Explainable and Reliable AI: Comparing Deep Learning with Adaptive Resonance - Stephen Grossberg

Abstract: This lecture compares and contrasts Deep Learning with Adaptive Resonance Theory, or ART. Deep Learning is often used to classify data. However, Deep Learning can experience catastrophic forgetting: At any stage of learning, an unpredictable part of its memory can collapse. It is thus unreliable. Even if it makes some accurate classifications, they are not explainable. It is thus untrustworthy. Deep Learning has these properties because it uses the back propagation algorithm, whose computational problems due to nonlocal weight transport during mismatch learning were described in the 1980s. Deep Learning became popular after very fast computers and huge online databases became available that enabled new applications despite these problems. ART models overcome 17 foundational computational problems of back propagation and Deep Learning. ART is a self-organizing, explainable production system that incrementally learns, using arbitrary combinations of unsupervised and supervised learning, to rapidly att