Steps towards human-level AI
A confluence of three factors is changing the kinds of AI experiments that can be done: (1) increasing computational power, (2) off-the-shelf representational resources, and (3) steady scientific progress, both in AI and in other areas of Cognitive Science. Consequently, I believe it is time for the field to spend more of its energy experimenting with larger-scale systems, and attempting to capture larger constellations of human cognitive abilities. This talk will summarize experiments with two larger-scale systems we have built at Northwestern: (1) Learning to solve AP Physics problems, in the Companions cognitive architecture. In an evaluation conducted by the Educational Testing Service, a Companion showed it was able to transfer knowledge across multiple types of variant problems. (2) Learning by reading, using the Learning Reader prototype. Learning Reader includes a novel process, rumination, where the system improves its learning by asking itself questions about material it has read.
Kenneth D. Forbus is the Walter P. Murphy Professor of Computer Science and Professor of Education at Northwestern University. His research interests include qualitative reasoning, analogy and similarity, sketch understanding, spatial reasoning, cognitive simulation, reasoning system design, articulate educational software, and the use of AI in computer gaming. He received his degrees from MIT (Ph.D. in 1984). He is a Fellow of the American Association for Artificial Intelligence, the Cognitive Science Society, and the Association for Computing Machinery. He serves on the editorial boards of Cognitive Science, the AAAI Press, and on the Advisory Board of the Journal of Game Development.