Landscape Of Artificial Intelligence Research Articles

Exploring the Role of Artificial Intelligence in Interreligious Discourse

This article delves into the rich tapestry of existing literature surrounding the intersection of Artificial Intelligence (AI) and Interreligious Dialogue (ID). Through a careful analysis of available scholarly works, the paper endeavors to shed light on the profound influence that AI exerts on discussions among individuals representing diverse religious backgrounds. By examining the dynamic interplay between AI and ID, the study seeks to unravel the intricate ways in which technology shapes and continues to shape conversations within this multifaceted context. The primary objective is to deepen our understanding of the opportunities and challenges that unfold at the crossroads of AI and ID. This exploration is poised to contribute valuable insights that extend beyond theoretical frameworks, providing practical implications for scholars, practitioners, and policymakers alike. In essence, the article aims to serve as a compass, navigating the complex terrain where AI and interreligious discourse converge. As the study unfolds, it specifically aims to identify distinct domains where AI can be harnessed to play a constructive role in fostering and facilitating interreligious dialogue. By elucidating the potential contributions of AI in this realm, the article strives to offer a forward-looking perspective that transcends the current situation. Ultimately, this endeavor not only maps the existing landscape of AI in interreligious dialogue but also charts a course for future research, exploration, and application in this evolving and significant field. Keywords: artificial intelligence, interreligious dialogue, ethical considerations, religious diversity, technology

Analysis of the Principle and Configuration of AI Chip and the State-of-art Applications

AI chips are specialized hardware components designed to accelerate AI operations, making them crucial for various industries. This study provides a comprehensive overview of artificial intelligence (AI) chips, their structure, principles, fabrication process, applications, limitations, and future outlooks. The article discusses the components commonly found in AI chip structures, including processing units, memory hierarchy, interconnects, and specialized hardware units. It delves into the principles guiding AI chip design, such as specialization, parallelism, low precision arithmetic, and hardware-software co-design. The fabrication process of AI chips involves intricate steps, including design, mask generation, deposition, lithography, and testing. Applications of AI chips span data centers, edge computing, autonomous vehicles, healthcare, NLP, and more. Despite their advancements, AI chips have limitations, such as energy efficiency and specificity, but the future holds promise with improvements in energy efficiency, specialized acceleration, hybrid architectures, and quantum AI chips. Overall, AI chips have transformed the AI landscape, setting the stage for continued innovation and impact in various sectors.

Artificial Intelligence and Ethics

A more covert aspect of Artificial Intelligence (AI) pertains to the ethical quandaries surrounding the actions of machines. In the case of Large Language Models (LLMs), hidden beneath their seemingly impeccable automated outputs lies a colossal amalgamation of trillions of compiled data points, comprising copied blogs, articles, essays, books, and artworks. This raises profound questions about copyright ownership and retribution for the original authors. But beyond intellectual property, another insidious facet of LLMs emerges – their propensity to cause harm to individuals through what can only be described as hallucinatory outputs. Victims of these AI- -generated delusions suffer defamation, and their plight remains largely unnoticed. Amidst the marvels of AI, the plight of the underpaid laborers who form the backbone of AI development is seldom acknowledged, a subject that warrants more profound discussion. Furthermore, as AI algorithms continue to permeate various aspects of society, they bring to the fore issues of bias. For instance, facial recognition technologies frequently exhibit skewed outcomes, leading to false accusations and grave consequences due to over-reliance on these technologies.
 The algorithmic schemes employed in CV selection for job applications or university admissions also raise concerns about fairness.
 The question of machines replacing the human workforce looms ever larger on the horizon. The potential socio-economic ramifications demand careful evaluation.
 Lastly, the extensive reliance of artificial intelligence on vast datasets, including copyrighted works, results in the creation of gargantuan data servers with an unimaginable environmental impact.
 The hidden aspects of artificial intelligence encompass a multitude of ethical dilemmas, spanning intellectual property rights, biases, labour conditions, societal impacts, and environmental considerations. A thorough and elaborate examination of these issues is essential to navigate the ever-evolving landscape of AI responsibly and ethically.

