AI monopoly and why it backfires on talent management

Masters or Slaves? AI and the Future of Humanity

Technology based on artificial intelligence (AI) is a revolutionary force that is changing economies, civilizations, and industries all over the world. AI, which has its roots in computer science and cognitive psychology, is a wide range of tools and methods designed to make robots capable of doing activities that have historically required human intellect. This abstract examines the many facets of artificial intelligence (AI) technology, including its fundamentals, uses, difficulties, and ramifications. Artificial Intelligence (AI) technology comprises several subfields such as robotics, computer vision, natural language processing, machine learning, and expert systems. Particularly, machine learning techniques have propelled incredible progress by allowing computers to learn from data and make judgments or predictions without the need for explicit programming. Natural language processing allows machines to comprehend, interpret, and produce human language, hence facilitating human-computer interaction. Machines can now see, analyze, and interpret visual data from the real world thanks to computer vision technology. Applications of AI technology may be found in a wide range of industries, including manufacturing, healthcare, finance, transportation, agriculture, education, and entertainment. AI-powered solutions help in drug discovery, medical imaging analysis, diagnosis, and customized therapy in the healthcare industry. AI algorithms are used in finance to power automated trading, fraud detection, risk assessment, and customer support. AI makes it possible for transportation to include predictive maintenance, traffic management, and driverless cars. Artificial Intelligence enhances supply chain management, quality assurance, and production processes in manufacturing. AI technology has the potential to revolutionize many industries, but it also comes with dangers and problems. These include privacy concerns, security hazards, ethical dilemmas, issues with prejudice and fairness, and effects on society and employment. Responsible AI methods, legal frameworks, multidisciplinary cooperation, and ethical standards are all necessary to meet these issues. Future prospects for AI technology development include the ability to solve challenging issues, spur creativity, increase productivity, and improve quality of life. But to fully utilize AI, one must take a comprehensive strategy that strikes a balance between the advancement of technology and ethical issues, human values, and social well-being. In summary, artificial intelligence (AI) technology is at the vanguard of innovation, presenting never-before-seen possibilities to transform whole sectors, spur economic expansion, and tackle global issues. AI has the ability to usher in a future of greater human-machine collaboration, innovation, and wealth through the promotion of collaboration, transparency, and ethical stewardship. the Ranking of the Artificial Intelligence using the TOPSIS Method . Interpretable Models is got the first rank whereas is the Ethical AI is having the Lowest rank. Keywords: Explainable AI (XAI), Interpretable Models, Ethical AI ,Responsible AI, Robustness and Adversarial Defense, Continual Learning, Federated Learning, Human-Centric AI, AI Governance and Policy

America’s major league soccer: artificial intelligence and the quest to become a world class league

The recent development of artificial intelligence (AI) technology is bringing about changes at a faster pace and on a larger scale than any other period in human history. With technological advancements overcoming the limitations of medical AI through training with databases, AI technology has made remarkable progress since the inception of deep learning for image processing with convolutional neural networks (CNN) in 2012. The recent advancements in natural language processing (NLP) have accelerated the utilization of AI through sophisticated natural language processing, enabling machines to identify and understand data regardless of the complexity of the language. This has laid the foundation for the rapid and precise development of generative AI. In the era where generative AI is being utilized without pausing in its developmental speed, we considered the civil liability of AI in our civil law principles, taking into account the inherent characteristics of AI such as unpredictability, opacity, and the black box effect. To do this, we first examined the legal liability considering the stages of AI technology development in discussing the tort liability caused by AI. Even “Weak AI,” created by AI developers, may fall under “Gefahr,” and while not all types, some may apply to strict liability in terms of risk liability. Furthermore, while reviewing civil liability applicable to AI under fault-based and no-fault liability, we also looked at the trends in the EU comparatively. In discussing no-fault liability, particularly under the Product Liability Act, we examined the possibility and implications of applying risk liability to pharmaceutical manufacturing using generative AI technology as a representative example to overcome the limitations of the existing Product Liability Act. Humanity currently lives in an era of rapid technological development and exploding big data, enjoying numerous benefits due to these advancements. As user convenience improves and massive added value is created through technological progress, the meaning of risk liability in the realm of civil liability can gain more significance. Generative AI has already drastically reduced the costs and time required for new drug development, providing substantial profits to pharmaceutical companies. However, even if the existing Product Liability Act is applied, it may be difficult to adequately remedy the harm to victims due to the reasonable alternative possibility defense regarding design defects. In the era of generative AI, we examined the possibility of applying enhanced risk liability by assuming the case of pharmaceutical manufacturing.

