The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)26 Aug

Why the Next AI Breakthrough May Come from Physics with Max Welling - #774

The conventional wisdom in AI is that the next breakthrough will come from more compute, more data, and larger models. But what if the next leap comes from somewhere else? In this episode, Max Welling—co-founder and CTO of CuspAI and professor at the University of Amsterdam—argues that physics may provide some of the ideas behind the next generation of AI systems. We begin with CuspAI’s work using generative AI to design entirely new materials for semiconductors, batteries, carbon capture, and clean energy. Max explains how foundation models for chemistry, agentic workflows, simulation, and automated experimentation are dramatically accelerating the search for new materials and reshaping scientific discovery. The conversation then broadens into a deeper question. Beyond giving AI new scientific problems to solve, can physics also teach us how to build better AI? Max explores surprising connections between machine learning and thermodynamics, why waves may become a new computational primitive for neural networks, and how concepts like symmetry breaking and statistical physics could inspire AI architectures beyond today’s scaling paradigm. 🗒️ Full show notes: https://twimlai.com/go/774.

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Episoder(797)

Why Jev Is Changing How We Build With AI with Diogo Almeida - #779

In this episode, Diogo Almeida, co-founder and CEO of TypeSafe, joins us to discuss Jev, TypeSafe’s recently released model for bringing fast, reliable intelligence directly into software. We explore ...

6 Okt 1h 30min

From Math Olympiads to Navier-Stokes: How Fast Is AI Progressing? with Greg Burnham - #778

AI systems have gone from struggling with grade-school math to helping solve research problems that have resisted mathematicians for decades, including Navier-Stokes. In this episode, Greg Burnham, w...

29 Sep 1h 7min

From Voice Agents to AI Avatars with Alexander Smola - #777

Voice AI has gotten remarkably good, but natural conversation remains a high bar. Small delays, awkward interruptions, or the wrong tone can quickly break the illusion—and adding vision and visual pre...

17 Sep 1h 4min

Do AI Tokenomics Matter More Than Model Benchmarks? with Chris Potts - #776

As reasoning models consume more tokens and AI systems become more expensive to run, understanding what those tokens actually buy is becoming increasingly important. In this episode, Stanford professo...

9 Sep 59min

World Models and the Future of Spatial AI with Justin Johnson - #775

In this episode, Justin Johnson, co-founder of World Labs, joins us to discuss world models and the emerging field of spatial AI. We explore why many researchers see capabilities beyond language as an...

1 Sep 1h 6min

Why Image Generation Needs More Than Bigger Models with Fatih Porikli - #773

Text-to-image models have become remarkably good at producing realistic images. But realism isn’t the same as correctness. Ask for several distinct people, a specific composition, or a high-resolution...

12 Aug 56min

Why Models Are AI’s Next Training Dataset with Damian Borth - #772

For more than a decade, AI has advanced by training ever-larger models on ever-larger datasets. But as high-quality training data becomes harder to find and pretraining grows increasingly expensive, r...

27 Jul 47min