This is event is co-sponsored by the Department of Computer Science's Distinguished Lecture Series.
Yann LeCun, Facebook AI Research & New York University
The Power and Limits of Deep Learning and AI
Deep learning is at the root of revolutionary progress in visual and auditory perception by computers, and is pushing the state of the art in natural language understanding, dialog systems and language translation. Deep learning systems are deployed everywhere from self-driving cars to content filtering, search, and medical image analysis. Almost all of the deployed applications of deep learning use supervised learning in which the machine is trained to predict human-provided annotations. While reinforcement learning has been very successful in games and a few real-world applications, it requires an inordinately large number of trials to learn complex concepts. In contrast, humans and animals learn vast amounts of knowledge about the world by observation, with very little feedback from intelligent teachers and very few interactions with the environment. Humans (and many animals) construct complex predictive models of the world that give them "common sense", allowing them to interpret percepts, to fill in missing information, to predict future events, and to plan a course of actions. Enabling machines to learn predictive models of the world is a major obstacle towards significant progress in AI. I will describe a number of promising approaches towards learning predictive models that can handle the intrinsic uncertainty of the natural world, particularly variations of adversarial training.
Yann LeCun is Director of AI Research at Facebook, and Silver Professor of Data Science, Computer Science, Neural Science, and Electrical Engineering at New York University, affiliated with the NYU Center for Data Science, the Courant Institute of Mathematical Science, the Center for Neural Science, and the Electrical and Computer Engineering Department.