Learn how invisible, mathematical watermarks help trace AI provenance, protect against misuse, and safeguard protein sequence design without impacting biological function.

Timecodes:

00:00 Introduction

00:34 What is a watermark?

04:35 SynthID

15:16 Images and video

19:28 SynthID Bio

28:00 Future of resilience

Learn more about SynthID Bio: https://deepmind.google/blog/introducing-synthid-bio/

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!

Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

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

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!

Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

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!

Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.