A theoretical framework for deep learning
In recent years, machine learning researchers has achieved impressive results. Though theory has been lagging behind, some of the main questions about deep learning are now being solved. I will review the state of three main puzzles which include 3 separate branches of mathematics, that is approximation, optimization and machine learning theory:
I will also discuss the future of AI. To create artifacts that are as intelligent as we are, we need several additional breakthroughs. A good bet is that several of them will come from interdisciplinary research between the natural science and the engineering of Intelligence. This vision is in fact at the core of the CBMM and of the new MIT Quest for Intelligence, of which I will outline organization and research strategy.