{"title":"人工智能驱动的纯数学和理论物理学研究","authors":"Yang-Hui He \n (, )","doi":"10.1038/s42254-024-00740-1","DOIUrl":null,"url":null,"abstract":"The past five years have seen a dramatic increase in the usage of artificial intelligence (AI) algorithms in pure mathematics and theoretical sciences. This might appear counter-intuitive as mathematical sciences require 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 and theoretical discovery as ‘top-down’, ‘bottom-up’ and ‘meta-mathematics’. We review the progress over the past few years, comparing and contrasting both the advances and the shortcomings of each approach. We believe that although the theorist is not in danger of being replaced by AI systems in the near future, the combination of human expertise and AI algorithms will become an integral part of theoretical research. Advances in artificial-intelligence-assisted mathematical investigations suggest that human–machine collaboration will be an integral part of future theoretical research.","PeriodicalId":19024,"journal":{"name":"Nature Reviews Physics","volume":null,"pages":null},"PeriodicalIF":44.8000,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"AI-driven research in pure mathematics and theoretical physics\",\"authors\":\"Yang-Hui He \\n (, )\",\"doi\":\"10.1038/s42254-024-00740-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The past five years have seen a dramatic increase in the usage of artificial intelligence (AI) algorithms in pure mathematics and theoretical sciences. This might appear counter-intuitive as mathematical sciences require 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 and theoretical discovery as ‘top-down’, ‘bottom-up’ and ‘meta-mathematics’. We review the progress over the past few years, comparing and contrasting both the advances and the shortcomings of each approach. We believe that although the theorist is not in danger of being replaced by AI systems in the near future, the combination of human expertise and AI algorithms will become an integral part of theoretical research. Advances in artificial-intelligence-assisted mathematical investigations suggest that human–machine collaboration will be an integral part of future theoretical research.\",\"PeriodicalId\":19024,\"journal\":{\"name\":\"Nature Reviews Physics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":44.8000,\"publicationDate\":\"2024-08-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nature Reviews Physics\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://www.nature.com/articles/s42254-024-00740-1\",\"RegionNum\":1,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PHYSICS, APPLIED\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature Reviews Physics","FirstCategoryId":"101","ListUrlMain":"https://www.nature.com/articles/s42254-024-00740-1","RegionNum":1,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PHYSICS, APPLIED","Score":null,"Total":0}
AI-driven research in pure mathematics and theoretical physics
The past five years have seen a dramatic increase in the usage of artificial intelligence (AI) algorithms in pure mathematics and theoretical sciences. This might appear counter-intuitive as mathematical sciences require 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 and theoretical discovery as ‘top-down’, ‘bottom-up’ and ‘meta-mathematics’. We review the progress over the past few years, comparing and contrasting both the advances and the shortcomings of each approach. We believe that although the theorist is not in danger of being replaced by AI systems in the near future, the combination of human expertise and AI algorithms will become an integral part of theoretical research. Advances in artificial-intelligence-assisted mathematical investigations suggest that human–machine collaboration will be an integral part of future theoretical research.
期刊介绍:
Nature Reviews Physics is an online-only reviews journal, part of the Nature Reviews portfolio of journals. It publishes high-quality technical reference, review, and commentary articles in all areas of fundamental and applied physics. The journal offers a range of content types, including Reviews, Perspectives, Roadmaps, Technical Reviews, Expert Recommendations, Comments, Editorials, Research Highlights, Features, and News & Views, which cover significant advances in the field and topical issues. Nature Reviews Physics is published monthly from January 2019 and does not have external, academic editors. Instead, all editorial decisions are made by a dedicated team of full-time professional editors.