IDEAS home Printed from https://ideas.repec.org/a/eee/respol/v51y2022i10s0048733322001275.html
   My bibliography  Save this article

Artificial intelligence in science: An emerging general method of invention

Author

Listed:
  • Bianchini, Stefano
  • Müller, Moritz
  • Pelletier, Pierre

Abstract

This paper offers insights into the diffusion and impact of artificial intelligence in science. More specifically, we show that neural network-based technology meets the essential properties of emerging technologies in the scientific realm. It is novel, because it shows discontinuous innovations in the originating domain and is put to new uses in many application domains; it is quick growing, its dimensions being subject to rapid change; it is coherent, because it detaches from its technological parents, and integrates and is accepted in different scientific communities; and it has a prominent impact on scientific discovery, but a high degree of uncertainty and ambiguity associated with this impact. Our findings suggest that intelligent machines diffuse in the sciences, reshape the nature of the discovery process and affect the organization of science. We propose a new conceptual framework that considers artificial intelligence as an emerging general method of invention and, on this basis, derive its policy implications.

Suggested Citation

  • Bianchini, Stefano & Müller, Moritz & Pelletier, Pierre, 2022. "Artificial intelligence in science: An emerging general method of invention," Research Policy, Elsevier, vol. 51(10).
  • Handle: RePEc:eee:respol:v:51:y:2022:i:10:s0048733322001275
    DOI: 10.1016/j.respol.2022.104604
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0048733322001275
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.respol.2022.104604?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Benjamin F. Jones, 2009. "The Burden of Knowledge and the "Death of the Renaissance Man": Is Innovation Getting Harder?," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 76(1), pages 283-317.
    2. Nicholas Bloom & Charles I. Jones & John Van Reenen & Michael Webb, 2020. "Are Ideas Getting Harder to Find?," American Economic Review, American Economic Association, vol. 110(4), pages 1104-1144, April.
    3. Schmoch, Ulrich, 2007. "Double-boom cycles and the comeback of science-push and market-pull," Research Policy, Elsevier, vol. 36(7), pages 1000-1015, September.
    4. Wang, Jian & Veugelers, Reinhilde & Stephan, Paula, 2017. "Bias against novelty in science: A cautionary tale for users of bibliometric indicators," Research Policy, Elsevier, vol. 46(8), pages 1416-1436.
    5. Philippe Aghion & Benjamin F. Jones & Charles I. Jones, 2018. "Artificial Intelligence and Economic Growth," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 237-282, National Bureau of Economic Research, Inc.
    6. Daron Acemoglu & Pascual Restrepo, 2020. "Robots and Jobs: Evidence from US Labor Markets," Journal of Political Economy, University of Chicago Press, vol. 128(6), pages 2188-2244.
    7. Jason Furman & Robert Seamans, 2019. "AI and the Economy," Innovation Policy and the Economy, University of Chicago Press, vol. 19(1), pages 161-191.
    8. Rosenberg, Nathan, 1992. "Scientific instrumentation and university research," Research Policy, Elsevier, vol. 21(4), pages 381-390, August.
    9. Trajtenberg, Manuel, 2018. "AI as the next GPT: a Political-Economy Perspective," CEPR Discussion Papers 12721, C.E.P.R. Discussion Papers.
    10. Jeffrey L. Furman & Florenta Teodoridis, 2020. "Automation, Research Technology, and Researchers’ Trajectories: Evidence from Computer Science and Electrical Engineering," Organization Science, INFORMS, vol. 31(2), pages 330-354, March.
    11. Pierre Azoulay & Joshua S. Graff Zivin & Gustavo Manso, 2011. "Incentives and creativity: evidence from the academic life sciences," RAND Journal of Economics, RAND Corporation, vol. 42(3), pages 527-554, September.
    12. Fontana, Magda & Iori, Martina & Montobbio, Fabio & Sinatra, Roberta, 2020. "New and atypical combinations: An assessment of novelty and interdisciplinarity," Research Policy, Elsevier, vol. 49(7).
    13. Rotolo, Daniele & Hicks, Diana & Martin, Ben R., 2015. "What is an emerging technology?," Research Policy, Elsevier, vol. 44(10), pages 1827-1843.
    14. Simone Vannuccini & Ekaterina Prytkova, 2021. "Artificial Intelligence’s New Clothes? From General Purpose Technology to Large Technical System," SPRU Working Paper Series 2021-02, SPRU - Science Policy Research Unit, University of Sussex Business School.
    15. Iain M. Cockburn & Rebecca Henderson & Scott Stern, 2018. "The Impact of Artificial Intelligence on Innovation: An Exploratory Analysis," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 115-146, National Bureau of Economic Research, Inc.
    16. Lee Fleming, 2001. "Recombinant Uncertainty in Technological Search," Management Science, INFORMS, vol. 47(1), pages 117-132, January.
    17. Robert J. Gordon, 2016. "Perspectives on The Rise and Fall of American Growth," American Economic Review, American Economic Association, vol. 106(5), pages 72-76, May.
    18. Maria Savona, 2019. "The Value of Data:Towards a Framework to Redistribute It," SPRU Working Paper Series 2019-21, SPRU - Science Policy Research Unit, University of Sussex Business School.
    19. Wolfgang Glänzel & András Schubert, 2001. "Double effort = Double impact? A critical view at international co-authorship in chemistry," Scientometrics, Springer;Akadémiai Kiadó, vol. 50(2), pages 199-214, February.
    20. Vivien Marx, 2013. "The big challenges of big data," Nature, Nature, vol. 498(7453), pages 255-260, June.
