The Deep Learning Revolution
There have been two very different paradigms for Artificial Intelligence: the logic-inspired paradigm focused on reasoning and language, and assumed that the core of intelligence was manipulation of symbolic expressions; the biologically-inspired paradigm focused on learning and perception, and assumed that the core of intelligence was learning the connection strengths in a neural network. With the advent of massively parallel computation and huge datasets, neural networks have proved to be far more effective than symbolic manipulation at solving some of the hardest problems in AI, including language modelling. In this lecture, I will speculate on what this tells us about the nature of human intelligence.
Professor Geoffrey Hinton FRS, known as the ‘godfather of deep learning’, received his PhD in Artificial Intelligence from Edinburgh University in 1978. After five years as a faculty member at Carnegie Mellon, he became a Fellow of the Canadian Institute for Advanced Research and moved to the Department of Computer Science at the University of Toronto. He is now an Engineering Fellow at Google and Chief Scientific Adviser of the Vector Institute.
Driven by the desire to understand the mechanisms of cognition in the human brain and how to apply them to machines that learn, Professor Hinton is considered the leading authority on machine learning. Professor Hinton was one of the researchers who introduced the backpropagation 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 experts; variational learning; and deep learning. His research group in Toronto made major breakthroughs in deep learning that revolutionised speech recognition and object classification. It was for his pioneering and sustained contributions to machine learning, including developments in deep neural networks, that Professor Hinton was awarded the IEEE/RSE James Clerk Maxwell Award in 2016.
Recently, Professor Hinton has been awarded the Association for Computing Machinery’s 2018 A.M. Turing Award along with Yoshua Bengio and Yann LeCun for their revolutionary work on deep neural networks.
Photos from the evening’s lecture and reception are now available.
The full lecture was also live-streamed.
View the recording now.
Professor Dame Anne Glover FRS PRSE
Professor Geoffrey E. Hinton FRS
Engineering Fellow at Google and Chief Scientific Adviser of the Vector Institute