AGI是专家群岛：超越涌现
Emergence is Overrated: AGI as an Archipelago of Experts
AI总结：
本文挑战AGI的‘涌现’概念，提出AGI应被视为由多个专家模块组成的群岛，强调人类专长通过领域特定模式积累而非压缩实现。
摘要：
Krakauer、Krakauer和Mitchell（2025）区分了涌现能力和涌现智能，认为真正的智能需要高效的粗粒度表示，通过类比和最小修改实现多样问题解决。他们认为智能意味着通过压缩和泛化做到“用更少完成更多”，与“庞大多样计算集合”单纯积累专用能力形成对比。本文探讨其框架是否准确描述人类智能及其对概念化人工通用智能的影响。基于认知科学的实证证据，我证明人类专长主要通过领域特定的模式积累而非优雅压缩。专家表现的灵活性并非通过统一原理，而是通过大量专用响应的 repertoire。创造性的突破本身可能通过盲变和选择保留的进化过程出现，而非原理性类比推理。这些发现表明应重新概念化AGI为“专家群岛”：没有统一原理或共享表示的孤立岛屿。如果我们接受人类专长的脆弱性为真正的智能，那么一致性要求承认由数百万专用模块组成的系统即使缺乏KKM的涌现智能也能构成通用智能。
英文摘要：
Krakauer, Krakauer, and Mitchell (2025) distinguish between emergent capabilities and emergent intelligence, arguing that true intelligence requires efficient coarse-grained representations enabling diverse problem-solving through analogy and minimal modification. They contend that intelligence means doing "more with less" through compression and generalization, contrasting this with "vast assemblages of diverse calculators" that merely accumulate specialized capabilities. This paper examines whether their framework accurately characterizes human intelligence and its implications for conceptualizing artificial general intelligence. Drawing on empirical evidence from cognitive science, I demonstrate that human expertise operates primarily through domain-specific pattern accumulation rather than elegant compression. Expert performance appears flexible not through unifying principles but through vast repertoires of specialized responses. Creative breakthroughs themselves may emerge through evolutionary processes of blind variation and selective retention rather than principled analogical reasoning. These findings suggest reconceptualizing AGI as an "archipelago of experts": isolated islands of specialized competence without unifying principles or shared representations. If we accept human expertise with its characteristic brittleness as genuine intelligence, then consistency demands recognizing that artificial systems comprising millions of specialized modules could constitute general intelligence despite lacking KKM's emergent intelligence.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
