Learning from Machine Learning

A machine learning podcast that explores more than just algorithms and data: Life lessons from the experts. Welcome to "Learning from Machine Learning," a podcast about the insights gained from a career in the field of Machine Learning and Data Science. In each episode, industry experts, entrepreneurs and practitioners will share their experiences and advice on what it takes to succeed in this rapidly-evolving field. But this podcast is not just about the technical aspects of ML. It will also delve into the ways machine learning is changing the world around us. From the implications of artificial intelligence to the ways machine learning is being applied in various sectors, a wide range of topics will be covered that are relevant to anyone interested in the intersection of technology and society. All interviews available on YouTube: Learning from Machi

Learning from Machine Learning is a technology podcast hosted by Seth Levine, with 14 episodes on record and a Required Pod Score of 80. PitchCentric scores this show on Booking Probability, Listen Score, and live audience signals refreshed every 24 hours.

Seth Levine hosts Learning from Machine Learning, a technology show with 14 episodes published.

Our AI reads these to draft pitches. Use them as grounding for a pitch that cites a real guest and a specific topic.

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  Episode #14

  Dan Bricklin: Lessons from Building the First Killer App | Learning from Machine Learning #14

  Oct 17, 20251h 13mS1

  <p>On this episode of Learning from Machine Learning, I had the pleasure of speaking with Dan Bricklin, co-creator of VisiCalc - the first electronic spreadsheet and the killer app that launched the personal computer revolution. We explored what five decades of platform shifts teach us about today's AI moment. </p><p>Dan's framework is simple but powerful: breakthrough innovations must be 100 times better, not incrementally better. The same questions he asked about spreadsheets apply to AI today: What is this genuinely better at? What does it enable? What trade-offs will people accept? Does it pay for itself immediately? </p><...

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  Episode #13

  Lukas Biewald | You think you're late, but you're early | Learning from Machine Learning #13

  Jul 1, 20251h 5mS1

  <p>On this episode of Learning from Machine Learning, I had the privilege of speaking with Lukas Biewald, co-founder and CEO of Weights & Biases. We traced his journey from programming games as a kid to building one of the most essential tools in AI development today. Lukas's career demonstrates that conviction often matters more than consensus—from surviving the AI winter in the mid-2000s when he was coached to remove "AI" from investor pitches, to the AlphaGo moment that changed everything and led him to take an unpaid internship at OpenAI in his thirties.</p><p>Lukas's philosophy on "au...

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  Episode #12

  Maxime Labonne: Designing beyond Transformers | Learning from Machine Learning #12

  May 28, 20251h 4mS1

  <p>On this episode of Learning from Machine Learning, I had the privilege of speaking with Maxime Labonne, Head of Post-Training at Liquid AI. We traced his journey from cybersecurity to the cutting edge of model architecture. Maxime shared how the future of AI isn't just about making models bigger—it's about making them smarter and more efficient.</p><p>Maxime's work demonstrates that challenging established paradigms requires taking steps backward to leap forward. His framework for data quality—accuracy, diversity, and complexity—offers a blueprint for anyone working with machine learning systems.</p><p>Most importantly, Maxime's perspective on learni...

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  Episode #11

  Aman Khan: Arize, Evaluating AI, Designing for Non-Determinism | Learning from Machine Learning #11

  Apr 29, 20251h 7mS1

  <p>On this episode of Learning from Machine Learning, I had the privilege of speaking with Aman Khan, Head of Product at Arize AI. Aman shared how evaluating AI systems isn't just a step in the process—it's a machine learning challenge in of itself. Drawing powerful analogies between mechanical engineering and AI, he explained, "Instead of tolerances in manufacturing, you're designing for non-determinism," reminding us that complexity often breeds opportunity.</p><p>Aman's journey from self-driving cars to ML evaluation tools highlights the critical importance of robust systems that can handle failure. He encourages teams to clearly define outcomes, br...

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  Episode #10

  Leland McInnes: UMAP, HDBSCAN & the Geometry of Data | Learning from Machine Learning #10

  Oct 25, 202455 minS1

  <p>In this episode of Learning from Machine Learning, we explore the intersection of pure mathematics and modern data science with Leland McInnes, the mind behind an ecosystem of tools for unsupervised learning including UMAP, HDBSCAN, PyNN Descent and DataMapPlot. As a researcher at the Tutte Institute for Mathematics and Computing, Leland has fundamentally shaped how we approach and understand complex data.</p><p>Leland views data through a unique geometric lens, drawing from his background in algebraic topology to uncover hidden patterns and relationships within complex datasets. This perspective led to the creation of UMAP, a breakthrough in dimensionality...

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