Deep learning pioneer Geoffrey Hinton has been elected to the U.S. National Academy of Sciences. (Photo: Johnny Guatto)
Deep learning pioneer Geoffrey Hinton of the University of Toronto Department of Computer Science has been elected to the U.S. National Academy of Sciences — considered one of the highest honours awarded to scientists worldwide in recognition of their distinguished and continuing achievements in original research.
Hinton, a U of T University Professor Emeritus, is a Companion of the Order of Canada and a Fellow of the Royal Societies of Canada and London.
This is the latest in an extensive list of accolades for Hinton, who is also chief scientific adviser at the Vector Institute for Artificial Intelligence and a recipient of the Association for Computing Machinery’s A. M. Turing Award, widely considered to be the Nobel Prize of computing.
Hinton’s ground-breaking research lies in deep learning and artificial neural networks — a field of artificial intelligence that mimics the way humans acquire certain types of knowledge.
Hinton and his collaborators developed the breakthrough approach — based on the backpropagation algorithm, a central mechanism by which artificial neural networks learn – that would realize the promise of neural networks and form the current foundation of that technology.
Hinton and his colleagues in Toronto built on that initial work with a number of critical developments that enhanced the potential of AI and helped usher in today’s revolution in deep learning with applications in speech and image recognition, self-driving vehicles, automated diagnosis of images and language, and more.
Hinton was awarded the Royal Medal from the Royal Society in 2022 for “pioneering work on algorithms that learn distributed representations in artificial neural networks and their application to speech and vision, leading to a transformation of the international information technology industry.”
He has published more than 250 articles, authored dozens of book chapters and trained over 50 graduate students.
With files from Chris Sasaki