P versus NP intuition for AI tasks

Geoffrey explains how some tasks remain hard while many mundane tasks become easy for scaled models, affecting jaggedness.

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Geoffrey Irving, Chief Scientist at the UK AI Security Institute, explains why our theoretical understanding of machine learning remains fragile even as models surpass experts on critical security tasks. He details AISI’s work on frontier model evaluations, red teaming, and threat modeling across biosecurity, cybersecurity, and loss-of-control risks. The conversation explores reward hacking, eval awareness, and why current safety techniques may struggle to deliver high reliability. Listeners will also hear how AISI is funding foundational research to build stronger guarantees for AI safety.

Nathan uses Granola to uncover blind spots in conversations and AI research. Try it at ⁠granola.ai/tcr⁠ with code TCR — and if you’re already using it, test his blind spot recipe here: ⁠https://bit.ly/granolablindspot⁠

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CHAPTERS:

(00:00) About the Episode

(04:09) From physics to ML

(08:52) AGI uncertainty and threats (Part 1)

(18:08) Sponsors: Serval | Claude

(21:29) AGI uncertainty and threats (Part 2)

(27:35) Control, autonomy, alignment (Part 1)

(34:02) Sponsor: Tasklet

(35:14) Control, autonomy, alignment (Part 2)

(38:44) Inside the UK AC

(51:02) Evaluations and jailbreaking

(01:01:17) Emerging capabilities and misuse