The advent of artificial intelligence (AI) has heralded a transformative era in various domains, including human resource (HR) management. This research paper explores the profound impact of AI-driven systems on HR practices and talent management. Traditional HR systems often face challenges such as inefficiencies, biases, and the inability to manage large volumes of data effectively. AI technologies, with their capabilities in data analytics, machine learning, and automation, offer innovative solutions to these challenges. This study investigates how AI can enhance various HR functions, including recruitment, performance management, employee engagement, and training. By leveraging AI, organizations can streamline their HR processes, reduce operational costs, and improve decision-making accuracy. Additionally, AI-driven talent management systems enable organizations to better identify, develop, and retain top talent, thereby fostering a more agile and competitive workforce. Using a mixed-methods approach, this research combines qualitative insights from industry case studies with quantitative data analysis to provide a comprehensive understanding of the benefits and challenges associated with AI implementation in HR. The findings reveal significant improvements in efficiency, accuracy, and employee satisfaction, while also highlighting ethical considerations such as data privacy and algorithmic bias. This paper concludes with practical recommendations for HR professionals seeking to integrate AI into their practices and suggests avenues for future research to address the emerging challenges and opportunities in AI-driven HR systems. The implications of this study are significant, offering valuable insights for both academic researchers and practitioners aiming to harness the potential of AI to revolutionize HR and talent management.

The modern stage of technological advancement is characterized by the dynamic development of artificial intelligence (AI) technologies and their integration into education. Of the several dozen artificial intelligence technologies used in various spheres of human activity, the most widely used in education are: a) machine learning, b) natural language processing, c) data science and d) intelligent tutoring system. On their basis, artificial intelligence tools are created, which have significant language teaching potential and in many ways change the traditional roles of the teacher and learners in the educational process. However, it should be noted that the integration of artificial intelligence technologies into education in general and foreign language teaching in particular is currently at the initial stage. Educators and learning designers conduct pilot studies investigating the abilities of specific artificial intelligence tools in the formation of foreign language aspects or the development of learners' foreign language communication skills. At the same time, the limited number of empirical research studies does not allow us to talk about the systematicity and comprehensiveness of foreign language teaching based on artificial intelligence technologies. One of the key differences between artificial intelligence technologies and modern information and communication technologies is their AI’s ability to provide a much wider range of feedback. It is owing to this advantage of artificial intelligence that innovative methods of teaching a foreign language will be based, creating new additional conditions for students to master a foreign language and raising the learning process to a new level in terms of the quality of solving learning tasks. However, the consideration of the types of feedback provided by AI tools has not been the subject of separate research, which determined the importance of this study. The aim of the study is to identify the types of feedback provided to learners by artificial intelligence technologies for the subsequent development of teaching methods (teaching technologies and/or typologies of tasks and assignments) based on them. The definition of the types of feedback provided to users by artificial intelligence tools was based on a review and analysis of research in the field of pedagogy and foreign language teaching methods. The sample of sources included research articles and reviews published in academic journals indexed in Scopus and Web of Science (Q1 and Q2), as well as Russian academic journals, included in the list of the Higher Attestation Commission of the Russian Federation (Categories 1 and 2) (pedagogical sciences). The following aspects of teaching methods were the subject of study in the review and analysis of academic papers: a) the artificial intelligence tool used for receiving feedback; b) the target audience of learners; c) the purpose of interaction with artificial intelligence; d) the form of activities; e) the type of feedback used. As a result, the following six types of feedback provided by artificial intelligence tools were identified in this study: a) educational and social; b) information and reference; c) methodological; d) analytical; e) evaluative; f) conditionally creative feedback.

Background Artificial intelligence (AI) technology is constantly and rapidly evolving and has the potential to benefit occupational therapy (OT) and OT clients. However, AI developments also pose risks and challenges, for example in relation to the ethical principles of OT. One way to support future AI technology aligned with OT ethical principles may be through human-centered AI (HCAI), an emerging branch within AI research and developments with a notable overlap of OT values and beliefs. Objective To explore the risks and challenges of AI technology, and how the combined expertise, skills, and knowledge of OT and HCAI can contribute to harnessing its potential and shaping its future, from the perspective of OT’s ethical values and beliefs. Results Opportunities for OT and HCAI collaboration related to future AI technology include ensuring a focus on 1) occupational performance and participation, while taking client-centeredness into account; 2) occupational justice and respect for diversity, and 3) transparency and respect for the privacy of occupational performance and participation data. Conclusion and Significance There is need for OTs to engage and ensure that AI is applied in a way that serves OT and OT clients in a meaningful and ethical way through the use of HCAI.

