone who talks about Artificial Intelligence, nowadays, means training deep neural nets on big data. Developing and using those patterns is a lot like the cognition historically attributed to our right brain hemispheres; it enables AI’s to react quickly and – very often – adequately. But we human beings also make good use of the sort of cognition historically attributed to our left brain hemisphere, reasoning slowly, logically, and causally. I will discuss this “other type of AI”– i.e., symbolic AI, which comprises a formal representation language, a “seed” knowledge base with hand-engineered default rules of common sense and domain knowledge written in that language, and an inference engine capable of producing hundreds-deep chains of deduction, induction, and abduction on that large knowledge base. I will describe the largest such platform, Cyc, a few commercial applications that were produced just by educating it as one might teach a new human employee, and give a short demo. We’ve made a lot of mistakes, and learned a lot of lessons, in the last four decades, in trying to get such an AI to operate on without having to compromise on speed or on the expressiveness of its representation. But it is important to remember that human beings’ “super-power” is our ability to harness both types of reasoning, and I believe that the most powerful AI solutions in the coming decade will likewise be hybrids of the two. That is the only path I see by which we will overcome the current dangerous inability of deep-learning AI’s to understand and explain their decisions, and will make AI’s far more trusted and – more importantly – far more trustworthy.
Bio: Dr. Douglas B. Lenat is the founder of the Cyc project, and founder and Co-CEO of Cycorp. Dr. Lenat has been a Professor of Computer Science at Carnegie-Mellon University and Stanford University, and has received numerous honors. These include being awarded the biennial IJCAI Computers and Thought Award, which is the highest honor in artificial intelligence; being named the first Fellow of the Association for the Advancement of Artificial Intelligence (AAAI); and being named a Fellow of the American Academy for the Advancement of Science (AAAS) and the Cognitive Science Society. He has authored over one hundred publications primarily in the areas of machine learning, automatic program synthesis, knowledge based systems, representation, and automated inference, and he is an editor of the J. Automated Reasoning, J. Learning Sciences, J. Applied Ontology, J. Applied Artificial Intelligence, and the Springer Artificial Intelligence series of books.