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Identifying and measuring developments in artificial intelligence: Making the impossible possible

Author

Listed:
  • Stefano Baruffaldi

    (Max Planck Institute for Innovation and Competition)

  • Brigitte van Beuzekom
  • Hélène Dernis
  • Dietmar Harhoff

    (Max Planck Institute for Innovation and Competition)

  • Nandan Rao
  • David Rosenfeld
  • Mariagrazia Squicciarini

Abstract

This paper identifies and measures developments in science, algorithms and technologies related to artificial intelligence (AI). Using information from scientific publications, open source software (OSS) and patents, it finds a marked increase in AI-related developments over recent years. Since 2015, AI-related publications have increased by 23% per year; from 2014 to 2018, AI-related OSS contributions grew at a rate three times greater than other OSS contributions; and AI-related inventions comprised, on average, more than 2.3% of IP5 patent families in 2017. China’s growing role in the AI space also emerges. The analysis relies on a three-pronged approach based on established bibliometric and patent-based methods, and machine learning (ML) implemented on purposely collected OSS data.

Suggested Citation

  • Stefano Baruffaldi & Brigitte van Beuzekom & Hélène Dernis & Dietmar Harhoff & Nandan Rao & David Rosenfeld & Mariagrazia Squicciarini, 2020. "Identifying and measuring developments in artificial intelligence: Making the impossible possible," OECD Science, Technology and Industry Working Papers 2020/05, OECD Publishing.
    • Handle: RePEc:oec:stiaaa:2020/05-en
    DOI: 10.1787/5f65ff7e-en
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    Citations

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    Cited by:

    1. Christian Rammer & Gastón P Fernández & Dirk Czarnitzki, 2021. "Artificial Intelligence and Industrial Innovation: Evidence from Firm-Level Data," Working Papers of Department of Economics, Leuven 674605, KU Leuven, Faculty of Economics and Business (FEB), Department of Economics, Leuven.
    2. Igna, Ioana & Venturini, Francesco, 2023. "The determinants of AI innovation across European firms," Research Policy, Elsevier, vol. 52(2).
    3. Venturini, Francesco, 2022. "Intelligent technologies and productivity spillovers: Evidence from the Fourth Industrial Revolution," Journal of Economic Behavior & Organization, Elsevier, vol. 194(C), pages 220-243.
    4. Bratanova, Alexandra & Pham, Hien & Mason, Claire & Hajkowicz, Stefan & Naughtin, Claire & Schleiger, Emma & Sanderson, Conrad & Chen, Caron & Karimi, Sarvnaz, 2022. "Differentiating artificial intelligence activity clusters in Australia," Technology in Society, Elsevier, vol. 71(C).
    5. Alexander V. Giczy & Nicholas A. Pairolero & Andrew A. Toole, 2022. "Identifying artificial intelligence (AI) invention: a novel AI patent dataset," The Journal of Technology Transfer, Springer, vol. 47(2), pages 476-505, April.
    6. Harald König & Martina F. Baumann & Christopher Coenen, 2021. "Emerging Technologies and Innovation—Hopes for and Obstacles to Inclusive Societal Co-Construction," Sustainability, MDPI, vol. 13(23), pages 1-13, November.
    7. Bratanova, Alexandra & Pham, Hien & Mason, Claire & Hajkowicz, Stefan & Naughtin, Claire & Schleiger, Emma & Sanderson, Conrad & Chen, Caron & Karimi, Sarvnaz, 2022. "Differentiating artificial intelligence capability clusters in Australia," MPRA Paper 113237, University Library of Munich, Germany.
    8. Hajkowicz, Stefan & Sanderson, Conrad & Karimi, Sarvnaz & Bratanova, Alexandra & Naughtin, Claire, 2023. "Artificial intelligence adoption in the physical sciences, natural sciences, life sciences, social sciences and the arts and humanities: A bibliometric analysis of research publications from 1960-2021," Technology in Society, Elsevier, vol. 74(C).
    9. Brea, Edgar, 2024. "The yin yang of AI: Exploring how commercial and non-commercial orientations shape machine learning innovation," Research Policy, Elsevier, vol. 53(6).
    10. Parteka, Aleksandra & Wolszczak-Derlacz, Joanna & Nikulin, Dagmara, 2024. "How digital technology affects working conditions in globally fragmented production chains: Evidence from Europe," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    11. Zuzanna Helena Zarach & Aleksandra Parteka, 2023. "Productivity effects of trade in natural resources—comparison with mechanisms of technological specialisation," The World Economy, Wiley Blackwell, vol. 46(9), pages 2684-2706, September.
    12. Fabien Petit & Florencia Jaccoud & Tommaso Ciarli, 2023. "Heterogeneous Adjustments of Labor Markets to Automation Technologies," CESifo Working Paper Series 10237, CESifo.
    13. Parteka, Aleksandra & Kordalska, Aleksandra, 2023. "Artificial intelligence and productivity: global evidence from AI patent and bibliometric data," Technovation, Elsevier, vol. 125(C).
    14. Duch-Brown, Néstor & Gomez-Herrera, Estrella & Mueller-Langer, Frank & Tolan, Songül, 2022. "Market power and artificial intelligence work on online labour markets," Research Policy, Elsevier, vol. 51(3).
    15. Rammer, Christian & Fernández, Gastón P. & Czarnitzki, Dirk, 2022. "Artificial intelligence and industrial innovation: Evidence from German firm-level data," Research Policy, Elsevier, vol. 51(7).
    16. Yulia Turovets & Konstantin Vishnevskiy & Artem Altynov, 2020. "How To Measure Ai: Trends, Challenges And Implications," HSE Working papers WP BRP 116/STI/2020, National Research University Higher School of Economics.
    17. Stefano Bianchini & Moritz Muller & Pierre Pelletier, 2023. "Drivers and Barriers of AI Adoption and Use in Scientific Research," Papers 2312.09843, arXiv.org, revised Feb 2024.

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