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| Format: | Preprint |
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2023
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| Online-Zugang: | https://arxiv.org/abs/2310.17688 |
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| author | Bengio, Yoshua Hinton, Geoffrey Yao, Andrew Song, Dawn Abbeel, Pieter Darrell, Trevor Harari, Yuval Noah Zhang, Ya-Qin Xue, Lan Shalev-Shwartz, Shai Hadfield, Gillian Clune, Jeff Maharaj, Tegan Hutter, Frank Baydin, Atılım Güneş McIlraith, Sheila Gao, Qiqi Acharya, Ashwin Krueger, David Dragan, Anca Torr, Philip Russell, Stuart Kahneman, Daniel Brauner, Jan Mindermann, Sören |
| author_facet | Bengio, Yoshua Hinton, Geoffrey Yao, Andrew Song, Dawn Abbeel, Pieter Darrell, Trevor Harari, Yuval Noah Zhang, Ya-Qin Xue, Lan Shalev-Shwartz, Shai Hadfield, Gillian Clune, Jeff Maharaj, Tegan Hutter, Frank Baydin, Atılım Güneş McIlraith, Sheila Gao, Qiqi Acharya, Ashwin Krueger, David Dragan, Anca Torr, Philip Russell, Stuart Kahneman, Daniel Brauner, Jan Mindermann, Sören |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_17688 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Managing extreme AI risks amid rapid progress Bengio, Yoshua Hinton, Geoffrey Yao, Andrew Song, Dawn Abbeel, Pieter Darrell, Trevor Harari, Yuval Noah Zhang, Ya-Qin Xue, Lan Shalev-Shwartz, Shai Hadfield, Gillian Clune, Jeff Maharaj, Tegan Hutter, Frank Baydin, Atılım Güneş McIlraith, Sheila Gao, Qiqi Acharya, Ashwin Krueger, David Dragan, Anca Torr, Philip Russell, Stuart Kahneman, Daniel Brauner, Jan Mindermann, Sören Computers and Society Artificial Intelligence Computation and Language Machine Learning 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. |
| title | Managing extreme AI risks amid rapid progress |
| topic | Computers and Society Artificial Intelligence Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2310.17688 |