Biological Foundations for Deep Learning: Towards Decision Networks
The basic principles of intelligence have been pursued by two parallel research communities – computer scientists developing artificial intelligence, and neuroscientists exploring the brain. Recent advances, particularly in deep learning, present a key opportunity for new homologies and cross-pollination. In this talk we will discuss some of the latest learning rules discovered by each community and their surprising convergence. We will then describe how these rules can be coordinated at scale to take learning networks from perception to decisions, to help solve mature enterprise problems that are ripe for AI applications.
Nathan Wilson is a scientist and entrepreneur focused on actualizing powerful new models of brain-based computation. After many years at MIT working on the mathematical logic of neural circuits, Nathan co-founded Nara Logics, a Cambridge, MA artificial intelligence company developing “synaptic intelligence” that automatically finds and refines connections across data for recommendations and decisions within enterprises. Nathan holds 14 patents in AI and his research has been featured in Nature, Science, PNAS, and the MIT Press. An enthusiastic writer and teacher, he has won departmental and university teaching awards and been highlighted in outlets ranging from TechCrunch and WIRED to Forbes, HuffPo, WSJ, Fast Company and National Geographic.