AI Came for Math. Now It’s Chasing the Rest of Science
AI Came for Math. Now It’s Chasing the Rest of Science
Save this storySave this storyLast week, I attended a talk at MIT by Neil Thompson, who studies progress in computing and artificial intelligence. Over the course of just half an hour, Thompson made a convincing case that several scientific fields are about to be upended by AI.
上周，我在麻省理工学院参加了尼尔·汤普森（Neil Thompson）的讲座，他专门研究计算和人工智能的进展。在短短半小时内，汤普森令人信服地指出，多个科学领域即将被人工智能彻底颠覆。
As mathematicians reel from the way AI is automating their field, Thompson’s latest study suggests that probability, statistics theory, economic theory, high-energy theory, gravitation and cosmology, and quantum physics may be next, thanks to the rapidly evolving capabilities of frontier models. Between mid-2024 and mid-2026, the study shows, the best AI models went from being able to correctly solve about 2 percent of theoretical problems in these fields to about 35 percent of such problems. And there are no signs that this progress will slow down.
正当数学家们因人工智能实现其领域自动化而感到震惊时，汤普森的最新研究表明，得益于前沿模型能力的快速演进，概率论、统计学理论、经济理论、高能物理理论、引力与宇宙学以及量子物理学可能将成为下一个目标。研究显示，在2024年中期至2026年中期之间，最顶尖的人工智能模型在这些领域解决理论问题的正确率从约2%提升到了约35%。而且，这种进步没有任何放缓的迹象。
“Within just a few years, we expect a lot of theory questions to be totally transformed in terms of AI’s ability to do them,” says Thompson. Broadly, Thompson’s lab digs into how advances in computer science impact society and affect innovation. This study—which Thompson conducted alongside Gabriel Manso, a PhD student, and Claudio Mayrink Verdun, a research scientist—took problems stated in research papers posted online between December 2025 and May 2026, and then tested whether different OpenAI models—o1, o3, GPT-5, GPT-5.1, GPT-5.4, and GPT-5.5—could solve them.
“我们预计在短短几年内，许多理论问题的解决方式将因人工智能的能力而发生彻底改变，”汤普森说道。总体而言，汤普森的实验室致力于研究计算机科学的进步如何影响社会和创新。这项研究由汤普森与博士生加布里埃尔·曼索（Gabriel Manso）及研究科学家克劳迪奥·梅林克·维尔登（Claudio Mayrink Verdun）共同开展，他们选取了2025年12月至2026年5月间在线发表的研究论文中的问题，并测试了不同的OpenAI模型（o1、o3、GPT-5、GPT-5.1、GPT-5.4和GPT-5.5）是否能够解决这些问题。
Crucially, solutions to these problems were found in papers published after the training cutoff for GPT-5.5 and preceding models. This means that the models they tested had to work out solutions for themselves, rather than relying on answers fed in as training data. (Such papers served as ground truth for testing the models’ solving skills.) The researchers also blocked internet access, which prevented the models they tested from looking up solutions online.
至关重要的是，这些问题的解决方案出现在GPT-5.5及之前模型的训练截止日期之后发表的论文中。这意味着他们测试的模型必须自行推导出解决方案，而不是依赖作为训练数据输入的答案。（这些论文作为测试模型解决能力的“基准真值”。）研究人员还切断了互联网访问，防止测试模型在线查找答案。
The areas covered by the study were limited—the trio focused on fields in which AI models could figure things out without needing to run simulations or experiments. But the work suggests that the kinds of shock waves currently being felt in math are likely to ripple out to other math- and theory-heavy areas of scientific inquiry.
该研究涵盖的领域有限——研究小组专注于那些人工智能模型无需运行模拟或实验即可得出结论的领域。但这项工作表明，数学领域目前感受到的冲击波很可能会波及其他以数学和理论为重的科学研究领域。
In September, OpenAI rocked the math world by announcing that an unreleased model had solved a long-standing, grand challenge in mathematics known as Navier-Stokes, which concerns equations that explain the behavior of fluid motion. The episode proved extra controversial after a mathematician at NYU—who had been working with another academic associated with Anthropic—accused OpenAI of gazumping them after learning of their progress.
9月，OpenAI宣布其一款未发布模型解决了数学界一个长期存在的重大难题——纳维-斯托克斯方程（Navier-Stokes），该方程涉及解释流体运动行为，这一消息震撼了数学界。此事件引发了额外的争议，因为纽约大学的一位数学家——他曾与另一位隶属于Anthropic的学者合作——指责OpenAI在获悉他们的研究进展后抢先发布了成果。
OpenAI and others have since announced hundreds more solutions to longstanding math problems, triggering something of a crisis in the field. Some mathematicians have called for limits on the use of AI, warning that the process of solving problems is often as useful as having the final answer.
此后，OpenAI及其他机构又宣布了数百个长期数学难题的解决方案，在数学界引发了一场危机。一些数学家呼吁限制人工智能的使用，并警告称，解决问题的过程往往与获得最终答案同样重要。
As with math, Thompson says, we should expect advances in other areas to have both positive and negative effects. He notes that theoretical advances sometimes drive practical advances in science and suggests that greater use of AI may mean that more progress happens this way.
汤普森表示，正如数学领域一样，我们应该预见到其他领域的进步也会带来积极和消极的影响。他指出，理论上的进步有时会推动科学实践的进步，并暗示更多地使用人工智能可能意味着这种进步会以更快的速度发生。
Some prominent AI researchers have already founded new labs focused on AI for science, and the big AI labs are all ramping up scientific research. These efforts explore how AI could be empowered to conduct real experiments alongside theoretical analysis. AI may mean that theorists are no longer required to solve key problems, but the researchers say that doesn’t mean that experts won’t be needed to make sense of solutions and to make sure they’re sound.
一些知名的人工智能研究人员已经成立了专注于“人工智能科学应用”的新实验室，各大人工智能实验室也都在加强科学研究。这些努力旨在探索如何赋予人工智能在进行理论分析的同时开展实际实验的能力。人工智能可能意味着理论家不再需要亲自解决关键问题，但研究人员表示，这并不意味着不再需要专家来解读解决方案并确保其严谨性。
“We are still going to need people to generate good ideas to connect the dots,” says Manso. He adds that for very complex problems, it’s possible that AI might make mistakes. (OpenAI, in fact, withdrew several solutions in its latest dataset post-publication.) “We also need ways to verify what was done, because it’s very easy for a model to tell you ‘this is the right answer,’ and when you look at the entire solution, you start to see maybe it is not.”
“我们仍然需要人类来提出好的想法，将各个点连接起来，”曼索说。他补充道，对于非常复杂的问题，人工智能可能会出错。（事实上，OpenAI在发布后撤回了其最新数据集中的几个解决方案。）“我们还需要验证工作成果的方法，因为模型很容易告诉你‘这是正确答案’，但当你审视整个解决方案时，你可能会发现事实并非如此。”
This is an edition of Will Knight’s AI Lab newsletter. Read previous newsletters here.
这是威尔·奈特（Will Knight）的“AI实验室”通讯。点击此处阅读往期通讯。