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.
Investigating the potential use of frontier AI models for offensive cyberattacks: A human uplift study
English Summary
The study investigated whether frontier AI models significantly enhance the capability of lower-skilled threat actors in offensive cyber operations. Key findings indicate that while AI access provides suggestive 'onboarding' uplift for novices in basic cyber skills, there is no statistically significant evidence that AI enables the successful completion of complex, end-to-end cyber attack chains. This suggests that the primary barrier to success remains the inherent difficulty of advanced cyber tasks, regardless of AI support. Policymakers must therefore balance the development of advanced AI capabilities with robust security guardrails, recognizing that while novice misuse is a concern, the threat from highly skilled threat actors remains a critical focus for mitigation.
中文摘要
本研究調查了前沿人工智慧模型是否能顯著提升技能較低的威脅行為者在攻擊性網路行動中的能力。主要發現指出，雖然AI的普及為初學者在基礎網路技能方面提供了具啟發性的「入門」提升，但缺乏統計學上顯著的證據證明AI能夠使攻擊者成功完成複雜的、端到端的網路攻擊鏈。這暗示了，無論是否有AI支持，進階網路任務的固有難度仍是成功的首要障礙。因此，政策制定者必須在開發先進AI能力與建立穩健的安全護欄之間取得平衡，並認識到，雖然初學者濫用是一個隱憂，但來自高技能威脅行為者的威脅仍是需要重點緩解的關鍵焦點。
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