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Machine Learning and Logic: Fast and Slow Thinking

Series: Distinguished Department Seminar

Speaker: Moshe Y. Vardi Rice University

Date/Time: Feb 16 18:30:00

Location: Microsoft teams

Abstract:
Computer science seems to be undergoing a paradigm shift. Much of earlier research was conducted in the framework of well-understood formal models. In contrast, some of the hottest trends today shun formal models and rely on massive data sets and machine learning. A cannonical example of this change is the shift in AI from logic programming to deep learning. I will argue that the correct metaphore for this development is not paradigm shift, but paradigm expansion. Just as General Relativity augments Newtonian Mechanics, rather than replace it -- we went to the moon, after all, using Newtonian Mechanics -- data-driven computing augments model-driven computing. In the context of Artificial Intelligence, machine learning and logic correspond to the two modes of human thinking: fast thinking and slow thinking. The challenge today is to integrate the model-driven and data-driven paradigms. I will describe one approach to such an integration -- making logic more quantitative.
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Host Faculty: Prof Deepak DSouza