{"title":"物理学与可信人工智能的经验差距","authors":"Savannah Thais","doi":"10.1038/s42254-024-00772-7","DOIUrl":null,"url":null,"abstract":"Understanding what cutting-edge AI models are doing ‘under the hood’ requires not just theoretical research but also well-controlled computational experiments. Savannah Thais explains why physics datasets may be the testing ground that AI developers need and how physicists can play a critical role in developing trustworthy AI.","PeriodicalId":19024,"journal":{"name":"Nature Reviews Physics","volume":"6 11","pages":"640-641"},"PeriodicalIF":44.8000,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Physics and the empirical gap of trustworthy AI\",\"authors\":\"Savannah Thais\",\"doi\":\"10.1038/s42254-024-00772-7\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Understanding what cutting-edge AI models are doing ‘under the hood’ requires not just theoretical research but also well-controlled computational experiments. Savannah Thais explains why physics datasets may be the testing ground that AI developers need and how physicists can play a critical role in developing trustworthy AI.\",\"PeriodicalId\":19024,\"journal\":{\"name\":\"Nature Reviews Physics\",\"volume\":\"6 11\",\"pages\":\"640-641\"},\"PeriodicalIF\":44.8000,\"publicationDate\":\"2024-10-07\",\"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/s42254-024-00772-7\",\"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/s42254-024-00772-7","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}
Understanding what cutting-edge AI models are doing ‘under the hood’ requires not just theoretical research but also well-controlled computational experiments. Savannah Thais explains why physics datasets may be the testing ground that AI developers need and how physicists can play a critical role in developing trustworthy AI.
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.
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