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المحتوى المقدم من Skeptics in the Pub Online. يتم تحميل جميع محتويات البودكاست بما في ذلك الحلقات والرسومات وأوصاف البودكاست وتقديمها مباشرة بواسطة Skeptics in the Pub Online أو شريك منصة البودكاست الخاص بهم. إذا كنت تعتقد أن شخصًا ما يستخدم عملك المحمي بحقوق الطبع والنشر دون إذنك، فيمكنك اتباع العملية الموضحة هنا https://ar.player.fm/legal.
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Too dangerous to publish? Navigating the high-stakes nature of AI research – Rosie Campbell

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Manage episode 374169622 series 3327627
المحتوى المقدم من Skeptics in the Pub Online. يتم تحميل جميع محتويات البودكاست بما في ذلك الحلقات والرسومات وأوصاف البودكاست وتقديمها مباشرة بواسطة Skeptics in the Pub Online أو شريك منصة البودكاست الخاص بهم. إذا كنت تعتقد أن شخصًا ما يستخدم عملك المحمي بحقوق الطبع والنشر دون إذنك، فيمكنك اتباع العملية الموضحة هنا https://ar.player.fm/legal.

As AI becomes increasingly advanced, it promises many benefits but also comes with risks. How can we mitigate these risks while preserving scientific inquiry and openness? Who is responsible for anticipating the impacts of AI research, and how can they do so effectively? What changes, if any, need to be made to the peer review process? In this talk, we’ll explore these tensions and how they are playing out right now in the AI community. AI is not the first high-stakes, ‘dual-use’ field to face these questions. Taking inspiration from fields like cybersecurity and biosecurity, we’ll look at possible approaches to responsible publication, their strengths and limitations, and how they might be used in practice for AI.

Rosie Campbell leads the Safety-Critical AI program at the Partnership on AI, a multistakeholder nonprofit shaping the future of responsible AI. Her main focus is on responsible publication and deployment practices for increasingly advanced AI. Previously, she was Assistant Director of the Center for Human-Compatible AI at UC Berkeley, a Research Engineer at BBC R&D, and cofounder of Manchester Futurists. Her academic background spans physics, philosophy, and computer science. Rosie is also a productivity nerd and enjoys thinking about how to optimize systems, and how to use reason and evidence to improve the world.

The music used in this episode is by Thula Borah and is used with permission.

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iconمشاركة
 
Manage episode 374169622 series 3327627
المحتوى المقدم من Skeptics in the Pub Online. يتم تحميل جميع محتويات البودكاست بما في ذلك الحلقات والرسومات وأوصاف البودكاست وتقديمها مباشرة بواسطة Skeptics in the Pub Online أو شريك منصة البودكاست الخاص بهم. إذا كنت تعتقد أن شخصًا ما يستخدم عملك المحمي بحقوق الطبع والنشر دون إذنك، فيمكنك اتباع العملية الموضحة هنا https://ar.player.fm/legal.

As AI becomes increasingly advanced, it promises many benefits but also comes with risks. How can we mitigate these risks while preserving scientific inquiry and openness? Who is responsible for anticipating the impacts of AI research, and how can they do so effectively? What changes, if any, need to be made to the peer review process? In this talk, we’ll explore these tensions and how they are playing out right now in the AI community. AI is not the first high-stakes, ‘dual-use’ field to face these questions. Taking inspiration from fields like cybersecurity and biosecurity, we’ll look at possible approaches to responsible publication, their strengths and limitations, and how they might be used in practice for AI.

Rosie Campbell leads the Safety-Critical AI program at the Partnership on AI, a multistakeholder nonprofit shaping the future of responsible AI. Her main focus is on responsible publication and deployment practices for increasingly advanced AI. Previously, she was Assistant Director of the Center for Human-Compatible AI at UC Berkeley, a Research Engineer at BBC R&D, and cofounder of Manchester Futurists. Her academic background spans physics, philosophy, and computer science. Rosie is also a productivity nerd and enjoys thinking about how to optimize systems, and how to use reason and evidence to improve the world.

The music used in this episode is by Thula Borah and is used with permission.

  continue reading

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