Open generative AI models areaway forward for science

被引：33

作者：

Spirling, Arthur ^{[1
]}

机构：

[1] NYU, Polit & Data Sci, New York, NY 10012 USA

来源：

NATURE | 2023年 / 616卷 / 7957期

关键词：

Ethics; Machine learning; Technology; Scientific community;

D O I：

10.1038/d41586-023-01295-4

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

Researchers should avoid the lure of proprietary models and develop transparent large language models to ensure reproducibility.

引用

页码：413 / 413

页数：1

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