Introduction Of Artificial Intelligence Research Articles

Problematic aspects of the implementation of artificial intelligence in the field of jurisprudence

The article is devoted to highlighting problematic aspects of the implementation of artificial intelligence in the field of jurisprudence, in particular, in certain branches of law. Due to the rapid development of neural network capabilities, artificial intelligence is a tool for quickly completing tasks and processing information in various spheres of society, including in jurisprudence. Therefore, for a comprehensive analysis, it is necessary to identify problem areas for the implementation of such a phenomenon. It is noted that today there is no unified approach to defining the concept of artificial intelligence. The authors provide several definitions of this concept for the most complete understanding of the essence of artificial intelligence. The legislation does not contain a normative legal act dedicated to the issue of the essence and procedure for using artificial intelligence, which gives rise to such dilemmas. Despite the widespread use of artificial intelligence capabilities among citizens, there is a lack of interest, funding and development of educational methods for the effective use of artificial intelligence in professional activities on the part of government authorities. It is also stated that the introduction of artificial intelligence into professional activities requires staffing with specialists who can service the software. The problems of introducing artificial intelligence in the field of legal proceedings are analyzed. The use of artificial intelligence by courts risks undermining the autonomy of courts and the independence of judges. Artificial intelligence is not able to comprehensively evaluate evidence and make decisions based on moral standards. The issues of transparency of courts, guaranteeing fundamental human rights and freedoms, and software reliability remain open. The legal status of artificial intelligence in civil law is considered. Since artificial intelligence is capable of creating a work, questions remain regarding the protection of copyrights using artificial intelligence and legal liability in case of copyright infringement. A debatable and controversial topic is the definition of artificial intelligence as a subject or object in a civil legal relationship. It has been determined that the introduction of artificial intelligence in criminal proceedings can become a factor in increasing cyber attacks on the neural network with the aim of stealing, destroying, and making data public.

Edge HPC Architectures for AI-Based Video Surveillance Applications

The introduction of artificial intelligence (AI) in video surveillance systems has significantly transformed security practices, allowing for autonomous monitoring and real-time detection of threats. However, the effectiveness and efficiency of AI-powered surveillance rely heavily on the hardware infrastructure, specifically high-performance computing (HPC) architectures. This article examines the impact of different platforms for HPC edge servers, including x86 and ARM CPU-based systems and Graphics Processing Units (GPUs), on the speed and accuracy of video processing tasks. By using advanced deep learning frameworks, a video surveillance system based on YOLO object detection and DeepSort tracking algorithms is developed and evaluated. This study thoroughly assesses the strengths, limitations, and suitability of different hardware architectures for various AI-based surveillance scenarios.

THE STUDY ON IMPACT OF ARTIFICIAL INTELLIGENCE IN HUMAN RESOURCE MANAGEMENT PRACTICES

In the present era of innovation, Artificial Intelligence (AI) has opened up tremendous opportunities in the workplace through robotics innovation, which envelops AI. Precision, Efficiency, and Flexibility are considered the potential benefits of Industry. The implementation of Industry requires a lot of changes, including the Human Resource (HR) function. In Industry, the HR capability is more critical and gives an upper hand to the organization. The HR capability should be more cautious and adaptable to adjust to the difficulties and requirements. This thesis study the contributions of AI in HR digitalization and practices in Industry. HR experts working in Information Technology (IT), Manufacturing, and administration are selected to participate in this review focusing on five AI applications in HR capability and three elements of HR readiness. The information collected was examined utilizing the different Microsoft word tools, excel formulas, and other mathematical formulas. The results uncovered that hierarchical organization examination is a fundamental part of acquiring sustainable development. Adaptability and human asset capability are upheld by each of the five components of AI application areas of HR. Well-being and Safety improvement were viewed as vital components under the AI application in HR. Detailed analysis of the need of artificial intelligence in human resource. The various AI tools to be used by this sector to enhance the organization, and finally check the improvement and growth in the organization with the introduction of AI in human resource management. Keywords- artificial intelligence, human resource management, healthcare industry

Digital transformation of accounting of agricultural industrial enterprises

This article outlines the role of digitalization at the state level, provides a review of current tools for digitalization of enterprises, in particular industrial agricultural enterprises, and the author proposes a vector for further automation and digitalization of enterprises, namely: the introduction of neural networks and artificial intelligence into the management and accounting system. The introduction of artificial intelligence in agriculture will increase the efficiency of agricultural enterprises, improve the monitoring and control system for the condition of crops and animals, as well as the financial condition of the enterprise as a whole, reduce the cost of producing agricultural products by optimizing the use of resources, and improve the quality of work at the enterprise.

