Yann LeCun, Director of Facebook AI Research and Silver Professor of Computer Science at New York University, will a give a public lecture, “How Could Machines Learn as Efficiently as Animals and Humans?,” on Tuesday, December 12, which will take place at 5:00 p.m. in Wolfensohn Hall on the Institute campus. This lecture is part of the Theoretical Machine Learning Lecture Series, curated by Sanjeev Arora, Visiting Professor in the School of Mathematics, and is made possible by a gift from Eric and Wendy Schmidt.
Deep learning has caused revolutions in computer perception and natural language understanding, but almost all of 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. However, 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 and with them, one can predict outcomes and plan courses of action. One could argue that prediction is the essence of intelligence in everyday life and science. These models may be the basis of common sense reasoning and intuition, allowing us to fill in missing information such as predicting the future from the past and present or the state of the world from noisy percepts. In this public lecture, LeCun will discuss the state of deep learning and promising principles and methods for predictive learning.
LeCun’s research interests include machine learning and artificial intelligence with applications to computer vision, natural language understanding, robotics, and computational neuroscience. He is best known for his contributions to deep learning and neural networks, particularly the convolutional network model, which is widely used in computer vision and speech recognition applications. LeCun has published over 190 papers and book chapters on these topics, as well as on handwriting recognition, image compression, and dedicated hardware for AI.
LeCun earned his Ph.D. in Computer Science from Université Pierre et Marie Curie (Paris) in 1987. After a postdoctoral position at the University of Toronto, he joined AT&T Bell Laboratories in 1988, later becoming the head of the Image Processing Research Department at AT&T Labs-Research. LeCun joined the faculty of New York University as a professor in 2003, following a brief period at the NEC Research Institute. He became the founding director of the NYU Center for Data Science, and in 2013, he was named Director of Facebook AI Research, remaining on the New York University faculty part-time. He held a visiting professor chair at Collège de France in 2015–16.
LeCun has received numerous awards for his contributions to the field including the 2014 IEEE Neural Network Pioneer Award, the 2015 IEEE Pattern Analysis and Machine Intelligence Distinguished Researcher Award, the 2016 Lovie Award for Lifetime Achievement, and an honorary doctorate from Instituto Politécnico Nacional, Mexico. He is also a member of the U.S. National Academy of Engineering, and serves on the boards of Institute for Pure & Applied Mathematics and the Institute for Computational and Experimental Research in Mathematics.
This event is free and open to the public, but registration is required. To register for this event, visit https://www.ias.edu/events/lecun-publiclecture. For more information on other public lectures and events at the Institute, visit http://www.ias.edu/events.