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At RAAIS 2026, Google DeepMind's Roberta Raileanu lays out a recipe for superhuman scientific discovery: AI systems that make groundbreaking discoveries across domains faster than people can. She walks through three ingredients - reinforcement learning to discover solutions where progress can be measured, open-ended divergent search to find new problems rather than climb known ones, and meta-learning to speed up discovery on problems no one has posed yet. The through-line: we can search for anything we can measure, but we still cannot measure what makes a discovery good. The bottleneck isn't the search. It's the signal.

Chapters:

00:00 - Introduction

00:51 - Defining superhuman scientific discovery

01:40 - The state of play: real progress, real plateau

06:58 - Ingredient one: discovery as reinforcement learning (Move 37, MLGym)

12:51 - Ingredient two: open-ended search and why greatness cannot be planned

18:30 - Rainbow Teaming: quality-diversity in practice

21:07 - Ingredient three: meta-learning the process of discovery (DiscoBench)

25:06 - The recipe, and the missing signal