Machine Learning: How Did We Get Here? Transistor (https://transistor.fm) https://feeds.transistor.fm/machine-learning-how-did-we-get-here 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. © 2026 Stanford Digital Economy Lab. All rights reserved. 9d09c6ac-16de-5082-a13d-e2037af939fa yes 7d60fcf0-1347-11f1-9d6e-2941c6bd53a2 Tom Mitchell Matty Smith en Fri, 10 Apr 2026 15:42:07 -0700 Fri, 10 Apr 2026 15:43:20 -0700 https://img.transistorcdn.com/EeNfYfm4-mFINhSH2kPMOUHTK3KJgp3D7PBxr6BV2DQ/rs:fill:0:0:1/w:1400/h:1400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS9iYjU3/Zjg4NDY1Mzk1ZDQ1/NTQwYmMxNDZjMTU5/MzJhMi5wbmc.jpg Machine Learning: How Did We Get Here? episodic Tom Mitchell | Stanford Digital Economy Lab | Carnegie Mellon University 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. Tom Mitchell literally wrote the book on machine learning. technology, history, machine learning, artificial intelligence, academia, Carnegie Mellon University, Stanford University, graduate studies, interviews Stanford Digital Economy Lab No No Machine Learning Meets Statistics with Michael I. Jordan 8 8 Machine Learning Meets Statistics with Michael I. Jordan full 193a1979-205d-4aa9-9707-b0cfe58ef88a https://share.transistor.fm/s/b039dd91 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 the intersection of statistics and machine learning.

Michael discusses his research trajectory, including how it has been inspired by ideas from control theory, statistics, and most recently economics.

]]> 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 the intersection of statistics and machine learning.

Michael discusses his research trajectory, including how it has been inspired by ideas from control theory, statistics, and most recently economics.

]]> Mon, 06 Apr 2026 03:00:00 -0700 Tom Mitchell | Stanford Digital Economy Lab | Carnegie Mellon University Tom Mitchell | Stanford Digital Economy Lab | Carnegie Mellon University 3667 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 the intersection of statistics and machine learning.

Michael discusses his research trajectory, including how it has been inspired by ideas from control theory, statistics, and most recently economics.

]]> technology, history, machine learning, artificial intelligence, academia, Carnegie Mellon University, Stanford University, graduate studies, interviews No Tom Mitchell Matty Smith Michael I. Jordan Machine Learning Theory with Leslie Valiant 7 7 Machine Learning Theory with Leslie Valiant full fc62e159-a2ff-4d64-8066-6537cb67767b https://share.transistor.fm/s/31820df8 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 Valiant.


Leslie tells us how he got interested in the problem, his contribution, the evolution of machine learning theory over the decades, and his advice to new researchers.

]]> 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 Valiant.


Leslie tells us how he got interested in the problem, his contribution, the evolution of machine learning theory over the decades, and his advice to new researchers.

]]> Mon, 30 Mar 2026 01:06:00 -0700 Tom Mitchell | Stanford Digital Economy Lab | Carnegie Mellon University Tom Mitchell | Stanford Digital Economy Lab | Carnegie Mellon University 1253 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 Valiant.


Leslie tells us how he got interested in the problem, his contribution, the evolution of machine learning theory over the decades, and his advice to new researchers.

]]> technology, history, machine learning, artificial intelligence, academia, Carnegie Mellon University, Stanford University, graduate studies, interviews No Tom Mitchell Matty Smith Leslie Valiant Decision Tree Learning with Ross Quinlan 6 6 Decision Tree Learning with Ross Quinlan full 26043b78-bdc9-42f1-ae48-6c940464b85d https://share.transistor.fm/s/89d6ca0d 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 decision trees, follow-on research that led to decision trees becoming one of the most popular machine learning algorithms, and his experience moving from academia into the commercial world.

]]> 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 decision trees, follow-on research that led to decision trees becoming one of the most popular machine learning algorithms, and his experience moving from academia into the commercial world.

]]> Mon, 23 Mar 2026 02:00:00 -0700 Tom Mitchell | Stanford Digital Economy Lab | Carnegie Mellon University Tom Mitchell | Stanford Digital Economy Lab | Carnegie Mellon University 1454 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 decision trees, follow-on research that led to decision trees becoming one of the most popular machine learning algorithms, and his experience moving from academia into the commercial world.

]]> technology, history, machine learning, artificial intelligence, academia, Carnegie Mellon University, Stanford University, graduate studies, interviews No Tom Mitchell Matty Smith Ross Quinlan Reinforcement Learning with Rich Sutton 5 5 Reinforcement Learning with Rich Sutton full 16a55284-83d0-4e76-bedb-020afa13037a https://share.transistor.fm/s/193e03e7 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 developed as a subfield of machine learning, the influence of Harry Kopf on his early thinking, his long-time collaboration with Andy Barto, his views about today's state of the art, and more.

