Mapping the development of a General Purpose Technology.
J. Klinger, J. Mateos-Garcia, and K. Stathoulopoulos
Automated by Russell Winch and Joel Klinger
The figures of this live paper are updated daily. Summary results are provided here; for full methodological workings please see the original paper.
In recent decades entirely new industries have
arisen in just a few years.
Traditional data for monitoring
industrial and academic
activity are slow, leading to
laggy public policy.
This means that the benefits of these
new industries may not be distributed evenly.
One technology which
is leading to rapid industrial change
is Deep Learning.
The growth of Deep Learning in scientific literature
has been rapid since the publication of seminal works in 2012.
The research since that time has, in no small part, been driven by the maturity of GPU technology and the relatively low cost of computing power.
Figure 1: Percentage of papers identified
as having a high Deep Learning content
This growth has been significantly more pronounced in some disciplines of computer science compared with others.
This allows us to naturally benchmark the growth of 'academic industries' which have been revolutionised by Deep Learning, against those which have remained relatively unchanged.
Figure 2: Shares of Deep Learning
activity by subject, before and after 2012
At the national level, there are
large differences in the relative
focus of Deep Learning and non-Deep Learning
For example, European countries tend to produce
more research in non-Deep Learning disciplines,
whereas the opposite is true for the Asia-region.
Transnational organisations (which are mainly US companies) are more comparable to Asian academic institutes in this respect, than to American academic researchers.
Figure 3: Distribution of
Deep Learning/non-Deep Learning
papers by country (top countries)
This geographic difference is echoed at the subnational level,
although almost all of most prolificly 'Deep Learning' city regions
perform more Deep Learning than non-Deep Learning
computer science research.
Figure 4: Distribution of
Deep Learning/non-Deep Learning
papers by city (top cities)
The question remains - has Deep Learning led to deep changes
in the international 'market leaders' in AI research?
Our research clearly shows that this is the case: that countries in the Asian-Pacific region have, since 2012, become the most specialised in Deep Learning research, well ahead of their European competitors.
North American and Japanese research is somewhat between the two extremes, although this discounts the considerable specialisation in Deep Learning research from transnational organisations based in these countries.
Figure 5: Comparison of changes in
Revealed Comparative Advantage (RCA)
between the period before 2012 and
afterwards, focusing on the top
countries by total level of DL activity.
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This project has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No. 770420 – EURITO.
Disclaimer: This Project has been produced with the assistance of the European Union. The contents of this publication are the sole responsibility of the Consortium and can in no way be taken to reflect the views of the European Union.