The study aimed to explore the possibilities of using artificial intelligence to improve cybersecurity systems in the context of increasing complexity and frequency of cyber threats. Analysis of the effectiveness of integrating machine and deep learning methods into the processes of detecting, assessing and neutralisation threats, as well as identification of the strengths and weaknesses of such approaches, were emphasised. The study substantiated the need to combine the technological capabilities of artificial intelligence with expert human experience to ensure comprehensive and adaptive protection of information systems. The study examined the potential application of artificial intelligence to improve cybersecurity systems, given the growing threats and complexity of modern cybercrime. The impact of machine learning and deep learning technologies on improving the effectiveness of traditional methods of protecting information systems was analysed. The study noted that despite the unchanged basic motivations of cybercriminals, their methods were becoming more sophisticated, which required new approaches to detecting and neutralising threats. The use of artificial intelligence was defined as one of the most promising areas of cybersecurity development, as it allows for the automation of risk assessment and incident response processes, reducing response times and increasing overall security efficiency. Particular attention was devoted to an analysis of the strengths and weaknesses of artificial intelligence in the context of cybersecurity. The necessity of integrating artificial intelligence with human intuition and experience was substantiated, as the combination of these components proved to be the most effective approach to ensuring comprehensive security. In addition, the potential risks and concerns associated with the use of artificial intelligence in cybersecurity were explored. The study concluded that a holistic approach that considers both technical and social aspects is needed to increase the maturity of cybersecurity systems. The importance of socially responsible use of artificial intelligence was emphasised to minimise potential threats and ensure the resilience of cyber systems to new challenges. The practical value of the study is to develop recommendations for the introduction of artificial intelligence into existing cybersecurity systems, which allows them to increase their resilience to new and complex cyber threats, as well as to identify potential risks and shortcomings in existing approaches to information security

Artificial intelligence is a new phenomenon that has occupied a prominent place in our present lives. Its presence in almost any industry that deals with any huge sheer volume of data are taking advantage of AI by integrating it into its day-to-day operation. AI has predictive power based on its data analytic functionality and some levels of autonomous learning, which its raw ingredient is just the massive sheer volume of data. Artificial intelligence is about extracting value from data, which has become the core business value when insight can be extracted. AI has various fundamental applications. This technology can be applied to many different sectors and industries. There has been a tremendous use of artificial intelligence in Nanotechnology research during the last decades. Convergence between artificial intelligence and Nanotechnology can shape the path for various technological developments and a large variety of disciplines. In this short communication, we present such innovative and dynamic sites utilizing artificial intelligence and its sub-sets of machine learning driven by deep learning in Nanotechnology.

Artificial Intelligence (AI) is an area of ​​research driven by innovation and development that culminates in computers, machines with human-like intelligence characterized by cognitive ability, learnability, adaptability and decision-making ability. The study found that AI is widely adopted and used in education, especially by educational institutions, in various forms. This article reviewed articles by various scientists from different countries. The paper discusses the prospects for the application of artificial intelligence and machine learning technologies in education and in everyday life. The history of the development of artificial intelligence is described, technologies of machine learning and neural networks are analyzed. An overview of already implemented projects for the use of artificial intelligence is given, a forecast of the most promising, according to the authors, directions for the development of artificial intelligence technologies for the next period is given. This article provides an analysis of how educational research is being transformed into an experimental science. AI is combined with the study of science into new ‘digital laboratories’, in which ownership of data, as well as power and authority in the production of educational knowledge, are redistributed between research complexes of computers and scientific knowledge.

Abstract The techniques and tools of artificial intelligence are reviewed. Chemical problems can serve as test domains for the development of new artificial intelligence methods. Artificial intelligence techniques can also be applied to the solution of practical problems in chemistry. Applications in chemistry include problems where it is necessary to encode chemical expertise, and where chemistry is merely an additional domain to which standard artificial intelligence techniques can be applied. Programs exploiting encoded chemical expertise can assist in solving problems of structure elucidation, synthesis planning, and experiment design. Robotics methods based on artifical intelligence and expert systems can enhance the performance of chemical instrumentation. Systems understanding natural language could improve the handling of chemical information, and artificial intelligence techniques may extend the capabilities of computer-assisted instruction.

As a burgeoning technology, artificial intelligence has been utilized in numerous domains, including stroke prevention, diagnosis, treatment, and rehabilitation, and has demonstrated considerable promise. The combination of artificial intelligence and big data can be utilized for accurate identification of stroke high-risk groups, automatic etiology classification, and assistance in the formulation of acute stroke and secondary prevention strategies, thereby enhancing the rehabilitation treatment effect for stroke patients. This article discusses the accomplishments made in artificial intelligence research for stroke prevention, diagnosis, treatment, and rehabilitation.

