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Science behind AI: the evolution of trend, mobility, and collaboration

Author

Listed:
  • Sha Yuan

    (Tsinghua University
    Beijing Academy of Artificial Intelligence)

  • Zhou Shao

    (Tsinghua University
    Nanjing University of Science and Technology)

  • Xingxing Wei

    (Beihang University)

  • Jie Tang

    (Tsinghua University)

  • Wendy Hall

    (University of Southampton)

  • Yongli Wang

    (Nanjing University of Science and Technology)

  • Ying Wang

    (The Ministry of Science and Technology of China)

  • Ye Wang

    (The Ministry of Science and Technology of China)

Abstract

In this era of interdisciplinary science, many scientific achievements, such as artificial intelligence (AI), have brought dramatic revolutions to human society. The increasing availability of digital data on scholarly outputs offers unprecedented opportunities to explore science of science (SciSci). Despite many significant works have been done on SciSci, substantial disciplinary differences in different domains make some insights inadequate within particular fields. One thing standing out is that knowledge concerning the science behind AI is sorely lacking. In this work, we study the evolution of AI from three dimensions, including the evolution of trend, mobility, and collaboration. We find that the AI research hotspots have shifted from theory to application. The USA, which has the largest number of distinguished AI scientists, appeals most to the global AI talents. The brain drain problem of AI scientists is increasingly serious in developing countries. The ties among the AI elites are highly clustered in the collaboration network. Overall, our work aims to serve as a starter and support the development of AI exploring in a visionary way. The related demos are available online in AMiner ( https://www.aminer.cn/ai10 , https://trend.aminer.org ).

Suggested Citation

  • Sha Yuan & Zhou Shao & Xingxing Wei & Jie Tang & Wendy Hall & Yongli Wang & Ying Wang & Ye Wang, 2020. "Science behind AI: the evolution of trend, mobility, and collaboration," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(2), pages 993-1013, August.
    • Handle: RePEc:spr:scient:v:124:y:2020:i:2:d:10.1007_s11192-020-03423-7
    DOI: 10.1007/s11192-020-03423-7
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    References listed on IDEAS

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    1. repec:nas:journl:v:115:y:2018:p:12608-12615 is not listed on IDEAS
    2. Cimini, Giulio & Zaccaria, Andrea & Gabrielli, Andrea, 2016. "Investigating the interplay between fundamentals of national research systems: Performance, investments and international collaborations," Journal of Informetrics, Elsevier, vol. 10(1), pages 200-211.
    3. Richard Van Noorden, 2012. "Global mobility: Science on the move," Nature, Nature, vol. 490(7420), pages 326-329, October.
    4. Albert-László Barabási & Chaoming Song & Dashun Wang, 2012. "Handful of papers dominates citation," Nature, Nature, vol. 491(7422), pages 40-40, November.
    5. Tao Jia & Dashun Wang & Boleslaw K. Szymanski, 2017. "Quantifying patterns of research-interest evolution," Nature Human Behaviour, Nature, vol. 1(4), pages 1-7, April.
    6. Alfredo Yegros-Yegros & Ismael Rafols & Pablo D’Este, 2015. "Does Interdisciplinary Research Lead to Higher Citation Impact? The Different Effect of Proximal and Distal Interdisciplinarity," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-21, August.
    7. Daniel E. Acuna & Stefano Allesina & Konrad P. Kording, 2012. "Predicting scientific success," Nature, Nature, vol. 489(7415), pages 201-202, September.
    8. Lindell Bromham & Russell Dinnage & Xia Hua, 2016. "Interdisciplinary research has consistently lower funding success," Nature, Nature, vol. 534(7609), pages 684-687, June.
    9. Wagner, Caroline S. & Roessner, J. David & Bobb, Kamau & Klein, Julie Thompson & Boyack, Kevin W. & Keyton, Joann & Rafols, Ismael & Börner, Katy, 2011. "Approaches to understanding and measuring interdisciplinary scientific research (IDR): A review of the literature," Journal of Informetrics, Elsevier, vol. 5(1), pages 14-26.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

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    5. Brea, Edgar, 2024. "The yin yang of AI: Exploring how commercial and non-commercial orientations shape machine learning innovation," Research Policy, Elsevier, vol. 53(6).
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    7. Alejandro Vega-Muñoz & Paloma Gónzalez-Gómez-del-Miño & Juan Felipe Espinosa-Cristia, 2021. "Recognizing New Trends in Brain Drain Studies in the Framework of Global Sustainability," Sustainability, MDPI, vol. 13(6), pages 1-27, March.
    8. Yinyu Jin & Sha Yuan & Zhou Shao & Wendy Hall & Jie Tang, 2021. "Turing Award elites revisited: patterns of productivity, collaboration, authorship and impact," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(3), pages 2329-2348, March.

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