Episode description

Hannah meets DeepMind co-founder and chief scientist Shane Legg, the man who coined the phrase ‘artificial general intelligence’, and explores how it might be built. Why does Shane think AGI is possible? When will it be realised? And what could it look like? Hannah also explores a simple theory of using trial and error to reach AGI and takes a deep dive into MuZero, an AI system which mastered complex board games from chess to Go, and is now generalising to solve a range of important tasks in the real world.

For questions or feedback on the series, message us on Twitter @DeepMind or email podcast@deepmind.com. 

Interviewees: DeepMind’s Shane Legg, Doina Precup, Dave Silver & Jackson Broshear

Credits

Presenter: Hannah Fry

Series Producer: Dan Hardoon

Production support: Jill Achineku

Sounds design: Emma Barnaby

Music composition: Eleni Shaw

Sound Engineer: Nigel Appleton

Editor: David Prest

Commissioned by DeepMind

Thank you to everyone who made this season possible! 

Further reading: 

Real-world challenges for AGI, DeepMind: https://deepmind.com/blog/article/real-world-challenges-for-agi

An executive primer on artificial general intelligence, McKinsey: https://www.mckinsey.com/business-functions/operations/our-insights/an-executive-primer-on-artificial-general-intelligence

Mastering Go, chess, shogi and Atari without rules, DeepMind: https://deepmind.com/blog/article/muzero-mastering-go-chess-shogi-and-atari-without-rules

What is AGI?, Medium: https://medium.com/intuitionmachine/what-is-agi-99cdb671c88e

A Definition of Machine Intelligence by Shane Legg, arXiv: https://arxiv.org/abs/0712.3329

Reward is enough by David Silver, ScienceDirect: https://www.sciencedirect.com/science/article/pii/S0004370221000862

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