1h 57 min
26 - AI Governance with Elizabeth Seger AXRP - the AI X-risk Research Podcast
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- Tecnología
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
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
1h 57 min