Physics and the empirical gap of trustworthy AI

被引：0
|
作者：
Thais, Savannah [1 ]
机构：
[1] Columbia Univ, Data Sci Inst, New York, NY 10027 USA
来源：
NATURE REVIEWS PHYSICS | 2024年
关键词：
D O I：
10.1038/s42254-024-00772-7
中图分类号：
O59 [应用物理学];
学科分类号：
摘要：
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.
引用
收藏
页码：640 / 641
页数：2
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