This paper presents information on how artificial intelligence has affected the worldwide labor market. Both positive and negative effects on the labor market are described

Belikov, A. S.

Belarusian State University of Informatics and Radioelectronics Repository

59-я Научная Конференция Аспирантов, Магистрантов и Студентов БГУИР, Минск, 2023 359  and they may not work well together. This can cause problems with network reliability, latency, and bandwidth, which affect distributed architectures. This fallacy is related to all the other network fallacies. Distributed architecture has more challenges than monolithic architecture besides the eight fallacies mentioned above. They are distributed transactions, distributed logging, cost maintenance and versioning and others. In conclusion, it should be mentioned that software architecture and infrastructure affect each other in modern software development. Software architecture is defined as a combination of four elements: structure, characteristics, decisions, and principles. Different architectural styles or patterns affect and are affected by these elements. The availability of computing resources and affordability of cloud infrastructure brought distributed architectural styles to the foreground of software development. The focus on microservices is defined by the popularity of this style for building complex software systems. The impact of cloud infrastructure enables cloud computing as a delivery model of computing resources over the internet. But besides the obvious advantages of such architectural style, microservices introduce to the system common fallacies of distributed architectures that affect network reliability, communication latency, bandwidth limitations, etc. Software architecture is not a static or isolated concept but a dynamic and interrelated one that evolves with changing requirements, environments, tools, metrics, and examples. Understanding the capabilities, trade-offs, vulnerabilities, and weaknesses of different styles and patterns in the current context is necessary. Software architects should also be able to adapt and migrate existing architectures to leverage new opportunities and overcome new challenges created by modern infrastructure innovations such as cloud computing, containerization, microservices, DevOps, and continuous delivery.  References: 1. Richards, M. Fundamentals of Software Architecture: An Engineering Approach / M. Richards, N. Ford. – Sebastopol : O’Reilly Media, Inc., 2020. – 401 p. 2. Richards, M. Software Architecture Patterns / M. Richards. – Sebastopol : O’Reilly Media, Inc., 2017. – 47 p. 3. Statista [Electronic resource] : Cloud computing – Statistics & Facts. – Mode of access: https://www.statista.com/topics/1695/cloud-computing/. – Date of access: 23.02.2023. 4. Cloudwards [Electronic resource] : Cloud Computing Statistics, Facts & Trends for 2023. – Mode of access: https://www.cloudwards.net/cloud-computing-statistics/. – Date of access: 19.02.2023. 5. Patel, H. Cloud Computing Deployment Models: A Comparative Study / H. Patel, N. Kansara // International Journal of Innovative Research in Computer Science & Technology. – 2021. Vol. 9, iss. 2. 6. Lv, Y. Infrastructure Smart Service System Based on Microservice Architecture from the Perspective of Informatization / Y. Lv, W. Tan // Mobile Information Systems. – 2022. Vol. 2022, article 1344720. 7. Waseem, M. A Systematic Mapping Study on Microservices Architecture in DevOps / M. Waseem, P. Liang, M. Shahin // Journal of Systems and Software. – 2020. Vol. 170, article 110798. 8. Francesco, P.D. Architecting with microservices: A systematic mapping study / P.D. Francesco, P. Lago, I. Malavolta // Journal of Systems and Software. – 2019. Vol. 150, 2019. 9. Microservice Architecture: Aligning Principles, Practices, and Culture / I. Nadareishvili [et al.]. – Sebastopol : O’Reilly Media, Inc., 2016. – 199 p. 10. Haris, M. A Systematic Review on Cloud Computing / M. Haris, R. Kahn // International Journal of Computer Sciences and Engineering. – 2018. Vol. 6, iss. 11. 11. Jaiswal, M. Cloud computing and Infrastructure / M. Jaiswal // International Journal of Computer Sciences and Engineering. – 2017. Vol. 4, iss. 2.  5. THE IMPACT OF ARTIFICIAL INTELLIGENCE ON THE LABOR MARKET Belikov A.S. Belarusian State University of Informatics and Radioelectronics Minsk, Republic of Belarus  Riabykh V.A. – Lecturer This paper presents information on how artificial intelligence has affected the worldwide labor market. Both positive and negative effects on the labor market are described. Artificial intelligence (AI) has already begun to have a significant impact on the labor market, and its influence is likely to grow in the coming years. While some jobs will be created as a result of AI, many others will become obsolete or change significantly. AI has the potential to automate many repetitive, mundane, and labor-intensive jobs that require minimal cognitive abilities, leading to significant improvements in efficiency and productivity in industries such as manufacturing, logistics, and customer service. However, this also raises concerns about the potential replacement of human workers in various enterprises. As AI technology continues to advance, machines can perform tasks previously done by humans with