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

Xuli Tang, Xin Li, Ying Ding, Min Song, Yi Bu

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)


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 0.87âh 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.

Original languageEnglish
Article number101094
JournalJournal of Informetrics
Issue number4
Publication statusPublished - 2020 Nov

Bibliographical note

Funding Information:
This work was supported by the National Social Science Foundation of China ( 71420107026 , 91646206 ) and the National Research Foundation of Korea (No. NRF-2019R1A2C2002577 ). This work was also supported by the Yonsei University Research Grant of 2020 . The financial support provided by Wuhan University (student exchange program) during the visit by Xuli Tang to the University of Texas at Austin is acknowledged. The support provided by the China Scholarship Council (CSC) during a visit by Xin Li to the University of Texas at Austin is also acknowledged (No. 201806270047 ). We thank Xiaoran Yan for his great help in the process of data collection. We also thank the Indiana University’s Supercomputer service of Cabornate and Karst, which provided abundant computing resources for this work.

Publisher Copyright:
© 2020 Elsevier Ltd.

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Library and Information Sciences


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