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Deep learning, deep change? Mapping the development of the Artificial Intelligence General Purpose Technology

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  • J. Klinger
  • J. Mateos-Garcia
  • K. Stathoulopoulos

Abstract

General Purpose Technologies (GPTs) that can be applied in many industries are an important driver of economic growth and national and regional competitiveness. In spite of this, the geography of their development and diffusion has not received significant attention in the literature. We address this with an analysis of Deep Learning (DL), a core technique in Artificial Intelligence (AI) increasingly being recognized as the latest GPT. We identify DL papers in a novel dataset from ArXiv, a popular preprints website, and use CrunchBase, a technology business directory to measure industrial capabilities related to it. After showing that DL conforms with the definition of a GPT, having experienced rapid growth and diffusion into new fields where it has generated an impact, we describe changes in its geography. Our analysis shows China's rise in AI rankings and relative decline in several European countries. We also find that initial volatility in the geography of DL has been followed by consolidation, suggesting that the window of opportunity for new entrants might be closing down as new DL research hubs become dominant. Finally, we study the regional drivers of DL clustering. We find that competitive DL clusters tend to be based in regions combining research and industrial activities related to it. This could be because GPT developers and adopters located close to each other can collaborate and share knowledge more easily, thus overcoming coordination failures in GPT deployment. Our analysis also reveals a Chinese comparative advantage in DL after we control for other explanatory factors, perhaps underscoring the importance of access to data and supportive policies for the successful development of this complex, `omni-use' technology.

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  • J. Klinger & J. Mateos-Garcia & K. Stathoulopoulos, 2018. "Deep learning, deep change? Mapping the development of the Artificial Intelligence General Purpose Technology," Papers 1808.06355, arXiv.org.
    • Handle: RePEc:arx:papers:1808.06355
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    2. Lee, Yong Suk & Kim, Taekyun & Choi, Sukwoong & Kim, Wonjoon, 2022. "When does AI pay off? AI-adoption intensity, complementary investments, and R&D strategy," Technovation, Elsevier, vol. 118(C).
    3. Rajat Kathuria & Mansi Kedia & Sashank Kapilavai, 2020. "Implications of AI on the Indian Economy," Indian Council for Research on International Economic Relations (ICRIER) Report 20-r-03, Indian Council for Research on International Economic Relations (ICRIER), New Delhi, India.
    4. Matheus E. Leusin & Bjoern Jindra & Daniel S. Hain, 2021. "An evolutionary view on the emergence of Artificial Intelligence," Papers 2102.00233, arXiv.org.
    5. Andrea Borsato & Andre Lorentz, 2022. "Data Production and the coevolving AI trajectories: An attempted evolutionary model," Working Papers of BETA 2022-09, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    6. Alessandra Colombelli & Elettra D’Amico & Emilio Paolucci, 2023. "When computer science is not enough: universities knowledge specializations behind artificial intelligence startups in Italy," The Journal of Technology Transfer, Springer, vol. 48(5), pages 1599-1627, October.
    7. Van Roy, Vincent & Vertesy, Daniel & Damioli, Giacomo, 2019. "AI and Robotics Innovation: a Sectoral and Geographical Mapping using Patent Data," GLO Discussion Paper Series 433, Global Labor Organization (GLO).
    8. Giacomo Damioli & Vincent Van Roy & Daniel Vertesy, 2021. "The impact of artificial intelligence on labor productivity," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 11(1), pages 1-25, March.
    9. Kerstin Hotte & Taheya Tarannum & Vilhelm Verendel & Lauren Bennett, 2022. "Exploring Artificial Intelligence as a General Purpose Technology with Patent Data -- A Systematic Comparison of Four Classification Approaches," Papers 2204.10304, arXiv.org.
    10. Krzysztof Rusek & Agnieszka Kleszcz & Albert Cabellos-Aparicio, 2023. "Bayesian inference of spatial and temporal relations in AI patents for EU countries," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(6), pages 3313-3335, June.
    11. Krzysztof Rusek & Agnieszka Kleszcz & Albert Cabellos-Aparicio, 2022. "Bayesian inference of spatial and temporal relations in AI patents for EU countries," Papers 2201.07168, arXiv.org.
    12. Raquel Ortega-Argilés, 2022. "The evolution of regional entrepreneurship policies: “no one size fits all”," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 69(3), pages 585-610, December.
    13. Su Jung Jee & So Young Sohn, 2023. "Firms’ influence on the evolution of published knowledge when a science-related technology emerges: the case of artificial intelligence," Journal of Evolutionary Economics, Springer, vol. 33(1), pages 209-247, January.

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