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Green Family Lecture Series: “Deep Learning and the Future of Artificial Intelligence” by Yann LeCun
The Institute for Pure and Applied Mathematics (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.
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?
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 audience. No RSVP is required.