The essence of intelligence is the ability to predict. Prediction, perception, planning/reasoning, attention, and memory are the pillars of intelligence. Both animals and humans learn to predict, learn how the world works, and acquire common sense largely without supervision, through observation and experimentation. This is a far cry from supervised learning—the basis of most recent successes in the application of deep learning. Significant progress in AI will require breakthroughs in unsupervised/predictive learning, as well as in reasoning, attention, and episodic memory. Yann LeCun describes several projects at FAIR and NYU on unsupervised learning for predicting videos using adversarial training, question answering with a new type of memory-augmented network, and various applications for vision and natural language understanding.
Yann LeCun is director of AI research at Facebook and Silver Professor at New York University, affiliated with the Courant Institute of Mathematical Sciences, the Center for Neural Science, and the Center for Data Science, for which he served as founding director until 2014. Over his career, Yann has held a wide range of positions, including a postdoc at the University of Toronto, head of the Image Processing Research department at AT&T Labs-Research, and a researcher at the NEC Research Institute, as well as the 2015–2016 annual visiting professor chair of computer science at Collège de France. His research interests include machine learning and artificial intelligence with applications to computer vision, natural language understanding, robotics, and computational neuroscience. Yann is best known for his work in deep learning and the invention of the convolutional network method, widely used for image, video, and speech recognition. He is the recipient of the 2014 IEEE Neural Network Pioneer Award and the 2015 IEEE Pattern Analysis and Machine Intelligence Distinguished Researcher Award. Yann holds a PhD in computer science from Université Pierre et Marie Curie (Paris).
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