AI’s Local Patterns of Innovation

Matheus Eduardo Leusin, Björn Jindra, Daniel Hain

Research output: Contribution to conferencePaperResearchpeer-review

Abstract

The concept of relatedness has been crucial to understanding the geography of innovation. It explains local specialisations patterns such as the probability that a location develops a given technology. Yet we still lack an understanding of how technological relatedness changes over time, and how these changes affect local technological development. At the example of Artificial Intelligence (AI) we investigate (i) how specialisation patterns linked to AI change over time, (ii) how these changes affect the local exploration of AI at country level, and (iii) how new capabilities related to AI are created by countries. Thereby, we focus on the US, Japan, South Korea, and China as the four countries leading AI development during the observation period (1974 - 2018). Using patent data, we apply a technological space perspective coupled with specialization indices to identify the dynamics occurring at local and technological levels. We find that the technological evolution of AI has little association with how it was locally developed. Instead, the local development of AI relates to countries’ existing knowledge bases, even in cases when it was weakly related AI
Original languageEnglish
Publication date2021
Number of pages37
Publication statusPublished - 2021
EventDRUID21 Conference - Copenhagen Business School, Frederiksberg, Denmark
Duration: 18 Oct 202120 Oct 2021
Conference number: 42
https://conference.druid.dk/Druid/?confId=62

Conference

ConferenceDRUID21 Conference
Number42
LocationCopenhagen Business School
CountryDenmark
CityFrederiksberg
Period18/10/202120/10/2021
Internet address

Keywords

  • Artificial intelligence
  • Technological space
  • Evolutionary economic geography
  • Technological relatedness
  • Knowledge complexity

Cite this