    21. Wagner, Caroline S. & Whetsell, Travis A. & Mukherjee, Satyam, 2019. "International research collaboration: Novelty, conventionality, and atypicality in knowledge recombination," Research Policy, Elsevier, vol. 48(5), pages 1260-1270.
    22. Lee, You-Na & Walsh, John P. & Wang, Jian, 2015. "Creativity in scientific teams: Unpacking novelty and impact," Research Policy, Elsevier, vol. 44(3), pages 684-697.
    23. Iain M. Cockburn & Rebecca Henderson & Scott Stern, 2018. "The Impact of Artificial Intelligence on Innovation," NBER Working Papers 24449, National Bureau of Economic Research, Inc.
    24. Joel Klinger & Juan Mateos-Garcia & Konstantinos Stathoulopoulos, 2021. "Deep learning, deep change? Mapping the evolution and geography of a general purpose technology," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 5589-5621, July.
    25. Ajay Agrawal & John McHale & Alexander Oettl, 2018. "Finding Needles in Haystacks: Artificial Intelligence and Recombinant Growth," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 149-174, National Bureau of Economic Research, Inc.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Stefano Bianchini & Giacomo Damioli & Claudia Ghisetti, 2023. "The environmental effects of the “twin” green and digital transition in European regions," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 84(4), pages 877-918, April.
    2. Xueyuan Gao & Hua Feng, 2023. "AI-Driven Productivity Gains: Artificial Intelligence and Firm Productivity," Sustainability, MDPI, vol. 15(11), pages 1-21, June.
    3. Christian Peukert & Margaritha Windisch, 2023. "The Economics of Copyright in the Digital Age," CESifo Working Paper Series 10687, CESifo.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Stefano Bianchini & Moritz Müller & Pierre Pelletier, 2022. "Artificial intelligence in science: An emerging general method of invention," Post-Print hal-03958025, HAL.
    2. Stefano Bianchini & Moritz Muller & Pierre Pelletier, 2020. "Deep Learning in Science," Papers 2009.01575, arXiv.org, revised Sep 2020.
    3. Ajay Agrawal & Joshua Gans & Avi Goldfarb, 2019. "Economic Policy for Artificial Intelligence," Innovation Policy and the Economy, University of Chicago Press, vol. 19(1), pages 139-159.
    4. 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.
    5. Gold, E. Richard, 2021. "The fall of the innovation empire and its possible rise through open science," Research Policy, Elsevier, vol. 50(5).
    6. Dongqing Lyu & Kaile Gong & Xuanmin Ruan & Ying Cheng & Jiang Li, 2021. "Does research collaboration influence the “disruption” of articles? Evidence from neurosciences," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(1), pages 287-303, January.
    7. Pierre Pelletier & Kevin Wirtz, 2023. "Sails and Anchors: The Complementarity of Exploratory and Exploitative Scientists in Knowledge Creation," Papers 2312.10476, arXiv.org.
    8. 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.
    9. Yue Wang & Ning Li & Bin Zhang & Qian Huang & Jian Wu & Yang Wang, 2023. "The effect of structural holes on producing novel and disruptive research in physics," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(3), pages 1801-1823, March.
    10. Bernardo S Buarque & Ronald B Davies & Ryan M Hynes & Dieter F Kogler, 2020. "OK Computer: the creation and integration of AI in Europe," Cambridge Journal of Regions, Economy and Society, Cambridge Political Economy Society, vol. 13(1), pages 175-192.
    11. Naude, Wim, 2019. "The race against the robots and the fallacy of the giant cheesecake: Immediate and imagined impacts of artificial intelligence," MERIT Working Papers 2019-005, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    12. Gries, Thomas & Naude, Wim, 2018. "Artificial intelligence, jobs, inequality and productivity: Does aggregate demand matter?," MERIT Working Papers 2018-047, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    13. Kwon, Seokbeom, 2022. "Interdisciplinary knowledge integration as a unique knowledge source for technology development and the role of funding allocation," Technological Forecasting and Social Change, Elsevier, vol. 181(C).
    14. Montobbio, Fabio & Staccioli, Jacopo & Virgillito, Maria Enrica & Vivarelli, Marco, 2022. "Robots and the origin of their labour-saving impact," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    15. Banal-Estañol, Albert & Macho-Stadler, Inés & Pérez-Castrillo, David, 2019. "Evaluation in research funding agencies: Are structurally diverse teams biased against?," Research Policy, Elsevier, vol. 48(7), pages 1823-1840.
    16. Ke, Qing, 2020. "Technological impact of biomedical research: The role of basicness and novelty," Research Policy, Elsevier, vol. 49(7).
    17. 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.
    18. Albert Banal-Estañol & Ines Macho-Stadler & David Pérez-Castrillo, 2016. "Key Success Drivers in Public Research Grants: Funding the Seeds of Radical Innovation in Academia?," CESifo Working Paper Series 5852, CESifo.
    19. Abbasiharofteh, Milad & Kogler, Dieter F. & Lengyel, Balázs, 2023. "Atypical combinations of technologies in regional co-inventor networks," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 52(10), pages 1-1.
    20. Nicolas Carayol, 2016. "The Right Job and the Job Right: Novelty, Impact and Journal Stratification in Science," Post-Print hal-02274661, HAL.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:respol:v:51:y:2022:i:10:s0048733322001275. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/respol .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.