Symbols and Rules with Deep Learning Ellie Pavlick, Stanford MLSys, 54

Episode 54 of the Stanford MLSys Seminar Series Implementing Symbols and Rules with Neural Networks Speaker: Ellie Pavlick Abstract: Many aspects of human language and reasoning are well explained in terms of symbols and rules. However, stateoftheart computational models are based on large neural networks which lack explicit symbolic representations of the type frequently used in cognitive theories. One response has been the development of neurosymbolic models which introduce explicit representations of symbols into neural network architectures or loss functions. In terms of Marr s levels of analysis, such approaches achieve symbolic reasoning at the computational level ( what the system does and why ) by introducing symbols and rules at the implementation and algorithmic levels. In this talk, I will consider an alternative: can neural networks (without any explicit symbolic components) nonetheless implement symbolic reasoning at the computational level I will describe several diagnostic tests of sym