Recent years have seen the dramatic rise of the usage of AI algorithms in pure mathematics and fundamental sciences such as theoretical physics. This is perhaps counter-intuitive since mathematical sciences require the rigorous definitions, derivations, and proofs, in contrast to the experimental sciences which rely on the modelling of data with error-bars. In this Perspective, we categorize the approaches to mathematical discovery as "top-down", "bottom-up" and "meta-mathematics", as inspired by historical examples. We review some of the progress over the last few years, comparing and contrasting both the advances and the short-comings in each approach. We argue that while the theorist is in no way in danger of being replaced by AI in the near future, the hybrid of human expertise and AI algorithms will become an integral part of theoretical discovery.

翻译：近年来，人工智能算法在纯数学和理论物理等基础科学中的运用显著增加。这或许有些反直觉，因为数学科学需要严格的定义、推导和证明，这与依赖带误差棒的数据建模的实验科学形成对比。在本视角论文中，我们借鉴历史案例，将数学发现的方法归类为“自上而下”、“自下而上”和“元数学”。我们回顾了过去几年的一些进展，比较和对比了每种方法的优势与不足。我们认为，尽管理论家在可预见的未来绝不会被人工智能取代，但人类专业知识与人工智能算法的结合将成为理论发现不可或缺的组成部分。