In recent years, artificial intelligence technologies have been widely used in computer vision, natural language processing, automatic driving, and other fields. However, artificial intelligence systems are vulnerable to adversarial attacks, which limit the applications of artificial intelligence (AI) technologies in key security fields. Therefore, improving the robustness of AI systems against adversarial attacks has played an increasingly important role in the further development of AI. This paper aims to comprehensively summarize the latest research progress on adversarial attack and defense technologies in deep learning. According to the target model’s different stages where the adversarial attack occurred, this paper expounds the adversarial attack methods in the training stage and testing stage respectively. Then, we sort out the applications of adversarial attack technologies in computer vision, natural language processing, cyberspace security, and the physical world. Finally, we describe the existing adversarial defense methods respectively in three main categories, i.e., modifying data, modifying models and using auxiliary tools.

The Theses deal with the theoretical foundations and methodological implications for scholarly research that arise from the development and implementation of artificial intelligence (AI) technologies into society’s daily life. The reader is introduced to age-old intellectual debates about AI and recent research concerning human-centered AI, artificial sociality (AS), and online culture. The paper presents the working definition of AI. It claims that AI has to be examined in relation to AS. The paper argues that the human-machine-interdependence is a new reality of artificial sociality. It envisages AI research as multidisciplinary and potentially a-disciplinary scientific activity. The questions the Theses raise: What should we be concerned about as artificial intelligence advances? Can AI technologies solve modern society’s problems and bring human beings to a new level of community and well-being? Are there ‘no-AI areas’ in society? Do human biases and prejudices influence AI technologies? The paper’s essential assertion is that the challenges posed by AI technologies and AS should be addressed apropos three P’s of the capitalist society: private property, profit, price.

Purpose: The purpose of the study is to examine the challenges faced by businesses in integrating and effectively utilizing artificial intelligence (AI) technology. It aims to provide a comprehensive understanding of how AI technologies generate business value and the anticipated benefits they offer. The study also seeks to identify the facilitators and inhibitors of AI adoption and usage, explore different types of AI use in the organizational environment, and analyze their first- and second-order impacts.
 Methodology: The study employed the comprehensive literature review research design. The researchers conducted a systematic search using predefined criteria in databases such as Scopus and Web of Science. The search yielded 21 relevant papers that were analyzed and synthesized for this study. The data collection method relied on the examination of existing literature. Data analysis involved identifying key themes, trends, and insights from the selected papers. The researchers conducted a qualitative analysis to extract relevant findings and synthesized the information to derive meaningful conclusions.
 Findings: The study revealed several insights regarding the integration and use of AI in businesses. This indicated that organizations struggle with understanding how AI technologies can generate value and how to effectively incorporate them into their operations. Lack of comprehensive knowledge about AI and its value generation processes was identified as a major barrier. Additionally, the study highlighted the facilitators and inhibitors of AI adoption and usage. It identified various types of AI applications in the organizational environment and explored their impacts on business operations. The findings shed light on the challenges businesses face in leveraging AI technology and suggested areas for further research.
 Recommendations: To practitioners: The study emphasizes the importance of acquiring comprehensive knowledge about AI technologies and their potential value generation processes. To policy makers: The study highlights the need for supportive policies and regulations to foster AI adoption. It suggests creating an enabling environment that promotes AI research and development. Theory and Validation: The study may have been informed by existing theories related to AI adoption, organizational change, or innovation. Practice: To practitioners, the study underscores the importance of understanding the value and potential of AI technologies. Policy: To policy makers, the study emphasizes the need for policy frameworks that promote AI adoption and address associated challenges.

This article explores applications of artificial intelligence (AI) technologies in Linguistic Landscape research. Traditionally, LL research has relied on manual data collection and analysis, often involving photographs of public signage, advertisements, and other visual language displays. However, this manual approach can present challenges, including time-consuming data collection, inconsistent data quality, and potential researcher bias. Two AI technologies in particular hold promise for addressing these challenges in LL research: computer vision (CV) and large language models (LLMs). CV automates the identification and extraction of text from images, improving data accuracy and enabling large-scale image analysis. LLMs, based on natural language processing, can detect, translate, and interpret multilingual text. This article explores the affordances and challenges of using AI technologies in LL research and discusses methods to improve data collection, enhance accuracy, and support the analysis of multilingual environments. It also raises ethical issues and limitations of the technologies.

