計算能力與人工智慧治理

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

計算能力，或稱為「運算」，對於人工智慧（AI）能力的開發和部署至關重要。因此，政府和公司已開始利用運算作為治理AI的手段。例如，政府正在投資國內運算能力，控制運算流向競爭對手國家，並對某些行業補貼運算訪問。然而，這些努力僅是運用運算來治理AI開發和部署的冰山一角。相對於AI的其他關鍵輸入（數據和算法），與AI相關的運算是一個特別有效的干預點：它是可檢測的、可排除的、可量化的，並通過極度集中的供應鏈生產。這些特點，再加上運算對尖端AI模型的獨特重要性，表明治理運算有助於實現共同的政策目標，如確保AI的安全和有益使用。更具體地說，政策制定者可以利用運算來促進對AI的監管可見性，分配資源以促進有益結果，並對不負責任或惡意的AI開發和使用實施限制。然而，儘管基於運算的政策和技術有助於在這些領域提供支援，但它們在實施準備方面存在顯著差異。一些想法目前正在試點，而其他一些受制於對基礎研究的需求。此外，對運算治理的天真或範圍不當的方法在隱私、經濟影響和權力集中等領域存在重大風險。最後，我們建議設置防護措施，以最小化來自運算治理的這些風險。

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.