Deep learning, deep change? Mapping the evolution and geography of a general purpose technology

Joel Klinger (), Juan Mateos-Garcia () and Konstantinos Stathoulopoulos ()
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Joel Klinger: Nesta
Juan Mateos-Garcia: Nesta
Konstantinos Stathoulopoulos: Nesta

Scientometrics, 2021, vol. 126, issue 7, No 10, 5589-5621

Abstract: Abstract General purpose technologies that can be applied in many industries are an important driver of economic growth and national and regional competitiveness but there is little research about their geographic dynamics and the role of industrial ecosystems in spurring their development. We address this with an analysis of Deep Learning, a core technique of artificial intelligence systems increasingly being recognized as the latest example of a transformational general purpose technology. We identify Deep Learning papers through a semantic analysis of a novel dataset from arXiv, a popular preprints website, and use CrunchBase, a technology business directory to map business capabilities. After showing that Deep Learning conforms to the definition of a general purpose technology, we study changes in its geography and its drivers revealing China’s rise in Deep Learning research. We also find that initial volatility in the geography of Deep Learning has been followed by consolidation suggesting that the window of opportunity for new entrants might be closing. We study the regional drivers of Deep Learning competitive advantage, finding that strong research clusters tend to appear in regions that specialise in research and industrial activities related to Deep Learning, underscoring the importance of supportive innovation ecosystems for the development of general purpose technologies.

Keywords: Artificial intelligence; Machine learning; Deep Learning; General purpose technology; Economic geography; Innovation ecosystem (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (13)

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DOI: 10.1007/s11192-021-03936-9

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