9 Matching Annotations
  1. Last 7 days
    1. Today's large language models (LLMs) are trained to align with user preferences through methods such as reinforcement learning. Yet models are beginning to be deployed not merely to satisfy users, but also to generate revenue for the companies that created them through advertisements

      这段陈述揭示了当前AI发展的一个关键悖论:模型训练的目标与实际商业用途之间存在根本性冲突。这种冲突可能导致AI行为偏离其原始设计意图,引发严重的信任问题。

  2. Apr 2026
    1. model alignment alone does not reliably guarantee the safety of autonomous agents.

      大多数人认为模型对齐(alignment)是确保AI系统安全的关键因素,但作者通过实验证明,即使是对齐良好的模型(如Claude Code)在计算机使用代理中也表现出高达73.63%的攻击成功率。这挑战了当前AI安全领域的核心假设,表明仅依赖模型对齐无法解决自主代理的安全问题。

    2. model alignment alone does not reliably guarantee the safety of autonomous agents

      大多数人认为通过模型对齐(alignment)可以有效保证AI代理的安全性,但作者认为这远远不够,因为实验显示即使使用对齐的Qwen3-Coder模型,Claude Code仍有73.63%的攻击成功率。这挑战了当前AI安全领域的主流观点,即单纯依靠模型对齐就能解决安全问题。

  3. Dec 2025
    1. Alignment as an operational problem. The book assumes that sufficiently advanced intelligences would recognize the value of cooperation, pluralism, and shared goals. A decade of observing misaligned incentives in human institutions amplified by algorithmic systems makes it clear that this assumption requires far more rigorous treatment. Alignment is not a philosophical preference. It is an engineering, economic, and institutional problem.

      The book did not address alignment, assumed it would sort itself out (in contrast to [[AI begincondities en evolutie 20190715140742]] how starting conditions might influence that. David recognises how algo's are also used to make diffs worse.

  4. Jun 2024
  5. May 2023
  6. Jun 2021