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Internationalizing AI: Evolution and Impact of Distance Factors

arXiv 2112.01231 ↗First posted 10 Nov 2021cs.CY cs.AIcited by 0

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Abstract (arXiv)

International collaboration has become imperative in the field of AI. However, few studies exist concerning how distance factors have affected the international collaboration in AI research. In this study, we investigate this problem by using 1,294,644 AI related collaborative papers harvested from the Microsoft Academic Graph (MAG) dataset. A framework including 13 indicators to quantify the distance factors between countries from 5 perspectives (i.e., geographic distance, economic distance, cultural distance, academic distance, and industrial distance) is proposed. The relationships were conducted by the methods of descriptive analysis and regression analysis. The results show that international collaboration in the field of AI today is not prevalent (only 15.7%). All the separations in international collaborations have increased over years, except for the cultural distance in masculinity/felinity dimension and the industrial distance. The geographic distance, economic distance and academic distances have shown significantly negative relationships with the degree of international collaborations in the field of AI. The industrial distance has a significant positive relationship with the degree of international collaboration in the field of AI. Also, the results demonstrate that the participation of the United States and China have promoted the international collaboration in the field of AI. This study provides a comprehensive understanding of internationalizing AI research in geographic, economic, cultural, academic, and industrial aspects.

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References · 10

  1. 2020Mapping the Landscape of Artificial Intelligence Applications against COVID-19cited by 14
  2. 2020The Pace of Artificial Intelligence Innovations: Speed, Talent, and Trial-and-Errorcited by 13
  3. 2014Team size matters: Collaboration and scientific impact since 1900cited by 8
  4. 2018International Research Collaboration: Novelty, Conventionality, and Atypicality in Knowledge Recombinationcited by 8
  5. 2019Machine Teaching by Domain Experts: Towards More Humane,Inclusive, and Intelligent Machine Learning Systemscited by 5
  6. 2016Growth of International Cooperation in Science: Revisiting Six Case Studiescited by 3
  7. 2020Characterizing Research Leadership on Geographical Weighted Collaboration Networkcited by 2
  8. 2020Mapping the co-evolution of artificial intelligence, robotics, and the internet of things over 20 years (1998-2017)cited by 2
  9. 2020Understanding Team Collaboration in Artificial Intelligence from the perspective of Geographic Distancecited by 2
  10. 2021Does the geographic proximity effect on knowledge spillovers vary across research fields?cited by 1

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