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 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 in this integrated space, 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.

翻译：人工智能已被证明是推动跨学科科学研究的变革性工具。然而，人工智能领域与科学界之间仍存在显著鸿沟，限制了人工智能方法在推动广泛科学发现方面的全部潜力。现有弥合这一鸿沟的努力通常依赖于对小规模文献样本的定性分析，对更广泛的人工智能赋能科学研究图景提供了有限的视角。在本工作中，我们对人工智能赋能科学文献进行了大规模分析，首先利用大语言模型从顶尖科学和人工智能会议/期刊的出版物中识别科学问题与人工智能方法。基于这一新数据集，我们定量地揭示了这一融合空间中人工智能方法与科学问题之间的关键差异，展现了跨科学学科实现更深层次人工智能融合的巨大机遇。此外，我们通过链接预测的视角，探讨了促进人工智能与科学界之间合作的潜力与挑战。我们的发现与工具旨在促进更具影响力的跨学科合作，并通过更深入、更广泛的人工智能融合来加速科学发现。