Deep learning is based on technical advances made by the neural network revolution in the 1980’s. Why did it take so long for neural networks to recognise speech and objects in images at human levels? What were the breakthroughs that made deep learning possible? Which industries will deep learning disrupt and how will deep learning change your life? These are some of the issues that this public lecture explores.
Professor Terrence Sejnowski received his PhD in physics from Princeton University. He was a postdoctoral fellow at Princeton University and the Harvard Medical School. He served on the faculty of Johns Hopkins University and was a Wiersma Visiting Professor of Neurobiology and a Sherman Fairchild Distinguished Scholar at Caltech. He is now an Investigator with the Howard Hughes Medical Institute and holds the Francis Crick Chair at The Salk Institute for Biological Studies. He is also a Professor of Biology at the University of California, San Diego, where he is co-director of the Institute for Neural Computation and co-director of the NSF Temporal Dynamics of Learning Center. He is a pioneer in computational neuroscience and his goal is to understand the principles that link brain to behaviour. His laboratory uses both experimental and modelling techniques to study the biophysical properties of synapses and neurons and the population dynamics of large networks of neurons. New computational models and new analytical tools have been developed to understand how the brain represents the world and how new representations are formed through learning algorithms for changing the synaptic strengths of connections between neurons. He has published over 500 scientific papers and 12 books, including The Computational Brain, with Patricia Churchland. Dr Sejnowski is the President of the Neural Information Processing Systems (NIPS) Foundation, which organises an annual conference attended by over 6000 researchers in machine learning and neural computation and is the founding editor-in-chief of Neural Computation published by the MIT Press. He is a member of the Institute of Medicine, National Academy of Sciences and the National Academy of Engineering, one of only ten current scientists elected to all three national academies.