Can artificial intelligence solve one of science’s most chaotic physics puzzles? In this episode, Professor Hannah Fry sits down with Peter Battaglia, Senior Director of Research at Google DeepMind, to explore how machine learning is transforming global weather forecasting. From providing critical early warnings for Category 5 storms like Hurricane Melissa to predicting renewable energy supply and agricultural impacts, see how models like GraphCast and WeatherNext 3 are building upon decades of numerical physics to reshape our understanding of the atmosphere.

Learn more about WeatherNext 3, our most advanced global weather AI model yet: https://deepmind.google/science/weathernext/

Timestamps:

00:00 Introduction

00:38 Hurricane Melissa

11:50 Why weather forecasting is hard

14:13 Traditional models vs AI models

21:55 Probabilistic forecasting

26:00 WeatherNext 3

33:00 Future outlook

43:44 Hannah's reflections

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Now, if you ask an AI a question, it will usually give you an absolute answer with unwavering authority, even if that answer turns out to be wrong. In fact, today's AI seems to be missing a fundamental human trait: self-doubt. Long before the current wave of large language models, one academic researcher was trying to give machines a sense of their own limitations. Zoubin Ghahramani has spent the last 30 years pioneering a type of intelligence built on the mathematics of uncertainty. Today, as a professor at Cambridge and VP of Research at Google DeepMind, Zoubin finds himself at the heart of another interesting debate: will improving machine uncertainty be one of the missing pieces to ever improving AI?

Timecodes:

• 00:00 Introduction
• 01:06 The role of uncertainty
• 07:45 Correctness vs confidence
• 09:40 Historical perspectives
• 16:10 Bayesian thinking in AI
• 26:30 Uncertainty in the real world
• 36:42 Future research and AGI

Please leave us a review on Spotify or Apple Podcasts if you enjoyed this episode. We always want to hear from our audience whether that's in the form of feedback, new idea or a guest recommendation!

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Neel and his team are trying to do something phenomenally difficult: understand an intelligence that didn't come with a manual. Together, they explore the cutting-edge "neuroscience" of artificial intelligence—revealing the surprising, elegant structures being discovered inside these networks (like spare autoencoders), the inherent limits of looking under the hood, and why interpretability is absolutely essential if we are to build safe, aligned and trustworthy AI as we move towards AGI. Learn more about this area of research via https://deepmind.google/

Timecodes

• 00:00 Introduction
• 02:41 Motivation for interpretability research
• 04:01 Mechanistic interpretability
• 08:14 Chain of thought monitoring
• 18:14 Interpretability techniques
• 35:00 Auditing models for safety
• 48:53 What comes next for interpretability

Please leave us a review on Spotify or Apple Podcasts if you enjoyed this episode. We always want to hear from our audience whether that's in the form of feedback, new idea or a guest recommendation!

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