In this episode, 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.

Further reading

• Genie 3
• SIMA, A generalist AI agent for 3D virtual environments
• SIMA, Google DeepMind: The Podcast
• xLand

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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.

Learn more and try the tools yourself:

• Gemini: https://gemini.google.com/
• Google Labs: https://labs.google/
• AI Studio: https://aistudio.google.com/
• Veo 3: https://deepmind.google/models/veo/
• Flow: https://labs.google/flow/

Thanks to everyone who made this possible, including but not limited to:

• Presenter: Professor Hannah Fry
• Series Producer: Dan Hardoon
• Editor: Rami Tzabar
• Commissioner & Producer: Emma Yousif
• Music composition: Eleni Shaw
• Audio engineer: Richard Courtice
• Production Manager: Dan Lazard
• Studio Manager: Nicholas Duke
• Video Director: Bernardo Resende
• Video Editor: Bilal Merhi
• Audio Engineer: Perry Rogantin
• Camera and Lighting Operator: Robert Messere
• Production Coordination: Zoey Roberts, Sarah Ellen Morton
• Visual Identity and Design: Rob Ashley
• Commissioned by Google DeepMind

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!

Further reading:

• Natural forests of the world: paper, data and benchmarks
• Forest loss drivers: paper, summary from WRI, and blog from GFW
• Forest loss drivers code: Google Earth Engine; at WRI; at GFW; or Zenodo.
• Deep learning based remote sensing (open source): Jeo, GeeFlow
• Species mapping paper: Arxiv
• Google resources: Google Earth Engine. Agri with Google, wildlife cameras
• Perch: code, paper
• Perch x coral reefs: Blog
• Agile Modelling: paper, code
• DolphinGemma: blog

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!