DOI: 10.33917/es-4.196.2024.46-53

The purpose of this article is to identify criteria for the influence of artificial intelligence technologies in payment systems on the consciousness of the population as their main user. When reviewing technologies, methods of scientific analysis were applied: the method of analysis and synthesis, abstraction, generalization, description, induction, deduction.

The article dwells on assessment of the implementation and impact of technologies based on artificial intelligence. It is shown that at the moment a situation has arisen where artificial intelligence tools have the opportunity to manipulate human consciousness. Accelerated development of human interaction in the virtual space resulted in both positive and negative consequences of the use of artificial intelligence technologies in payment systems within the framework of the new social reality. Based on the identified advantages and disadvantages, criteria for the impact of artificial intelligence technologies on the consciousness of the population were discovered and identified.

References:

1. Velichkovskiy B.B. Soznanie [Conscience]. Bol’shaya rossiyskaya entsiklopediya, available at: https://bigenc.ru/c/soznanie-f1153b

2. Tekhnologii iskusstvennogo intellekta i mashinnogo obucheniya [Artificial Intelligence and Machine Learning Technologies]. NIU VShE, 2021, 5 marta, available at: https://hsbi.hse.ru/articles/tekhnologii-iskusstvennogo-intellekta-i-mashinnogo-obucheniya/

3. 12 variantov ispol’zovaniya II i mashinnogo obucheniya v finansakh [12 Ways to Use AI and Machine Learning in Finance]. Generativnyy analiz dannykh, 2020, 10 aprelya, available at: https://zephyrnet.com/ru/12-use-cases-of-ai-and-machine-learning-in-finance/

4. Fedorov D. Chto takoe NLP? [What is NLP?]. Renovatsio, 2023, available at: https://renovatsio.rf/media/natural-language-processing

5. Vot polozhenie NLP v finansakh. Ty dolzhen znat’ [Here is the Position of NLP in Finance. You Should Know]. Shaip, 2023, 26 oktyabrya, available at: https://ru.shaip.com/in-the-media/here-are-the-applications-of-nlp-in-finance-you-need-to-know/

6. Perspektivy razvitiya chat-botov v bankovskom sektore: golosovoy II, prognozirovanie povedeniya, analiz nastroeniya [Prospects for the Development of Chatbots in the Banking Sector: Voice AI, Behavior Forecasting, Mood Analysis]. TalkBank, 2022, 21 yanvarya, available at: https://business.talkbank.io/tpost/ lp3s2ai1c1-perspektivi-razvitiya-chat-botov-v-banko

7. Vershinin O. Neyronnye seti: printsip raboty, perspektivy i 159 sovremennykh neyronok [Neural Networks: the Principle of Operation, Prospects and 159 Modern Neurons]. Neiros, 2023, 17 noyabrya, available at: https://neiros.ru/blog/automation/neyronnye-seti-printsip-raboty-perspektivy-i-159-sovremennykhneyronok/

8. Kak rabotayut neyronnye seti? [How do Neural Networks Work?]. Productstar, 2023, 16 marta, available at: https://blog.productstar.ru/kak-rabotayutnejronnye-seti/

9. Prediktivnaya analitika: instruktsiya po primeneniyu II pri prognozirovanii [Predictive Analytics: Instructions for using AI in Forecasting]. SBER Pro, 2023, 23 avgusta, available at: https://sber.pro/publication/prediktivnaya-analitika-instrukcziya-po-primeneniyu-ii-pri-prognozirovanii/

DOI: 10.33917/es-5.191.2023.94-99

In modern engineering of complex technical systems [1] digital twins and artificial intelligence systems started to be applied, while these subsystems have their own methods and tools for systemic, mathematical and computer modeling. Lack of a normalized approach to combining data from disparate sub-systems into a single system results in a “one-off” assembly methodology or in creation of unique digital models and intelligent systems, which impedes their further transformation into more complex both technical and intelligent systems. In this regard, the search for a standard form of representing such subsystems into a single system becomes relevant, along with the task of developing a methodology for the unified design and production of intelligent systems based on the use of model-oriented system engineering [2, 3, 4]. The work names and systematizes methods and techniques for such developments, demonstrates a standard approach to a normalized platform representation of models of various subsystems, which initially have their own methods and presentation tools; results of a normalized policy for constructing a platform of models from various subsystems for an anthropomorphic robot and spacecraft are described. Within the framework of the presented example, complementary methods of digital multiphysics modeling, digital twins and machine learning are positioned.

References:

1. Romanov A.A. Prikladnoi sistemnyi inzhiniring [Applied Systems Engineering]. Moscow, FIZMATLIT, 2015.

2. Kondrat’ev V.V. Model’no-orientirovannyi sistemnyi inzhiniring 2.0 [Model-Based Systems Engineering 2.0]. Moscow, MFTI, 2021.

3. Garichev S.N., Gorbachev R.A., Davydenko E.V., Dzhaparov B.A., Kondrat’ev V.V. Model’no-orientirovannyi inzhiniring fiziko-tekhnicheskikh, informatsionnykh i intellektual’nykh system [Model-based Engineering of Physical, Technical, Information and Intelligent Systems]. Trudy MFTI, 2022, vol.

14, no 2.

4. Aleshchenko A.S., Klishev O.P., Kondrat’ev V.V., Rasskazov A.A. Sovmeshchenie arkhitekturnykh i matematicheskikh modelei v sistemnom inzhiniringe tekhnicheskikh system [Combination of Architectural and Mathematical Models in System Engineering of Technical Systems]. Kosmonavtika i raketostroenie, 2021, no 5.

5. Potyupkin A.Yu., Chechkin A.V. Iskusstvennyi intellekt. Na baze informatsionno-sistemnoi izbytochnosti [Artificial Intelligence. Based on InformationSystem Redundancy]. Mosc ow, Kurs, 2022.

6. GOST R 57700-37–2021. Komp’yuternye modeli i modelirovanie. Tsifrovye dvoiniki izdeliya. Obshchie polozheniya [GOST R 57700-37–2021. Computer

models and simulation. Digital twins of products. General provisions]. Kodeks. URL: https://docs.cntd.ru/document/1200180928

7. Borovkov A.I., Burdakov S.F., Klyavin O.I., et al. Komp’yuternyi inzhiniring [Computer Engineering]. Ucheb. posobie. Saint Petersburg, Izd-vo Politekhn.

un-ta, 2012.

8. Peredovye proizvodstvennye tekhnologii: vozmozhnosti dlya Rossii. Ekspertno-analiticheskii doklad: Monografiya [Advanced Manufacturing Technologies: Opportunities for Russia. Expert Analytical Report: Monograph]. Pod red. A.I. Borovkova. Saint Petersburg, Politekh-Press, 2020.

DOI: 10.33917/mic-5.100.2021.87-102

The article deals with a description and analysis of the policy of modernization of the healthcare sector implemented by the Japanese government on the basis of artificial technology, provides particular examples of some research projects and cases of practical application of the described technologies, identifies problem areas of the policy being implemented and projects being developed.

Modernization of the healthcare sector and medical services based on using of the latest digital technologies, in particular, artificial intelligence technology, is one of the key current global trends. In Russia, the digital transformation of healthcare is defined as one of the key tasks and is carried out within the framework of the National Project «Healthcare».

The study of successful examples of the introduction of artificial intelligence technology, as well as problems that hinder or slow down the integration of this technology and ways to overcome them, can be a valuable lesson for countries also involved in the development of national strategies for the development of artificial intelligence.