IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i15p2552-d869181.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/15/2552/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/15/2552/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hadi Jahanshahi & Süleyman Uzun & Sezgin Kaçar & Qijia Yao & Madini O. Alassafi, 2022. "Artificial Intelligence-Based Prediction of Crude Oil Prices Using Multiple Features under the Effect of Russia–Ukraine War and COVID-19 Pandemic," Mathematics, MDPI, vol. 10(22), pages 1-14, November.
    2. Damjan Vlaj & Andrej Zgank, 2022. "Acoustic Gender and Age Classification as an Aid to Human–Computer Interaction in a Smart Home Environment," Mathematics, MDPI, vol. 11(1), pages 1-22, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kumar, Sanjesh & Singh, Baljeet, 2019. "Barriers to the international diffusion of technological innovations," Economic Modelling, Elsevier, vol. 82(C), pages 74-86.
    2. Robertson, Jeandri & Ferreira, Caitlin & Botha, Elsamari & Oosthuizen, Kim, 2024. "Game changers: A generative AI prompt protocol to enhance human-AI knowledge co-construction," Business Horizons, Elsevier, vol. 67(5), pages 499-510.
    3. Thorvaldur Gylfason, 2019. "Inequality Undermines Democracy and Growth," CESifo Working Paper Series 7486, CESifo.
    4. Qiliang Mao & Xianzhuang Mao, 2021. "Cultural barriers, institutional distance, and spatial spillovers: Evidence from regional industrial evolution in China," Growth and Change, Wiley Blackwell, vol. 52(3), pages 1440-1481, September.
    5. Kazemzadeh, Emad & Fuinhas, José Alberto & Koengkan, Matheus & Shadmehri, Mohammad Taher Ahmadi, 2023. "Relationship between the share of renewable electricity consumption, economic complexity, financial development, and oil prices: A two-step club convergence and PVAR model approach," International Economics, Elsevier, vol. 173(C), pages 260-275.
    6. James Temitope Dada & Folorunsho Monsur Ajide & Mamdouh Abdulaziz Saleh Al-Faryan & Mosab I. Tabash, 2024. "The moderating effect of economic complexity in the shadow economy-renewable energy transition nexus: evidence from African economies," Economic Change and Restructuring, Springer, vol. 57(6), pages 1-27, December.
    7. Enrico Bergamini & Georg Zachmann, 2020. "Exploring EU’s Regional Potential in Low-Carbon Technologies," Sustainability, MDPI, vol. 13(1), pages 1-28, December.
    8. Alje van Dam & Koen Frenken, 2019. "Variety, Complexity and Economic Development," Papers 1903.07997, arXiv.org.
    9. Siliang Tong & Nan Jia & Xueming Luo & Zheng Fang, 2021. "The Janus face of artificial intelligence feedback: Deployment versus disclosure effects on employee performance," Strategic Management Journal, Wiley Blackwell, vol. 42(9), pages 1600-1631, September.
    10. Diogo Ferraz & Fernanda P. S. Falguera & Enzo B. Mariano & Dominik Hartmann, 2021. "Linking Economic Complexity, Diversification, and Industrial Policy with Sustainable Development: A Structured Literature Review," Sustainability, MDPI, vol. 13(3), pages 1-29, January.
    11. Alje van Dam & Koen Frenken, 2020. "Vertical vs. Horizontal Policy in a Capabilities Model of Economic Development," Papers in Evolutionary Economic Geography (PEEG) 2037, Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography, revised Aug 2020.
    12. Olimpia Neagu, 2019. "The Link between Economic Complexity and Carbon Emissions in the European Union Countries: A Model Based on the Environmental Kuznets Curve (EKC) Approach," Sustainability, MDPI, vol. 11(17), pages 1-27, August.
    13. Balland, Pierre-Alexandre & Broekel, Tom & Diodato, Dario & Giuliani, Elisa & Hausmann, Ricardo & O'Clery, Neave & Rigby, David, 2022. "Reprint of The new paradigm of economic complexity," Research Policy, Elsevier, vol. 51(8).
    14. Koch, Philipp, 2021. "Economic complexity and growth: Can value-added exports better explain the link?," Economics Letters, Elsevier, vol. 198(C).
    15. Alcalá, Francisco & Solaz, Marta, 2018. "International Relocation of Production and Growth," CEPR Discussion Papers 13422, C.E.P.R. Discussion Papers.
    16. Vu, Trung V., 2020. "Economic complexity and health outcomes: A global perspective," Social Science & Medicine, Elsevier, vol. 265(C).
    17. Sèna Kimm Gnangnon, 2022. "Do Aid for Trade Flows Help Reduce the Shadow Economy in Recipient Countries?," Economies, MDPI, vol. 10(12), pages 1-33, December.
    18. Patrick Herron & Aashish Mehta & Cong Cao & Timothy Lenoir, 2016. "Research diversification and impact: the case of national nanoscience development," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(2), pages 629-659, November.
    19. Paulo Ferreira & Éder J.A.L. Pereira & Hernane B.B. Pereira, 2020. "From Big Data to Econophysics and Its Use to Explain Complex Phenomena," JRFM, MDPI, vol. 13(7), pages 1-10, July.
    20. Reto Föllmi & Angela Fuest & Philipp an de Meulen & Martin Micheli & Torsten Schmidt & Lina Zwick, 2018. "Openness and productivity of the Swiss economy," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 154(1), pages 1-21, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:15:p:2552-:d:869181. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.