Bulletin of Chinese Academy of Sciences (Chinese Version)
Keywords
artificial intelligence, discipline system, forms of intelligence, foundational disciplines, AI empowerment
Abstract
The discipline of artificial intelligence studies the theories, methods, systems, applications, enabling functions, ethics, and governance of artificial intelligence, and is a typical interdisciplinary field. With the rapid development of artificial intelligence in recent years, its disciplinary connotations and system architecture urgently require renewed examination. Based on the analysis of development trends of artificial intelligence, this paper elucidates the connotations of the AI discipline from four perspectives: theoretical methods, forms of intelligence, disciplinary integration, and application empowerment. It further proposes a disciplinary system framework for artificial intelligence comprising foundational supporting disciplines, core body of knowledge, major forms of intelligence, and application-oriented extensions. In response to emerging developments and evolving demands, systematic efforts should be made to advance the development of the artificial intelligence discipline by strengthening its disciplinary foundations, deepening interdisciplinary integration, enhancing application enablement, and improving systems for talent development, thereby promoting its open evolution and high-quality development.
First page
1079
Last Page
1088
Language
Chinese
Publisher
Bulletin of Chinese Academy of Sciences
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
References
[1] Turing A M. Computing machinery and intelligence. Mind, 1950, 59: 433-460.
[2] McCulloch W S, Pitts W. A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biology, 1990, 52(1): 99-115.
[3] Newell A, Simon H. The logic theory machine: A complex information processing system. IRE Transactions on Information Theory, 1956, 2(3): 61-79.
[4] Rosenblatt F. The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 1958, 65(6): 386-408.
[5] Rumelhart D E, Hinton G E, Williams R J. Learning representations by back-propagating errors. Nature, 1986, 323: 533-536.
[6] Brooks R A. Intelligence without representation. Artificial Intelligence, 1991, 47(1-3): 139-159.
[7] Sutton R S, Barto A G. Reinforcement Learning: An Introduction. 2nd ed. Cambridge: MIT Press, 2018.
[8] Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need. Advances in Neural Information Processing Systems, 2017, 30: 5998-6008.
[9] Bommasani R, Hudson D A, Adeli E, et al. On the opportunities and risks of foundation models. (2021-08-16)[2026-06-12]. https://arxiv.org/abs/2108.07258.
[10] Ouyang L, Wu J, Xu J, et al. Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 2022, 35: 27730-27744.
[11] Yao S, Zhao J, Yu D, et al. ReAct: Synergizing reasoning and acting in language models. (2022-10-06)[2026-06-12]. https://arxiv.org/abs/2210.03629.
[12] 浙江大学创新范式研究组. 创新范式——日用而不觉的变革力量. 杭州: 浙江大学出版社, 2024. Innovation Paradigm Research Group, Zhejiang University. Innovation Paradigms—The Transformative Forces Hidden in Plain Sight. Hangzhou: Zhejiang University Press, 2024. (in Chinese)
[13] 陈凯华, 王硕, 张超, 等. 加强面向场景需求的公共治理信息技术预见. 中国科学院院刊, 2025, 40(10): 1653-1662. Chen K H, Wang S, Zhang C, et al. Enhancing scenario-oriented technology foresight on information technology for public governance. Bulletin of Chinese Academy of Sciences, 2025, 40(10): 1653-1662. (in Chinese)
Recommended Citation
Discipline Group of Advisory Project on AI-empowered Scientific Research, Academic Divisions of the Chinese Academy of Sciences
(2026)
"Disciplinary system of artificial intelligence: Connotation, architecture, and development suggestions,"
Bulletin of Chinese Academy of Sciences (Chinese Version): Vol. 41
:
Iss.
6
, Article 2.
DOI: https://doi.org/10.3724/j.issn.1000-3045.20260514006
Available at:
https://bulletinofcas.researchcommons.org/journal/vol41/iss6/2


