A system for classification of technologies in the field of artificial intelligence for personnel forecasting

A system for classification of technologies in the field of artificial intelligence for personnel forecasting

Автор: Gurtov Valerii A., Averyanov Aleksandr O., Korzun Dmitrii Zh., Smirnov Nikolai V.

Журнал: Economic and Social Changes: Facts, Trends, Forecast @volnc-esc-en

Рубрика: Science, technology and innovation studies

Статья в выпуске: 3 т.15, 2022 года.

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The development of the Russian economy, including through large-scale introduction of digital technology and artificial intelligence technology, requires appropriate resources. Qualified personnel is one of them. The need for trained specialists poses important questions to state institutions - whom to train and in what quantity; this, in turn, demands a detailed elaboration on the issue of staffing requirement. The article presents the results of development of a classification system of artificial intelligence technology for solving personnel forecasting problems. Theoretical significance of the research findings consists in the creation of a classification system that structures existing knowledge about technologies in the field of artificial intelligence and has the potential to gain new knowledge. The novelty of the approach to the classification of artificial intelligence technologies consists in using a three-component structure of technologies “methods tools - application areas” and adjusting the classification to suit the tasks of forecasting the demand of the economy for personnel with competencies in the field of artificial intelligence. The classification is based on the results of analysis of scientific publications on AI (journals of the first quartile Q1 and conferences of the A/A* level). “The Systematic Literature Review” method was used for their research. All thematic publications indexed in Scopus were also analyzed. Practical significance of the results is revealed in relation to the tasks of personnel forecasting in the field of artificial intelligence. The developed classification makes it possible to structure the personnel need at different levels of refinement of artificial intelligence technologies. Another direction in the development of the proposed classification is to compare competencies (knowledge, skills and practical experience) in popular groups of professions with components of artificial intelligence technologies (methods, tools, applications) to design educational programs in the relevant field. The proposed classification has the potential for development: one of the ways is an expert assessment of priority areas for the development of artificial intelligence. The article presents an overview of the results of application of the classification.

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Digital economy, staffing requirement, forecasting, artificial intelligence, technologies, classification, frontiers

Короткий адрес: https://sciup.org/147238045

IDR: 147238045   |   DOI: 10.15838/esc.2022.3.81.6

Список литературы A system for classification of technologies in the field of artificial intelligence for personnel forecasting

