Yann LeCun


Yann Lecun, Facebook // Artificial Intelligence

Published on Dec 18, 2014

Professor Yann LeCun, Director of AI Research at Facebook, sat down for a fireside chat at December 2014's edition of Data Driven NYC to discuss deep learning and the future of artificial intelligence.
 

Yann LeCun, Director of AI Research at Facebook - Deep Learning

Published on Jan 21, 2016

The Institute for Scientific Computing Research (ISCR) sponsored this talk entitled "Deep Learning" on April 16, 2015, at the Lawrence Livermore National Laboratory. The talk was presented by Yann LeCun, director of AI research at Facebook and professor of data science, computer science, neural science and electrical engineering at NYU.
 

Deep Learning and the Future of AI | Yann LeCun | Talk 1/2


Deep Learning and the Future of AI | Yann LeCun | Q&A 2/2

Published on Sep 6, 2016

Over the last few years, rapid progress in AI have enabled our smartphones, social networks, and search engines to understand our voice, recognize our faces, and identifiy objects in our photos with very good accuracy. These improvements are due in large part to the emergence of a new class of machine learning methods known as Deep Learning. A particular type of deep learning system called convolutional network (ConvNet) has been particularly successful for image and speech recognition.

But we are still quite far from emulating the learning abilities of animal of humans. A key element we are missing is predictive (or unsupervised) learning: the ability of a machine to model the environment, predict possible futures and understand how the world works by observing it and acting in it, a very active topic of research at the moment.

Yann LeCun,
Facebook AI Research & New York University.

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Yann LeCun is Director of AI Research at Facebook, and Silver Professor of Dara 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.

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.
 

RI Seminar: Yann LeCun : The Next Frontier in AI: Unsupervised Learning

Published on Nov 18, 2016
Yann LeCun
Director of AI Research at Facebook, Professor of Computer Science, New York University

November 18, 2016

Abstract
The rapid progress of AI in the last few years are largely the result of advances in deep learning and neural nets, combined with the availability of large datasets and fast 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 and medical image understanding. But all of these systems currently use supervised learning in which the machine is trained with inputs labeled by humans. The challenge of the next several years is to let machines learn from raw, unlabeled data, such as video or text. This is known as unsupervised learning. AI systems today do not possess "common sense", which humans and animals acquire by observing the world, acting in it, and understanding the physical constraints of it. Some of us see unsupervised learning as the key towards machines with common sense. Approaches to unsupervised learning will be reviewed. This presentation assumes some familiarity with the basic concepts of deep learning.


Speaker Biography
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. 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.
 

Artificial Intelligence in the 21st Century - Yann LeCun

Published on Nov 1, 2017

Yann LeCun is the director of AI Research at Facebook (since December 2013), and Silver Professor at New York University.

Recorded: Oct 17, 2017
 

Yann LeCun - Power & limits of deep learning

Published on Nov 19, 2017

Yann LeCun is Director of AI Research at Facebook, and Silver Professor of Dara 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.

Recorded Nov 1st, 2017
 

The Great AI Debate - NIPS2017 - Yann LeCun

Published on Jan 31, 2018

The first ever debate at a Neural Information Processing Systems conference.
Position: Interpetability is necessary for machine learning
For: Rich Caruana, Patrice Simard
Against: Kilian Weinberger, Yann LeCun.
December 9th, 2017
 

Yann LeCun - The next step toward Artificial Intelligence

Published on Jul 27, 2018

Recorded July 16th 2018

Yann LeCun is the Chief AI Scientist for Facebook AI Research (FAIR), joining Facebook in December 2013. He is also a Silver Professor at New York University on a part time basis, mainly affiliated with the NYU Center for Data Science, and the Courant Institute of Mathematical Science.
 

Yann LeCun: Deep Learning, Convolutional Neural Networks, and Self-Supervised Learning | AI Podcast

Published on Aug 31, 2019

Yann LeCun is one of the fathers of deep learning, the recent revolution in AI that has captivated the world with the possibility of what machines can learn from data. He is a professor at New York University, a Vice President & Chief AI Scientist at Facebook, co-recipient of the Turing Award for his work on deep learning. He is probably best known as the founding father of convolutional neural networks, in particular their early application to optical character recognition. This conversation is part of the Artificial Intelligence podcast.
 

Yann LeCun: Dark Matter of Intelligence and Self-Supervised Learning | Lex Fridman Podcast #258

Jan 22, 2022

Yann LeCun is the Chief AI Scientist at Meta, professor at NYU, Turing Award winner, and one of the seminal researchers in the history of machine learning.

Outline:

0:00 - Introduction
0:36 - Self-supervised learning
10:55 - Vision vs language
16:46 - Statistics
22:33 - Three challenges of machine learning
28:22 - Chess
36:25 - Animals and intelligence
46:09 - Data augmentation
1:07:29 - Multimodal learning
1:19:18 - Consciousness
1:24:03 - Intrinsic vs learned ideas
1:28:15 - Fear of death
1:36:07 - Artificial Intelligence
1:49:56 - Facebook AI Research
2:06:34 - NeurIPS
2:22:46 - Complexity
2:31:11 - Music
2:36:06 - Advice for young people
 

Yann Lecun: Meta AI, Open Source, Limits of LLMs, AGI & the Future of AI | Lex Fridman Podcast #416

Mar 7, 2024

Yann LeCun is the Chief AI Scientist at Meta, professor at NYU, Turing Award winner, and one of the most influential researchers in the history of AI.

Outline:

0:00 - Introduction
2:18 - Limits of LLMs
13:54 - Bilingualism and thinking
17:46 - Video prediction
25:07 - JEPA (Joint-Embedding Predictive Architecture)
28:15 - JEPA vs LLMs
37:31 - DINO and I-JEPA
38:51 - V-JEPA
44:22 - Hierarchical planning
50:40 - Autoregressive LLMs
1:06:06 - AI hallucination
1:11:30 - Reasoning in AI
1:29:02 - Reinforcement learning
1:34:10 - Woke AI
1:43:48 - Open source
1:47:26 - AI and ideology
1:49:58 - Marc Andreesen
1:57:56 - Llama 3
2:04:20 - AGI
2:08:48 - AI doomers
2:24:38 - Joscha Bach
2:28:51 - Humanoid robots
2:38:00 - Hope for the future
 
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