A new scientific research paradigm driven by AI and its applications in academic disciplines

Authors

Jiang YU, Institutes of Science and Development, Chinese Academy of Sciences, Beijing 100190, China; School of Public Policy and Management, University of Chinese Academy of Sciences, Beijing 100049, ChinaFollow
Yue ZHANG, Institutes of Science and Development, Chinese Academy of Sciences, Beijing 100190, China; School of Public Policy and Management, University of Chinese Academy of Sciences, Beijing 100049, ChinaFollow
Yi ZHOU, Institutes of Science and Development, Chinese Academy of Sciences, Beijing 100190, China; Global Development Institute, the University of Manchester, Manchester M13 9PL, UK

Keywords

artificial intelligence(AI) new paradigm of scientific research evolutionary process disciplinary applications

Document Type

Disciplinary Development

Abstract

The artificial intelligence (AI) -driven paradigm of scientific research, deeply embedded through the integration of data, computing power, and algorithms, has triggered profound changes in the research process, thinking logic, and organizational patterns. This study systematically summarizes the main characteristics and forms of the AI-driven new scientific research paradigm. It proposes that the evolution of the AI-driven research paradigm is shifting from “automated research” to “model-based research” and “intelligent research.” The depth and scope of AI applications in scientific research continue to expand, and this will drive significant changes in research organization and governance models. In addition, this study discusses the applicability of AI in various fields based on the characteristics of scientific disciplines and analyzes successful cases of the AI-driven new paradigm in various academic fields. It also examines the challenges confronted by key players such as research funding agencies, database construction operators, science and technology leading enterprises, research institutions and researchers, as well as pointing out the implications and making suggestions to explore the AI-driven research applications.

First page

362

Last Page

370

Language

Chinese

Publisher

Bulletin of Chinese Academy of Sciences

References

1 薛菁华, 徐慧婷, 陈广玉. 全球科研范式数字化转型趋势研究. 竞争情报, 2022, 18(6): 54-63.Xue J H, Xu H T, Chen G Y. Study on the trend of digital transformation of global scientific research paradigm. Competitive Intelligence, 2022, 18(6): 54-63. (in Chinese)

2 喻思南. 人工智能，为科研注入智慧动能. 人民日报, 2022-10-20(14). Yu S N. Artificial intelligence, adding intelligent kinetic energy to research. People’s Daily, 2022-10-20(14). (in Chinese)

3 Tolle K M, Tansley D S W, Hey A J G. The fourth paradigm: Data-intensive scientific discovery. Proceedings of the IEEE, 2011, 99(8): 1334-1337.

4 丁大尉. 大数据技术带来科学知识生产新模式. 中国社会科学报, 2022-07-26(A04). Ding D W. The “fourth paradigm” of scientific research: Big data technologies bring about a new paradigm of scientific knowledge production. Chinese Social Sciences Today, 2022-07-26 (A04). (in Chinese)

5 陈套. 推动科研范式升级强化国家战略科技力量. 中国科技奖励, 2020, (8): 67-68.Chen T. Promoting the upgrading of scientific research paradigms and strengthen the national strategic science and technology force. China Awards for Science and Technology, 2020, (8): 67-68. (in Chinese)

6 汪洋, 周园春, 王彦棡, 等. 适度超前推动科研基础平台建设，支撑我国高水平科技自立自强. 中国科学院院刊, 2022, 37(5): 652-660.Wang Y, Zhou Y C, Wang Y G, et al. Promoting infrastructure construction in advance to support sci-tech selfreliance and self-strengthening at higher level. Bulletin of Chinese Academy of Sciences, 2022, 37(5): 652-660. (in Chinese)

7 北京科学智能研究院. 科学智能（AI4S）全球发展观察与展望. 北京: 北京科学智能研究院, 2022.Beijing Institute of Scientific Intelligence. Artificial Intelligence for Sciences (AI4S) —A Global Outlook 2023 Edition. Beijing: Beijing Institute of Scientific Intelligence,2022. (in Chinese)

8 Li R B, Xia H D, Huang Q, et al. Nonlinearity in mass spectrometry for quantitative multi-component gas analysis in reaction processes. Analytica Chimica Acta, 2022, 1194: 339412.

9 Xu Y J, Liu X, Cao X, et al. Artificial intelligence: A powerful paradigm for scientific research. The Innovation, 2021, 2(4): 100179.

10 孙建军, 李阳. 科学大数据：范式重塑与价值实现. 图书与情报, 2017, (5): 20-26.Sun J J, Li Y. Science big data: Paradigm remodeling and value realization. Library & Information, 2017, (5): 20-26. (in Chinese)

11 Jumper J, Evans R, Pritzel A, et al. Highly accurate protein structure prediction with AlphaFold. Nature, 2021, 596: 583-589.

12 Araujo R P, Liotta L A. Universal structures for adaptation in biochemical reaction networks. Nature Communications, 2023, 14: 2251.

13 余江, 孟庆时, 张越, 等. 数字创新：创新研究新视角的探索及启示. 科学学研究, 2017, 35(7): 1103-1111.Yu J, Meng Q S, Zhang Y, et al. Digital innovation: Exploration and enlightenment of the new perspective of innovation research, Studies in Science of Science, 2017, 35 (7): 1103-1111. (in Chinese)

14 鲁鸣鸣, 王建新 “. 人工智能+X”交叉学科科研创新能力培养模式探索. 工业和信息化教育, 2021, (10): 1-5. Lu M M, Wang J X. Exploration of “Artificial Intelligence + X” interdisciplinary research and innovation capacity cultivation model. Industry and Information Technology Education, 2021, (10): 1-5. (in Chinese)

15 余江, 陈凤, 方元欣. 面向世界科技强国建设的科教融合新体系初探. 科教发展研究, 2022, 2(3): 55-78. Yu J, Chen F, Fang Y X. A preliminary exploration of the new system of research-education integration for the construction of a scientific and technological powerhouse. Research on Science, 2022, 2(3): 55-78. (in Chinese)

16 王飞跃, 缪青海. 人工智能驱动的科学研究新范式：从 AI4S 到智能科学. 中国科学院院刊, 2023, 38(4): 536-540. Wang F Y, Miao Q H. Novel paradigm for AI-driven scientific research: From AI4S to intelligent science. Bulletin of Chinese Academy of Sciences, 2023, 38(4): 536-540. (in Chinese)

Recommended Citation

YU, Jiang; ZHANG, Yue; and ZHOU, Yi (2024) "A new scientific research paradigm driven by AI and its applications in academic disciplines," Bulletin of Chinese Academy of Sciences (Chinese Version): Vol. 40 : Iss. 2 , Article 13.
DOI: https://doi.org/10.16418/j.issn.1000-3045.20230411001
Available at: https://bulletinofcas.researchcommons.org/journal/vol40/iss2/13