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Machine Learning: How Did We Get Here?
Machine Learning: How Did We Get Here?

Five Decades of Neural Networks with Geoffrey Hinton

23 February 2026 45:37 Tom Mitchell | Stanford Digital Economy Lab

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About this episode

Tom sits down with Geoffrey Hinton, University Professor Emeritus at the University of Toronto, and co-winner of the ACM Turing Award and of the 2024 Nobel Prize in Physics.

Geoffrey explains how he got into the field, from his days as an aspiring carpenter to his conversion to a neural network researcher.  He explains the burst of neural network progress in the mid-1980s when the backpropagation training algorithm came into widespread use, and the re-emergence of deep neural networks in 2012 when he and his students soundly defeated the best computer vision methods around.

Geoffrey discusses his early realization that those GPUs being sold to accelerate video games were the perfect hardware to accelerate neural networks as well, his journey from academia to Google, the competition among the big AI companies, and his views on where AI is and might be headed.

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