Stefano Bianchini, Moritz Müller, Pierre Pelletier
arXiv 3 Sep 2020 · Computers and Society · 1 citations (OpenAlex)
arXiv:2009.01575 · PDF · DOI · OpenAlex · Extracted main text
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 arXiv.org 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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Cockburn, I. M., R. Henderson, and S. Stern (2018) The impact of artificial intelligence on innovation | 1.000 | 10 | 5 | 100% |
| 2 | Klinger, J., J. C. Mateos-Garcia, and K. Stathoulopoulos (2020) Deep learning, deep change? Mapping R&D ecosystems for a General Purpose Technology | 1.000 | 6 | 4 | 100% |
| 3 | Agrawal, A., J. McHale, and A. Oettl (2018) Finding needles in haystacks: Artificial intelligence and recombinant growth | 1.000 | 5 | 3 | 100% |
| 4 | Fleming, L (2001) Recombinant uncertainty in technological search | 1.000 | 5 | 3 | 100% |
| 5 | Wang, J., R. Veugelers, and P. Stephan (2017) Bias against novelty in science: A cautionary tale for users of bibliometric indicators | 0.974 | 13 | 3 | 92% |
| 6 | Uzzi, B., S. Mukherjee, M. Stringer, and B. Jones (2013) Atypical combinations and scientific impact | 0.956 | 8 | 3 | 88% |
| 7 | Hassabis, D., D. Kumaran, C. Summerfield, and M. Botvinick (2017) Neuroscience-inspired artificial intelligence | 0.874 | 5 | 2 | 100% |
| 8 | Agrawal, A., J. Gans, and A. Goldfarb (2018) Prediction machines: The simple economics of artificial intelligence | 0.843 | 3 | 3 | 100% |
| 9 | LeCun, Y., Y. Bengio, and G. Hinton (2015) Deep learning | 0.843 | 3 | 3 | 100% |
| 10 | O'Neil, C (2016) Weapons of math destruction: How big data increases inequality and threatens democracy | 0.843 | 3 | 3 | 100% |
Showing the top 10 of 84 scored citations.