Machine Learning: How Did We Get Here?

Tom speaks with Ross Quinlan, whose algorithms C4.5 and ID3 helped establish decision trees as one of the most popular approaches in machine learning, and who founded RuleQuest Research, which accelerated the commercial adoption of machine learning.

Ross (published as "JR Quinlan") describes a sabbatical visit to Stanford University where he took a course that drove him to invent the first successful learning algorithm for ...

Tom interviews Rich Sutton, Research Scientist at Keen Technologies, Professor of Computing Science at the University of Alberta and co-winner of the 2024 ACM Turing Award for his foundational research on reinforcement learning.

Rich discusses why the common framing of machine learning as 'supervised learning' is insufficient, and how reinforcement learning reframes the problem. He discusses how reinforcement learning has d...

Tom discusses the chaotic evolution of the field of machine learning with Tom Dietterich, Distinguished Professor Emeritus at Oregon State University.

Tom has made numerous research contributions to the field, and has served in professional roles from Executive Editor of the journal Machine Learning, to President of the Association for the Advancement of Artificial Intelligence. He shares his encyclopedic knowledge of the f...

Tom sits down with Yann LeCun, the Jacob T. Schwartz Professor of Computer Science at NYU, and Executive Chairman of Advanced Machine Intelligence Labs.

Yann is co-winner of the 2018 ACM Turing Award for his research in neural network learning. Yann takes us from his days as a postdoc working with Geoffrey Hinton, through his days as Chief AI Scientist at Facebook/Meta. His simultaneous roles as a Professor at NYU and Chief...

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 int...

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 w...