Google DeepMind: The Podcast

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

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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

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Timecodes:

00:00 Intro

1:07 Defining AI agents

4:44 Agentic exploration in science and research

15:46 Delegation between agents

22:46 Agentic security and traps

29:31 Building an agentic economy

33:22 Cognitive monoculture

36:29 Distributed intelligence 

To read the research, search for: Distributional AGI Safety, May 2026 Intelligent AI Delegation, February 2026 Virtual Agent Economies, September 2025 

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Seoul, March 2016. Two players sit hunched over a 19x19 grid covered in a sea of black and white stones. They are playing the ancient game of Go - a game of unimaginable complexity long thought impossible for a machine to master. On one side is Lee Sedol (Sae Dol), a legendary 18-time Go world champion. On the other, AlphaGo, a neural network based AI system built on a powerful technique called reinforcement learning. In the blink of an eye, the world changed. Exactly one decade later, we look back at the match that sparked the modern AI revolution. From algorithmic discovery to the solving of scientific grand challenges like protein folding, the foundation was laid right there on that wooden board. 

Join Hannah Fry, Pushmeet Kohli (VP, Science) and Thore Graepel (AlphaGo team & Distinguished Research Scientist) as they unpick the legacy of AlphaGo. 

🎥 AlphaGo https://youtu.be/WXuK6gekU1Y 

🎥 The Thinking Game: https://youtu.be/d95J8yzvjbQ

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