光学(聚焦)
工程伦理学
政治学
工程类
物理
光学
作者
Qun Hao,Fengli Xu,Yong Li,James Evans
出处
期刊:Cornell University - arXiv
日期:2024-12-10
标识
DOI:10.48550/arxiv.2412.07727
摘要
The rapid rise of AI in science presents a paradox. Analyzing 67.9 million research papers across six major fields using a validated language model (F1=0.876), we explore AI's impact on science. Scientists who adopt AI tools publish 67.37% more papers, receive 3.16 times more citations, and become team leaders 4 years earlier than non-adopters. This individual success correlates with concerning on collective effects: AI-augmented research contracts the diameter of scientific topics studied, and diminishes follow-on scientific engagement. Rather than catalyzing the exploration of new fields, AI accelerates work in established, data-rich domains. This pattern suggests that while AI enhances individual scientific productivity, it may simultaneously reduce scientific diversity and broad engagement, highlighting a tension between personal advancement and collective scientific progress.
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