AI-DRIVEN TALENT ANALYTICS FOR STRATEGIC HR DECISION-MAKING IN THE UNITED STATES OF AMERICA: A REVIEW

This paper review ways AI and analytics are transforming HR decision-making in American organizations. It explores the adoption of these technologies in the U.S., their impact on optimizing talent management, and the broader implications for organizational growth and employee well-being. In the rapidly evolving landscape of Human Resources (HR), the integration of Artificial Intelligence (AI)-driven talent analytics has emerged as a transformative force. This review explores the application of AI-driven talent analytics in the United States, specifically focusing on its role in strategic HR decision-making. The analysis delves into the current state of AI adoption in HR practices, the challenges and opportunities it presents, and the impact on organizational performance. Key topics covered include predictive analytics, talent acquisition, workforce planning, and employee engagement. Through a comprehensive examination of existing literature and case studies, this review aims to provide insights into the ways AI-driven talent analytics shapes strategic HR decisions and contributes to organizational success in the U.S. context. The findings underscore the imperative for HR professionals and organizational leaders to navigate the evolving landscape of AI in talent management, fostering a deeper understanding of its potential benefits and challenges for informed decision-making.
 Keywords: Artificial Intelligence; Talent Analytics; Human Resources; Decision-Making; USA.

ATTRACTING INNOVATIVE ARTIFICIAL INTELLIGENCE TOOLS TO THE CRYPTOCURRENCY MARKET

The purpose of the article is to conduct a study and determine the importance and benefits of using artificial intelligence (AI) tools for certain areas of the cryptocurrency market, in particular, detecting and preventing fraud in the cryptocurrency market, as well as the possibilities of using AI chatbots in trading and in the formation of investment portfolios. The article covers the analysis of the growth of digital currencies and the increasing number of hacker attacks, defines the role of AI in ensuring security, considers methods of fraud detection and prevention, and analyses development prospects. AI plays a crucial role in identifying suspicious transactions and preventing fraud. The study aims to investigate the benefits and potential risks of AI bots. AI's integration has transformed the market, enabling more informed decision-making, improved investment strategies, and higher returns. The article emphasizes the need for ongoing research into the evolving landscape of AI in cryptocurrency, discussing challenges, and the potential to finance sphere. The integration of AI in fraud detection has proven advantageous, enabling real-time data analysis and pattern recognition, enhancing security for investors. The article also addresses concerns such as algorithmic bias and the displacement of traders and using AI chat bots. While acknowledging the risks, it highlights the positive impact of AI on efficiency, reliability, and security in the cryptocurrency marketThe article presents an algorithmisation of the possibilities and procedures for engaging artificial intelligence in the fight against fraud, as well as recommendations for market participants and regulators. The presented results highlight the existing innovative potential of AI to improve the security and efficiency of the functioning of participants in the cryptocurrency market. Overall, the article suggests that AI's transformative influence in the cryptocurrency market is a game changer, shaping the industry's future and presenting opportunities for growth and innovation.

ARTIFICIAL INTELLIGENCE AND NATIONAL SECURITY: PERSPECTIVE OF THE GLOBAL SOUTH

More than six decades since its inception, Artificial Intelligence (AI) stands at the cusp of a transformative shift. The global perspective on AI has evolved optimistically, as it increasingly permeates every facet of human life. AI is revolutionizing national security strategies and capabilities worldwide, but its impact on the Global South remains a topic of growing significance and concern. Every nation actively seeks to bolster internal security through AI-driven initiatives, including surveillance, cyber security, and autonomous technologies. This review paper delves into AI's role in analyzing vast datasets, uncovering patterns, and identifying security threats and challenges focusing specifically on the Global South. It considers the potential advantages AI offers in enhancing national security capabilities while addressing concerns surrounding its integration. Drawing from existing literature, it presents a comprehensive analysis of AI's prospective future in the cyber and national security domains within these nations. Ultimately, this paper aims to answer whether AI serves as a facilitator in strengthening internal security or poses unforeseen challenges and raises the importance of capacity-building, technology transfer, and international cooperation. It provides valuable insights into the evolving landscape of AI in the context of national security in the Global South.

Machine Learning Approaches to Predict Asthma Exacerbations: A Narrative Review.

The implementation of artificial intelligence (AI) and machine learning (ML) techniques in healthcare has garnered significant attention in recent years, especially as a result of their potential to revolutionize personalized medicine. Despite advances in the treatment and management of asthma, a significant proportion of patients continue to suffer acute exacerbations, irrespective of disease severity and therapeutic regimen. The situation is further complicated by the constellation of factors that influence disease activity in a patient with asthma, such as medical history, biomarker phenotype, pulmonary function, level of healthcare access, treatment compliance, comorbidities, personal habits, and environmental conditions. A growing body of work has demonstrated the potential for AI and ML to accurately predict asthma exacerbations while also capturing the entirety of the patient experience. However, application in the clinical setting remains mostly unexplored, and important questions on the strengths and limitations of this technology remain. This review presents an overview of the rapidly evolving landscape of AI and ML integration into asthma management by providing a snapshot of the existing scientific evidence and proposing potential avenues for future applications.

