[IGPP Everyone] [EPSS Everyone] IPAM invites you to the Green Family Lecture Series featuring Yann LeCun - February 5 and 6, 2018

[Brown, Carlene]

IPAM invites you to the first Green Family Lectures of 2018 featuring Yann LeCun, director of Facebook's artificial intelligence research and professor at NYU. No RSVP is required.  Both lectures will take place in the Ackerman Grand Ballroom (UCLA Ackerman Student Union).


Monday, February 5 at 4:30 pm:

"Deep Learning and the Future of Artificial Intelligence"

The rapid progress of AI in the last few years is largely the result of advances in deep learning and neural nets, combined with the availability of large datasets and fast hardware for numerical computing (GPUs). We now have systems that can recognize images with an accuracy that rivals that of humans. This will lead to revolutions in several domains such as autonomous transportation, medical image analysis and personalized medicine. Similarly dramatic progress have been achieved in speech recognition, natural language understanding, and language translation. AI will profoundly transform society and cause major shifts in many industries. But all of the current systems are trained through supervised learning, where the machine is trained with inputs labeled by humans. To make significant progress in AI, researchers are working on new forms of learning where machines learn like humans and animals, learning how the world works and building predictive models of the world by observation and action. Will future autonomous machines ultimately acquire "common sense" and learn how to behave like humans and other animals? What will be their impact on society?

This lecture will be accessible to a general audience.

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Tuesday, February 6 at 4:30 pm:

"AI Breakthroughs & Obstacles to Progress, Mathematical and Otherwise"

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.

This lecture will be accessible to a general scientific audience.


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.



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