IMPLEMENTATION OF ARTIFICIAL INTELLIGENCE IN ENERGY CONSUMPTION CALCULATIONS TO REDUCE EXCESS GENERATION IN THE CONTEXT OF UKRAINE'S RECOVERY

In authors' opinion, the relevance of implementing artificial intelligence in the calculation of energy consumption in order to reduce excess generation lies in several key aspects: 1) efficient use of resources (by analysing data and predicting energy consumption patterns using artificial intelligence, the operation of the energy system can be optimised, ensuring efficient use of energy resources and avoiding excessive electricity generation); 2) reduction of losses (artificial intelligence can help identify and eliminate problematic segments in energy systems, leading to a reduction in energy losses during transport and distribution); 3) consumption forecasting (artificial intelligence can predict and respond to energy consumption peaks, ensuring the stability of energy supply and avoiding overloading of energy systems); 4) resource conservation and emissions reduction (efficient management of energy consumption using artificial intelligence can lead to reduced fuel consumption and greenhouse gas emissions, promoting more sustainable and environmentally friendly development). Artificial intelligence is a field of computer science that deals with the creation of programs and systems capable of performing tasks that typically require human intellectual abilities. These systems can exhibit cognitive functions such as image recognition, language understanding, decision making, self-learning and planning. Artificial intelligence uses methods and techniques from computer science, mathematics, linguistics, philosophy and other fields to design and implement intelligent systems. Artificial intelligence in the energy sector is the application of AI methods and technologies to optimise energy production, transmission, distribution and consumption. This includes the development of algorithms and systems that can automatically analyse large amounts of data, predict energy demand, optimise energy processes, maintain the stability of energy networks and reduce energy losses. The application of artificial intelligence to energy can help increase the efficiency of energy production, reduce environmental impact and improve the reliability of energy systems. The subject of the study is the introduction of artificial intelligence in the calculation of energy consumption in order to reduce excess generation in the context of Ukraine's recovery. The research methods for introducing artificial intelligence into the calculation of energy consumption in order to reduce excess generation in the context of Ukraine's recovery are a system of general scientific and special methods of scientific knowledge. The purpose of the study is to determine the possibilities of introducing artificial intelligence into the calculation of energy consumption to reduce excess generation in the context of Ukraine's recovery. Results. Investing in the use of artificial intelligence to calculate energy consumption in order to reduce excess generation in the context of Ukraine's recovery can be done through various investment instruments, including: venture capital (investments in start-ups and companies developing artificial intelligence technologies to optimise energy consumption); project financing (financing of specific projects using artificial intelligence to analyse and optimise energy consumption); corporate investments (investments in the development of in-house artificial intelligence systems for energy efficiency management at industrial enterprises); stock market (investments in shares and bonds of companies specialising in the development and implementation of innovative technologies for the energy sector); crowdfunding (raising funds from individual investors on platforms dedicated to the development of artificial intelligence projects in the field of energy efficiency).

