Shane Gu (Google Brain) - Data Science
Date: - Location: Eurecom
Abstract : Many supervised learning and generative modeling applications (e.g. computer vision, NLP, molecular biology, etc.) have experienced exponential progresses with exponential growths in data and computation. Recent models such as DALL-E and GPT-3 are essentially perpetual learning machines, capable of learning new concepts and capabilities by simply ingesting more data without much more human engineering. Reinforcement learning (RL) applications such as robotics control, however, are mostly limited to successes in narrow domains, where learned knowledge is often non-transferrable. How can we develop a continual learning system for RL agents that automatically grow in capabilities without human interventions? I will discuss progress and challenges, on topics including (1) algorithmic RL research and sample- and compute-efficiencies, (2) reset-free learning, (3) environment diversity and robotics, (4) environment engineering and optimizability, (5) roles of physics simulators, (6) self-supervised RL algorithms, and (7) universal metrics for robot intelligence. Bio : Shixiang Shane Gu (???) is a Senior Research Scientist at Google Brain and a Visiting Associate Professor at the University of Tokyo, researching deep learning, reinforcement learning, probabilistic machine learning and robotics. Shane holds PhD in Machine Learning from the University of Cambridge and the Max Planck Institute for Intelligent Systems, supervised by Richard E. Turner, Zoubin Ghahramani, and Bernhard Schölkopf. Shane holds B.ASc. in Engineering Science from the University of Toronto, supervised by the thesis advisor Geoffrey E. Hinton. Shane previously was also a visiting scholar at the Department of Computer Science at Stanford University hosted by Emma Brunskill. Shane's academic work received Best Paper Award at CoRL 2019, Google Focused Research Award, Cambridge-Tübingen PhD Fellowship, and NSERC Scholarship, and was featured in Google Research Blogpost and MIT Technology Review. Data Science Seminars: https://ds.eurecom.fr/seminars/ https://mediaserver.eurecom.fr/channels/#data-science-seminars (internal)
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