]]> 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 developed as a subfield of machine learning, the influence of Harry Kopf on his early thinking, his long-time collaboration with Andy Barto, his views about today's state of the art, and more.

]]> Mon, 16 Mar 2026 02:00:00 -0700 Tom Mitchell | Stanford Digital Economy Lab | Carnegie Mellon University Tom Mitchell | Stanford Digital Economy Lab | Carnegie Mellon University 2063 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 developed as a subfield of machine learning, the influence of Harry Kopf on his early thinking, his long-time collaboration with Andy Barto, his views about today's state of the art, and more.

]]> technology, history, machine learning, artificial intelligence, academia, Carnegie Mellon University, Stanford University, graduate studies, interviews No Tom Mitchell Matty Smith Rich Sutton The Chaotic Evolution of the Field with Tom Dietterich 4 4 The Chaotic Evolution of the Field with Tom Dietterich full 48bf0bea-36b4-4331-a7fc-eff7d73ad8b6 https://share.transistor.fm/s/cabe8ce5 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 field and its evolution, describing waves of alternative paradigms, the interaction of theory with practice, the interaction of statisticians with computer scientists, some of his main research results, and his experience spinning off a machine learning startup company.

]]> 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 field and its evolution, describing waves of alternative paradigms, the interaction of theory with practice, the interaction of statisticians with computer scientists, some of his main research results, and his experience spinning off a machine learning startup company.

]]> Mon, 09 Mar 2026 02:00:00 -0700 Tom Mitchell | Stanford Digital Economy Lab | Carnegie Mellon University Tom Mitchell | Stanford Digital Economy Lab | Carnegie Mellon University 3927 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 field and its evolution, describing waves of alternative paradigms, the interaction of theory with practice, the interaction of statisticians with computer scientists, some of his main research results, and his experience spinning off a machine learning startup company.

]]> technology, history, machine learning, artificial intelligence, academia, Carnegie Mellon University, Stanford University, graduate studies, interviews No Tom Mitchell Matty Smith Thomas Dietterich A University and Corporate Perspective with Yann LeCun 3 3 A University and Corporate Perspective with Yann LeCun full 8b4db9d7-6512-467c-afc9-aaa336ba12db https://share.transistor.fm/s/da3e9ae4 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 AI Scientist at a large AI provider give Yann a unique perspective on how technological advances and commercial forces combined to get us to today's state of the art.

]]> 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 AI Scientist at a large AI provider give Yann a unique perspective on how technological advances and commercial forces combined to get us to today's state of the art.

]]> Mon, 02 Mar 2026 01:00:00 -0800 Tom Mitchell | Stanford Digital Economy Lab | Carnegie Mellon University Tom Mitchell | Stanford Digital Economy Lab | Carnegie Mellon University 4841 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 AI Scientist at a large AI provider give Yann a unique perspective on how technological advances and commercial forces combined to get us to today's state of the art.

]]> technology, history, machine learning, artificial intelligence, academia, Carnegie Mellon University, Stanford University, graduate studies, interviews No Tom Mitchell Matty Smith Yann LeCun Five Decades of Neural Networks with Geoffrey Hinton 2 2 Five Decades of Neural Networks with Geoffrey Hinton full b4e0cdd7-c548-4650-ad9b-3d33e49bc33e https://share.transistor.fm/s/bdf7111e 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 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.

]]> Sun, 22 Feb 2026 22:44:32 -0800 Tom Mitchell | Stanford Digital Economy Lab | Carnegie Mellon University Tom Mitchell | Stanford Digital Economy Lab | Carnegie Mellon University 2737 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.

]]> technology, history, machine learning, artificial intelligence, academia, Carnegie Mellon University, Stanford University, graduate studies, interviews No Tom Mitchell Matty Smith Geoffrey Hinton The History of Machine Learning with Tom Mitchell 1 1 The History of Machine Learning with Tom Mitchell full 3623d93d-98ec-4889-a8d6-014acb8e01d4 https://share.transistor.fm/s/20afc5bc 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.

]]> 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.

]]> Sun, 22 Feb 2026 22:42:28 -0800 Tom Mitchell | Stanford Digital Economy Lab | Carnegie Mellon University Tom Mitchell | Stanford Digital Economy Lab | Carnegie Mellon University 4061 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.

]]> technology, history, machine learning, artificial intelligence, academia, Carnegie Mellon University, Stanford University, graduate studies, interviews No Tom Mitchell Matty Smith