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.
Verified Machine Learning Infrastructure: Formal Methods for Trustworthy Artificial Intelligence Deployment
English Summary
The RAND report, published in 2026, argues that the rapid advancement of frontier AI capabilities by late 2026 necessitates immediate action to secure the underlying machine learning infrastructure. Utilizing formal methods – mathematical techniques for software verification – offers a potential solution to bolster the security of AI systems before these capabilities become fully operational. The report identifies key vulnerabilities within AI inference and training stacks and highlights the urgency driven by converging cyber threats. Recommendations are offered for a collaborative roadmap involving AI labs, formal methods experts, hardware vendors, and government agencies to establish verified machine learning infrastructure.
中文摘要
2026 年 RAND 報告認為，截至 2026 年末，前沿人工智能能力的快速發展要求立即採取行動以確保基礎的機器學習基礎設施。利用形式化方法——用於軟體驗證的數學技術——提供了一種潛在的解決方案，以增強人工智能系統的安全性，在這些能力完全投入使用之前。報告識別了人工智能推理和訓練堆疊中的關鍵漏洞，並突出了由 converging 的網絡威脅所帶來的緊迫性。報告提出了 AI 實驗室、形式化方法專家、硬體供應商和政府機構合作制定經過驗證機器學習基礎設施的合作路線圖的建議。
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