Revolutionizing Radiology: Exploring Applications and Advancements in AI for Imaging Diagnostics

Artificial intelligence (AI) has revolutionized the field of medical imaging diagnostics, offering unprecedented opportunities for accurate and efficient disease detection and diagnosis. The field of radiology is undergoing a profound transformation due to the rapid advancements in artificial intelligence (AI). AI algorithms, particularly deep learning, have demonstrated remarkable capabilities in analyzing medical images, leading to significant improvements in diagnostic accuracy, efficiency, and patient care. This paper provides a comprehensive overview of the current landscape of AI in radiology, exploring its diverse applications and the groundbreaking advancements that are revolutionizing the field.

Mapping the Bibliometrics Landscape of AI in Medicine: Methodological Study.

Artificial intelligence (AI), conceived in the 1950s, has permeated numerous industries, intensifying in tandem with advancements in computing power. Despite the widespread adoption of AI, its integration into medicine trails other sectors. However, medical AI research has experienced substantial growth, attracting considerable attention from researchers and practitioners. In the absence of an existing framework, this study aims to outline the current landscape of medical AI research and provide insights into its future developments by examining all AI-related studies within PubMed over the past 2 decades. We also propose potential data acquisition and analysis methods, developed using Python (version 3.11) and to be executed in Spyder IDE (version 5.4.3), for future analogous research. Our dual-pronged approach involved (1) retrieving publication metadata related to AI from PubMed (spanning 2000-2022) via Python, including titles, abstracts, authors, journals, country, and publishing years, followed by keyword frequency analysis and (2) classifying relevant topics using latent Dirichlet allocation, an unsupervised machine learning approach, and defining the research scope of AI in medicine. In the absence of a universal medical AI taxonomy, we used an AI dictionary based on the European Commission Joint Research Centre AI Watch report, which emphasizes 8 domains: reasoning, planning, learning, perception, communication, integration and interaction, service, and AI ethics and philosophy. From 2000 to 2022, a comprehensive analysis of 307,701 AI-related publications from PubMed highlighted a 36-fold increase. The United States emerged as a clear frontrunner, producing 68,502 of these articles. Despite its substantial contribution in terms of volume, China lagged in terms of citation impact. Diving into specific AI domains, as the Joint Research Centre AI Watch report categorized, the learning domain emerged dominant. Our classification analysis meticulously traced the nuanced research trajectories across each domain, revealing the multifaceted and evolving nature of AI's application in the realm of medicine. The research topics have evolved as the volume of AI studies increases annually. Machine learning remains central to medical AI research, with deep learning expected to maintain its fundamental role. Empowered by predictive algorithms, pattern recognition, and imaging analysis capabilities, the future of AI research in medicine is anticipated to concentrate on medical diagnosis, robotic intervention, and disease management. Our topic modeling outcomes provide a clear insight into the focus of AI research in medicine over the past decades and lay the groundwork for predicting future directions. The domains that have attracted considerable research attention, primarily the learning domain, will continue to shape the trajectory of AI in medicine. Given the observed growing interest, the domain of AI ethics and philosophy also stands out as a prospective area of increased focus.

Clustering the AI Landscape: Navigating Global Insights from Leading AI Indexes

Abstract This study develops a scorecard validation model for evaluating key Artificial Intelligence (AI) indexes, aiming to provide a comprehensive framework for assessing the multifaceted nature of AI development. Focusing on four significant AI indexes and one AI report from 2021 to 2023, the research employs both expert judgment and advanced text mining techniques, including k-means clustering. This dual approach facilitates a detailed examination of AI indexes, highlighting their strengths, weaknesses, and overall market comprehensiveness. The findings contribute to understanding the AI sector’s evolution, offering critical insights for policy formulation and strategic decision-making in AI. Acknowledging the inherent subjectivity in the evaluation process and potential data biases, the paper suggests future research avenues, including cross-sectoral and regional analyses of AI trends and a deeper exploration of ethical considerations in AI. This study serves as a valuable resource for stakeholders navigating the complex AI landscape, providing a structured method for comparing and understanding AI advancements.

Medical Use of Intelligent Systems

This essay delves into the ethical implications of integrating artificial intelligence (AI) into medical practice, addressing a critical gap in the existing research. As AI-driven medical systems have gained prominence over the past decade, there is an urgent need to explore the moral dimensions of their utilization. This study holds significant relevance for academics, healthcare professionals, and policymakers, especially those involved in intelligent medical systems. The exploration begins by dissecting the moral dilemmas surrounding AI applications in healthcare, exploring both technological justifications and concerns. The paper evaluates existing taxonomies developed by researchers in the field, shedding light on the issues that emerge during the development of intelligent medical systems. By scrutinizing the ethical reasons behind healthcare practitioners' adoption of intelligent systems, this research contributes valuable insights for a comprehensive understanding of the subject. This analysis not only informs scholars but also provides crucial guidance to government officials and medical professionals navigating the intricate landscape of AI in medicine.

