Green Family Lecture Series; “AI Breakthroughs & Obstacles to Progress, Mathematical and Otherwise” by Yann LeCun

The Institute for Pure and Applied Mathematics (IPAM) invites you to attend the 2018 Green Family Lectures featuring Yann LeCun, Director of AI Research at Facebook and Professor at NYU.

Deep learning is causing revolutions in computer perception and natural language understanding. But almost all these successes largely rely on supervised learning, where the machine is required to predict human-provided annotations. For game AI, most systems use model-free reinforcement learning, which requires too many trials to be practical in the real world.  But animals and humans seem to learn vast amounts of knowledge about how the world works through mere observation and occasional actions. Good predictive world models are an essential component of intelligent behavior: with them, one can predict outcomes and plan courses of actions. One could argue that prediction is the essence of intelligence.  Good predictive models may be the basis of intuition, reasoning and “common sense”, allowing us to fill in missing information: predicting the future from the past and present, the past from the present, or the state of the world from noisy percepts.  After a brief presentation of the state of the art in deep learning, some promising principles and methods for predictive learning will be discussed.

Speaker bio:
Yann LeCun is Director of Facebook’s Artificial Intelligence Research and Silver Professor at NYU, affiliated with the Courant Institute and the Center for Data Science. He received a PhD in Computer Science from Université Pierre et Marie Curie (Paris). After a postdoc at the University of Toronto, he joined AT&T Bell Labs, and became head of Image Processing Research at AT&T Labs in 1996. He joined NYU in 2003 and Facebook in 2013. His current interests include AI, machine learning, computer vision, mobile robotics, and computational neuroscience. He has been a member of IPAM’s Science Advisory Board since 2008 and has organized several IPAM programs.

This lecture will be accessible to a general scientific audience. No RSVP is required.