Разное
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.
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In this video, I would like to put up my compiled list of Top programming language. This video is divided in 4 sections.
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The new Science Teaching and Student Services building at the University of Minnesota will have 10 Active Learning Classrooms. Active Learning Classrooms allow for students to experience a more interactive and conversational educational environment. With round tables for discussion and high-tech accessories for interactivity, these classrooms will service more than 125 class sections this fall.
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In the spirit of ideas worth spreading, TEDx is a program of local, self-organized events that bring people together to share a TED-like experience. At a TEDx event, TEDTalks video and live speakers combine to spark deep discussion and connection in a small group. These local, self-organized events are branded TEDx, where x = independently organized TED event. The TED Conference provides general guidance for the TEDx program, but individual TEDx events are self-organized.* (*Subject to certain rules and regulations)
Sacramento Kings guard Buddy Hield talks about learning from new coach Luke Walton, his third coach in the NBA, and the upcoming international flight to India.
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Hello Bayesian Ninjas! A general introduction of machine learning algorithms! happy hunting! The white noise tutorial part was made entirely in MATLAB!
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My 85 year old Italian grandmother learns to use Google home
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Обучающий ролик посвященный сложному для понимания элемента с турника-Заднее сальто,при этом раскачиваться назад. Прошу прощения за дикцию,можно сказать первый опыт.Очень старался объяснить доступным языком.Буду рад выслушать замечания по поводу самого процесса обучения.Извиняюсь перед иностранцами,я думаю им тяжело будет понять...
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We've developed a new framework for reinforcement learning, a subset of machine learning. This video shows the framework applied to an autonomous RC car that learns to drift around a truck.
From helping farmers in Japan sort cucumbers to helping doctors in India diagnose eye disease, machine learning is changing the way people -- inside and outside of Google -- use code to solve problems and improve lives.
Google engineers and researchers (in order of appearance): Maya Gupta, Jeff Dean, Jay Yagnik, Francoise Beaufays, John Giannandrea, Fernando Pereira, Dana Movshovitz-Attias, Rajat Monga, Lily Peng
More on Machine Learning at Google :
Blog: http://research.google.com/pub....s/ArtificialIntellig
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Machine Learning and Deep Neural Nets Explained: https://www.youtube.com/watch?v=bHvf7Tagt18
In this video Scott Hanselman delivers one of the best and personal demos, he'll show you how a combined solution using technologies such as IoT devices, cloud platforms, Machine Learning and Office API's can come together to solve even the most complex problems.
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