Summary of Bridging Ai and Science: Implications From a Large-scale Literature Analysis Of Ai4science, by Yutong Xie et al.
Bridging AI and Science: Implications from a Large-Scale Literature Analysis of AI4Science
by Yutong Xie, Yijun Pan, Hua Xu, Qiaozhu Mei
First submitted to arxiv on: 27 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Digital Libraries (cs.DL); Information Retrieval (cs.IR)
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a large-scale analysis of the AI4Science literature, aiming to bridge the gap between AI and scientific communities. The authors used large language models to identify scientific problems and AI methods in top science and AI publications, revealing substantial opportunities for deeper AI integration across disciplines. They also explored link prediction as a lens for facilitating collaboration between AI and scientific communities, highlighting potential challenges and opportunities. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using artificial intelligence (AI) to help scientists do their work better. Right now, there’s a big gap between the people who make AI tools and the scientists who use them. To fix this problem, the authors looked at lots of papers in science and AI journals to see what kinds of problems scientists are trying to solve with AI. They found some surprising patterns that show where AI can really help make a difference. The goal is to bring together people from both sides to work together more effectively. |