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.
Open-Weight AI Models May Increase Biological Misuse Risks: Assessing Anti-Refusal Tampering, Capability Enhancement, and Publicly Available Uncensored Models
English Summary
The RAND report argues that open-weight Large Language Models (LLMs) significantly increase biological misuse risks because their public availability allows for unauthorized modification and removal of safety safeguards. Key evidence shows that moderately skilled actors can cheaply and reliably tamper with these models—primarily by removing refusal mechanisms—thereby making existing dual-use biological knowledge freely accessible without developer controls. This presents a critical policy challenge: balancing the substantial benefits of open research and transparency against the risk of unrestricted redistribution in sensitive domains like biosecurity. Policymakers must therefore develop strategies to mitigate the threat of tampering and misuse while preserving the scientific and economic advantages of open-source AI.
中文摘要
RAND報告指出，開放權重大型語言模型（LLMs）顯著增加了生物濫用風險，因為其公開可用性允許未經授權的修改和移除安全防護措施。關鍵證據顯示，即使是具備中等技能的行為者，也能低成本且可靠地篡改這些模型——主要方式是移除其拒絕機制——從而使現有的兩用生物知識在缺乏開發者控制的情況下自由流通。這提出了重大的政策挑戰：如何在平衡開放研究和透明度的巨大益處與敏感領域（如生物安全）不受限制的再分配風險之間取得平衡。因此，政策制定者必須制定策略，在維護開源人工智慧的科學和經濟優勢的同時，減輕篡改和濫用威脅。
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