J. Klinger, J. Mateos-Garcia, K. Stathoulopoulos
arXiv 20 Aug 2018 · Computers and Society · 9 citations (OpenAlex)
arXiv:1808.06355 · PDF · DOI · OpenAlex · Extracted main text
General Purpose Technologies (GPTs) that can be applied in many industries are an important driver of economic growth and national and regional competitiveness. In spite of this, the geography of their development and diffusion has not received significant attention in the literature. We address this with an analysis of Deep Learning (DL), a core technique in Artificial Intelligence (AI) increasingly being recognized as the latest GPT. We identify DL papers in a novel dataset from ArXiv, a popular preprints website, and use CrunchBase, a technology business directory to measure industrial capabilities related to it. After showing that DL conforms with the definition of a GPT, having experienced rapid growth and diffusion into new fields where it has generated an impact, we describe changes in its geography. Our analysis shows China's rise in AI rankings and relative decline in several European countries. We also find that initial volatility in the geography of DL has been followed by consolidation, suggesting that the window of opportunity for new entrants might be closing down as new DL research hubs become dominant. Finally, we study the regional drivers of DL clustering. We find that competitive DL clusters tend to be based in regions combining research and industrial activities related to it. This could be because GPT developers and adopters located close to each other can collaborate and share knowledge more easily, thus overcoming coordination failures in GPT deployment. Our analysis also reveals a Chinese comparative advantage in DL after we control for other explanatory factors, perhaps underscoring the importance of access to data and supportive policies for the successful development of this complex, `omni-use' technology.
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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 | Iain M Cockburn, Rebecca Henderson, and Scott Stern (2018) The impact of artificial intelligence on innovation | 1.000 | 9 | 4 | 100% |
| 2 | Timothy F Bresnahan and Manuel Trajtenberg (1995) General purpose technologies ‘engines of growth’? | 0.737 | 3 | 2 | 100% |
| 3 | Avi Goldfarb and Daniel Trefler (2018) Ai and international trade | 0.644 | 2 | 2 | 100% |
| 4 | I. Goodfellow et al (2016) Deep learning | 0.644 | 2 | 2 | 100% |
| 5 | Bronwyn H Hall and Manuel Trajtenberg (2004) Uncovering gpts with patent data | 0.644 | 2 | 2 | 100% |
| 6 | Erik Brynjolfsson, Daniel Rock, and Chad Syverson (2017) Artificial intelligence and the modern productivity paradox: A clash of expectations and statistics | 0.585 | 3 | 1 | 100% |
| 7 | Paul A David (1990) The dynamo and the computer: an historical perspective on the modern productivity paradox | 0.511 | 2 | 1 | 100% |
| 8 | Koen Frenken, Frank Van Oort, and Thijs Verburg (2007) Related variety, unrelated variety and regional economic growth | 0.511 | 2 | 1 | 100% |
| 9 | César A Hidalgo and Ricardo Hausmann (2009) The building blocks of economic complexity | 0.511 | 2 | 1 | 100% |
| 10 | Steven Klepper (1996) Entry, exit, growth, and innovation over the product life cycle | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 44 scored citations.