Evaluating Computer Science Students' Experiences and Motivation Towards Learning Artificial Intelligence

This study investigates the experiences and motivations of Saudi Arabian computer science students (aged 18 and above) in their pursuit of knowledge in Artificial Intelligence (AI). It employs a cross-sectional design using web-based surveys. Findings indicate that students recognize AI's transformative potential in computer science and express a willingness to embrace it in their careers. However, confidence levels vary regarding using AI tools, understanding healthcare AI, and assessing AI's impact on computer science education. The study emphasizes the significance of intrinsic motivation, experiential learning, and pedagogical strategies like collaborative learning in AI education. Additionally, it underscores the importance of addressing gender and diversity considerations to create inclusive AI learning environments. In conclusion, this research provides valuable insights into computer science students' experiences and motivations in AI education. It offers practical implications for enhancing AI pedagogy, reducing barriers to learning, and promoting diversity and inclusivity in the AI field. Educators can empower students to navigate the dynamic AI landscape effectively by tailoring educational approaches to individual learner needs.

Use of Artificial Intelligence in Pakistani Journalism: Navigating Challenges and Future Paths in TV Newsrooms

This research investigates the landscape of artificial intelligence (AI) integration in Pakistani journalism, exploring perceived benefits, challenges, ethical considerations, and the overall outlook on this transformative process. Drawing on interviews with industry professionals, the study contributes theoretical insights to the Diffusion of Innovations theory, unveiling varying degrees of AI integration awareness and perspectives within the Pakistani news industry. A human-centric ethical perspective is introduced, emphasizing concerns about job displacement and the evolving role of journalists. The delicate balance between AI’s efficiency gains and ethical considerations is explored, with practical implications highlighting the need for targeted education, transparent communication, and cost-effective solutions to overcome barriers and foster widespread AI adoption. The research underscores the significance of continuous adaptation to technological advancements for seamless integration. As Pakistan cautiously navigates the AI landscape in news production, this study lays the groundwork for theoretical frameworks and practical strategies, providing valuable insights for the ethical, efficient, and responsible integration of AI technologies.

Revolutionizing Drug Formulation: Harnessing Artificial Intelligence and Machine Learning for Enhanced Stability, Formulation Optimization, and Accelerated Development

The integration of Artificial Intelligence (AI) and Machine Learning (ML) in the field of pharmaceutical drug formulation has sparked a paradigm shift in the way drug stability is predicted, formulations are optimized, and drug development is expedited. This review delves into the transformative impact of AI and ML techniques on pharmaceutical research and development. It highlights how predictive models driven by AI algorithms are effectively simulating drug degradation pathways and stability profiles, enabling scientists to make informed decisions during formulation design. Moreover, the utilization of ML algorithms to analyze vast datasets has led to the discovery of optimal formulations by identifying critical relationships between formulation variables, excipients, and drug properties. This approach not only reduces experimentation time and costs but also enhances the likelihood of developing robust and effective drug products. Furthermore, AI-powered drug development platforms are shortening the timeline for candidate selection, preclinical evaluations, and clinical trials, thereby accelerating the entire drug development process. This article explores the evolving landscape of AI and ML in drug formulation, discusses challenges, and anticipates future prospects in this transformative field.

Artificial Intelligence in Healthcare: Opportunities and Challenges

The rapid advancement of Artificial Intelligence (AI) has permeated virtually every sector, and healthcare is no exception. This research paper undertakes a comprehensive exploration of the vast opportunities and intricate challenges entailed in integrating AI into healthcare systems. It delves into how AI can revolutionize disease diagnosis, customize treatment plans, accelerate drug discovery, and streamline administrative tasks. Additionally, the paper navigates through the complex web of data privacy concerns, ethical dilemmas, and the essential need for a balanced and thoughtful approach to the implementation of AI. Drawing insights from existing AI-driven healthcare systems and a comprehensive review of the current literature, this paper presents an all-encompassing view of the AI landscape. The ensuing discussion underscores the remarkable strides that AI has made within the realm of healthcare, while also underscoring the multifaceted challenges that must be addressed for AI to reach its full potential while safeguarding ethical principles and patient well-being

The Advances and Utility of Artificial Intelligence and Robotics in Regional Anesthesia: An Overview of Recent Developments.

