Challenges Of Artificial Intelligence Research Articles

Navigating the Future: AI-Driven Project Management in the Digital Era

This research paper explores the implications of Artificial Intelligence (AI) in project management within the digital era. It investigates the evolution of project management methodologies, the key concepts and technologies driving AI integration, real-world case studies showcasing AI implementation, challenges, future directions, and opportunities in AI-driven project management. The findings reveal that AI offers significant benefits, including improved resource allocation, risk management, and communication, yet pose challenges such as ethical considerations, data privacy, and integration hurdles. Future directions suggest emerging trends in AI adoption, potential innovations, and opportunities for research and development. Overall, the paper highlights the transformative potential of AI-driven project management while emphasizing the need for addressing challenges to ensure successful implementation and maximize benefits.

LEVERAGING QUANTUM COMPUTING FOR INCLUSIVE AND RESPONSIBLE AI DEVELOPMENT: A CONCEPTUAL AND REVIEW FRAMEWORK

This paper proposes a novel conceptual framework that integrates the advanced capabilities of quantum computing to address the urgent need for responsible and inclusive Artificial Intelligence (AI) development. It reviews current challenges in AI, such as bias, lack of inclusivity, and the computational limitations faced by classical computing methods in solving complex societal problems. By harnessing quantum computing, this framework aims to overcome these barriers, enabling faster, more efficient AI solutions that are ethically grounded and universally accessible. By adopting a holistic approach that integrates technical innovation with ethical considerations and stakeholder engagement, we believe that quantum computing can serve as a catalyst for the development of AI technologies that are not only more advanced but also more inclusive, responsible, and beneficial for society as a whole. This concept paper serves as a foundational framework for further research, collaboration, and action in the intersection of quantum computing and AI, with the ultimate goal of harnessing the transformative potential of these technologies to address pressing societal challenges and promote human well-being.
 Keywords: Quantum Computing, AI, Development, Responsible.

Recruiting Revolution: How AI is Streamlining Hiring

Artificial Intelligence (AI), a fundamental aspect of the Fourth Industrial Revolution, has become a pivotal concern across various industries, including business management. It is increasingly applied in fields such as marketing, human resource management (HRM), and manufacturing. Within HRM, AI and machine learning technologies are utilized to enhance employee experience, provide personalized training, and analyze HR data for informed decision-making. This integration of technology into HRM, traditionally a people-focused domain, represents a significant shift. AI technology adoption in HRM has surged in prominent settings. For instance, IBM has leveraged AI solutions like Blue Matching, CogniPay, and Watson Career Coach to streamline HR operations. Blue Matching predicts mobile tasks based on employee profiles, while CogniPay aids managers in decision-making by evaluating performance. Watson Career Coach offers personalized career guidance. However, notable cases like Amazon’s AI Recruitment Program have highlighted concerns regarding biases. Artificial intelligence is reshaping various aspects of our lives, including recruitment practices. As more businesses harness AI for candidate selection and evaluation, there’s a growing interest among HR managers in adopting AI solutions. However, this technological advancement also brings challenges, such as the displacement of existing job roles and the need for additional professionals to manage the evolving landscape. Understanding the potential benefits and challenges of AI in recruitment is essential for informed decision-making in HRM.

Lifetime policy reuse and the importance of task capacity

A long-standing challenge in artificial intelligence is lifelong reinforcement learning, where learners are given many tasks in sequence and must transfer knowledge between tasks while avoiding catastrophic forgetting. Policy reuse and other multi-policy reinforcement learning techniques can learn multiple tasks but may generate many policies. This paper presents two novel contributions, namely 1) Lifetime Policy Reuse, a model-agnostic policy reuse algorithm that avoids generating many policies by optimising a fixed number of near-optimal policies through a combination of policy optimisation and adaptive policy selection; and 2) the task capacity, a measure for the maximal number of tasks that a policy can accurately solve. Comparing two state-of-the-art base-learners, the results demonstrate the importance of Lifetime Policy Reuse and task capacity based pre-selection on an 18-task partially observable Pacman domain and a Cartpole domain of up to 125 tasks.

