  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.
