计算能力与人工智能治理

Computing Power and the Governance of Artificial Intelligence

February 13, 2024

作者: Girish Sastry, Lennart Heim, Haydn Belfield, Markus Anderljung, Miles Brundage, Julian Hazell, Cullen O'Keefe, Gillian K. Hadfield, Richard Ngo, Konstantin Pilz, George Gor, Emma Bluemke, Sarah Shoker, Janet Egan, Robert F. Trager, Shahar Avin, Adrian Weller, Yoshua Bengio, Diane Coyle

cs.AI

摘要

计算能力，或称为“计算”，对人工智能（AI）能力的开发和部署至关重要。因此，政府和公司已开始利用计算作为治理人工智能的手段。例如，政府正在投资国内计算能力，控制计算资源流向竞争国家，并为某些行业补贴计算资源的获取。然而，这些努力仅仅触及了利用计算来治理人工智能开发和部署的表面。与AI的其他关键输入（数据和算法）相比，与AI相关的计算是一种特别有效的干预点：它是可检测的、可排除的、可量化的，并且是通过极度集中的供应链生产的。这些特征，加上计算对尖端AI模型的独特重要性，表明治理计算可以有助于实现共同的政策目标，例如确保AI的安全和有益使用。更具体地说，决策者可以利用计算促进对AI的监管可见性，分配资源以促进有益结果，并对不负责任或恶意的AI开发和使用实施限制。然而，尽管基于计算的政策和技术在这些领域有助于实现潜在的作用，但它们在实施准备方面存在显著的差异。一些想法目前正在试点，而另一些则受制于对基础研究的需求。此外，对计算治理的天真或范围不明确的方法在隐私、经济影响和权力集中等领域存在重大风险。最后，我们建议设定防护措施，以减少计算治理带来的这些风险。

English

Computing power, or "compute," is crucial for the development and deployment of artificial intelligence (AI) capabilities. As a result, governments and companies have started to leverage compute as a means to govern AI. For example, governments are investing in domestic compute capacity, controlling the flow of compute to competing countries, and subsidizing compute access to certain sectors. However, these efforts only scratch the surface of how compute can be used to govern AI development and deployment. Relative to other key inputs to AI (data and algorithms), AI-relevant compute is a particularly effective point of intervention: it is detectable, excludable, and quantifiable, and is produced via an extremely concentrated supply chain. These characteristics, alongside the singular importance of compute for cutting-edge AI models, suggest that governing compute can contribute to achieving common policy objectives, such as ensuring the safety and beneficial use of AI. More precisely, policymakers could use compute to facilitate regulatory visibility of AI, allocate resources to promote beneficial outcomes, and enforce restrictions against irresponsible or malicious AI development and usage. However, while compute-based policies and technologies have the potential to assist in these areas, there is significant variation in their readiness for implementation. Some ideas are currently being piloted, while others are hindered by the need for fundamental research. Furthermore, naive or poorly scoped approaches to compute governance carry significant risks in areas like privacy, economic impacts, and centralization of power. We end by suggesting guardrails to minimize these risks from compute governance.