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Google DeepMind: The Podcast

Hannah Fry

Join mathematician and broadcaster Professor Hannah Fry as she goes behind the scenes of the world-leading research lab to uncover the extraordinary ways AI is transforming our world. No hype. No spin, just compelling discussions and grand scientific ambition.

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52 episodes
Avg 39 min
English

Episodes 52

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#33
Thursday · 38 min
From deepfakes to DNA: the science of watermarking AI
How do you know if what you're seeing is real? Professor Hannah Fry sits down with Pushmeet Kohli (VP of Science) and Jeremy Ratcliffe (RS) to break down SynthID, the watermarking technology expanding from text, images, and video straight into biology.
#32
September 9 · 44 min
Can AI help us better predict the weather?
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
#31
August 26 · 44 min
The mathematics of AI uncertainty
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. As a professor at Cambridge and VP of Research at Google DeepMind, Zoubin finds himself at the heart of an interesting debate: will improving machine uncertainty be one of the missing pieces to ever improving AI?
#30
July 10 · 53 min
Understanding the inner thoughts of AI
What if you were to peer inside the ‘mind’ of AI? You wouldn't find fully formed thoughts, just vast arrays of numbers. In this episode, Professor Hannah Fry is joined by Neel Nanda, to shine a light on an ongoing open area of research, interpretability.
#29
June 23 · 42 min
When millions of AI agents meet
The conversation of the moment is focused on one topic: AI agents. Unlike traditional language models that simply respond to a prompt, autonomous agents can execute multi-step plans and perform complex tasks on your behalf. But what happens when millions of these agents are not just working for us, but transacting, negotiating, and delegating to one another? Nenad Tomašev, Senior Staff Research Scientist at Google DeepMind, joins host Hannah Fry to discuss the theoretical framework of a future
#28
March 10 · 53 min
10 Years of AlphaGo: The Turning Point for AI | Thore Graepel & Pushmeet Kohli
Join Hannah Fry, Pushmeet Kohli (VP, Science) and Thore Graepel (AlphaGo team & Distinguished Research Scientist) as they unpick the legacy of AlphaGo.
#27
Dec 16, 2025 · 50 min
The Future of Intelligence with Demis Hassabis (Co-founder and CEO of DeepMind)
In our final episode of the season, Professor Hannah Fry sits down with Google DeepMind co-founder and CEO Demis Hassabis for their annual check-in. Together, they look beyond the product launches to the scientific and technological questions that will define the next decade. Demis shares his vision for the path to AGI - from solving "root node" problems in fusion energy and material science to the rise of world models and simulations. They also explore what's beyond the frontier and the import
#26
Dec 11, 2025 · 47 min
The Arrival of AGI with Shane Legg (co-founder of DeepMind)
Shane Legg, Chief AGI Scientist and co-founder of Google DeepMind, has been talking about artificial general intelligence, or AGI, for more than a decade. So what’s changed? According to Shane, now is the time to take it seriously. In this episode, Shane walks Professor Hannah Fry through his AGI framework, breaking down the levels from minimal to full AGI, and his timelines for each. They discuss the economic and societal shifts AGI will bring, the potential impact on work, and the extraordinar
#25
Nov 28, 2025 · 47 min
AlphaFold: Grand Challenge to Nobel Prize with John Jumper
Five years ago, AlphaFold 2 solved the protein structure prediction problem, unlocking new avenues of biological research and providing our first major proof point that AI can be a powerful tool to advance science. The profound scientific and societal value of this work was even recognized in 2024 with the Nobel Prize in Chemistry. In this episode, Hannah is joined by John Jumper, one of the most instrumental scientists behind AlphaFold. He shares stories about his Nobel win, the tool's unexpect
#24
Nov 6, 2025 · 48 min
Waymo: The future of autonomous driving with Vincent Vanhoucke
How do you train an AI to drive safer than a human? Professor Hannah Fry sits down with Waymo Distinguished Engineer Vincent Vanhoucke to break down the complexities of autonomous driving—from the "closed loop" problem of real-world traffic to using generative AI for simulation. Plus, Hannah moves from theory to practice, taking a Waymo for a spin in California to experience it firsthand.
#23
Oct 16, 2025 · 25 min
Pt.2 Beyond Phishing: Cyber Threats in the Age of AI with Four Flynn
In part two, Hannah and Four tackle the human element at the heart of cybersecurity: Who are the bad actors, and what motivates them? They dissect the evolving strategies designed to stop social engineering attacks - like passkeys and risk-based authentication - and confront the complex security and privacy challenges that may be introduced by autonomous agents.
