Journal of Siberian Federal University. Humanities & Social Sciences / Scientific Potential of Siberian Cities after the Approval of the Artificial Intelligence Development Strategy in Russia

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Issue
Journal of Siberian Federal University. Humanities & Social Sciences. 2024 17 (12)
Authors
Blanutsa, Viktor I.
Contact information
Blanutsa, Viktor I. : V. B. Sochava Institute of Geography SB RAS Irkutsk, Russian Federation;
Keywords
development strategy; machine learning; publication activity; local scientific community; Siberian Federal District
Abstract

The article presents the results of the verification of the implementation of the installations of the “National Strategy for the Development of Artificial Intelligence for the Period up to 2030” in the Siberian Federal District and Siberian cities in 2020–2022. Of the three main approaches to assessing the scientific potential of the territory – resource, qualification and bibliometric – the latter was chosen. For regions and cities, the study of publication activity in the field of artificial intelligence has not been conducted before. From the national strategy, the guidelines for increasing the number of publications, researchers and organizations are highlighted. They are presented in the form of three initial hypotheses of our study. Three types of journal articles devoted to the discussion, use and development of artificial intelligence technologies were taken into account. The author’s semantic search algorithm was used to select articles on artificial intelligence. More than five hundred articles of Siberian scientists have been identified. Based on them, all hypotheses for the federal district have been confirmed. The distribution of publications, researchers and organizations by Siberian cities is given. The situation in them is not the same. The results obtained can be used to monitor the National Strategy and localization of companies developing artificial intelligence technologies. Five possible directions for further research are presented.

Pages
2310–2321
EDN
EVCBST
Paper at repository of SibFU
https://elib.sfu-kras.ru/handle/2311/154277

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