IDEAS home Printed from https://ideas.repec.org/p/eti/dpaper/20010.html

Science and Technology Co-evolution in AI: Empirical Understanding through a Linked Dataset of Scientific Articles and Patents

Author

Listed:
  • Kazuyuki MOTOHASHI

Abstract

The linked dataset of AI research articles and patents reveals that a substantial public sector contribution is found for AI development. In addition, the role of researchers who are involved both in publication and patent activities, particularly in the private sector, increased over time. That is, open science that is publicly available through research articles and propriety technology that is protected by patents are intertwined in AI development. In addition, the impact of data science, measured by AI research articles on innovation, is analyzed by patent citation analysis. It is found that patents invented by AI paper authors are more likely to have more forward citations by other applicants (non-self-citation), in wider technology fields (greater generality index). This implies that the nature of general purpose technology (GPT) for data science is elevated by the fact that patent inventors are also involved with scientific activities and published as research authors.

Suggested Citation

  • Kazuyuki MOTOHASHI, 2020. "Science and Technology Co-evolution in AI: Empirical Understanding through a Linked Dataset of Scientific Articles and Patents," Discussion papers 20010, Research Institute of Economy, Trade and Industry (RIETI).
  • Handle: RePEc:eti:dpaper:20010
    as

    Download full text from publisher

    File URL: https://www.rieti.go.jp/jp/publications/dp/20e010.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. David Silver & Julian Schrittwieser & Karen Simonyan & Ioannis Antonoglou & Aja Huang & Arthur Guez & Thomas Hubert & Lucas Baker & Matthew Lai & Adrian Bolton & Yutian Chen & Timothy Lillicrap & Fan , 2017. "Mastering the game of Go without human knowledge," Nature, Nature, vol. 550(7676), pages 354-359, October.
    2. Marina Flamand & F. Manlay & Johannes van Der Pol, 2017. "Patent intelligence for technology intelligence," Post-Print hal-02152019, HAL.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ruilu Yang & Qiang Wu & Yundong Xie, 2023. "Are scientific articles involving corporations associated with higher citations and views? an analysis of the top journals in business research," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(10), pages 5659-5685, October.
    2. Sheer, Lia, 2022. "Sitting on the Fence: Integrating the two worlds of scientific discovery and invention within the firm," Research Policy, Elsevier, vol. 51(7).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bo Hu & Jiaxi Li & Shuang Li & Jie Yang, 2019. "A Hybrid End-to-End Control Strategy Combining Dueling Deep Q-network and PID for Transient Boost Control of a Diesel Engine with Variable Geometry Turbocharger and Cooled EGR," Energies, MDPI, vol. 12(19), pages 1-15, September.
    2. Chao, Xiangrui & Ran, Qin & Chen, Jia & Li, Tie & Qian, Qian & Ergu, Daji, 2022. "Regulatory technology (Reg-Tech) in financial stability supervision: Taxonomy, key methods, applications and future directions," International Review of Financial Analysis, Elsevier, vol. 80(C).
    3. Qingyan Li & Tao Lin & Qianyi Yu & Hui Du & Jun Li & Xiyue Fu, 2023. "Review of Deep Reinforcement Learning and Its Application in Modern Renewable Power System Control," Energies, MDPI, vol. 16(10), pages 1-23, May.
    4. Yuchen Zhang & Wei Yang, 2022. "Breakthrough invention and problem complexity: Evidence from a quasi‐experiment," Strategic Management Journal, Wiley Blackwell, vol. 43(12), pages 2510-2544, December.
    5. Michael Curry & Alexander Trott & Soham Phade & Yu Bai & Stephan Zheng, 2022. "Analyzing Micro-Founded General Equilibrium Models with Many Agents using Deep Reinforcement Learning," Papers 2201.01163, arXiv.org, revised Feb 2022.
