Machine Learning: How Did We Get Here?

Tom Mitchell | Stanford Digital Economy Lab | Carnegie Mellon University

Neural Networks

Reinforcement Learning

Deep Learning

Machine Learning

JEPA (joint Embedding Predictive Architecture)

Graphical Models

Artificial Intelligence

Convolutional Networks

Self-Supervised Learning

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... more

Publishes	Weekly	Episodes	6	Founded	a month ago
Number of Listeners		Categories	TechnologyHistory

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Latest Episodes

Decision Tree Learning with Ross Quinlan

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. more

Yesterday • 24 min

Reinforcement Learning with Rich Sutton

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 t... more

16 Mar 2026 • 34 min

The Chaotic Evolution of the Field with Tom Dietterich

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 fr... more

9 Mar 2026 • 1 hr 5 min

A University and Corporate Perspective with Yann LeCun

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... more

2 Mar 2026 • 1 hr 20 min

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Recent Guests

Rich Sutton

Pioneer of reinforcement learning

University of Alberta; Collaboration with Andrew Barto; ACM Turing Award recipient

Episode: Reinforcement Learning with Rich Sutton

Tom Dietterich

Pioneer of machine learning, former president of AAAI

Oregon State University (collaborator on projects)

Episode: The Chaotic Evolution of the Field with Tom Dietterich

Yann LeCun

Pioneer in neural networks, currently exploring world models and hierarchical planning

Facebook/Meta AI Research (past), NYU, NYU Center for Data Science (past)

Episode: A University and Corporate Perspective with Yann LeCun

Geoffrey Hinton

Pioneer in neural networks and deep learning

Geoffrey Hinton's own affiliations mentioned in episode (CMU, Google past, etc.)

Episode: Five Decades of Neural Networks with Geoffrey Hinton

Host

Tom Mitchell

Host of the series and interviewer, affiliated with Carnegie Mellon University; also connected to Stanford Digital Economy Lab as a producing entity.

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Apple Podcasts	#210	United States/Technology
Apple Podcasts	#182	Canada/Technology
Apple Podcasts	#203	United Kingdom/Technology
Apple Podcasts	#231	France/Technology
Apple Podcasts	#48	Sweden/Technology
Apple Podcasts	#51	Finland/Technology
Apple Podcasts	#74	Norway/Technology
Apple Podcasts	#128	Switzerland/Technology
Apple Podcasts	#194	India/Technology
Apple Podcasts	#208	Indonesia/Technology
Apple Podcasts	#225	Chile/Technology
Apple Podcasts	#229	Brazil/Technology
Apple Podcasts	#233	South Korea/Technology

Talking Points

Recent interactions between the hosts and their guests.

The Chaotic Evolution of the Field with Tom Dietterich

Q: Final question. If there's a new PhD student just entering right now, and they just heard you talk about these different thesis topics, what advice do you have for them on how to get started, how to conduct themselves?

The guidance emphasizes building a broad toolbox of techniques, identifying a personal 'secret weapon' such as ability to explain and frame field developments, and solving problems in a staged way—start with small problems, gradually tackle larger, more complex ones, while keeping an eye on fundamental questions and real-world impact.

Reinforcement Learning with Rich Sutton

Q: What were the things going on at the time in your head that made you think this was an interesting direction to look?

He discusses the motivation rooted in learning from experience and rewards/penalties, contrasted with supervised approaches, and the historical shift from reinforcement to supervised framing before returning to RL as a distinct field.

Reinforcement Learning with Rich Sutton

Q: Can you define reinforcement learning in one sentence?

Reinforcement learning is learning from experience by trial and error to achieve a goal.

A University and Corporate Perspective with Yann LeCun

Q: How did the shift from symbolic methods to neural networks unfold in your view?

He explains the Snowbird Snowbird workshop and the mix of Bayesian methods and kernel methods taking over the field, which pushed neural nets to become a smaller but persistent focus; eventual demonstrations of neural nets' capabilities rekindled broader interest and led to the deep learning revolution.

A University and Corporate Perspective with Yann LeCun

Q: What got you into this field and what motivated you early on?

LeCun describes being inspired by the intelligence question, discovering early literature on neural nets in France, and noticing a revival of interest in learning systems after meeting the foundational figures in the field, which motivated his pursuit of practical training methods like backpropagation.

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Frequently Asked Questions About Machine Learning: How Did We Get Here?

What is Machine Learning: How Did We Get Here? about and what kind of topics does it cover?

This series curates candid conversations with early pioneers and current leaders in machine learning, tracing the field's evolution from symbolic and rule-based ideas to data-driven approaches. Episodes thread together foundational concepts, key breakthroughs, and the sociotechnical shifts that shaped modern AI—covering neural networks, reinforcement learning, kernel methods, and the transformer era—while weaving in personal stories, collaborations, and interdisciplinary influences. Notable throughlines include the tension between theory and practice, the role of large-scale experimentation and industry labs, and thoughtful discussions on AI safety, governance, and societal impact. The format tends to blend historical context with technical... more

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Recent guests on Machine Learning: How Did We Get Here? include:

1. Rich Sutton
2. Tom Dietterich
3. Yann LeCun
4. Geoffrey Hinton

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