Hosted by Unknown Host · 🇧🇷 BR · EN · 14 episodes
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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.
Unknown Host hosts Machine Learning: How Did We Get Here?, a history show with 14 episodes published.
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 knowled
Show notesTom 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 cogni
Show notesTom 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 speec
Show notesWhat 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. J
Show notesTom 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. N
Show notesTom 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 th
Show notesTom 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 lear
Show notesWhat 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 Prof
Show notesTom 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
Show notesTom 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 l
Show notesTom 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 se
Show notesTom 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
Show notesTom 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, f
Show notesTom 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
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Machine Learning: How Did We Get Here? is hosted by Unknown Host. The show is categorised under History (history) and has published 14 episodes.
Machine Learning: How Did We Get Here? has published 14 episodes.
Machine Learning: How Did We Get Here? regularly covers History, Technology. It sits in the History category, with a history focus.
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Episodes of Machine Learning: How Did We Get Here? average 46 minutes. a focused format where a clear narrative arc and tight preparation matter most.
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