    6. Elsisi, Mahmoud & Amer, Mohammed & Dababat, Alya’ & Su, Chun-Lien, 2023. "A comprehensive review of machine learning and IoT solutions for demand side energy management, conservation, and resilient operation," Energy, Elsevier, vol. 281(C).
    7. Minkyu Shin & Jin Kim & Bas van Opheusden & Thomas L. Griffiths, 2023. "Superhuman Artificial Intelligence Can Improve Human Decision Making by Increasing Novelty," Papers 2303.07462, arXiv.org, revised Apr 2023.
    8. Wang, Xuan & Wang, Rui & Jin, Ming & Shu, Gequn & Tian, Hua & Pan, Jiaying, 2020. "Control of superheat of organic Rankine cycle under transient heat source based on deep reinforcement learning," Applied Energy, Elsevier, vol. 278(C).
    9. Jiacheng Zhang & Haolan Zhang, 2025. "Towards Human-like Artificial Intelligence: A Review of Anthropomorphic Computing in AI and Future Trends," Mathematics, MDPI, vol. 13(13), pages 1-49, June.
    10. Annika Österdiekhoff & Nils Wendel Heinrich & Nele Russwinkel & Stefan Kopp, 2026. "Giving AI agents a sense of control facilitates reinforcement learning in multitasking scenarios," PLOS ONE, Public Library of Science, vol. 21(2), pages 1-27, February.
    11. Yunping Bai & Yifu Xu & Shifan Chen & Xiaotian Zhu & Shuai Wang & Sirui Huang & Yuhang Song & Yixuan Zheng & Zhihui Liu & Sim Tan & Roberto Morandotti & Sai T. Chu & Brent E. Little & David J. Moss & , 2025. "TOPS-speed complex-valued convolutional accelerator for feature extraction and inference," Nature Communications, Nature, vol. 16(1), pages 1-13, December.
    12. Daníelsson, Jón & Macrae, Robert & Uthemann, Andreas, 2022. "Artificial intelligence and systemic risk," Journal of Banking & Finance, Elsevier, vol. 140(C).
    13. Ying Jin & Zhuoran Yang & Zhaoran Wang, 2025. "Is Pessimism Provably Efficient for Offline Reinforcement Learning?," Mathematics of Operations Research, INFORMS, vol. 50(4), pages 2738-2793, November.
    14. Xiaotian Liu & Yijie Peng & Gongbo Zhang & Ruihan Zhou, 2025. "An Efficient Node Selection Policy for Monte Carlo Tree Search with Neural Networks," INFORMS Journal on Computing, INFORMS, vol. 37(4), pages 785-807, July.
    15. Zhang, Xi & Wang, Qin & Bi, Xiaowen & Li, Donghong & Liu, Dong & Yu, Yuanjin & Tse, Chi Kong, 2024. "Mitigating cascading failure in power grids with deep reinforcement learning-based remedial actions," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
    16. Yang, Yucheng & Wang, Chiyuan & Schaab, Andreas & Moll, Benjamin, 2025. "Structural Reinforcement Learning for Heterogeneous Agent Macroeconomics," CEPR Discussion Papers 20980, Centre for Economic Policy Research.
    17. Sichen Ding & Gaiyun Liu & Li Yin & Jianzhou Wang & Zhiwu Li, 2024. "Detection of Cyber-Attacks in a Discrete Event System Based on Deep Learning," Mathematics, MDPI, vol. 12(17), pages 1-21, August.
    18. Li, Munan & Wang, Liang, 2025. "Leveraging patent classification based on deep learning: The case study on smart cities and industrial Internet of Things," Journal of Informetrics, Elsevier, vol. 19(1).
    19. Wang, Peixiang & Xu, Qihang & Li, Yufei & Chen, Qunlong & Tao, Jinghan & Qin, Wei & Huang, Heng & Zou, Ying, 2025. "Learning-based hybrid algorithms for container relocation problem with storage plan," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 197(C).
    20. Dumitru Alexandru BODISLAV & Raluca Iuliana GEORGESCU, 2025. "Neuroeconomics in the Age of AI: how machine learning alters human decision-making at the neural level," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania / Editura Economica, vol. 0(2(643), S), pages 301-312, Summer.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eti:dpaper:20010. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: TANIMOTO, Toko (email available below). General contact details of provider: https://edirc.repec.org/data/rietijp.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.