  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 hours in 2019,
over 1,064 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.
