David Lindner

@davidlindner.bsky.social

Making AI safer at Google DeepMind davidlindner.me

Excited to share some technical details about our approach to scheming and deceptive alignment as outlined in Google's Frontier Safety Framework! (1) current models are not yet capable of realistic scheming (2) CoT monitoring is a promising mitigation for future scheming

@vkrakovna.bsky.social · last yr.

As models advance, a key AI safety concern is deceptive alignment / "scheming" – where AI might covertly pursue unintended goals. Our paper "Evaluating Frontier Models for Stealth and Situational Awareness" assesses whether current models can scheme. arxiv.org/abs/2505.01420

Super excited this giant paper outlining our technical approach to AGI safety and security is finally out! No time to read 145 pages? Check out the 10 page extended abstract at the beginning of the paper

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We are excited to release a short course on AGI safety! The course offers a concise and accessible introduction to AI alignment problems and our technical / governance approaches, consisting of short recorded talks and exercises (75 minutes total). deepmindsafetyresearch.medium.com/1072adb7912c

Introducing our short course on AGI safety

We are excited to release a short course on AGI safety for students, researchers and professionals interested in this topic. The course…

deepmindsafetyresearch.medium.com

Want to join one of the best AI safety teams in the world? We're hiring at Google DeepMind! We have open positions for research engineers and research scientists in the AGI Safety & Alignment and Gemini Safety teams. Locations: London, Zurich, New York, Mountain View and SF

By default, LLM agents with long action sequences use early steps to undermine your evaluation of later steps; a big alignment risk. Our new paper mitigates this, keeps the ability for long-term planning, and doesnt assume you can detect the undermining strategy. 👇

David Lindner@davidlindner.bsky.social · 2y ago

New Google DeepMind safety paper! LLM agents are coming – how do we stop them finding complex plans to hack the reward? Our method, MONA, prevents many such hacks, *even if* humans are unable to detect them! Inspired by myopic optimization but better performance – details in🧵

New Google DeepMind safety paper! LLM agents are coming – how do we stop them finding complex plans to hack the reward? Our method, MONA, prevents many such hacks, *even if* humans are unable to detect them! Inspired by myopic optimization but better performance – details in🧵

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So excited to share our Google DeepMind team's new Nature paper on GenCast, an ML-based probabilistic weather forecasting model: www.nature.com/articles/s41... It represents a substantial step forward in how we predict weather and assess the risk of extreme events. 🌪️🧵

Probabilistic weather forecasting with machine learning - Nature

GenCast, a probabilistic weather model using artificial intelligence for weather forecasting, has greater skill and speed than the top operational medium-range weather forecast in the world and provid...

nature.com

New paper on evaluating instrumental self-reasoning ability in frontier models 🤖🪞 We propose a suite of agentic tasks that are more diverse than prior work and give us a more representative picture of how good models are at eg. self-modification and embedded reasoning

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