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.
AI Licensing Comes Full Circle
English Summary
The concept of mandatory AI licensing and pre-release testing is resurfacing as a critical policy concern, driven by the emergence of highly capable, vulnerable models and renewed political interest in regulation. While the U.S. government is considering an executive order modeled after drug approval processes, the author argues that simply adapting existing regulations is insufficient. For effective policy, the U.S. must craft a framework tailored to AI's unique, continuously evolving nature, focusing heavily on rigorous pre-release evaluation methods. Crucially, any licensing regime must be paired with robust post-market oversight and enforcement mechanisms to manage the risks posed by advanced, rapidly advancing AI systems.
中文摘要
強制性AI許可和發布前測試的概念正重新浮現為一項關鍵的政策議題。這主要是由於高度能力但又易受漏洞的模型出現，以及政治界對監管興趣的重燃。儘管美國政府正在考慮仿效藥品審批流程的行政命令，但本文作者認為，僅僅適應現有的法規是遠遠不夠的。若要制定有效的政策，美國必須建立一套針對AI獨特且持續演變的性質量身打造的框架，並重點關注嚴格的發布前評估方法。至關重要的是，任何許可制度都必須搭配強健的上市後監管和執法機制，才能有效管理先進、快速發展的AI系統所帶來的風險。
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