The integration of artificial intelligence (AI) and robotics in regional anesthesia has brought about transformative changes in acute pain management for surgical procedures. This review explores the evolving landscape of AI and robotics applications in regional anesthesia, outlining their potential benefits, challenges, and ethical considerations. AI-driven pain assessment, real-time guidance for needle placement during nerve blocks, and predictive modeling solutions for nerve blocks have the potential to enhance procedural precision and improve patient outcomes. Robotic technology aids in accurate needle insertion, reducing complications and improving pain relief. This review also highlights the ethical and safety considerations surrounding AI implementation, emphasizing data security and professional training. While challenges such as costs and regulatory hurdles exist, ongoing research and clinical trials demonstrate the practical utility of these technologies. In conclusion, AI and robotics have the potential to reshape regional anesthesia practice, ultimately improving patient care and procedural accuracy in pain management.

Ethical Considerations of Using ChatGPT in Health Care.

ChatGPT has promising applications in health care, but potential ethical issues need to be addressed proactively to prevent harm. ChatGPT presents potential ethical challenges from legal, humanistic, algorithmic, and informational perspectives. Legal ethics concerns arise from the unclear allocation of responsibility when patient harm occurs and from potential breaches of patient privacy due to data collection. Clear rules and legal boundaries are needed to properly allocate liability and protect users. Humanistic ethics concerns arise from the potential disruption of the physician-patient relationship, humanistic care, and issues of integrity. Overreliance on artificial intelligence (AI) can undermine compassion and erode trust. Transparency and disclosure of AI-generated content are critical to maintaining integrity. Algorithmic ethics raise concerns about algorithmic bias, responsibility, transparency and explainability, as well as validation and evaluation. Information ethics include data bias, validity, and effectiveness. Biased training data can lead to biased output, and overreliance on ChatGPT can reduce patient adherence and encourage self-diagnosis. Ensuring the accuracy, reliability, and validity of ChatGPT-generated content requires rigorous validation and ongoing updates based on clinical practice. To navigate the evolving ethical landscape of AI, AI in health care must adhere to the strictest ethical standards. Through comprehensive ethical guidelines, health care professionals can ensure the responsible use of ChatGPT, promote accurate and reliable information exchange, protect patient privacy, and empower patients to make informed decisions about their health care.

Combining Bibliometric and Social Network Analysis to Understand the Scholarly Publications on Artificial Intelligence

This article aims to conduct a comprehensive study employing bibliometric and social network analysis to explore scholarly publications in artificial intelligence (AI). A co-authorship network analysis of countries/regions and institutions, a thematic analysis based on the co-occurrence of keywords, and a Spearman rank correlation test of social network analysis are conducted using VOSviewer and SPSS, respectively. According to the research power analysis, the United States and China are the most significant contributors to the relevant publications and hold dominant positions in the co-authorship network. Universities play a crucial role in promoting and carrying out relevant research. AI has been increasingly applied to address new problems and challenges in various fields in recent years. The Spearman rank correlation analysis indicates that research performance in AI is significantly and positively correlated with social network indicators. This article reveals a systematic picture of the research landscape of AI, which can provide a potential guide for future research.

An initial interpretation of the U.S. Department of Education's AI report: Implications and recommendations for academic libraries

This article provides an analysis of the U.S. Department of Education's report on Artificial Intelligence (AI) and its implications for academic libraries. It delves into the report's key points, including the importance of AI literacy, the need for educator involvement in AI design and implementation, and the necessity of preparing for AIrelated issues. The author discusses how these points impact academic libraries and offers actionable recommendations for library leaders. It emphasizes the need for libraries to promote AI literacy, involve librarians in AI implementation, develop guidelines for AI use, prepare for AI issues, and collaborate with other stakeholders. The article concludes with a call to action for academic libraries to take a proactive approach to AI, ensuring its effective, ethical, and responsible use in library services and operations. This analysis serves as a roadmap for academic libraries navigating the evolving landscape of AI in education.

The transformative impact of artificial intelligence on hr practices and employee experience: A review

This review paper explores the profound impact of Artificial Intelligence (AI) on Human Resources (HR) practices and employee experience. With the rapid advancement of AI technologies, organizations are increasingly integrating AI-driven solutions into various HR functions. This paper provides a comprehensive examination of the current state of AI adoption in HR, delving into its implications for recruitment, training, performance management, and employee engagement. Additionally, it explores the effects of AI on the overall employee experience, including job satisfaction, well-being, and work-life balance. The review identifies the benefits, challenges, and ethical considerations associated with the integration of AI in HR practices. Finally, future directions and recommendations are provided for organizations and HR professionals seeking to navigate the evolving landscape of AI in the workplace.