50 Shades of Understanding: Why the Veneer of Intelligence is Not Enough

Almost everyone who talks about AI, nowadays, means neural net-based machine learning from big data. That fast pattern-finding and -using is a lot like what our right brain hemispheres do. But we also have a left brain hemisphere, which reasons more slowly, logically, and causally. Human beings' super-power is harnessing both types of reasoning, and I believe that the most powerful AI solutions in the coming decade will likewise be hybrids of right-brain-like "thinking fast" and left-brain-like "thinking slow." The ability (versus inability) to rationalize their decisions will make near-future AI systems like autonomous cars, household robots, automated assistants, and AI-powered social media far more trusted and far more trustworthy (versus a string of unfortunately newsworthy disasters). We'll recap the methods AI has developed for this sort of symbolic representation and automated reasoning, the progress over the last several decades, and the current state of the art.

Dr. Doug Lenat, a prolific author and pioneer in artificial intelligence, focuses on applying large amounts of structured knowledge to information management tasks. As the head of Cycorp, Dr. Lenat leads groundbreaking research in software technologies, including the formalization of common sense, the semantic integration of - and efficient inference over - massive information sources, the use of explicit contexts to represent and reason with inconsistent knowledge, and the use of existing structured knowledge to guide and strengthen the results of automated information extraction from unstructured sources. Doug is applying these technologies commercially in the healthcare information and energy industries, and for the U.S. government in intelligence analysis and K-12 education. Previously, he was a professor in Stanford University's computer science department and the principal scientist at Microelectronics and Computer Technology Corporation. Doug was also one of the original fellows of the American Association for Artificial Intelligence. He serves on the Rule Interchange Format and OWL 1.1 working groups of the World Wide Web Consortium, and he is the recipient of the biannual International Joint Conference on Artificial Intelligence Computers and Thought Award.