#22
Oct 9, 2025 · 46 min
Pt.1 Beyond Phishing: Cyber Threats in the Age of AI with Four Flynn
Social engineering, cyberattacks, and the fog of war - all topics covered in this interview with the VP of Security and Privacy at Google DeepMind. Hannah Fry and Four Flynn take us behind the scenes of Operation Aurora, the monumental 2009 attack on Google that forever changed the landscape of cybersecurity. They discuss the defender's dilemma, the constant battle between attackers and defenders in the digital world, and how AI can potentially help mitigate some of the most complex vulnerabilit
#21
Aug 22, 2025 · 56 min
Genie 3: An Infinite World Model with Shlomi Fruchter and Jack Parker-Holder
Professor Hannah Fry speaks with Jack Parker-Holder and Shlomi Fruchter about Genie 3, a general-purpose world model that can generate an unprecedented diversity of interactive environments. The conversation covers how this model's auto-regressive nature allows for the creation of consistent, explorable worlds from text or image prompts, and how its capabilities differ from video generation models like Veo.
#20
Jul 10, 2025 · 29 min
A Whistle Stop Tour of AI Creation with Paige Bailey
This week's episode is a slight departure from our usual deep dives. Join Paige Bailey, DevRel lead, as she guides Hannah Fry though some of her favorite AI tools. Having spent years understanding the 'what' of these models, Hannah finally gets to experience the 'how’ – from generating prompts and 'vibe coding', to creating her own version of the infamous spaghetti meme.
#19
Jun 19, 2025 · 40 min
The Nature of AI: Solving the Planet's Data Gap with Drew Purves
Could we use the sound of a coral reef to determine its health? Do we know where all the world's natural forests are? And are we on the verge of using AI to decode animal communication? In this episode, Professor Hannah Fry is joined by Drew Purves, Nature Lead at Google DeepMind. They explore how AI is helping ecologists map forests and species for the first time in history and how this data can be used to better understand the root causes of deforestation. They also explore bioaoustics and how
#18
Jun 5, 2025 · 45 min
A Quest for a Cure: AI Drug Design with Isomorphic Labs
In this episode, host Hannah Fry is joined by Max Jaderberg and Rebecca Paul of Isomorphic Labs to explore the future of drug discovery in the age of AI. They discuss how new technology, particularly AlphaFold 3, is revolutionizing the field by predicting the structure of life’s molecules, paving the way for faster and more efficient drug discovery.
#17
May 22, 2025 · 44 min
Redefining Robotics with Carolina Parada
In this episode, Hannah is joined by Carolina Parada, Senior Director and Head of Robotics at Google DeepMind. They explore the recent leap forward in robotic capabilities, highlighting advancements in multimodal understanding and embodied reasoning, which enable robots to interact with the physical world with unprecedented generality. They dig into the two-system approach - ‘slow and fast thinking‘ - that enables both complex reasoning and rapid, reactive movements. Using examples from robots
#16
May 8, 2025 · 36 min
AI and the Future of Health with Joelle Barral
In this episode, Professor Hannah Fry interviews Joelle Barral, Senior Director of Research at Google DeepMind, about AI in healthcare. They discuss existing AI applications including image analysis for diabetic retinopathy and the expansion of diagnostic tools as a result of multi-modal models. The conversation highlights AI's potential to improve healthcare delivery, personalize treatment, expand access worldwide, and ultimately, bring back the joy of practicing medicine.
#15
Apr 24, 2025 · 40 min
Consciousness, Reasoning and the Philosophy of AI with Murray Shanahan
In this episode, Hannah is once again joined by Murray Shanahan, Professor of Cognitive Robotics at Imperial College London and Principal Scientist at Google DeepMind, for a philosophical deep dive on AI. They explore everything from consciousness and metacognition in animals, to symbolic AI and neural networks. Murray also shares insights into his involvement with the film 'Ex Machina' and discusses the idea of reasoning, anthropomorphism, and the future of AI.
#14
Apr 10, 2025 · 49 min
Is Human Data Enough? With David Silver
In this episode of Google DeepMind: The Podcast, VP of Reinforcement Learning, David Silver, describes his vision for the future of AI, exploring the concept of the "era of experience" versus the current "era of human data". Using AlphaGo and AlphaZero as examples, he highlights how these systems surpassed human capabilities by engaging in reinforcement learning without prior human knowledge. This approach contrasts with large language models, which depend on human data and feedback. Silver emph

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