论文标题
人工智能创新的速度：速度，才华和反复试验
The Pace of Artificial Intelligence Innovations: Speed, Talent, and Trial-and-Error
论文作者
论文摘要
人工智能的创新（AI）的速度比以往任何时候都要快。但是，很少有研究能够衡量或描绘AI领域的创新速度越来越高。在本文中，我们将有关AI和语义学者AI的数据结合在一起，从三个角度来探索AI创新的节奏：AI出版物，AI播放器和AI更新（反复试验和错误）。提出了一个研究框架和三个新颖的指标，即平均时间间隔（ATI），创新速度（IS）和更新速度（美国），以衡量AI领域的创新速度。结果表明：（1）在2019年，每小时提交了3个以上的AI预印象，超过148倍，比1994年快148倍。此外，与2019年每小时每0.87小时每0.87小时每0.87小时提交一个深度学习的预印气，比1994年更快地提高了1,064次。比1990年代快的175倍。 （3）至于AI更新（反复试验），每41天提交了一次更新的AI预印本，大约有33％的AI预启动已在2019年至少进行了两次更新。此外，如2019年所报道的那样，在2019年报道的是，在AI Preprints中平均需要0.2岁，即可获得第一批比赛量，比2000年的Faster the First First Pertints of First Prepints of 2000乘以20000年度的Faster。 AI的这种迅速速度说明了AI创新的普及。对AI领域的系统和细粒度的分析使得可以肖像AI创新的速度，并证明可以采用拟议的方法来了解其他快速增长的领域，例如癌症研究和纳米科学。
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.
