Subvolts take Google DeepMind explores using games to train AI agents for real-world tasks. Sep 25, 2024 - Developers
- Learn about using game environments to train AI agents and explore research like SIMA for building more capable systems.
- Business
- Understand how frontier labs are developing AI agents that can perform a wide range of tasks, potentially impacting future automation.
This episode features a key researcher discussing practical AI agent development and a specific research project.
Gaming, Goats & General Intelligence with Frederic Besse
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From Subvolts
Summary and analysis by Subvolts editorial, AI-assisted.
Google DeepMind's podcast features Research Engineering Team Lead Frederic Besse discussing how games serve as effective training grounds for AI agents. Besse and host Hannah Fry explore research like SIMA (Scalable Instructable Multiworld Agent) and the future of AI agents capable of performing diverse tasks online and in the real world.
This episode explains how video games are like training gyms for AI. By practicing in these controlled game worlds, AI agents learn to understand rules and act on their own. This research helps build AI that can do many different jobs, both on computers and in the real world.
This episode offers insight into Google DeepMind's approach to developing AI agents, using gaming environments for training. It's for anyone interested in the practical application of AI research, particularly in creating agents that can understand and execute complex instructions. The discussion on SIMA connects directly to current frontier lab efforts in building more capable and general-purpose AI systems.
- HostHannah Fry
- GuestFrederic BesseResearch Engineering Team Lead, Google DeepMind
- Guest: Frederic Besse, Research Engineering Team Lead
- Show: Google DeepMind: The Podcast
- Length: 40 minutes
- Research Highlight: SIMA (Scalable Instructable Multiworld Agent)
- Training Method: Games as training grounds for agents
Games are a very good training ground for agents. Think about it. Perfectly packaged, neatly constrained environments where agents can run wild, work out the rules for themselves, and learn how to handle autonomy. In this episode, Research Engineering Team Lead, Frederic Besse, joins Hannah as they discuss important research like SIMA (Scalable Instructable Multiworld Agent) and what we can expect from future agents that can understand and safely carry out a wide range of tasks - online and in the real world.
Further reading:
Thanks to everyone who made this possible, including but not limited to:
- Presenter: Professor Hannah Fry
- Series Producer: Dan Hardoon
- Editor: Rami Tzabar, TellTale Studios
- Commissioner & Producer: Emma Yousif
- Production support: Mo Dawoud
- Music composition: Eleni Shaw
- Camera Director and Video Editor: Tommy Bruce
- Audio Engineer: Perry Rogantin
- Video Studio Production: Nicholas Duke
- Video Editor: Bilal Merhi
- Video Production Design: James Barton
- Visual Identity and Design: Eleanor Tomlinson
- Commissioned by Google DeepMind
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53 minQuestions people ask
- What is SIMA?
- SIMA stands for Scalable Instructable Multiworld Agent. It is a research project discussed in the episode.
- Why are games good for training AI agents?
- Games provide neatly constrained environments where agents can learn the rules and practice autonomy.
Page generated Oct 19, 2026. Summaries are Subvolts' own; the audio, artwork and notes belong to Hannah Fry.