An Artificial Intelligence–Supported Medicinal Chemistry Project: An Example for Incorporating Artificial Intelligence Within the Pharmacy Curriculum

ObjectiveThis study aims to integrate and use AI to teach core concepts in a medicinal chemistry course and to increase the familiarity of pharmacy students with AI in pharmacy practice and drug development. Artificial intelligence (AI) is a multidisciplinary science that aims to build software tools that mimic human intelligence. AI is revolutionizing pharmaceutical research and patient care. Hence, it is important to include AI in pharmacy education to prepare a competent workforce of pharmacists with skills in this area. MethodsAI principles were introduced in a required medicinal chemistry course for first-year pharmacy students. An AI software, KNIME, was used to examine structure-activity relationships for 5 drugs. Students completed a data sheet that required comprehension of molecular structures and drug-protein interactions. These data were then used to make predictions for molecules with novel substituents using AI. The familiarity of students with AI was surveyed before and after this activity. ResultsThere was an increase in the number of students indicating familiarity with use of AI in pharmacy (before vs after: 25.3% vs 74.5%). The introduction of AI stimulated interest in the course content (> 60% of students indicated increased interest in medicinal chemistry) without compromising the learning outcomes. Almost 70% of students agreed that more AI should be taught in the PharmD curriculum. ConclusionThis is a successful and transferable example of integrating AI in pharmacy education without changing the main learning objectives of a course. This approach is likely to stimulate student interest in AI applications in pharmacy.

The Attitudes of Czech Teachers Towards the Use of Artificial Intelligence in Schools

RESEARCH OBJECTIVE: The objective of this research study was to find out the subjective feelings of Czech teachers regarding the introduction of artificial intelligence into schools.
 THE RESEARCH PROBLEM AND METHODS: The following research questions follow from the research objective: Are Czech teachers worried about the introduction of artificial intelligence into schools? Do Czech teachers see artificial intelligence as a tool that will help them? Do Czech teachers have experience with the use of artificial intelligence? What experience do teachers have with artificial intelligence? To achieve the research objective and answer the research questions, the in-depth interview method with primary and secondary school teachers was used.
 THE PROCESS OF ARGUMENTATION: The first part of the article discusses the concept of artificial intelligence. The second part discussed the use of artificial intelligence in Czech schools. The third part presents the results of a research study was to find out the subjective feelings of Czech teachers regarding the introduction of artificial intelligence into schools.
 RESEARCH RESULTS: As part of the research, it was found that some teachers are concerned about the advent of artificial intelligence and its use in education, especially that students will “misuse” AI to cheat and plagiarize. Most teachers see the application of artificial intelligence in schools as inevitable and realize that it is their task to teach students to use AI effectively. Teachers are also aware that with the implementation of AI into schools, the teaching system will also have to change.
 CONCLUSIONS, RECOMMENDATIONS AND APPLICABLE VALUE OF RESEARCH: We can expect that teachers will consider artificial intelligence as a tool that helps them and makes their work easier. A big impetus will be the integration of artificial intelligence into tools that teachers commonly work with, such as Office 365 or Google Workspace.

New possibilities of artificial intelligence in medicine: a narrative review

The purpose of the narrative review is to provide a descriptive analysis of the emerging capabilities of artificial intelligence (AI) to improve the diagnosis, prevention and treatment of various diseases. The article discusses which modern AI tools can be used in clinical practice, healthcare organization and medical education. The paper considers various aspects of medical AI systems, which are mainly computer support systems for medical decision-making in the process of clinical work. Much attention is paid to the possibilities of generative AI in medicine. Potential applications of AI in clinical practice have been investigated, highlighting promising prospects for both practitioners and their patients. The limitations associated with the use of AI in various fields of medicine are described, and possible ways of solving them are suggested. The problems of information security and ethical constraints associated with the introduction of AI are outlined. The broad integration of AI into public health will enhance clinical and management decision support, speed up disease diagnosis, and improve the overall quality and accessibility of healthcare services.

Artificial Intelligence-Powered Pulse Sequences in Nuclear Magnetic Resonance and Magnetic Resonance Imaging: Historical Trends, Current Innovations and Perspectives

This review article explores the historical background and recent advances in the application of artificial intelligence (AI) in the development of radiofrequency pulses and pulse sequences in nuclear magnetic resonance spectroscopy (NMR) and imaging (MRI). The introduction of AI into this field, which traces back to the late 1970s, has recently witnessed remarkable progress, leading to the design of specialized frameworks and software solutions such as DeepRF, MRzero, and GENETICS-AI. Through an analysis of literature and case studies, this review tracks the transformation of AI-driven pulse design from initial proof-of-concept studies to comprehensive scientific programs, shedding light on the potential implications for the broader NMR and MRI communities. The fusion of artificial intelligence and magnetic resonance pulse design stands as a promising frontier in spectroscopy and imaging, offering innovative enhancements in data acquisition, analysis, and interpretation across diverse scientific domains.