Undergraduate and graduate students' conceptual understanding of model classification outcomes under the lens of scientific argumentation

AbstractRecent advancements in artificial intelligence (AI) and machine learning (ML) have driven research and development across multiple industries to meet national economic and technological demands. Consequently, companies are investing in AI, ML, and data analytics workforce development efforts to digitalize operations and enhance global competitiveness. As such, evidence‐based educational research around ML is essential to provide a foundation for the future workforce as they face complex AI challenges. This study explored students' conceptual ML understanding through a scientific argumentation framework, where we examined how they used evidence and reasoning to support claims about their ML models. This framework lets us gain insight into students' conceptualizations and helped scaffold student learning via a cognitive apprenticeship model. Thirty students in a mechanical engineering classroom at Purdue University experimented with neural network ML models within a computational notebook to create visual claims (ML models) with textual explanations of their evidence and reasoning. Accordingly, we qualitatively analyzed their learning artifacts to examine their underfit, fit, and overfit models and explanations. It was found that some students tended toward technical explanations while others used visual explanations. Students with technically dominant explanations had higher proficiency in generating correctly fit models but lacked explanatory evidence. Conversely, students with visually dominant explanations provided evidence but lacked technical reasoning and were less accurate in identifying fit models. We discuss implications for both groups of students and offer future research directions to examine how positive pedagogical elements of learning design can optimize ML educational material and AI workforce development.

Artificial intelligence (AI) meets biomaterials and biomedicine

With the rapid progress of information technology and life sciences, artificial intelligence (AI) technology has substantially changed the way in many areas of biomaterials and biomedicine, including biomaterials and formulation design, drug development, preclinical study, clinical diagnosis and treatment, as well as health management. This perspective outlines the key issues of AI in the fields of biomaterials and biomedicine applications, and analyzes some opportunities and challenges of AI in the biomedical and clinical development. The gap between experts from multiple disciplines and fields needs to be narrowed, and common participation should be applied to open the next frontier of integrated AI-biomedicine.

Application and challenges of artificial intelligence in cybersecurity

This paper delves into the multi-faceted role of Artificial Intelligence (AI) in the field of cybersecurity. It conducts an in-depth analysis of two common security problems, spam email and DDos/Dos attacks. It focuses on the application of AI in resolving these problems and further on Event Management (SIEM) and Intrusion Detection Systems (IDS). Furthermore, this article outlines and discusses five key challenges that AI may encounter throughout its development, including issues related to reliability, unidentified threats, data privacy, ethical considerations, and the importance of an explainable AI. The paper underscores the remarkable potential of AI to improve our defences against emerging cybersecurity threats. It highlights the growing importance of addressing ethical and legal issues in the evolving technological environment. Finally, the article offers a comprehensive summary and valuable insights into the future prospects of AI in the realm of cybersecurity, confirming its crucial role in shaping the security landscape in the digital era.

The Risks and Challenges of Artificial Intelligence in Endocrinology.

Artificial intelligence (AI) holds the promise of addressing many of the numerous challenges healthcare faces, which include a growing burden of illness, an increase in chronic health conditions and disabilities due to aging and epidemiological changes, higher demand for health services, overworked and burned-out clinicians, greater societal expectations, and rising health expenditures. While technological advancements in processing power, memory, storage, and the abundance of data have empowered computers to handle increasingly complex tasks with remarkable success, AI introduces a variety of meaningful risks and challenges. Among these are issues related to accuracy and reliability, bias and equity, errors and accountability, transparency, misuse, and privacy of data. As AI systems continue to rapidly integrate into healthcare settings, it is crucial to recognize the inherent risks they bring. These risks demand careful consideration to ensure the responsible and safe deployment of AI in healthcare.

Tree boosting methods for balanced and imbalanced classification and their robustness over time in risk assessment

Most real-world classification problems deal with imbalanced datasets, posing a challenge for Artificial Intelligence (AI), i.e., machine learning algorithms, because the minority class, which is of extreme interest, often proves difficult to be detected. This paper empirically evaluates tree boosting methods' performance given different dataset sizes and class distributions, from perfectly balanced to highly imbalanced. For tabular data, tree-based methods such as XGBoost, stand out in several benchmarks due to detection performance and speed. Therefore, XGBoost and Imbalance-XGBoost are evaluated. After introducing the motivation to address risk assessment with machine learning, the paper reviews evaluation metrics for detection systems or binary classifiers. It proposes a method for data preparation followed by tree boosting methods including hyper-parameter optimization. The method is evaluated on private datasets of 1 thousand (K), 10K and 100K samples on distributions with 50, 45, 25, and 5 percent positive samples. As expected, the developed method increases its recognition performance as more data is given for training and the F1 score decreases as the data distribution becomes more imbalanced, but it is still significantly superior to the baseline of precision-recall determined by the ratio of positives divided by positives and negatives. Sampling to balance the training set does not provide consistent improvement and deteriorates detection. In contrast, classifier hyper-parameter optimization improves recognition, but should be applied carefully depending on data volume and distribution. Finally, the developed method is robust to data variation over time up to some point. Retraining can be used when performance starts deteriorating.

