The article argues that the Federal Reserve's recent rate hike decision, while justifiable on inflation grounds, highlights a critical lack of an underlying, transparent policy framework. The core problem is that the Fed's decisions appear discretionary, leading to market uncertainty because the committee's judgment, rather than clear data, dictates policy shifts. The author proposes that the Fed adopt a formal monetary policy rule—an algebraic formula linking the target rate to indicators like inflation and unemployment—to replace subjective guidance. Implementing such a rule would provide market predictability, enhance transparency, and shield the Fed from political attacks by making deviations from the standard easily quantifiable.
Advancing U.S.–UK Cooperation to Secure Frontier Artificial Intelligence
English Summary
The RAND–Oxford report argues that the U.S. and UK must establish coordinated bilateral frameworks to secure frontier AI systems against sophisticated nation-state threats. Current AI lab defenses are insufficient against advanced state actors, though both countries possess deep expertise in protecting critical technologies. The report proposes a cluster-based framework organizing AI security into five interconnected domains—access controls, supply chain, monitoring/response, personnel security, and physical security—enabling risk-informed, modular implementation without requiring wholesale industry restructuring. Key recommendations include establishing joint threat intelligence infrastructure, accelerating hardware security R&D, extending government personnel vetting to labs, coordinating supply-chain security, expanding red-teaming, conducting joint crisis exercises, and establishing common security standards.
中文摘要
RAND–Oxford報告指出，美國與英國必須建立協調的雙邊框架，以保護前沿人工智能系統免受複雜的國家級威脅。儘管兩國在保護關鍵技術方面具有深厚的專業知識，但目前人工智能實驗室的防禦措施對先進國家行為體的威脅防護不足。該報告提出了一個群集式框架，將人工智能安全組織為五個相互連接的領域——訪問控制、供應鏈、監測/應對、人員安全和物理安全——實現風險知情的模組化實施，無需行業全面重組。主要建議包括建立聯合威脅情報基礎設施、加速硬件安全研發、將政府人員審查延伸至實驗室、協調供應鏈安全、擴大紅隊測試、進行聯合危機演習，以及建立共同安全標準。
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