  • Abbass H. (2021). Editorial: What is artificial intelligence? IEEE Transactions on Artificial Intelligence, 2(2), 94–95. DOI: 10.1109/tai.2021.3096243 (in Russian).
  • Baksanskii O.E. (2005). Kognitivnye nauki. Ot poznaniya k deistviyu [Cognitive Sciences. From Cognition to Action]. Moscow: URSS.
  • Bokovoy A., Muravuev K., Yakovlev K. (2020). Map-Merging Algorithms for Visual SLAM: Feasibility Study and Empirical Evaluation. DOI: 10.1007/978-3-030-59535-7_4
  • Buevich A., Sergeev A., Shichkin A., Baglaeva E. (2021). A two-step combined algorithm based on NARX neural network and the subsequent prediction of the residues improves prediction accuracy of the greenhouse gases concentrations. Neural Computing and Applications, 33. DOI: 10.1007/s00521-020-04995-4
  • Gurtov V., Pitukhina M., Sigova S. (2015). Hi-tech skills anticipation for sustainable development in Russia. International Journal of Management, Knowledge and Learning, 3(2), 3–17.
  • Gurtov V.A., Ershova N.Yu., Sigova S.V. (2013). In-demand competencies for solving “tasks of the future” in priority areas of science, technology and technology: Embedding in OOP. In: Mat-ly Mezhdunar. nauch.-metod. Konf. “Vysokie intellektual'nye tekhnologii i innovatsii v natsional’nykh issledovatel’skikh universitetakh”: plenarnye doklady (28 fevralya – 01 marta 2013 g.) [Proceedings of the International Scientific and Methodological Conference “High Intellectual Technologies and Innovations in National Research Universities”: Plenary Reports (February 28 – March 1, 2013)]. Saint Petersburg: Izdatel’stvo Politekhnicheskogo universiteta (in Russian).
  • Gust H., Kühnberger K.-U. (2006). The Relevance of Artificial Intelligence for Human Cognition.
  • Jin C. (2020). Relevance between artificial intelligence and cognitive science. In: Proceedings of the 2020 International Symposium on Artificial Intelligence in Medical Sciences (ISAIMS 2020). DOI: 10.1145/3429889.3429917
  • Kalinovskaya I.N. (2021). Creation of programs for the development of human resources of the organization using artificial intelligence technology. In: Koroleva A.A. et al. (Eds.). Tendentsii ekonomicheskogo razvitiya v XXI veke: Mat-ly III Mezhdunar. nauch. konf., Minsk, 1 marta 2021 g [Trends of Economic Development in the 21st Century: Materials of the 3rd International Scientific Conference, March 1, 2021]. Minsk: Belorusskii gosudarstvennyi universitet (in Russian).
  • Khokhlova M., Migniot C., Morozov A., Sushkova O., Dipanda A. (2019). Normal and pathological gait classification LSTM model. Artificial Intelligence in Medicine, 94, 54–66. DOI: 10.1016/j.artmed.2018.12.007
  • Kolin K.K. (2019). A new stage in the development of artificial intelligence: National strategies, trends and forecasts. Strategicheskie prioritety=Strategic Priorities, 2(22), 4–12 (in Russian).
  • Kuznetsov N.V., Lizyaeva V.V., Prokhorova T.A., Lesnykh Yu.G. (2020). Training personnel for the implementation of the national program “digital economy of the Russian Federation”. Sovremennye problemy nauki i obrazovaniya=Modern Problems of Science and Education, 1. Available at: https://science-education.ru/ru/article/view?id=29520. DOI: 10.17513/spno.29520 (accessed: March 24, 2022; in Russian).
  • Leksin V.N. (2020). Artificial intelligence in economy and policy nowadays. Article 1. Artificial intelligence as new economic and political reality. Rossiiskii ekonomicheskii zhurnal=Russian Economic Journal, 4, 3–30. DOI: 10.33983/0130-9757-2020-6-3-32 (in Russian).
  • Lu H., Li Y. (2019). Editorial: Cognitive science and artificial intelligence for human cognition and communication. Mobile Networks and Applications, 25, 995–996. DOI: 10.1007/s11036-019-01265-z
  • Lyubimov A.P. (2020). Main approaches to the definition of “artificial intelligect”. Nauchno-tekhnicheskaya informatsiya. Seriya 2: Informatsionnye protsessy i sistemy=Scientific and Technical Information. Series 2: Information Processes and Systems, 9, 1–6. DOI: 10.36535/0548-0027-2020-09-1 (in Russian).
  • Migurenko R.A. (2010). Human competencies and artificial intelligence. Izvestiya Tomskogo politekhnicheskogo universiteta=Bulletin of the Tomsk Polytechnic University. Geo Assets Engineering, 6(317), 85–89 (in Russian).
  • Razin A.V. (2019). Ethics of artificial intelligence. Filosofiya i obshchestvo=Philosophy and Society, 1(90), 57–73 (in Russian).
  • Romanova O.A., Ponomareva A.O. (2020). Industrial policy: New realities, formation and implementation issues. Ekonomicheskie i sotsial’nye peremeny: fakty, tendentsii, prognoz=Economic and Social Changes: Facts, Trends, Forecast, 13(2), 25–40. DOI: 10.15838/esc.2020.2.68.2 (in Russian).
  • Sigova S.V., Serebryakov A.G., Luksha P.O. (2013). Creating the list of competences in demand: First Russian experience. Nepreryvnoe obrazovanie: XXI vek=Lifelong Education: The 21st century, 1. Available at: http://lll21.petrsu.ru/journal/atricle.php?id=1946 (in Russian).
  • Snyder H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333–339. DOI: 10.1016/j.jbusres.2019.07.039
  • Ushakov E.V. (2017). Filosofiya tekhniki i tekhnologii [Philosophy of Engineering and Technology]. Moscow: Yurait.
  • Vartanov A.V., Ivanov V., Vartanova I. (2020). Facial expressions and subjective assessments of emotions. Cognitive Systems Research, 59, 319–328. DOI: 10.1016/j.cogsys.2019.10.005
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