Machine Learning: How Did We Get Here?

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 and AI efforts at several top firms, and is now one of the top AI venture capitalists in China.

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 Professor of Psychology and (by Courtesy) of Linguistics and Computer Science and Director of the Center for Mind, Brain, Computation and Technology at Stanford University. He discusses his 50 year journey modeling cognition in the brain with artificial neural networks, and his role in the 1980s emergence of neural networks in machine learning.

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 Engineering, and is currently CEO of Insitro, a company at the intersection of machine learning and human biology.

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 community, and paved the way for decades of follow-on research leading to today's self-driving cars.

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.

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.

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 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 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 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 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 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.