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Technology

Machine Learning: How Did We Get Here?

by Tom Mitchell | Stanford Digital Economy Lab | Carnegie Mellon University
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Latest episodes

  • From Philosophy to Machine Learning with Bruce Buchanan

    May 18, 2026 · 38 min

    Tom sits down with Bruce Buchanan, a PhD Philosopher turned machine learning researcher.  Bruce produced a key milestone for machine learning in the 1970s by creating the first program that discovered new symbolic knowledge publishable in…

  • AI Agents to Model Human Cognition with John Laird

    May 11, 2026 · 33 min

    Tom chats with John Laird, who has spent the past 40 years trying to build an AI agent that accomplishes the full range of human cognitive abilities, beginning with his 1980s PhD research on the SOAR model of human cognition with Allen…

  • Machine Learning and Speech Recognition with Kai-Fu Lee

    May 4, 2026 · 39 min

    Tom meets with Kai-Fu Lee, a pioneer in using machine learning to significantly advance speech recognition. Kai-Fu, former president of Google China and now Chairman of Sinovation Ventures and CEO of 01.AI, has led speech, machine learning…

  • Machine Learning meets Cognitive Neuroscience with Jay McClelland

    April 27, 2026 · 1 hr 3 min

    What is the relationship between neural network approaches in machine learning, and real neural networks in the brain? Today's guest Jay McClelland is a cognitive scientist who has spent decades studying this question. Jay is Lucie Stern…

  • Learning Probabilistic Models with Daphne Koller

    April 20, 2026 · 40 min

    Tom interviews Daphne Koller, a Stanford professor turned serial entrepreneur. Daphne is widely known for her research at the intersection of machine learning and probabilistic reasoning. Daphne is a member of the U.S. National Academy of…

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  • Self-Driving Cars in the 1980s (!) with Dean Pomerleau

    April 13, 2026 · 33 min

    Tom meets with Dr. Dean Pomerleau, who as a CMU PhD student in the 1980s was the first person to demonstrate that a neural network could be trained to automatically steer a self-driving vehicle. Dean's results shocked the research…

  • Machine Learning Meets Statistics with Michael I. Jordan

    April 6, 2026 · 1 hr 1 min

    Tom sits down with Michael I. Jordan, Director of Rearch at Inria and Professor Emeritus of the Departments of EECS and Statistics, University of California, Berkeley. Michael has been a major contributor to machine learning, especially at…

  • Machine Learning Theory with Leslie Valiant

    March 30, 2026 · 21 min

    What would a "theory" of machine learning tell us? In this episode Tom meets with the person who invented what is now the widely accepted definition of supervised machine learning: Turing Award recipient and Harvard Professor Leslie…

  • Decision Tree Learning with Ross Quinlan

    March 23, 2026 · 24 min

    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…

  • Reinforcement Learning with Rich Sutton

    March 16, 2026 · 34 min

    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…

  • The Chaotic Evolution of the Field with Tom Dietterich

    March 9, 2026 · 1 hr 5 min

    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…

  • A University and Corporate Perspective with Yann LeCun

    March 2, 2026 · 1 hr 21 min

    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…

Full episode archive on Apple Podcasts →

About the show

Tom Mitchell literally wrote the book on machine learning. In this series of candid conversations with his fellow pioneers, Tom traces the history of the field through the people who built it. Behind the tech are stories of passion, curiosity, and humanity. Tom Mitchell is the University Founders Professor at Carnegie Mellon University, a Digital Fellow at the Stanford Digital Economy Lab, and the author of Machine Learning, a foundational textbook on the subject. This podcast is produced by the Stanford Digital Economy Lab.