Opportunities and Challenges in AI Based Modern Power System

Nowadays, the power grid has transformed into a dynamic and extensive resource generation and management system. This transformation is primarily driven by the widespread adoption of renewable energy sources and the utilization of intelligent information and communication technologies to handle dynamic workloads. The smart grid encompasses various innovative operations, including power electrification, intelligent information integration at the physical layer, and intricate interconnections. These operations leverage data-driven deep learning, big data, and machine learning paradigms to effectively analyze and control transient issues within the electric power system. Artificial intelligence (AI) has emerged as a crucial tool in addressing and resolving challenges associated with transient stability assessment (TSA) and power generation control. In this research paper, we present a comprehensive review that explores the role of AI and its sub-procedures in tackling TSA problems. The article outlines an AI-based intelligent power system structure, along with the rationality of applying AI to transient situations in power system TSA. Distinguishing itself from other reviews, this paper delves into the AI-based TSA framework and design process, highlighting intelligent applications and their analytical capabilities in addressing power system transient problems. Furthermore, our analysis extends beyond AI alone, as we also explore the integration of big data, which aligns seamlessly with AI. The paper discusses future trends, opportunities, challenges, and open issues pertaining to AI-Big data based transient stability assessment in the smart power grid.

LEGAL CHALLENGES OF ARTIFICIAL INTELLIGENCE AND ROBOTICS: A COMPREHENSIVE REVIEW

The paper presents an insightful overview of the intricate legal challenges posed by the proliferation of Artificial Intelligence (AI) and Robotics. This comprehensive review explores the multifaceted dimensions of the evolving legal landscape, addressing issues at the intersection of technology and law. Key focal points include the accountability and liability frameworks for autonomous AI systems, ethical considerations in the deployment of intelligent machines, and the complex dynamics of data privacy in the age of pervasive automation. The review delves into the intricate legal nuances surrounding intellectual property rights, particularly as AI systems contribute to creative outputs and innovation. It navigates the blurred lines between human and machine authorship, raising fundamental questions about ownership and protection in this digital era. Moreover, the paper emphasizes the global nature of these challenges, highlighting the imperative for international cooperation to formulate harmonized legal standards. As AI and robotics revolutionize industries and societal frameworks, the analysis underscores the critical need for adaptive and anticipatory legal frameworks. It explores how existing legal paradigms are grappling with the unprecedented speed of technological advancements and the ethical dilemmas arising from the delegation of decision-making to intelligent algorithms. The paper sets the stage for a thorough examination of the legal intricacies surrounding AI and robotics. It advocates for a proactive and collaborative approach, involving legal experts, technologists, ethicists, and policymakers in crafting robust frameworks that balance innovation with ethical, privacy, and accountability considerations. This review serves as a foundational resource for understanding and addressing the legal challenges inherent in the transformative era of Artificial Intelligence and Robotics.
 Keywords: Artificial intelligence, Robotics, Legal, AI challenges, Ethics, Review.

Don’t Fear Artificial Intelligence, Question the Business Model: How Surveillance Capitalists Use Media to Invade Privacy, Disrupt Moral Autonomy, and Harm Democracy

This paper analyzes the causes, consequences, and logic of surveillance capitalism, delineating how behavioral surplus became the latest form of accumulation and questioning its ethical, legal, and material implications. The purpose of this project is to provide a decisively human response to an otherwise reductive, totalizing political economic system that uses equally reductive technology. Using history, political economy, and media ethics, it shows how surveillance capitalists use artificial intelligence (AI) to disrupt the privacy necessary for identity work and distort the moral autonomy necessary for democratic worldmaking. Exploiting human psychology and emotional vulnerabilities, surveillance capitalists interfere with our ability to become better versions of our personal and collective selves. We must therefore reject surveillance capitalism and embrace a more inconclusive understanding of democracy informed by care. While experts and technocrats can endlessly debate the potential outcomes and possibilities, the challenges of AI and an abusive surveillance capitalist system must ultimately be answered by a caring citizenry with equally resilient social institutions.

