Explore every episode of the podcast Machine Learning: How Did We Get Here?

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.

Tom Mitchell, Founders University Professor at Carnegie Mellon University kicks off the podcast with this recording of his February 2026 seminar talk on “The History of Machine Learning.”

He takes us from the writings of early philosophers about whether it is even possible to form correct general laws given only specific examples, to today’s machine learning algorithms that underlie a trillion dollar AI economy. Along the way we see the thoughts and recollections of many of the pioneers in the field, in the form of excerpts from upcoming podcast episodes featuring full interviews with each.

Tom discusses the wonderful creativity and diversity of approaches explored during the 1980s, the integration of statistics and probability into the field in the 1990s and early 2000s, and the amazing progress over the past decade that has brought us today’s AI systems.  He reflects in the end on what we should learn from this history.

Recorded at Carnegie Mellon University.