The Power and Limits of Deep Learning: Talk on July 11

Reminder of the below talk later this week.  Appears to be very worth while, will be attending.

VIP Reminder: July 11 Talk on Deep Learning with 2018 ACM A.M. Turing Laureate Yann LeCun
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If you haven't done so already, register now for the next free ACM TechTalk, "The Power and Limits of Deep Learning," presented on Thursday, July 11 at 1 PM ET/10 AM PT by Yann LeCun, VP & Chief AI Scientist at Facebook, Silver Professor at NYU, and 2018 ACM A.M Turing Award Laureate. Mehran Sahami, Associate Chair for Education at Stanford's Computer Science Department and Past Chair of the ACM Education Board, will moderate the questions and answers session.

Leave your comments and questions with our speaker now and any time before the live event on ACM's Discourse Page. And check out the page after the webcast for extended discussion with your peers in the computing community, as well as further resources on Deep Learning.

(If you'd like to attend but can't make it to the virtual event, you still need to register to receive a recording of the TechTalk when it becomes available.)

Deep Learning (DL) has enabled significant progress in computer perception, natural language understanding, and control. Almost all these successes rely on supervised learning, where the machine is required to predict human-provided annotations, or model-free reinforcement learning, where the machine learns policies that maximize rewards. Supervised learning paradigms have been extremely successful for an increasingly large number of practical applications such as medical image analysis, autonomous driving, virtual assistants, information filtering, ranking, search and retrieval, language translation, and many more. Today, DL systems are at the core of search engines and social networks. DL is also used increasingly widely in the physical and social sciences to analyze data in astrophysics, particle physics, and biology, or to build phenomenological models of complex systems. An interesting example is the use of convolutional networks as computational models of human and animal perception. But while supervised DL excels at perceptual tasks, there are two major challenges to the next quantum leap in AI: (1) getting DL systems to learn tasks without requiring large amounts of human-labeled data; (2) getting them to learn to reason and to act. These challenges motivate some the most interesting research directions in AI.

Duration: 60 minutes (including audience Q&A)

Presenter:
Yann LeCun, VP & Chief AI Scientist at Facebook; Silver Professor at NYU; 2018 ACM A.M Turing Award Laureate 
Yann LeCun is VP and Chief AI Scientist at Facebook and Silver Professor at NYU affiliated with the Courant Institute and the Center for Data Science. He was the founding Director of Facebook AI Research and of the NYU Center for Data Science. He received an EE Diploma from ESIEE (Paris) in 1983 and a PhD in Computer Science from Université Pierre et Marie Curie (Paris) in 1987. After a postdoc at the University of Toronto, he joined AT&T Bell Laboratories. He became head of the Image Processing Research Department at AT&T Labs-Research in 1996, and joined NYU in 2003 after a short tenure at the NEC Research Institute. In late 2013, LeCun became Director of AI Research at Facebook, while remaining on the NYU Faculty part-time. He was visiting professor at Collège de France in 2016. His 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 work in deep learning and the invention of the convolutional network method which is widely used for image, video, and speech recognition. He is a member of the US National Academy of Engineering, the recipient of 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, the University of Pennsylvania Pender Award, and honorary doctorates from IPN, Mexico and EPFL. He is the recipient of the 2018 ACM Turing Award (with Geoffrey Hinton and Yoshua Bengio) for "conceptual and engineering breakthroughs that have made deep neural networks a critical component of computing."

Moderator:
Mehran Sahami, Associate Chair for Education, Stanford University CS Department; Past Chair, ACM Education Board 
In 2007, Mehran Sahami joined the faculty of the Computer Science Department at Stanford University. From 2001 to 2006, he taught in the CS department at Stanford as a Lecturer. From 2002-2007, he was a full-time Senior Research Scientist at Google. After moving to Stanford, he continued to consult at Google on a part-time basis until 2010. His research interests include computer science education, machine learning, and information retrieval on the Web. Previously, he has worked for several years as a Senior Engineering Manager at Epiphany. Prior to working at Epiphany, he completed his PhD in the Computer Science Department at Stanford.

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