))
Governing AI Through Compute Providers
Fetch error
Hmmm there seems to be a problem fetching this series right now.
Last successful fetch was on October 24, 2024 13:55 (
What now? This series will be checked again in the next hour. If you believe it should be working, please verify the publisher's feed link below is valid and includes actual episode links. You can contact support to request the feed be immediately fetched.
Manage episode 445593716 series 3604081
コンテンツは Jean Jane によって提供されます。エピソード、グラフィック、ポッドキャストの説明を含むすべてのポッドキャスト コンテンツは、Jean Jane またはそのポッドキャスト プラットフォーム パートナーによって直接アップロードされ、提供されます。誰かがあなたの著作物をあなたの許可なく使用していると思われる場合は、ここで概説されているプロセスに従うことができますhttps://ja.player.fm/legal。
This Episode reviews the main themes and important ideas presented in the research paper "Governing-Through-the-Cloud: The Intermediary Role of Compute Providers in AI Regulation". It focuses on the potential for leveraging compute providers as regulatory intermediaries to oversee the development and deployment of frontier AI systems, defined as "highly capable general-purpose AI models" demanding substantial compute resources.
Key Themes:- Compute providers as regulatory intermediaries: The paper proposes that compute providers, due to their unique position in the AI supply chain, can act as effective intermediaries for AI governance, similar to financial institutions in anti-money laundering efforts.
- Focus on frontier AI: The proposed framework specifically targets actors developing and deploying frontier AI systems, leveraging their substantial compute usage as a screening mechanism. This targeted approach aims to ensure regulatory effectiveness and proportionality.
- Four governance capacities of compute providers: The paper outlines four key capacities that compute providers can utilize for effective AI governance:
- Securing: Ensuring physical and cybersecurity measures to protect AI models, intellectual property, and sensitive data.
- Record keeping: Collecting and maintaining information on customer identities and compute usage, enabling transparency and post-incident attribution.
- Verification: Actively verifying customer identities, activities, and properties of AI systems to ensure compliance with regulations.
- Enforcement: Restricting or limiting compute access for non-compliant customers or workloads.
- Technical feasibility and challenges: The paper acknowledges both the technical feasibility and challenges associated with implementing these governance capacities. It suggests several existing technical capabilities, including data collection practices and confidential computing techniques, that can be leveraged for effective oversight. It also identifies potential challenges, such as data privacy concerns and evasion tactics employed by malicious actors.
Compute providers are already extensive data collectors: For billing, optimization, and legal compliance, compute providers collect a wide range of
- Workload classification and compute accounting are feasible: Existing data attributes and techniques can likely enable compute providers to verify
Confidential computing could enable detailed workload verification: Emerging "confidential computing" techniques could enable compute providers
US policy provides a case study
International coordination is crucial: The paper emphasizes the need for international cooperation to ensure the effectiveness and durability of
Hosted on Acast. See acast.com/privacy for more information.
79 つのエピソード
シェア
