On this special episode of the podcast, Emilia Javorsky interviews Sean Ekins about the dangers of AI drug discovery. They talk about how Sean discovered an extremely toxic chemical (VX) by reversing an AI drug discovery algorithm.
Timestamps: 00:00 Introduction 00:46 Sean’s professional journey 03:45 Can computational models replace animal models? 07:24 The risks of AI drug discovery 12:48 Should scientists disclose dangerous discoveries? 19:40 How should scientists handle dual-use technologies? 22:08 Should we open-source potentially dangerous discoveries? 26:20 How do we control autonomous drug creation? 31:36 Surprising chemical discoveries made by black-box AI systems 36:56 How could the dangers of AI drug discovery be mitigated?
Peter Wildeford discusses methods for forecasting AI progress and why he sees AI as neither a bubble nor a normal technology, covering economic effects, national security, cyber capabilities, robotics, export controls, and prediction markets.
Inria researcher Carina Prunkl discusses why AI evaluation struggles to keep pace with general-purpose systems, including jagged capabilities, missed real-world behavior, misuse risks, de-skilling, red teaming, and layered safeguards.
Li-Lian Ang from Blue Dot Impact discusses how to build a workforce to defend against AI-driven risks, including engineered pandemics, cyber attacks, job disempowerment, and concentrated power, using a defense-in-depth framework for uncertain AI progress.
