Bootstrapping Cognitive Systems
Kenneth D. Forbus
Annotation, even by crowdsourcing, is a trap: People learn without others having privileged access to their mental states. Today’s ML systems require a cadre of technical experts to train them, using massively more data than people require, whereas people manage their own learning processes over a lifetime. Understanding how to build cognitive systems that can learn well from small amounts of data, expressed in forms natural to people, and able to manage their own learning over extended periods would be a revolutionary advance. In the Companion cognitive architecture, we are exploring ways to do this, using a combination of analogy and relational representations. This talk will describe several recent advances we have made, including learning human behavior from Kinect data, analogical chaining for commonsense reasoning, and co-learning of language disambiguation and reasoning using unannotated data. Ideas for scaling an analogical approach to cognitive systems to human-sized knowledge bases and potential applications along the way will also be discussed.
Kenneth D. Forbus is the Walter P. Murphy Professor of Computer Science and Professor of Education at Northwestern University. He received his degrees from MIT (Ph.D. in 1984). His research interests include qualitative reasoning, analogical reasoning and learning, spatial reasoning, sketch understanding, natural language understanding, cognitive architecture, reasoning system design, intelligent educational software, and the use of AI in interactive entertainment. He is a Fellow of the Association for the Advancement of Artificial Intelligence, the Cognitive Science Society, and the Association for Computing Machinery. He has received the Humboldt Award and has served as Chair of the Cognitive Science Society.