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Managing extreme AI risks amid rapid progress

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arxiv 2310.17688 v3 pith:DOMK3IPQ submitted 2023-10-26 cs.CY cs.AIcs.CLcs.LG

Managing extreme AI risks amid rapid progress

Yoshua Bengio , Geoffrey Hinton , Andrew Yao , Dawn Song , Pieter Abbeel , Trevor Darrell , Yuval Noah Harari , Ya-Qin Zhang
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Lan Xue Shai Shalev-Shwartz Gillian Hadfield Jeff Clune Tegan Maharaj Frank Hutter At{\i}l{\i}m G\"une\c{s} Baydin Sheila McIlraith Qiqi Gao Ashwin Acharya David Krueger Anca Dragan Philip Torr Stuart Russell Daniel Kahneman Jan Brauner S\"oren Mindermann
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classification cs.CY cs.AIcs.CLcs.LG
keywords riskssystemsextremeautonomousconsensusgovernancelackmechanisms
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Artificial Intelligence (AI) is progressing rapidly, and companies are shifting their focus to developing generalist AI systems that can autonomously act and pursue goals. Increases in capabilities and autonomy may soon massively amplify AI's impact, with risks that include large-scale social harms, malicious uses, and an irreversible loss of human control over autonomous AI systems. Although researchers have warned of extreme risks from AI, there is a lack of consensus about how exactly such risks arise, and how to manage them. Society's response, despite promising first steps, is incommensurate with the possibility of rapid, transformative progress that is expected by many experts. AI safety research is lagging. Present governance initiatives lack the mechanisms and institutions to prevent misuse and recklessness, and barely address autonomous systems. In this short consensus paper, we describe extreme risks from upcoming, advanced AI systems. Drawing on lessons learned from other safety-critical technologies, we then outline a comprehensive plan combining technical research and development with proactive, adaptive governance mechanisms for a more commensurate preparation.

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