greater speed, precision, and longer hours, 59-я Научная Конференция Аспирантов, Магистрантов и Студентов БГУИР, Минск, 2023 360  which may lead to job displacement and unemployment. As a result, the labor market may experience a decrease in the demand for low-skilled workers who could be restored by machines [1]. However, it is noteworthy that while AI may change certain job roles, it also possesses the potential to generate fresh employment opportunities in domains such as software engineering, data analytics, and AI maintenance. These vocations need specialized expertise that are in high demand, and AI may provide prospects for laborers to acquire new skills and transition into these novel job roles. The influence of AI on the labor market will differ depending on the industry and locality. Certain corporations may experience significant workforce reduction owing to automation, while others may encounter job expansion. All this depends on such aspects as economic growth, labor legislation and technological infrastructure. With the pace of technological change accelerating, workers must engage in lifelong learning to stay up-to-date with new developments in their field. This may involve taking courses, attending workshops, or participating in online training programs to acquire new skills and knowledge [2]. Another drawback of AI is its deficiency in emotional intelligence, a quality of human resources. Although AI can process and analyze vast amounts of data, it is incapable of comprehending emotions or grasping the subtleties of human interaction. This could pose a challenge in domains like healthcare and customer service, where empathy and emotional intelligence play a vital role. This can be a hindrance in fields such as art, music, and writing, where originality is paramount. If the data used to train an AI system contains biases or discrimination, the AI system will also be discriminatory. AI algorithms are merely impartial as the data on which they are trained. This can result in significant ramifications in domains such as borrowing, recruitment, and the legal system. AI technologies are also impacting the education sector, as schools and universities incorporate AI into their curricula and use it to personalize learning experiences for students. It could lead to changes in the skills and knowledge required for future jobs [3]. Governments have an obligation to ensure that the benefits of AI are shared widely across society. This involves creating policies and regulations that promote the responsible use of AI, as well as providing support for workers who are displaced by automation. Additionally, governments can invest in education and training programs to assist workers in transitioning to new careers and industries [4].  The impact of AI on the labor market is complex and multifaceted. While AI has the potential to create new jobs and increase efficiency and productivity, it can also displace workers and exacerbate social inequality. This poses various challenges, including job displacement, the need for re-skilling and upskilling, and the potential for exacerbating existing inequalities. Therefore, it is essential to address these challenges and ensure that the benefits of AI are shared equitably among workers. Governments, businesses, and individuals must work together to prepare for the future of work and ensure that the benefits of AI are distributed fairly and equitably.  References: 1. Artificial Intelligence’s Environmental Costs and Promise [Electronic resource]. – Mode of access: https://www.cfr.org/blog/artificial-intelligences-environmental-costs-and-promise. – Date of access: 13.03.2023. 2. The impact of Artificial Intelligence on the labor market [Electronic resource]. – Mode of access: https://www.oecd.org/publications/the-impact-of-artificial-intelligence-on-the-labour-market-7c895724-en.htm. – Date of access: 23.03.2023. 3. AI and Jobs: Evidence from Online Vacancies [Electronic resource]. ─ Mode of access: https://conference.nber.org/conf_papers/f143876.pdf. ─ Date of access: 23.03.2023. 4. How AI boosts industry profits and innovation  [Electronic resource]. ─ Mode of access: https://www.accenture.com/fr- fr/_acnmedia/36DC7F76EAB444CAB6A7F44017CC3997.pdf. ─ Date of access: 22.03.2023.  6. THE USE OF NEURAL NETWORKS IN PSEUDO-RANDOM NUMBER GENERATORS DEVELOPMENT Boltak S.V. Belarusian State University of Informatics and Radioelectronics                                                       Minsk, Republic of Belarus Liakh Y.V. – Senior Lecturer  The information about using different types of neural networks for developing pseudo-random number generators is presented in this paper.  Random number generators are widely used in computer security issues and cryptography. Most cryptographic procedures require random numbers: generating the key, authentication, and identification. One of the major problems in cryptography today is developing a pseudo-random number generator (PRNG) with acceptable quality in terms of randomness. There are many methods that can help generate pseudo-random numbers. 

The impact of artificial intelligence on the labor market

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The impact of artificial intelligence on the labor market

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