Conceptual Understanding of Deep Learning

Virtual Google Workshop

May 17th 9am-4pm PST

Goal: How does the Brain/Mind (perhaps even an artificial one) work at an algorithmic level? While deep learning has produced tremendous technological strides in recent decades, there is an unsettling feeling of a lack of “conceptual” understanding of why it works and to what extent it will work in the current form. The goal of the workshop is to bring together theorists and practitioners to develop an understanding of the right algorithmic view of deep learning, characterizing the class of functions that can be learned, coming up with the right learning architecture that may (provably) learn multiple functions, concepts and remember them over time as humans do, theoretical understanding of language, logic, RL, meta learning and lifelong learning. (workshop-summary)

The speakers and panelists include Turing award winners Geoffrey Hinton, Leslie Valiant, and Godel Prize winner Christos Papadimitriou, and experts from diverse backgrounds including, ML/AI, algorithms, theory, and neuroscience.

Panel Discussion: There will also be a panel discussion on the fundamental question of “Is there a mathematical model for the Mind?” We will explore basic questions such as “is there a provable algorithm that captures the essential capabilities of the mind?”, “how do we remember complex phenomena?”, “how is a knowledge graph created automatically?”, “how do we learn new concepts, function and action hierarchies over time?” and “why do human decisions seem so interpretable?” (full list of questions)

Participation Links: The recording of the entire workshop is available over Youtube.

Youtube Live Link:

Twitter: #ConceptualDLWorkshop, Retweet, @rinapy