The Imperative of Upholding Academic Integrity in the Face of Artificial Intelligence Challenges

Effective scholarly communication, whether oral or written, is inherently challenging. Scientific discourse relies on objective facts rather than subjective opinions. Hence, it necessitates grounding in evidence derived from research, ensuring the arguments presented are objective and supported by factual findings. Generating scholarly information, even in a single sentence, is a laborious process, demanding considerable time, financial resources, and substantial energy. In some instances, continuous or multi-year research is imperative. Nevertheless, technological advancements have revolutionized this landscape. Writing can now be expedited through the use of available artificial intelligence (AI) technologies, ranging from free versions to premium services. For example, Jenni AI functions akin to a "magical assistant," generating text on requested topics. ELICIT AI excels in grid synthesis, ResearchPal automates literature reviews, and others provide various functionalities. This poses a temptation to researchers, making AI a double-edged sword in the realm of publication integrity.

The Application and Challenges of Artificial Intelligence in Recruitment Interviews

In the wake of rapid technological advancements, artificial intelligence (AI) has found widespread application across numerous sectors, including the recruitment industry. This study conducts a comprehensive examination of AI’s current application in recruitment interviews, underscoring the necessity for proactive collaboration between AI and human experts in practice. This approach is aimed at harnessing AI’s strengths in data processing and human’s adeptness in emotional cognition, to jointly drive innovation and progression in recruitment interview techniques. Additionally, the study analyzes the myriad challenges presented by this integration and envisages potential future directions for development. The in-depth analyses and proposed directions for improvement presented in this study are intended to guide practitioners in the recruitment industry and serve as a research foundation and inspiration for future scholars in the fields of artificial intelligence technology and human resource management.

The application and challenges of artificial intelligence in the fashion and luxury industry

The concept of artificial intelligence (AI) involves the scientific and technological simulation of human intelligence, utilizing technologies such as deep learning, virtual reality, natural language processing, deep learning and more. Its core objective is to enable computers to process human- like abilities in perception, understanding, reasoning, learning, and decision-making. AI has achieved significant achievements and offers huge potential and applications across numerous areas, including the fashion and luxury industry. There, this article is to examine the application of AI technology in the fashion and luxury industry, specifically focusing on its utilization in personalized customer experience, market promotion and sales strategies, product design and innovation, as well as inventory and supply chain management. Additionally, this article aims to analyze the key existing issues and challenges brought about by these applications. This article a prospectus on the current focal points and future prospects of this research topic.

Evaluating the understanding of the ethical and moral challenges of Big Data and AI among Jordanian medical students, physicians in training, and senior practitioners: a cross-sectional study

AimsTo examine the understanding of the ethical dilemmas associated with Big Data and artificial intelligence (AI) among Jordanian medical students, physicians in training, and senior practitioners.MethodsWe implemented a literature-validated questionnaire to examine the knowledge, attitudes, and practices of the target population during the period between April and August 2023. Themes of ethical debate included privacy breaches, consent, ownership, augmented biases, epistemology, and accountability. Participants’ responses were showcased using descriptive statistics and compared between groups using t-test or ANOVA.ResultsWe included 466 participants. The greater majority of respondents were interns and residents (50.2%), followed by medical students (38.0%). Most participants were affiliated with university institutions (62.4%). In terms of privacy, participants acknowledged that Big Data and AI were susceptible to privacy breaches (39.3%); however, 59.0% found such breaches justifiable under certain conditions. For ethical debacles involving informed consent, 41.6% and 44.6% were aware that obtaining informed consent posed an ethical limitation in Big Data and AI applications and denounced the concept of “broad consent”, respectively. In terms of ownership, 49.6% acknowledged that data cannot be owned yet accepted that institutions could hold a quasi-control of such data (59.0%). Less than 50% of participants were aware of Big Data and AI’s abilities to augment or create new biases in healthcare. Furthermore, participants agreed that researchers, institutions, and legislative bodies were responsible for ensuring the ethical implementation of Big Data and AI. Finally, while demonstrating limited experience with using such technology, participants generally had positive views of the role of Big Data and AI in complementing healthcare.ConclusionJordanian medical students, physicians in training and senior practitioners have limited awareness of the ethical risks associated with Big Data and AI. Institutions are responsible for raising awareness, especially with the upsurge of such technology.