Introduction: The application of artificial intelligence (AI) in talent management is reforming recruitment, workforce development, and employee engagement. Traditional human resource (HR) practices are being displaced by AI technologies that accelerate processes, eliminate discriminatory hiring, and facilitate improved decision-making. Purpose: This chapter explains how AI employs data predictions, machine learning (ML), and chatbots to enable automated recruitment. This, in turn, allows rapid screening of candidates’ skills, matching of jobs, and more diverse hiring. AI also enables tracking the progress of workers, real-time performance measurement, and personalized learning plans, thus ensuring employee satisfaction and preventing staff turnover. Scope: The study examines AI applications in HR, including predictive analytics, ML, and chatbots for hiring, performance tracking, and employee retention strategies. It also discusses ethical challenges such as algorithmic bias and data privacy. Methodology: A qualitative analysis of AI-driven HR tools and case studies from companies implementing AI in talent management is conducted to assess its impact on efficiency and fairness. Findings: AI accelerates hiring, improves diversity, enhances performance tracking, and personalizes learning plans, reducing turnover. However, risks like biased algorithms and data security concerns necessitate ethical AI implementation with human oversight. Proper AI integration can create a data-driven, inclusive, and competitive talent management system, balancing technological advancements with human expertise.

Background. Higher education in this digital era is faced with significant changes, especially with the development of artificial intelligence (AI) technology. Purpose. This research aims to explore the potential and limitations of integrating AI technology in improving the quality of distance learning and present findings that can guide the development of AI-based pedagogy. Method. This research method adopts a quantitative survey approach to detail the integration of artificial intelligence (AI) technology in the context of distance learning in higher education. A total of 20 students were randomly selected as respondents, with sample selection using the purposive sampling method. This process ensures maximum representation of students who have significant experience with the integration of AI technology in their learning. Data was collected through questionnaires focused on effectiveness, adaptability of material, and level of interactivity during learning. Next, descriptive and inferential statistical analysis will analyze patterns and relationships between variables to explore the effectiveness of AI technology, the factors that influence it, and its impact on student learning experiences. Results. Survey results show that the majority of students actively use AI technology, especially several times a week, and express a high level of satisfaction with the use of AI technology in distance learning. Virtual Reality or Augmented Reality learning experiences were considered to benefit the most, even though all respondents experienced challenges or obstacles in using AI technology. Conclusion. The conclusions of this research emphasize the need to address these challenges to maximize the benefits of integrating AI technology in increasing the effectiveness and efficiency of distance learning in higher education.

Background. Higher education in this digital era is faced with significant changes, especially with the development of artificial intelligence (AI) technology. Purpose. This research aims to explore the potential and limitations of integrating AI technology in improving the quality of distance learning and present findings that can guide the development of AI-based pedagogy. Method. This research method adopts a quantitative survey approach to detail the integration of artificial intelligence (AI) technology in the context of distance learning in higher education. A total of 20 students were randomly selected as respondents, with sample selection using the purposive sampling method. This process ensures maximum representation of students who have significant experience with the integration of AI technology in their learning. Data was collected through questionnaires focused on effectiveness, adaptability of material, and level of interactivity during learning. Next, descriptive and inferential statistical analysis will analyze patterns and relationships between variables to explore the effectiveness of AI technology, the factors that influence it, and its impact on student learning experiences. Results. Survey results show that the majority of students actively use AI technology, especially several times a week, and express a high level of satisfaction with the use of AI technology in distance learning. Virtual Reality or Augmented Reality learning experiences were considered to benefit the most, even though all respondents experienced challenges or obstacles in using AI technology. Conclusion. The conclusions of this research emphasize the need to address these challenges to maximize the benefits of integrating AI technology in increasing the effectiveness and efficiency of distance learning in higher education.

Background. Higher education in this digital era is faced with significant changes, especially with the development of artificial intelligence (AI) technology. Purpose. This research aims to explore the potential and limitations of integrating AI technology in improving the quality of distance learning and present findings that can guide the development of AI-based pedagogy. Method. This research method adopts a quantitative survey approach to detail the integration of artificial intelligence (AI) technology in the context of distance learning in higher education. A total of 20 students were randomly selected as respondents, with sample selection using the purposive sampling method. This process ensures maximum representation of students who have significant experience with the integration of AI technology in their learning. Data was collected through questionnaires focused on effectiveness, adaptability of material, and level of interactivity during learning. Next, descriptive and inferential statistical analysis will analyze patterns and relationships between variables to explore the effectiveness of AI technology, the factors that influence it, and its impact on student learning experiences. Results. Survey results show that the majority of students actively use AI technology, especially several times a week, and express a high level of satisfaction with the use of AI technology in distance learning. Virtual Reality or Augmented Reality learning experiences were considered to benefit the most, even though all respondents experienced challenges or obstacles in using AI technology. Conclusion. The conclusions of this research emphasize the need to address these challenges to maximize the benefits of integrating AI technology in increasing the effectiveness and efficiency of distance learning in higher education.