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26 - AI Governance with Elizabeth Seger

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Manage episode 385970673 series 2844728
内容由Daniel Filan提供。所有播客内容(包括剧集、图形和播客描述)均由 Daniel Filan 或其播客平台合作伙伴直接上传和提供。如果您认为有人在未经您许可的情况下使用您的受版权保护的作品,您可以按照此处概述的流程进行操作https://zh.player.fm/legal

The events of this year have highlighted important questions about the governance of artificial intelligence. For instance, what does it mean to democratize AI? And how should we balance benefits and dangers of open-sourcing powerful AI systems such as large language models? In this episode, I speak with Elizabeth Seger about her research on these questions.

Patreon: patreon.com/axrpodcast

Ko-fi: ko-fi.com/axrpodcast

Topics we discuss, and timestamps:

- 0:00:40 - What kinds of AI?

- 0:01:30 - Democratizing AI

- 0:04:44 - How people talk about democratizing AI

- 0:09:34 - Is democratizing AI important?

- 0:13:31 - Links between types of democratization

- 0:22:43 - Democratizing profits from AI

- 0:27:06 - Democratizing AI governance

- 0:29:45 - Normative underpinnings of democratization

- 0:44:19 - Open-sourcing AI

- 0:50:47 - Risks from open-sourcing

- 0:56:07 - Should we make AI too dangerous to open source?

- 1:00:33 - Offense-defense balance

- 1:03:13 - KataGo as a case study

- 1:09:03 - Openness for interpretability research

- 1:15:47 - Effectiveness of substitutes for open sourcing

- 1:20:49 - Offense-defense balance, part 2

- 1:29:49 - Making open-sourcing safer?

- 1:40:37 - AI governance research

- 1:41:05 - The state of the field

- 1:43:33 - Open questions

- 1:49:58 - Distinctive governance issues of x-risk

- 1:53:04 - Technical research to help governance

- 1:55:23 - Following Elizabeth's research

The transcript: https://axrp.net/episode/2023/11/26/episode-26-ai-governance-elizabeth-seger.html

Links for Elizabeth:

- Personal website: elizabethseger.com

- Centre for the Governance of AI (AKA GovAI): governance.ai

Main papers:

- Democratizing AI: Multiple Meanings, Goals, and Methods: arxiv.org/abs/2303.12642

- Open-sourcing highly capable foundation models: an evaluation of risks, benefits, and alternative methods for pursuing open source objectives: papers.ssrn.com/sol3/papers.cfm?abstract_id=4596436

Other research we discuss:

- What Do We Mean When We Talk About "AI democratisation"? (blog post): governance.ai/post/what-do-we-mean-when-we-talk-about-ai-democratisation

- Democratic Inputs to AI (OpenAI): openai.com/blog/democratic-inputs-to-ai

- Collective Constitutional AI: Aligning a Language Model with Public Input (Anthropic): anthropic.com/index/collective-constitutional-ai-aligning-a-language-model-with-public-input

- Against "Democratizing AI": johanneshimmelreich.net/papers/against-democratizing-AI.pdf

- Adversarial Policies Beat Superhuman Go AIs: goattack.far.ai

- Structured access: an emerging paradigm for safe AI deployment: arxiv.org/abs/2201.05159

- Universal and Transferable Adversarial Attacks on Aligned Language Models (aka Adversarial Suffixes): arxiv.org/abs/2307.15043

Episode art by Hamish Doodles: hamishdoodles.com

  continue reading

33集单集

Artwork
icon分享
 
Manage episode 385970673 series 2844728
内容由Daniel Filan提供。所有播客内容(包括剧集、图形和播客描述)均由 Daniel Filan 或其播客平台合作伙伴直接上传和提供。如果您认为有人在未经您许可的情况下使用您的受版权保护的作品,您可以按照此处概述的流程进行操作https://zh.player.fm/legal

The events of this year have highlighted important questions about the governance of artificial intelligence. For instance, what does it mean to democratize AI? And how should we balance benefits and dangers of open-sourcing powerful AI systems such as large language models? In this episode, I speak with Elizabeth Seger about her research on these questions.

Patreon: patreon.com/axrpodcast

Ko-fi: ko-fi.com/axrpodcast

Topics we discuss, and timestamps:

- 0:00:40 - What kinds of AI?

- 0:01:30 - Democratizing AI

- 0:04:44 - How people talk about democratizing AI

- 0:09:34 - Is democratizing AI important?

- 0:13:31 - Links between types of democratization

- 0:22:43 - Democratizing profits from AI

- 0:27:06 - Democratizing AI governance

- 0:29:45 - Normative underpinnings of democratization

- 0:44:19 - Open-sourcing AI

- 0:50:47 - Risks from open-sourcing

- 0:56:07 - Should we make AI too dangerous to open source?

- 1:00:33 - Offense-defense balance

- 1:03:13 - KataGo as a case study

- 1:09:03 - Openness for interpretability research

- 1:15:47 - Effectiveness of substitutes for open sourcing

- 1:20:49 - Offense-defense balance, part 2

- 1:29:49 - Making open-sourcing safer?

- 1:40:37 - AI governance research

- 1:41:05 - The state of the field

- 1:43:33 - Open questions

- 1:49:58 - Distinctive governance issues of x-risk

- 1:53:04 - Technical research to help governance

- 1:55:23 - Following Elizabeth's research

The transcript: https://axrp.net/episode/2023/11/26/episode-26-ai-governance-elizabeth-seger.html

Links for Elizabeth:

- Personal website: elizabethseger.com

- Centre for the Governance of AI (AKA GovAI): governance.ai

Main papers:

- Democratizing AI: Multiple Meanings, Goals, and Methods: arxiv.org/abs/2303.12642

- Open-sourcing highly capable foundation models: an evaluation of risks, benefits, and alternative methods for pursuing open source objectives: papers.ssrn.com/sol3/papers.cfm?abstract_id=4596436

Other research we discuss:

- What Do We Mean When We Talk About "AI democratisation"? (blog post): governance.ai/post/what-do-we-mean-when-we-talk-about-ai-democratisation

- Democratic Inputs to AI (OpenAI): openai.com/blog/democratic-inputs-to-ai

- Collective Constitutional AI: Aligning a Language Model with Public Input (Anthropic): anthropic.com/index/collective-constitutional-ai-aligning-a-language-model-with-public-input

- Against "Democratizing AI": johanneshimmelreich.net/papers/against-democratizing-AI.pdf

- Adversarial Policies Beat Superhuman Go AIs: goattack.far.ai

- Structured access: an emerging paradigm for safe AI deployment: arxiv.org/abs/2201.05159

- Universal and Transferable Adversarial Attacks on Aligned Language Models (aka Adversarial Suffixes): arxiv.org/abs/2307.15043

Episode art by Hamish Doodles: hamishdoodles.com

  continue reading

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