From Traditionalism to Algorithms: Embracing Artificial Intelligence for Effective University Teaching and Learning

The current study examines the impact of integrating artificial intelligence (AI) in higher education in Saudi Arabia, specifically focusing on the roles of university teachers and students’ learning outcomes. It investigates the transformation of teaching practices, and the challenges faced by university teachers during AI integration, and identifies opportunities for collaboration and innovation. A quantitative research method using a survey questionnaire was employed to gather data and inform practical recommendations. The findings indicate that university teachers recognize the potential benefits of AI in education, but also highlight the need to address challenges for effective implementation. Participants perceive AI as a tool that enables more creative and engaging teaching methods, automates tasks, and brings about changes in teaching approaches. Difficulties in utilizing AI effectively and adapting to new teaching methods were identified. AI also offers opportunities for innovation, collaboration, and creating engaging learning environments. There were slight variations in perspectives based on gender and current role, with female university teachers and teaching assistants/professors perceiving higher impact and opportunities for collaboration and innovation. Participants with higher AI expertise perceived a greater impact of AI and fewer challenges. Those who aligned with the educational purposes of AI saw greater impact and opportunities for collaboration while acknowledging usage difficulties.

Analyzing Ghana's Pharmacy Act, 1994 (Act 489) Regarding Quality Control and Negligence Liability Measures for Artificial Intelligence Pharmacy Systems

The objective of this systematic review was to assess the adequacy of current medication management in Ghana considering the risks posed by increased artificial intelligence (AI) automation in pharmacies worldwide A qualitative comparative approach was used despite reviewed the Ghana 1994 Pharmacy Act against recognition of AI challenges and international governance guidelines . The results revealed flaws in terms of quality prerequisites, transparency checklists and liability mechanisms developed for AI systems compared to existing regulations of the manual process. Outdated approaches to patient care that fail to ensure patient safety or address threats to the accuracy of recommendations from data collection biases and technical errors. Proposed changes include a requirement for usability testing before approving AI pharmacy deployments and the creation of a review board to review post-implementation systems for validity. Updating regulations to deal with modern equipment puts innovation and responsible regulation in the fast-paced healthcare industry. This study contributes significantly to preliminary research on AI policy readiness in the Ghanaian legal context, and suggests a feasible methodology for exploring qualitative differences for use in companies and countries competing for technology a disturbing, increasingly beyond the date code. Early government reform helps keep pace with the realities of adoption.

REVIEW OF AI TECHNIQUES IN FINANCIAL FORECASTING: APPLICATIONS IN STOCK MARKET ANALYSIS

This scholarly inquiry delves into the burgeoning intersection of Artificial Intelligence (AI) and financial forecasting, particularly within the stock market domain. The study's backdrop is set against the rapid evolution of AI techniques, which have significantly altered the landscape of financial analysis. The primary aim is to dissect and evaluate the impact of AI on stock market predictions, juxtaposing its capabilities against traditional forecasting methods while navigating through the ethical and practical complexities inherent in AI implementation. The scope of the paper encompasses a comprehensive review of AI's evolution in financial analysis, its comparative effectiveness, and the sector-specific applications in stock markets. Methodologically, the study employs a systematic review of existing literature, focusing on peer-reviewed articles that shed light on the performance, challenges, and future prospects of AI in stock market forecasting. The findings reveal AI's profound potential in enhancing market efficiency and volatility understanding, albeit tempered by challenges such as data quality issues, model interpretability, and the need for robust regulatory frameworks. The main conclusions underscore AI's transformative role in financial forecasting, highlighting its ability to analyze vast datasets and predict market trends with heightened accuracy. However, the study also acknowledges the limitations within AI models, emphasizing the necessity for a balanced approach that integrates AI with traditional methods and continuous algorithmic refinement. Recommendations advocate for collaborative efforts between technologists, ethicists, and financial experts to develop ethically sound, transparent, and effective AI applications. In summary, this paper offers a panoramic view of AI's role in financial forecasting, serving as a guidepost for future explorations in this field. It underscores the immense possibilities and intricate challenges of AI in the dynamic landscape of stock market analysis, paving the way for a new era of data-driven decision-making in finance.
 Keywords: Artificial Intelligence, Financial Forecasting, Stock Market Prediction, Machine Learning, Ethical Considerations, Regulatory Frameworks.

Progress and challenges of artificial intelligence in skin of color.

Progress and challenges of artificial intelligence in skin of color.