Why AI Evaluation Science Can't Keep Up (with Carina Prun...

What this episode covers

Carina Prunkl is a researcher at Inria. She joins the podcast to discuss how to assess the capabilities and risks of general-purpose AI. We examine why systems can solve hard coding and math problems yet still fail at simple tasks, why pre-deployment tests often miss real-world behavior, and how faster capability gains can increase misuse risks. The conversation also covers de-skilling, red teaming, layered safeguards, and warning signs that AIs might undermine oversight.LINKS:Carina Prunkl personal websiteCHAPTERS: (00:00) Episode Preview (01:04) Introducing the report (02:10) Jagged frontier capabilities (05:29) Formal reasoning progress (12:36) Risks and evaluation science (19:00) Funding evaluation capacity (24:03) Autonomy and de-skilling (31:32) Authenticity and AI companions (41:00) Defense in depth methods (48:34) Loss of control risks (53:16) Where to read report PRODUCED BY: https://aipodcast.ing SOCIAL LINKS: Website: https://podcast.futureoflife.org Twitter (FLI): https://x.com/FLI_org Twitter (Gus): https://x.com/gusdocker LinkedIn: https://www.linkedin.com/company/future-of-life-institute/ YouTube: https://www.youtube.com/channel/UC-rCCy3FQ-GItDimSR9lhzw/ Apple: https://geo.itunes.apple.com/us/podcast/id1170991978 Spotify: https://open.spotify.com/show/2Op1WO3gwVwCrYHg4eoGyP

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How long is this episode of Future of Life Institute Podcast?

This episode is 54 minutes long.

When was this Future of Life Institute Podcast episode published?

This episode was published on April 17, 2026.

What is this episode about?

Carina Prunkl is a researcher at Inria. She joins the podcast to discuss how to assess the capabilities and risks of general-purpose AI. We examine why systems can solve hard coding and math problems yet still fail at simple tasks, why...

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