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Review of Artificial Intelligence and Machine Learning Technologies: Classification, Restrictions, Opportunities and Challenges

Author

Listed:
  • Ravil I. Mukhamediev

    (Institute of Automation and Information Technologies, Satbayev University (KazNRTU), Almaty 050013, Kazakhstan
    Institute of Information and Computational Technologies, Almaty 050010, Kazakhstan)

  • Yelena Popova

    (Baltic International Academy, 1/4 Lomonosov Str., LV-1003 Riga, Latvia)

  • Yan Kuchin

    (Institute of Automation and Information Technologies, Satbayev University (KazNRTU), Almaty 050013, Kazakhstan
    Institute of Information and Computational Technologies, Almaty 050010, Kazakhstan)

  • Elena Zaitseva

    (Faculty of Management Science and Informatics, University of Zilina, 010 26 Žilina, Slovakia)

  • Almas Kalimoldayev

    (Higher School of Economics and Business, Al-Farabi Kazakh National University (KazNU), Almaty 050040, Kazakhstan)

  • Adilkhan Symagulov

    (Institute of Automation and Information Technologies, Satbayev University (KazNRTU), Almaty 050013, Kazakhstan
    Institute of Information and Computational Technologies, Almaty 050010, Kazakhstan)

  • Vitaly Levashenko

    (Faculty of Management Science and Informatics, University of Zilina, 010 26 Žilina, Slovakia)

  • Farida Abdoldina

    (Office of Academic Excellence and Methodology, Almaty Management University, Almaty 050060, Kazakhstan)

  • Viktors Gopejenko

    (International Radio Astronomy Centre, Ventspils University of Applied Sciences, Inzhenieru Str., 101, LV-3601 Ventspils, Latvia
    Department of Natural Science and Computer Technologies, ISMA University of Applied Sciences, Lomonosov Str., 1, LV-1011 Riga, Latvia)

  • Kirill Yakunin

    (Institute of Automation and Information Technologies, Satbayev University (KazNRTU), Almaty 050013, Kazakhstan
    Institute of Information and Computational Technologies, Almaty 050010, Kazakhstan
    School of Digital Technologies, Almaty Management University, Almaty 050060, Kazakhstan)

  • Elena Muhamedijeva

    (Institute of Information and Computational Technologies, Almaty 050010, Kazakhstan)

  • Marina Yelis

    (Institute of Automation and Information Technologies, Satbayev University (KazNRTU), Almaty 050013, Kazakhstan
    Institute of Information and Computational Technologies, Almaty 050010, Kazakhstan)

Abstract

Artificial intelligence (AI) is an evolving set of technologies used for solving a wide range of applied issues. The core of AI is machine learning (ML)—a complex of algorithms and methods that address the problems of classification, clustering, and forecasting. The practical application of AI&ML holds promising prospects. Therefore, the researches in this area are intensive. However, the industrial applications of AI and its more intensive use in society are not widespread at the present time. The challenges of widespread AI applications need to be considered from both the AI (internal problems) and the societal (external problems) perspective. This consideration will identify the priority steps for more intensive practical application of AI technologies, their introduction, and involvement in industry and society. The article presents the identification and discussion of the challenges of the employment of AI technologies in the economy and society of resource-based countries. The systematization of AI&ML technologies is implemented based on publications in these areas. This systematization allows for the specification of the organizational, personnel, social and technological limitations. This paper outlines the directions of studies in AI and ML, which will allow us to overcome some of the limitations and achieve expansion of the scope of AI&ML applications.

Suggested Citation

  • Ravil I. Mukhamediev & Yelena Popova & Yan Kuchin & Elena Zaitseva & Almas Kalimoldayev & Adilkhan Symagulov & Vitaly Levashenko & Farida Abdoldina & Viktors Gopejenko & Kirill Yakunin & Elena Muhamed, 2022. "Review of Artificial Intelligence and Machine Learning Technologies: Classification, Restrictions, Opportunities and Challenges," Mathematics, MDPI, vol. 10(15), pages 1-25, July.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:15:p:2552-:d:869181
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    References listed on IDEAS

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    1. Vincent Van Roy, 2020. "AI Watch - National strategies on Artificial Intelligence: A European perspective in 2019," JRC Research Reports JRC119974, Joint Research Centre.
    2. Cesar A. Hidalgo & Ricardo Hausmann, 2009. "The Building Blocks of Economic Complexity," Papers 0909.3890, arXiv.org.
    3. Jarrahi, Mohammad Hossein, 2018. "Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making," Business Horizons, Elsevier, vol. 61(4), pages 577-586.
    4. Abbas Mardani & Mehrbakhsh Nilashi & Jurgita Antucheviciene & Madjid Tavana & Romualdas Bausys & Othman Ibrahim, 2017. "Recent Fuzzy Generalisations of Rough Sets Theory: A Systematic Review and Methodological Critique of the Literature," Complexity, Hindawi, vol. 2017, pages 1-33, October.
    5. Daekyum Kim & Sang-Hun Kim & Taekyoung Kim & Brian Byunghyun Kang & Minhyuk Lee & Wookeun Park & Subyeong Ku & DongWook Kim & Junghan Kwon & Hochang Lee & Joonbum Bae & Yong-Lae Park & Kyu-Jin Cho & S, 2021. "Review of machine learning methods in soft robotics," PLOS ONE, Public Library of Science, vol. 16(2), pages 1-24, February.
    6. Kirill Yakunin & Maksat Kalimoldayev & Ravil I. Mukhamediev & Rustam Mussabayev & Vladimir Barakhnin & Yan Kuchin & Sanzhar Murzakhmetov & Timur Buldybayev & Ulzhan Ospanova & Marina Yelis & Akylbek Z, 2021. "KazNewsDataset: Single Country Overall Digital Mass Media Publication Corpus," Data, MDPI, vol. 6(3), pages 1-12, March.
    7. Mihai Duguleană & Victor-Alexandru Briciu & Ionuț-Alexandru Duduman & Octavian Mihai Machidon, 2020. "A Virtual Assistant for Natural Interactions in Museums," Sustainability, MDPI, vol. 12(17), pages 1-17, August.
    8. Sergey I. Nikolenko, 2021. "Synthetic Data for Deep Learning," Springer Optimization and Its Applications, Springer, number 978-3-030-75178-4, June.
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