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

Highlights：

• Investigating the pace of AI innovations has thus become increasingly relevant. Yet, few studies have managed to measure or depict this increasing velocity of innovations in AI.

• A research framework and three measures, i.e., Average Time Interval (ATI), Innovation Speed (IS), and Update Speed (US), are proposed, to explore the pace of AI from the perspective of AI papers, AI players, and trial and error. The 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.

• 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.

• 5.26 new researchers entered into the field of AI each hour in 2019, more than 175 times faster than in the 1990s.

• An updated version of the AI preprint was submitted to arXiv every 41 days, with around 33% of arXiv’s AI preprints being updated twice or more in 2019. It took about 0.2 year on average for AI preprints to get their first citations from 2014–2019, around 5 times faster than from 2000–2007.

摘要

•Investigating the pace of AI innovations has thus become increasingly relevant. Yet, few studies have managed to measure or depict this increasing velocity of innovations in AI.•A research framework and three measures, i.e., Average Time Interval (ATI), Innovation Speed (IS), and Update Speed (US), are proposed, to explore the pace of AI from the perspective of AI papers, AI players, and trial and error. The 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.•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.•5.26 new researchers entered into the field of AI each hour in 2019, more than 175 times faster than in the 1990s.•An updated version of the AI preprint was submitted to arXiv every 41 days, with around 33% of arXiv’s AI preprints being updated twice or more in 2019. It took about 0.2 year on average for AI preprints to get their first citations from 2014–2019, around 5 times faster than from 2000–2007.