St. Petersburg, Russian Federation
Pushkin, St. Petersburg, Russian Federation
Russian Federation
This paper explores the impact of digital transformation on the media industry, focusing on the use of artificial intelligence technologies. The authors examine changes in approaches to content creation, processing, and personalization, including automated moderation, enhancement of visual content quality, generation of new media formats, and protection against falsifications. Purpose: to analyze how the implementation of AI technologies transforms the processes of content creation, processing, and personalization. Results: AI technology contributes to the automation of moderation, improves the quality of visual materials, facilitates the generation of new media formats, and enhances content protection against falsification. Practical significance: the potential of AI technologies lies in enhancing the efficiency and quality of media production, as well as in outlining directions for their integration into various stages of the media cycle.
digital journalism, digitalization, machine learning, convolutional neural networks, generative adversarial networks
1. Chervonyaschiy V. V. Cifrovaya transformaciya zhurnalistiki: rol' kreativa i didzhital-tehnologiy // Prakticheskiy marketing. 2023. № 5 (311). S. 40–48. DOI:https://doi.org/10.24412/2071-3762-2023-5311-40-48.
2. Polovneva M. V. Analiz razvitiya i primeneniya tehnologii chat-bot // Teoriya i praktika sovremennoy nauki. 2018. № 6 (36). S. 917–920.
3. Morozova A. A., Arsent'eva A. D. Problemy i perspektivy ispol'zovaniya iskusstvennogo intellekta v sfere massmedia: mnenie rossiyskoy auditorii // Znak: problemnoe pole mediaobrazovaniya. 2022. № 2 (44). S. 150–158. DOI:https://doi.org/10.47475/2070-0695-2022-10219.
4. Chto takoe obuchenie s uchitelem v mashinnom obuchenii // Skypro Wiki. URL: http://sky.pro/wiki/python/chto-takoe-obuchenie-s-uchitelem-v-mashinnom-obuchenii (data obrascheniya: 18.12.2024).
5. Marshalko D. A., Kubanskih O. V. Arhitektura svertochnyh neyronnyh setey // Uchenye zapiski Bryanskogo gosudarstvennogo universiteta. 2019. № 4 (16). S. 10–13.
6. Sayfutdinov A. V. Svertochnye neyronnye seti dlya resheniya zadach komp'yuternogo zreniya // Universum: tehnicheskie nauki. 2023. № 10 (115), Ch. 1. S. 42–44. DOI:https://doi.org/10.32743/UniTech.2023.115.10.16127.
7. Chto takoe obuchenie bez uchitelya v mashinnom obuchenii // Skypro Wiki. URL: http://sky.pro/wiki/python/chto-takoe-obuchenie-bez-uchitelya-v-mashinnom-obuchenii (data obrascheniya: 20.12.2024).
8. Levin A. O., Belov Yu. S. Ispol'zovanie generativno-sostyazatel'nyh setey dlya generacii izobrazheniy po tekstu // Nauchnoe obozrenie. Tehnicheskie nauki. 2023. № 2. S. 11–15. DOI:https://doi.org/10.17513/srts.1427.
9. Averchenkov A. V., Androsov A. A., Malahov Yu. A. Analiz i primenenie generativno-sostyazatel'nyh setey dlya polucheniya izobrazheniy vysokogo kachestva // Ergodizayn. 2020. № 4 (10). S. 167–176. DOI:https://doi.org/10.30987/2658-4026-2020-4-167-176.
10. Primenenie generativno-sostyazatel'nyh neyrosetey dlya generacii izobrazheniy / E. V. Il'inskaya, E. N. Golysheva, A. A. Medvedev, N. S. Masalitin // Nauchnyy rezul'tat. Informacionnye tehnologii. 2024. T. 9, № 1. S. 73–78. DOI:https://doi.org/10.18413/2518-1092-2024-9-1-0-8.