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

• URL: http://arxiv.org/abs/2412.09628v2
• Date: Tue, 18 Feb 2025 03:53:05 GMT
• Title: Bridging AI and Science: Implications from a Large-Scale Literature Analysis of AI4Science
• Authors: Yutong Xie, Yijun Pan, Hua Xu, Qiaozhu Mei,
• Abstract summary: We present a large-scale analysis of the AI4Science literature. We quantitatively highlight key disparities between AI methods and scientific problems. We explore the potential and challenges of facilitating collaboration between AI and scientific communities.
• Score: 25.683422870223076
• License:
• 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.

Related papers

• Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation [58.064940977804596]
  A plethora of new AI models and tools has been proposed, promising to empower researchers and academics worldwide to conduct their research more effectively and efficiently. Ethical concerns regarding shortcomings of these tools and potential for misuse take a particularly prominent place in our discussion.
  arXiv  Detail & Related papers  (2025-02-07T18:26:45Z)
• Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges [11.232704182001253]
  This paper examines the current state of AI for scientific discovery, highlighting recent progress in large language models and other AI techniques applied to scientific tasks. We then outline key challenges and promising research directions toward developing more comprehensive AI systems for scientific discovery.
  arXiv  Detail & Related papers  (2024-12-16T03:52:20Z)
• AI Expands Scientists' Impact but Contracts Science's Focus [11.634306888037273]
  We analyze 67.9 million research papers across six major fields using a validated language model. 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.
  arXiv  Detail & Related papers  (2024-12-10T18:24:17Z)
• AI for social science and social science of AI: A Survey [47.5235291525383]
  Recent advancements in artificial intelligence have sparked a rethinking of artificial general intelligence possibilities. The increasing human-like capabilities of AI are also attracting attention in social science research.
  arXiv  Detail & Related papers  (2024-01-22T10:57:09Z)
• On the Opportunities of Green Computing: A Survey [80.21955522431168]
  Artificial Intelligence (AI) has achieved significant advancements in technology and research with the development over several decades. The needs for high computing power brings higher carbon emission and undermines research fairness. To tackle the challenges of computing resources and environmental impact of AI, Green Computing has become a hot research topic.
  arXiv  Detail & Related papers  (2023-11-01T11:16:41Z)
• Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems [268.585904751315]
  New area of research known as AI for science (AI4Science) Areas aim at understanding the physical world from subatomic (wavefunctions and electron density), atomic (molecules, proteins, materials, and interactions), to macro (fluids, climate, and subsurface) scales. Key common challenge is how to capture physics first principles, especially symmetries, in natural systems by deep learning methods.
  arXiv  Detail & Related papers  (2023-07-17T12:14:14Z)
• The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligence [67.70415658080121]
  Recent advances in machine learning and AI are disrupting technological innovation, product development, and society as a whole. AI has contributed less to fundamental science in part because large data sets of high-quality data for scientific practice and model discovery are more difficult to access. Here we explore and investigate aspects of an AI-driven, automated, closed-loop approach to scientific discovery.
  arXiv  Detail & Related papers  (2023-07-09T21:16:56Z)
• Quantifying the Benefit of Artificial Intelligence for Scientific Research [2.4700789675440524]
  We estimate both the direct use of AI and the potential benefit of AI in scientific research. We find that the use of AI in research is widespread throughout the sciences, growing especially rapidly since 2015. Our analysis reveals considerable potential for AI to benefit numerous scientific fields, yet a notable disconnect exists between AI education and its research applications.
  arXiv  Detail & Related papers  (2023-04-17T08:08:50Z)
• AI for Science: An Emerging Agenda [30.260160661295682]
  This report documents the programme and the outcomes of Dagstuhl Seminar 22382 "Machine Learning for Science: Bridging Data-Driven and Mechanistic Modelling" The transformative potential of AI stems from its widespread applicability across disciplines, and will only be achieved through integration across research domains. Alongside technical advances, the next wave of progress in the field will come from building a community of machine learning researchers, domain experts, citizen scientists, and engineers.
  arXiv  Detail & Related papers  (2023-03-07T20:21:43Z)
• Learning from learning machines: a new generation of AI technology to meet the needs of science [59.261050918992325]
  We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery. The distinct goals of AI for industry versus the goals of AI for science create tension between identifying patterns in data versus discovering patterns in the world from data.
  arXiv  Detail & Related papers  (2021-11-27T00:55:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.