自动化解题与相关研究不是一回事。
这是 Anthropic 一篇客座文章的核心观点，作者是哈佛大学物理学家 Matthew Schwartz 教授。他描述了自己如何不再试图把 Claude 当作人类研究者来使用，而是转而寻找“Claude 形状的问题”，即那些能发挥当今 AI 模型优势的任务。Schwartz 目前还是 Anthropic 的访问研究员。
这一方法催生了BootLoops，一个用于精确科学计算的开源工具框架。其源代码可在 GitHub 上获取。
据 Schwartz 称，借助该工具框架，Claude 发现了粒子物理学、生态学、群体遗传学、经济学和语言学之间的联系。但这些结果往往只有在领域专家介入并确定方向之后，才具有科学价值。
Schwartz 用“凸包”的概念来说明 AI 如何助力科学。人类知识是碎片化的，各个领域朝不同方向延伸，而它们之间的空白则无人探索。一个实验室可能花 20 年用单一方法研究一组基因，却对邻近的基因和其他方法不闻不问。像 BootLoops 这样的 AI 工具框架，本应通过在这些参差不齐的知识前沿之间建立联系来填补这些空白。
三个月内产出横跨 18 个领域的 36 篇手稿
在三个月里，团队与 19 位合著者一起，在 18 个领域产出了 36 篇手稿。Schwartz 从粒子物理计算入手，具体是散射振幅和椭圆积分。几周之内，Claude 使用 BootLoops 计算了 30 个积分，其中 15 个复现了已知结果，15 个是首次计算得出。
Then he expanded the search to other fields. In ecology, Claude solved a 20-year-old equation from neutral biodiversity theory that had previously been impossible to compute at scale. When applied to data, the results showed that tree species composition on Barro Colorado Island in the Panama Canal is changing 4.5 times faster than the theory allows. Ecologist James O'Dwyer then helped turn that finding into a better predictive model.
In population genetics, the team analyzed 5.7 billion mutation pairs from the1000 Genomes Projectand found evidence for a mechanism called gene conversion. Other projects included an AI data editor for economics journals that automatically checked 4,452 replication packages (published as anNBER Working Paper), as well as aword stress databasecovering 6,072 languages, built with three linguists.
"Python for engineers is now obsolete"
Schwartz describes how fast AI is changing science, to the point where planning ahead is becoming nearly impossible. Why apply for a three-year grant to fund a calculation that an AI model might solve overnight?
Training PhD students has also become an open question. Since his earlier post onVibe Physics, that feeling has only grown stronger. In some fields like computer science, the disruption is alarming. Two years ago, he would have called a "Python for Engineers" course "essential," but today it's "unnecessary" because Claude can handle those tasks.
Building ML models to study physical phenomena is another area AI can now take over. Even deep knowledge of neural networks offers limited returns when Claude can implement the current state of ML research on command.
"Look at everything yourself"
Schwartz also warns about the models' weaknesses. Claude likes to declare victory too early, and phrases like "done, with one asterisk" often mean "not done at all." It misjudges how long tasks will take and tends to brute-force calculations instead of finding more elegant solutions. Automated checks aren't reliable, and Claude's conclusions can be wrong even when its calculations are correct.
除了可靠性之外，该模型倾向于那些陈旧、被大量引用的争论，而非真正全新的问题。而且这些项目是“计算和 token 密集型的”，Schwartz 说。
Schwartz 还认为对重大数学问题和头条新闻的关注是有风险的。不切实际的期望可能会分散人们对那些今天已经奏效的生产性应用的注意力。真正的进步仍将建立在坚实的基础之上，只是速度更快。科学方法本身并未受到威胁，人类的引导和品味仍然不可或缺，Schwartz 说。
没有炒作的 AI 新闻——由人类精选
订阅 THE DECODER，享受无广告阅读、每周 AI 新闻通讯、我们每年六期的独家“AI Radar”前沿报告、完整档案访问权限，以及评论区访问权限。