T. Jefferson Coolidge Professor of Computer Science and Applied Mathematics
What Needs to be Added to Machine Learning?
Supervised learning is a cognitive phenomenon which has proved amenable both to theoretical analysis as well as exploitation as a technology. However, not all of cognition can be accounted for by supervised learning. The question we ask here is whether one can build on the success of machine learning to address the broader goals of artificial intelligence. We regard reasoning as the major component of cognition that needs to be added. We suggest that the central challenge therefore is to unify the formulation of these two phenomena, learning and reasoning, into a single framework with a common semantics. We propose Robust Logic for this role, as a framework with a satisfactory theoretical basis. Testing it experimentally on a significant scale remains a major challenge for the future.
This lecture will be held in Wu & Chen Auditorium, 101 Levine Hall (3330 Walnut Street). A reception will follow the lecture.