IDEAS home Printed from
   My bibliography  Save this paper

Deep Learning in Science


  • Stefano Bianchini
  • Moritz Muller
  • Pierre Pelletier


Much of the recent success of Artificial Intelligence (AI) has been spurred on by impressive achievements within a broader family of machine learning methods, commonly referred to as Deep Learning (DL). This paper provides insights on the diffusion and impact of DL in science. Through a Natural Language Processing (NLP) approach on the publication corpus, we delineate the emerging DL technology and identify a list of relevant search terms. These search terms allow us to retrieve DL-related publications from Web of Science across all sciences. Based on that sample, we document the DL diffusion process in the scientific system. We find i) an exponential growth in the adoption of DL as a research tool across all sciences and all over the world, ii) regional differentiation in DL application domains, and iii) a transition from interdisciplinary DL applications to disciplinary research within application domains. In a second step, we investigate how the adoption of DL methods affects scientific development. Therefore, we empirically assess how DL adoption relates to re-combinatorial novelty and scientific impact in the health sciences. We find that DL adoption is negatively correlated with re-combinatorial novelty, but positively correlated with expectation as well as variance of citation performance. Our findings suggest that DL does not (yet?) work as an autopilot to navigate complex knowledge landscapes and overthrow their structure. However, the 'DL principle' qualifies for its versatility as the nucleus of a general scientific method that advances science in a measurable way.

Suggested Citation

  • Stefano Bianchini & Moritz Muller & Pierre Pelletier, 2020. "Deep Learning in Science," Papers 2009.01575,, revised Sep 2020.
  • Handle: RePEc:arx:papers:2009.01575

    Download full text from publisher

    File URL:
    File Function: Latest version
    Download Restriction: no

    References listed on IDEAS

    1. Bresnahan, Timothy F. & Trajtenberg, M., 1995. "General purpose technologies 'Engines of growth'?," Journal of Econometrics, Elsevier, vol. 65(1), pages 83-108, January.
    2. 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.
    3. 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.
    4. 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.
    5. Jason Furman & Robert Seamans, 2019. "AI and the Economy," Innovation Policy and the Economy, University of Chicago Press, vol. 19(1), pages 161-191.
    6. Georg Graetz & Guy Michaels, 2018. "Robots at Work," The Review of Economics and Statistics, MIT Press, vol. 100(5), pages 753-768, December.
    7. 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.
    8. 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.
    9. Can Huang & Ad Notten & Nico Rasters, 2011. "Nanoscience and technology publications and patents: a review of social science studies and search strategies," The Journal of Technology Transfer, Springer, vol. 36(2), pages 145-172, April.
    10. 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.
    11. Henry Small, 1973. "Co‐citation in the scientific literature: A new measure of the relationship between two documents," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 24(4), pages 265-269, July.
    12. 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.
    13. 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.
    14. 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.
    15. Martin L. Weitzman, 1998. "Recombinant Growth," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 113(2), pages 331-360.
    16. Vivien Marx, 2013. "The big challenges of big data," Nature, Nature, vol. 498(7453), pages 255-260, June.
    17. 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.
    18. 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).
    19. 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.
    20. 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.
    21. Alessandro Annoni & Peter Benczur & Paolo Bertoldi & Blagoj Delipetrev & Giuditta De Prato & Claudio Feijoo & Enrique Fernandez Macias & Emilia Gomez Gutierrez & Maria Iglesias Portela & Henrik Junkle, 2018. "Artificial Intelligence: A European Perspective," JRC Research Reports JRC113826, Joint Research Centre.
    22. Ying Huang & Jannik Schuehle & Alan L. Porter & Jan Youtie, 2015. "A systematic method to create search strategies for emerging technologies based on the Web of Science: illustrated for ‘Big Data’," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(3), pages 2005-2022, December.
    23. Rosenberg, Nathan, 1992. "Scientific instrumentation and university research," Research Policy, Elsevier, vol. 21(4), pages 381-390, August.
    24. Lee Fleming, 2001. "Recombinant Uncertainty in Technological Search," Management Science, INFORMS, vol. 47(1), pages 117-132, January.
    Full references (including those not matched with items on IDEAS)


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

    Cited by:

    1. Caloffi, Annalisa & Colovic, Ana & Rizzoli, Valentina & Rossi, Federica, 2023. "Innovation intermediaries' types and functions: A computational analysis of the literature," Technological Forecasting and Social Change, Elsevier, vol. 189(C).

    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. Bianchini, Stefano & Müller, Moritz & Pelletier, Pierre, 2022. "Artificial intelligence in science: An emerging general method of invention," Research Policy, Elsevier, vol. 51(10).
    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. Gold, E. Richard, 2021. "The fall of the innovation empire and its possible rise through open science," Research Policy, Elsevier, vol. 50(5).
    5. 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.
    6. Pierre Pelletier & Kevin Wirtz, 2023. "Sails and Anchors: The Complementarity of Exploratory and Exploitative Scientists in Knowledge Creation," Papers 2312.10476,
    7. 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.
    8. 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.
    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. 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.
    12. 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.
    13. Nicolas Carayol, 2016. "The Right Job and the Job Right: Novelty, Impact and Journal Stratification in Science," Post-Print hal-02274661, HAL.
    14. Jakub Growiec, 2019. "The Hardware–Software Model: A New Conceptual Framework of Production, R&D, and Growth with AI," Working Paper series 19-18, Rimini Centre for Economic Analysis.
    15. 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.
    16. 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).
    17. Basso, Henrique S. & Jimeno, Juan F., 2021. "From secular stagnation to robocalypse? Implications of demographic and technological changes," Journal of Monetary Economics, Elsevier, vol. 117(C), pages 833-847.
    18. Czarnitzki, Dirk & Fernández, Gastón P. & Rammer, Christian, 2023. "Artificial intelligence and firm-level productivity," Journal of Economic Behavior & Organization, Elsevier, vol. 211(C), pages 188-205.
    19. 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).
    20. Ke, Qing, 2020. "Technological impact of biomedical research: The role of basicness and novelty," Research Policy, Elsevier, vol. 49(7).

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:


    Access and download statistics


    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:arx:papers:2009.01575. 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: arXiv administrators (email available below). General contact details of provider: .

    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.