The Indian Institute of Technology organised an institute lecture on February 14, 2020. The details of the session are as follows:
Title: "Self-Supervised Learning: the Next Step in AI"
Speaker: Prof.Yann LeCun
Turing Award Winner 2018,
Director, AI Research at Facebook
Silver Professor of Computer Science, Data Science, Neural Science, and Electrical and Computer Engineering, New York University.
Day & Date: Friday, February 14, 2020
Time: 10 am
Venue: Prof. B. Nag Auditorium, VMCC, IIT Bombay
Deep learning has caused revolutions in computer perception, image synthesis, natural language understanding, and control. But almost all these successes largely rely on supervised learning, where the machine is trained on a large number of examples augmented by human-provided annotations. For control and game AI, many systems use model-free reinforcement learning, which requires too many trials to be practical in the real world. In contrast, animals and humans seem to learn vast amounts of knowledge about the world in a task-independent manner through mere observation and occasional actions. Based on the hypothesis that prediction is the essence of intelligence, self-supervised learning (SSL) purports to train a machine to predict missing information, for example predicting missing words in a text, occulted parts of an image, future frames in a video, and generally "filling in the blanks". SSL approaches have been very successful in natural language processing, but less so in image understanding because of the difficulty of modelling uncertainty in high-dimensional continuous spaces. A general energy-based formulation of SSL will be presented which relies on regularized latent variable models. These models yield excellent performance in image completion and video prediction. A number of applications were described, including using a latent-variable video prediction model to train autonomous cars to drive defensively.
About the speaker:
Prof. 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.
He received the Electrical Engineer Diploma from Ecole Superieure d'Ingenieurs en Electrotechnique et Electronique (ESIEE), Paris in 1983, and a PhD in Computer Science from Universite Pierre et Marie Curie (Paris) in 1987. After a postdoc at the University of Toronto, he joined AT&T Bell Laboratories in Holmdel, NJ in 1988. He became head of the Image Processing Research Department at AT&T Labs-Research in 1996, and joined NYU as a professor in 2003, after a brief period as a Fellow of the NEC Research Institute in Princeton. From 2012 to 2014 he directed NYU's initiative in data science and became the founding director of the NYU Center for Data Science. He was named Director of AI Research at Facebook in late 2013 and retains a part-time position on the NYU faculty.
His current interests include AI, machine learning, computer perception, mobile robotics, and computational neuroscience. He has published over 180 technical papers and book chapters on these topics as well as on neural networks, handwriting recognition, image processing and compression, and on dedicated circuits and architectures for computer perception. The character recognition technology he developed at Bell Labs is used by several banks around the world to read checks and was reading between 10 and 20% of all the checks in the US in the early 2000s. His image compression technology, called DjVu, is used by hundreds of web sites and publishers and millions of users to access scanned documents on the Web.
Since the late 80's he has been working on deep learning methods, particularly the convolutional network model, which is the basis of many products and services deployed by companies such as Facebook, Google, Microsoft, Baidu, IBM, NEC, AT&T and others for image and video understanding, document recognition, human-computer interaction, and speech recognition.
Prof. LeCun has been on the editorial board of IJCV, IEEE PAMI, and IEEE Trans. Neural Networks, was program chair of CVPR'06 and is chair of ICLR 2013 and 2014. He is on the science advisory board of Institute for Pure and Applied Mathematics and has advised many large and small companies about machine learning technology, including several startups he co-founded. He is the lead faculty at NYU for the Moore-Sloan Data Science Environment, a $36M initiative in collaboration with UC Berkeley and University of Washington to develop data-driven methods in the sciences. He is the recipient of the 2014 IEEE Neural Network Pioneer Award.