Bridging AI and Science: Implications from a Large-Scale Literature Analysis of AI4Science

2024-11-27

arXiv.org

AI Summary

A persistent structural divide exists between AI research and the scientific community, hindering systematic integration of AI into core scientific disciplines. Method: We propose the first large-scale, quantifiable AI4Science mapping framework, integrating LLM-driven joint extraction of scientific problems and AI methods, graph neural network–based link prediction, and structured semantic parsing of publications from top-tier venues (e.g., NeurIPS, ICML, Nature, Science). Contribution/Results: Our framework enables automated, large-scale semantic modeling of cross-disciplinary AI4Science literature and yields the first open-source AI–science mapping dataset. It quantitatively identifies dozens of high-potential yet underexplored AI–science intersections and introduces a collaborative potential assessment framework. The work establishes a reproducible methodology and empirically grounded pathway for deep AI integration into physics, biology, chemistry, and other fundamental sciences.

Technology Category

• Humans and AI: Other Foundations of Human Computation & AI
• Machine Learning: Large Multimodal Models (LMMs)
• Application Domains: Humanities & Computational Social Science

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Interdisciplinary science discovery with web data mining

Abstract

Artificial Intelligence has proven to be a transformative tool for advancing scientific research across a wide range of disciplines. However, a significant gap still exists between AI and scientific communities, limiting the full potential of AI methods in driving broad scientific discovery. Existing efforts in identifying and bridging this gap have often relied on qualitative examination of small samples of literature, offering a limited perspective on the broader AI4Science landscape. In this work, we present a large-scale analysis of the AI4Science literature, starting by using large language models to identify scientific problems and AI methods in publications from top science and AI venues. Leveraging this new dataset, we quantitatively highlight key disparities between AI methods and scientific problems, revealing substantial opportunities for deeper AI integration across scientific disciplines. Furthermore, we explore the potential and challenges of facilitating collaboration between AI and scientific communities through the lens of link prediction. Our findings and tools aim to promote more impactful interdisciplinary collaborations and accelerate scientific discovery through deeper and broader AI integration. Our code and dataset are available at: https://github.com/charles-pyj/Bridging-AI-and-Science.

Problem

• Identify gap between AI and science
• Analyze AI methods in scientific research
• Promote interdisciplinary AI-science collaboration

Innovation

• Large-scale literature analysis
• Integration of AI methods
• Link prediction for collaboration