Geoffrey Hinton

Geoffrey Hinton

Geoffrey Hinton received his BA in Experimental Psychology from Cambridge in 1970 and his PhD in Artificial Intelligence from Edinburgh in 1978.  He did postdoctoral work at Sussex University and the University of California San Diego and spent five years as a faculty member in the Computer Science department at Carnegie-Mellon University. He then became a fellow of the Canadian Institute for Advanced Research and moved to the Department of Computer Science at the University of Toronto. He spent three years from 1998 until 2001 setting up the Gatsby Computational Neuroscience Unit at University College London and then returned to the University of Toronto where he is now an emeritus distinguished professor. From 2004 until 2013 he was the director of the program on “Neural Computation and Adaptive Perception” which is funded by the Canadian Institute for Advanced Research. From 2013 to 2023 he worked half-time at Google where he became a Vice President and Engineering Fellow.

Geoffrey Hinton is a fellow of the Royal Societythe Royal Society of Canadathe Association for the Advancement of Artificial Intelligence and a former president of the Cognitive Science Society. He is an honorary foreign member of the American Academy of Arts and Sciences, the US National Academy of Engineering and the US National Academy of Science. He has received honorary doctorates from the University of Edinburgh, the University of Sussex, the University of Sherbrooke and the University of Toronto. His awards include the David E. Rumelhart prize, the IJCAI award for research excellence, the Killam prize for Engineering , The NSERC Herzberg Gold Medal, the IEEE Frank Rosenblatt medal, the IEEE James Clerk Maxwell Gold medal, the NEC C&C award, the BBVA award, the Honda Prize, the Princess of Asturias Award and the ACM Turing Award.

Geoffrey Hinton designs machine learning algorithms. His aim is to discover a learning procedure that is efficient at finding complex structure in large, high-dimensional datasets and to show that this is how the brain learns to see. He was one of the researchers who introduced the back-propagation algorithm and the first to use backpropagation for learning word embeddings. His other contributions to neural network research include Boltzmann machines, distributed representations, time-delay neural nets, mixtures of expertsvariational learningproducts of experts and deep belief nets.    His research group in Toronto made major breakthroughs in deep learning that have revolutionized speech recognition and object classification.

Share this speaker
  • Organization
  • Profession
    Advisor for the Learning in Machines & Brains

Are you sure you want to remove this speaker?