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The pace of artificial intelligence innovations: Speed, talent, and trial-and-error

Author

Listed:
  • Tang, Xuli
  • Li, Xin
  • Ding, Ying
  • Song, Min
  • Bu, Yi

Abstract

Innovations in artificial intelligence (AI) are occurring at speeds faster than ever witnessed before. However, few studies have managed to measure or depict this increasing velocity of innovations in the field of AI. In this paper, we combine data on AI from arXiv and Semantic Scholar to explore the pace of AI innovations from three perspectives: AI publications, AI players, and AI updates (trial and error). A research framework and three novel indicators, Average Time Interval (ATI), Innovation Speed (IS) and Update Speed (US), are proposed to measure the pace of innovations in the field of AI. The results show that: (1) in 2019, more than 3 AI preprints were submitted to arXiv per hour, over 148 times faster than in 1994. Furthermore, there was one deep learning–related preprint submitted to arXiv every 0.87 h in 2019, over 1064 times faster than in 1994. (2) For AI players, 5.26 new researchers entered into the field of AI each hour in 2019, more than 175 times faster than in the 1990s. (3) As for AI updates (trial and error), one updated AI preprint was submitted to arXiv every 41 days, with around 33 % of AI preprints having been updated at least twice in 2019. In addition, as reported in 2019, it took, on average, only around 0.2 year for AI preprints to receive their first citations, which is 5 times faster than 2000–2007. This swift pace in AI illustrates the increase in popularity of AI innovation. The systematic and fine-grained analysis of the AI field enabled to portrait the pace of AI innovation and demonstrated that the proposed approach can be adopted to understand other fast-growing fields such as cancer research and nano science.

Suggested Citation

  • Tang, Xuli & Li, Xin & Ding, Ying & Song, Min & Bu, Yi, 2020. "The pace of artificial intelligence innovations: Speed, talent, and trial-and-error," Journal of Informetrics, Elsevier, vol. 14(4).
    • Handle: RePEc:eee:infome:v:14:y:2020:i:4:s1751157720301991
    DOI: 10.1016/j.joi.2020.101094
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    2. Xuli Tang & Xin Li & Feicheng Ma, 2022. "Internationalizing AI: evolution and impact of distance factors," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(1), pages 181-205, January.
    3. Li, Xin & Tang, Xuli & Cheng, Qikai, 2022. "Predicting the clinical citation count of biomedical papers using multilayer perceptron neural network," Journal of Informetrics, Elsevier, vol. 16(4).
    4. Hajibabaei, Anahita & Schiffauerova, Andrea & Ebadi, Ashkan, 2022. "Gender-specific patterns in the artificial intelligence scientific ecosystem," Journal of Informetrics, Elsevier, vol. 16(2).
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    6. Gómez-Talal Ismael & Azizsoltani Mana & Bischoff Jared & Ghaharian Kasra & Singh Ashok, 2025. "A systematic review of machine learning applications in hotel occupancy forecasting," European Journal of Tourism, Hospitality and Recreation, Sciendo, vol. 15(2), pages 311-327.
    7. Bettina Falckenthal & Manuel Au-Yong-Oliveira & Cláudia Figueiredo, 2025. "Intergenerational Tacit Knowledge Transfer: Leveraging AI," Societies, MDPI, vol. 15(8), pages 1-22, July.
    8. Yong Qin & Zeshui Xu & Xinxin Wang & Marinko Skare, 2024. "Artificial Intelligence and Economic Development: An Evolutionary Investigation and Systematic Review," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(1), pages 1736-1770, March.
    9. Jinqing Yang & Zhifeng Liu & Yong Huang, 2024. "From informal to formal: scientific knowledge role transition prediction," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(8), pages 4909-4935, August.

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