technology
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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.
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Contact path
Public web form
Booking probability
27%
Guest openness
Selective
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Signup to Generate a PitchTom 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.
Recent episodes include "From Philosophy to Machine Learning with Bruce Buchanan" (18 May 2026, 38 minutes), "AI Agents to Model Human Cognition with John Laird" (11 May 2026, 33 minutes), and "Machine Learning and Speech Recognition with Kai-Fu Lee" (4 May 2026, 39 minutes).
Machine Learning: How Did We Get Here? publishes roughly weekly, with episodes averaging 46 minutes across 14 episodes.
Booking probability for Machine Learning: How Did We Get Here? is 27%, and the host is rated Selective on guests. Machine Learning: How Did We Get Here? requires a Pod Score of 80 or higher. Machine Learning: How Did We Get Here?'s contact route on record: public web form.
The strongest fits for Machine Learning: How Did We Get Here? are Scientific Research & Development and IT Services & Software Development. Machine Learning: How Did We Get Here? has a technology audience.
Machine Learning: How Did We Get Here? is a technology show with 14 episodes published.
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Episode #14
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...
Episode #13
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...
Episode #12
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...
Episode #11
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...
Episode #10
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...
Episode #9
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...
Episode #8
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...
Episode #7
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...
Episode #6
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...
Episode #5
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...
Episode #4
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...
Episode #3
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...
Episode #2
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...
Episode #1
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...
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