A Review Article on the Transformative Impact of Artificial Intelligence-powered Autopsy in Forensic Medicine

Forensic medicine has traditionally relied on conventional autopsy methods for determining the cause of death and supporting criminal investigations. However, the introduction of artificial intelligence (AI) has sparked a new era in forensic medicine, fundamentally altering the autopsy process. This comprehensive review investigates the transformative impact of AI-powered autopsy techniques on forensic medicine, emphasizing the advancements, challenges, and future possibilities. Through a meticulous analysis of current research and case studies, the article illustrates how AI significantly improves accuracy, efficiency, and objectivity in forensic investigations, resulting in more dependable outcomes. The integration of AI into autopsy procedures represents a revolutionary shift in forensic medicine, with emerging technologies and methodologies reshaping investigative practices. Alongside traditional autopsies, the utilization of VIRTOPSY—an advanced imaging technique—is increasingly prominent, further enhancing the forensic examination process. By delving into recent advancements, applications, and potential developments, this review provides a holistic overview of how AI is reshaping forensic investigations, enhancing reliability, and contributing to justice. This review underscores the transformative role of AI in forensic medicine, highlighting its potential to reshape practices and contribute significantly to societal well-being.

Ethics and artificial intelligence

The introduction of artificial intelligence (AI) into radiology promises to enhance efficiency and improve diagnostic accuracy, yet it also raises manifold ethical questions. These include data protection issues, the future role of radiologists, liability when using AI systems, and the avoidance of bias. To prevent data bias, the datasets need to be compiled carefully and to be representative of the target population. Accordingly, the upcoming European Union AI act sets particularly high requirements for the datasets used in training medical AI systems. Cognitive bias occurs when radiologists place too much trust in the results provided by AI systems (overreliance). So far, diagnostic AI systems are used almost exclusively as "second look" systems. If diagnostic AI systems are to be used in the future as "first look" systems or even as autonomous AI systems in order to enhance efficiency in radiology, the question of liability needs to be addressed, comparable to liability for autonomous driving. Such use of AI would also significantly change the role of radiologists.

МЕТОДОЛОГИЯ СОЗДАНИЯ ИНДИВИДУАЛЬНОЙ МОДЕЛИ ЗДОРОВЬЯ ЧЕЛОВЕКА С ИСПОЛЬЗОВАНИЕМ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА

Background. In the modern world, the role of artificial intelligence in healthcare is becoming increasingly significant, providing new opportunities to transform traditional methods of diagnosis, treatment and medical data management. This technological breakthrough not only improves the efficiency of medical procedures, but also opens up new per-spectives in the prevention and treatment of diseases. The aim. To emphasize the need to create a personalized health model using artificial intelligence to help an individual achieve and maintain optimal health and well-being. In the field of personalized treatment, artificial intelligence plays an important role, taking into account the unique characteristics of each patient. Algorithms analyze genetic information, medical history and responses to previous therapies to develop optimal treatment plans. This opens the way to individualized medicine, where the approach to each patient is based on his or her unique characteristics. Despite all the positive aspects, the introduction of artificial intelligence in healthcare also raises questions of data privacy, ethical issues and technology security. However, if these issues are resolved, artificial intelligence promises to significantly improve the quality and accessibility of medical treatment, opening new horizons in healthcare. Results. In this article, we describe breakthroughs in artificial intelligence technologies and biomedical applications, identify problems of using and further development in medical artificial intelligence systems and summarize the economic, legal and social consequences of using artificial intelligence in healthcare, and propose a scheme for constructing a model of individual human health using artificial intelligence. Conclusion. The results of the analysis of modern scientific literature allow us to draw a conclusion about the potential for creating more effective and personalized approaches to the problem of individual health using integrated artificial intelligence technologies. The proposed methodology can serve as the basis for the development of innovative deci-sion support systems in medicine and improving the quality of medical care.

