IDEAS home Printed from https://ideas.repec.org/a/alq/rufejo/rfej_2023_12_87-100.html
   My bibliography  Save this article

The Potential of Neural Network Models on the Example of ChatGPT: Opportunities, Limitations and Application in the Analysis of Foreign Trade

Author

Listed:
  • Fedor Igorevich ARZHAEV

    (Russian Foreign Trade Academy, Moscow, Russia)

  • Mikhail Aleksandrovich KOKAREV

    (Russian Foreign Trade Academy, Moscow, Russia)

Abstract

Contemporary neural network models are facing a rapid growth of application in a wide range of applied tasks. Consequently, this topic is popular in public discourse, but is oversimplified – the attention is given to individual products, such as ChatGPT. Thus, there is a certain contradiction – it is not the neural networks that are gaining popularity, but just one of their kin. In addition to that the potential for using neural networks is significantly exaggerated in public discourse, just as their importance in generating new knowledge. Based on the identified issues, the study aims to prove the neural networks’ viability as a tool of applied analysis and a financial product with full respect to their potential and usage limitations in the analysis of international relations. The following tasks have been solved to achieve this goal: the possibilities and limitations of using neural network models have been identified; the public discourse on the relevant topic has been analyzed; the potential of ChatGPT as a successful commercial project has been highlighted; the potential for the analysis and management of foreign economic relations is identified. The most significant results of the study include: proof of the applied significance of the development and use of neural network models; revelation of the trend nature of interest to the individual products based on neural networks; proof that ChatGPT is a powerful driver of the transformation of the global high-tech market.

Suggested Citation

  • Fedor Igorevich ARZHAEV & Mikhail Aleksandrovich KOKAREV, 2023. "The Potential of Neural Network Models on the Example of ChatGPT: Opportunities, Limitations and Application in the Analysis of Foreign Trade," Russian Foreign Economic Journal, Russian Foreign Trade Academy Ministry of economic development of the Russian Federation, issue 12, pages 87-100, December.
  • Handle: RePEc:alq:rufejo:rfej_2023_12_87-100
    DOI: 10.24412/2072-8042-2023-12-87-100
    as

    Download full text from publisher

    File URL: http://repec.vavt.ru/RePEc/alq/rufejo/rfej_2023_12_87-100.pdf
    Download Restriction: no

    File URL: https://libkey.io/10.24412/2072-8042-2023-12-87-100?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Keywords

    Neural network models; limitations; potential; ChatGPT; trends;
    All these keywords.

    JEL classification:

    • F10 - International Economics - - Trade - - - General

    Statistics

    Access and download statistics

    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:alq:rufejo:rfej_2023_12_87-100. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Anna Chernyavskaya (email available below). General contact details of provider: https://edirc.repec.org/data/vavtmru.html .

    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.