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The limits of AI automation in Google DeepMind's ML research

Google DeepMind's Gemini 4 Argon safety report finds no significant end-to-end ML R&D acceleration, despite automation of some tasks.

8 Sources, 1h ago, first seen 1h ago

TLDR

A contributor to Google DeepMind's Gemini 4 Argon Safety & Security Report says its assessment found no significant end-to-end acceleration of ML R&D and rated the current risk from AI-driven ML R&D low. AI agents do substantial coding work, but about 88% of pull requests still need multi-turn human steering or human code authorship. Across 1,389 internal Argon agent trajectories, larger and longer tasks saw less autonomous success and more human intervention.

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8 Sources

Haydn Belfield@HaydnBelfield1/9 In the Gemini 4 Argon Safety & Security Report, we published our first holistic empirical assessment of ML R&D automation and acceleration inside Google DeepMind — combining capability benchmarks, a macro capability index, data on AI-written code and trajectory outcomes, and a survey of senior frontier researchers. Our main findings are that: 1. There is currently no significant end-to-end acceleration of ML R&D, AI-driven or otherwise. 2. While some components of ML R&D are being automated and there have been many examples of AI-driven breakthroughs and optimizations, we are far from full end-to-end automation. 3. We therefore conclude that the risk from AI-driven ML R&D is currently low. Thread below🧵1h
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    8 Sources

    Haydn Belfield@HaydnBelfield1/9 In the Gemini 4 Argon Safety & Security Report, we published our first holistic empirical assessment of ML R&D automation and acceleration inside Google DeepMind — combining capability benchmarks, a macro capability index, data on AI-written code and trajectory outcomes, and a survey of senior frontier researchers. Our main findings are that: 1. There is currently no significant end-to-end acceleration of ML R&D, AI-driven or otherwise. 2. While some components of ML R&D are being automated and there have been many examples of AI-driven breakthroughs and optimizations, we are far from full end-to-end automation. 3. We therefore conclude that the risk from AI-driven ML R&D is currently low. Thread below🧵1h
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