An Introductory Guide to Artificial Intelligence in Interventional Radiology: Part 2: Implementation Considerations and Harms.

The introduction of artificial intelligence (AI) in interventional radiology (IR) will bring about new challenges and opportunities for patients and clinicians. AI may comprise software as a medical device or AI-integrated hardware and will require a rigorous evaluation that should be guided based on the level of risk of the implementation. A hierarchy of risk of harm and possible harms are described herein. A checklist to guide deployment of an AI in a clinical IR environment is provided. As AI continues to evolve, regulation and evaluation of the AI medical devices will need to continue to evolve to keep pace and ensure patient safety.

Enabling Human-Autonomy Teaming in Aviation: A Framework to Address Human Factors in Digital Assistants Design

The introduction of artificial intelligence (AI) tools in aviation necessitates more research into human-autonomy teaming in these domain settings. This paper describes the development of a design framework for supporting Human Factors novices in considering human factors, improving human-autonomy collaboration, and maintaining safety when developing AI tools for aviation settings. Combining elements of Hierarchical Task Analysis, Coactive Design, and Types and Levels of Autonomy, the design framework provides guidance in three phases: modelling and understanding the existing system and associated tasks; producing a new function allocation for optimal Human-Autonomy Teaming (HAT); and assessing HAT-related risks of the proposed design. In this framework, designers generate a comprehensive set of design considerations to support subsequent development processes. Framework limitations and future research avenues are discussed.

МЕЖДУ ИНДЕКСАЛЬНЫМ И ИКОНИЧЕСКИМ: ФОТОГРАФИИ В КОНТЕКСТЕ РАЗВИТИЯ НЕЙРОННЫХ СЕТЕЙ

The rapid development of technoscience calls into question the usual ways of perception and traditional practices of information analysis, in particular, one of the challenges for the communication society has been the development of artificial intelligence capable of creating images that are almost indistinguishable from paintings and photographs, this is clearly visible in the discussions that are developing today around photography and push us to think about the semiotic shift taking place in this field. The article observes three ways for a photographer to work with neural networks, each of which has its own limits of applicability: image processing, image creation based on the style of a famous photographer, image generation "from scratch". The key examples of neural networks at the disposal of photographers and artists are analyzed, their features and expressive capabilities are indicated, as well as those aesthetic and semiological issues that are issues that are actualized by the introduction of artificial intelligence into photographic practice. Based on the results of the study, the authors come to the conclusion that the semiotic shifts occurring in the field of photography challenge traditional forms of representation and have a fundamental impact on how the categories of authorship and viewership can be understood in the modern sociocultural process.

The Effects of Learner Characteristics on Learning Flow and Learning Satisfaction in the Flipped Learning of Chinese Beauty Major College Students

The advancement of information and communication technology, a central element in the Fourth Industrial Revolution, is influencing the way people think and act with the introduction of artificial intelligence, augmented reality, and the Internet of Things. In education, there's a notable shift from a teacher-centric approach to a learner-centric environment, emphasizing the flipped learning teaching method that facilitates self-directed learning. This study examined how learner characteristics influence learning flow (classroom class and online class) and learning satisfaction in order to establish a foundation for the effective implementation of beauty education in a flipped learning environment. An empirical study was conducted from July 10 to August 10, 2023, involving 166 surveys administered to beauty-related students in China. The data were analyzed using SPSS 25.0, employing techniques such as frequency analysis, factor analysis, reliability analysis, descriptive statistics, correlation analysis, and multiple regression analysis. The research findings indicated high reliability scores for learner characteristics (3.98), learning flow (3.75), and learning satisfaction (3.77) within the flipped learning environment. Correlation analyses between learner characteristics, learning flow, and learning satisfaction revealed statistically significant correlation, especially in terms of the correlation between learning attachment of learner characteristics and learning self-efficacy in learning satisfaction. In particular, a higher level of learning attachment in learner characteristics was associated with a statistically significant positive effect on learning satisfaction, whether in online or classroom learning contexts. The research findings suggest that flipped learning classes can improve students' satisfaction by boosting their learning attachment, leading to increased learning self-efficacy. This study highlights the importance of a flipped learning instructional approach when designing courses to enhance learning flow and attachment, addressing academic self-regulation and self-determination motivation.

