发表机构
Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出“稀缺性反转”概念，指出当机器使科学推理变得充裕时，实验、验证等资源成为新瓶颈，并探讨其对科学实践与国家实验室的影响。
AI 中文摘要
人工智能可能会改变科学中阻碍进展的部分。设想一个世界，其中机器系统在大多数可通过计算机完成的科学工作上比人类更优秀、更快速且成本更低。我们的问题是，在那个世界中，什么会限制科学的发展。文献综述、假设生成、软件开发、模拟和分析可能变得充裕，而实验、观测、有充分支持的结论以及负责任的机构权威仍然稀缺。届时，科学将受到一系列不同资源的约束。在本文中，我们将这种变化称为稀缺性反转，并考虑其四个组成部分：选择、物理访问、验证和组织选择。这种变化首先出现在数学和编码/软件/算法设计领域，在这些领域中，整个科学循环可以在计算内部运行。对于国家实验室而言，这种变化可能引人注目。它们独特的作用是通过结合受控实验、受保护数据、专家判断和负责任的权威，将充裕的机器推理转化为可信赖的结果。实际问题是，当推理充足而可信证据稀缺时，设施、验证、来源追踪、资源分配和科学治理应如何改变。
英文摘要
AI could change which parts of science impede progress. Consider a world in which machine systems are better, faster, and cheaper than people at most scientific work that can be done through a computer. Our question is what would limit science in that world. Literature synthesis, hypothesis generation, software development, simulation, and analysis could become abundant, while experiments, observations, well-supported conclusions, and accountable institutional au- thority remain scarce. Science would then be constrained by a different set of resources. In this paper, we call this change the scarcity inversion and consider four parts of it: selection, physical access, validation, and organizational choice. This change is arriving first in mathematics and coding/software/algorithm design, where the whole scientific loop can run inside computation. For national laboratories, the change could be striking. Their distinctive role is to turn abundant machine reasoning into trustworthy results by combining controlled experiments, protected data, expert judgment, and accountable authority. The practical question is how facilities, verification, provenance, resource allocation, and scientific governance should change when reasoning is plentiful and trustworthy evidence is scarce.
Comments14 pages, 1 figure
