Physics and the empirical gap of trustworthy AI

IF 44.8 1区物理与天体物理 Q1 PHYSICS, APPLIED

Nature Reviews Physics Pub Date : 2024-10-07 DOI:10.1038/s42254-024-00772-7

Savannah Thais

引用次数: 0

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.

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物理学与可信人工智能的经验差距

要了解尖端人工智能模型在 "引擎盖 "下做了什么，不仅需要理论研究，还需要控制良好的计算实验。Savannah Thais 解释了为什么物理数据集可能是人工智能开发人员所需的试验场，以及物理学家如何在开发值得信赖的人工智能方面发挥关键作用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Nature Reviews Physics Multiple-

CiteScore

47.80

自引率

0.50%

发文量

122

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.