Embracing the future of Artificial Intelligence in the classroom: the relevance of AI literacy, prompt engineering, and critical thinking in modern education

The present discussion examines the transformative impact of Artificial Intelligence (AI) in educational settings, focusing on the necessity for AI literacy, prompt engineering proficiency, and enhanced critical thinking skills. The introduction of AI into education marks a significant departure from conventional teaching methods, offering personalized learning and support for diverse educational requirements, including students with special needs. However, this integration presents challenges, including the need for comprehensive educator training and curriculum adaptation to align with societal structures. AI literacy is identified as crucial, encompassing an understanding of AI technologies and their broader societal impacts. Prompt engineering is highlighted as a key skill for eliciting specific responses from AI systems, thereby enriching educational experiences and promoting critical thinking. There is detailed analysis of strategies for embedding these skills within educational curricula and pedagogical practices. This is discussed through a case-study based on a Swiss university and a narrative literature review, followed by practical suggestions of how to implement AI in the classroom.

The Relationship of the Global Al Index and the Level of Employment: A Cluster Approach in Assessing Cross-Country Differences

The article substantiates the problem of measuring and analyzing the «response» of the employment level to the introduction of artificial intelligence (AI) in the economic and social spheres. The authors propose methods for studying the interdependence of integral and component assessments of the development of artificial intelligence and the level of employment for a set of countries representing different continents and economic groups. An assessment was made based on the first ever Global AI Index (GAII) published by Tortoise Media in 2023 for 62 countries and cluster analysis methods, including differentiation of countries by general level and components of artificial intelligence. The values of AI sub-indices were taken into account, characterizing such components as the presence of a state strategy for the implementation of AI, its commercial basis, use for scientific research and development, the formation of an operating environment, infrastructure development, support for «talents» - intellectual leaders (including institutional ones) in the field of AI. Based on the results of cluster analysis, the Russian Federation’s place in the group of countries characterized by a relatively average overall assessment of the development of artificial intelligence and leading in the implementation of statestrategic programs for the introduction of AI into public life has been established.The results of the analysis and modeling of trends in scatter diagrams constructed for selected clusters of countries show the multidirectionality and ambiguous strength of the existing relationship between the development of artificial intelligence for individual components of the Global Index and the level of employment. At the same time, the existing relationship between the level of employment and the integral assessment of the Global AI Index was assessed as statistically weak for all clusters of countries. Conclusions were drawn about the need to take into account the identified differences in statistical estimates (both by country and by AI components) when predicting the impact of AI on changes in the level and structure of employment.As this topic is filled with statistical research, the conclusions drawn from the results of the study will be deepened and continued by the authors. At the same time, according to the authors, the formulated conclusions, which are preliminary at this stage, indicate the relevance, theoretical and practical significance of the problem of assessing the impact of AI on employment, as well as the ambiguity of its solution in different countries.

AI, doping and ethics: On why increasing the effectiveness of detecting doping fraud in sport may be morally wrong

In this article, our aim is to show why increasing the effectiveness of detecting doping fraud in sport by the use of artificial intelligence (AI) may be morally wrong. The...

Use of Artificial Intelligence in Teacher Training

The purpose of the study is to consider the issue and identify problems with the introduction of artificial intelligence in education and, in particular, teacher training. The study is based on online questionnaires that were sent to teachers (375 people) of higher educational institutions and schools in Russia and China. The results of the study suggest that teacher training based on artificial intelligence can improve students' knowledge, but at this stage, the use of technology should be combined with the traditional learning approach. The research may be of interest to teachers, students, parents, school, and university administrations, as well as to a wide range of people interested in modern education trends. The results obtained with the help of this study can be considered when planning a strategy for introducing artificial intelligence into education, making decisions about its share in learning with due regard to a specific area of study.