Cosmetic dermatology is a growing field as more patients are seeking treatments for esthetic concerns. Traditionally, practitioners and patients utilize their own perceptions, current beauty standards, and manual observation to determine their satisfaction with cosmetic interventions. Artificial intelligence (AI) can be introduced into cosmetic dermatology to provide objective data-driven recommendations to both dermatologists and patients. The purpose of this paper is to compose a unified review that illustrates the various facets of artificial intelligence and formulate a hypothesis regarding the new implications of artificial intelligence in cosmetic dermatology specifically. A comprehensive search on PubMed was conducted to identify the available information related to AI in cosmetic dermatology. The search was conducted using a combination of keywords including "cosmetic dermatology" and "artificial intelligence." The current literature indicates that AI models offer personalized, efficient, and result-driven outputs that can enhance cosmetic outcomes, patient satisfaction, and overall experience. Artificial intelligence integration in cosmetic dermatology shows a promising future, offering the ability to analyze vast data sets and deliver a tailored patient experience. By incorporating AI into cosmetic dermatology, there is an opportunity to balance evidence-based decision-making with the artistic human touch of cosmetic dermatologists.

Artificial intelligence is increasingly being used in medical practice to complete tasks that were previously completed by the physician, such as visit documentation, treatment plans and discharge summaries. As artificial intelligence becomes a routine part of medical care, physicians increasingly trust and rely on its clinical recommendations. However, there is concern that some physicians, especially those younger and less experienced, will become over-reliant on artificial intelligence. Over-reliance on it may reduce the quality of clinical reasoning and decision-making, negatively impact patient communications and raise the potential for deskilling. As artificial intelligence becomes a routine part of medical treatment, it is imperative that physicians recognise the limitations of artificial intelligence tools. These tools may assist with basic administrative tasks but cannot replace the uniquely human interpersonal and reasoning skills of physicians. The purpose of this feature article is to discuss the risks of physician deskilling based on increasing reliance on artificial intelligence.

The author states his intention to present an overview of the area of artificial intelligence as it relates to psychiatry, giving the practicing psychiatrist without computer experience insight into a field of ever increasing relevance to psychological understanding. He succeeds only partially, as his attempts to provide a rigorous presentation will cause many members of his intended audience to lay aside a book that provides an elegant presentation of a wide range of topics relating to the description and representation (in artificial intelligence terms) of the complexities of human logic and language, with particular reference to psychopathologic states. After defining artificial intelligence, he attempts to describe the fallacies of modeling approaches to problem solving. He correctly states that it is not necessary that the computer solve problems in the same manner as the human clinician. He may lose the computer novice in his brief references to serial versus parallel processing,

The present article aims to discuss the possibility of including the sphere of artificial intelligence production within the domain of artificial intelligence ethics and investigate its moral implications. In the first section, the role of human labour in the artificial intelligence production processes is considered, with particular reference to the distinction between high-skilled and low-skilled jobs, their differential distribution in the production process itself, and the labour conditions of ghost workers, in order to analyse the main ethical issues emerging within the field. In the second section, some aspects of the existing critical literature concerning artificial intelligence and labour are discussed, focusing on Marxist and decolonial scholarship and more precisely on its lack of consideration of the global value chain through which artificial intelligence AI production processes are structured. Finally, the possibility and limits of an ethics of artificial intelligence production are reconsidered by assuming the centrality of workers’ struggles and agency along artificial intelligence's global value chain.

<p>The focus of this research would be to see how Artificial Intelligence (AI) has changed education.<br> Education today is essential to an individual's and society's progress. The purpose of the study was to uncover and develop the usage of Artificial Intelligence (AI) in the educational sphere, as well as to come up with previously unknown applications. Artificial Intelligence investigates the fresh talent for educational technology innovation. Artificial intelligence has evolved into a critical element of today's education sector and a critical technique for gaining market competitive advantages. The uses of artificial intelligence in education are discussed in this paper, as well as existing tools and applications, market and research trends, opportunities, risks, and present limitations of AI in education.<br> Based on multiple intelligence framework analysis and machine learning, this paper integrates multiple intelligence theory and artificial intelligence technology, notices the teaching situation through smart voice and image interaction, and provides smart teaching auxiliary services for teachers and students. This study provides a smart and innovative education paradigm and, based on that model, creates a smart teaching assistant system. We concentrate on artificial intelligence-assisted innovation education with multidisciplinary integration as the objective, so that teachers and students may interact more effectively than before. The study's findings efficiently achieve tailored instruction, enjoyable learning, and the growth of unique talents with varied abilities. We examined several works in relevant fields and sub-domains such as big data in education, educational data mining (EDM), and learning analytics for our in-depth review study. Educational applications are examined from several angles in this paper. On one side, a detailed description of the platforms and tools produced as a result of the study is provided. On the other hand, it recognizes the limitations, prospective obstacles, and areas for future improvement, and this serves as a foundation for future e-learning research.</p>