Challenges Of Artificial Intelligence Research Articles

ROLE OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN OPTIMIZING INVENTORY MANAGEMENT ACROSS GLOBAL INDUSTRIAL MANUFACTURING & SUPPLY CHAIN: A MULTI-COUNTRY REVIEW

This study examines the impact of Artificial Intelligence (AI) and Machine Learning (ML) on inventory management within global industrial manufacturing and supply chains, particularly in the context of Industry 4.0. Through a comparative analysis across several countries, the research analyzes quantitative and qualitative data to assess the adoption and integration of these technologies and their implications for supply chain optimization. The research methodology includes a comprehensive literature review using multiple databases and expert interviews conducted within a specific timeframe. The study identifies a significant favorable influence of AI and ML on enhancing efficiency, reducing costs, and improving real-time data analytics and predictive maintenance. It highlights the evolution from theoretical potential to practical applications, with an increased focus on regulatory compliance and data integrity, reflecting the industry's maturation in digital integration. Furthermore, the study explores the strategic role of AI and ML in process design and the holistic adoption of Industry 4.0 principles across the supply chain. The findings contribute to the academic literature by detailing the benefits and challenges of AI and ML implementation, offering insights for future research and practical applications in the supply chain sector. The conclusion emphasizes the transformative potential of AI and ML, advocating for their strategic implementation to foster resilience and adaptability in supply chain networks.

Horizon of Healthcare: AI's Evolutionary Journey and Future Implications

This meta-analysis offers a comprehensive review of the literature surrounding the integration of Artificial Intelligence (AI) in healthcare. Drawing upon an extensive array of scholarly articles, research papers, and industry reports, this study synthesizes the current state of knowledge regarding AI's applications, challenges, and future directions within the healthcare sector. The analysis encompasses various dimensions of AI implementation, including diagnostic assistance, treatment optimization, patient management, and administrative streamlining. Additionally, it examines the methodological approaches employed in AI healthcare research, assessing the efficacy and reliability of AI-driven interventions across diverse medical domains. Furthermore, this meta-analysis explores the ethical, legal, and social implications associated with AI adoption in healthcare, shedding light on issues of data privacy, algorithmic bias, and patient autonomy. Through a systematic examination of existing literature, this study elucidates key trends, knowledge gaps, and emerging research directions in the evolving intersection of AI and healthcare, providing valuable insights for policymakers, practitioners, and researchers alike.

Cite space-based research on the use of artificial intelligence in the field of literature and museums

In recent years, with the rapid development and application of artificial intelligence technology, image technology based on artificial intelligence has gradually become a research hotspot in the field of cultural relic restoration. This article will explore the application of artificial intelligence based image technology in the field of cultural relic and museum restoration. Firstly, the importance of restoring cultural relics and artifacts was introduced, as well as the limitations and shortcomings of traditional restoration methods. Secondly, elaborate on the development and application of artificial intelligence technology, as well as its potential and advantages in the field of cultural relic restoration. Next, we will introduce the application of artificial intelligence based image technology in cultural relic restoration, including image enhancement, segmentation, recognition, and reconstruction. Finally, the application prospects and challenges of artificial intelligence based image technology in the field of cultural relic restoration were summarized, and future research directions and suggestions were proposed.

Security risk models against attacks in smart grid using big data and artificial intelligence.

The need to update the electrical infrastructure led directly to the idea of smart grids (SG). Modern security technologies are almost perfect for detecting and preventing numerous attacks on the smart grid. They are unable to meet the challenging cyber security standards, nevertheless. We need many methods and techniques to effectively defend against cyber threats. Therefore, a more flexible approach is required to assess data sets and identify hidden risks. This is possible for vast amounts of data due to recent developments in artificial intelligence, machine learning, and deep learning. Due to adaptable base behavior models, machine learning can recognize new and unexpected attacks. Security will be significantly improved by combining new and previously released data sets with machine learning and predictive analytics. Artificial Intelligence (AI) and big data are used to learn more about the current situation and potential solutions for cybersecurity issues with smart grids. This article focuses on different types of attacks on the smart grid. Furthermore, it also focuses on the different challenges of AI in the smart grid. It also focuses on using big data in smart grids and other applications like healthcare. Finally, a solution to smart grid security issues using artificial intelligence and big data methods is discussed. In the end, some possible future directions are also discussed in this article. Researchers and graduate students are the audience of our article.

Exploring the Impact of ChatGPT on Educational Outcomes in Beijing High Schools

As technology continues to advance rapidly, its integration into education has become a focal point for researchers and educators worldwide. In particular, the emergence of artificial intelligence (AI) tools like ChatGPT presents a promising avenue for transforming traditional teaching methods and enhancing student learning experiences. The findings of this study demonstrate that the integration of ChatGPT into Beijing high school classrooms significantly enhances the learning environment. Through its interactive nature, ChatGPT fosters greater engagement among students, leading to improved educational outcomes. Moreover, the study highlights the potential of ChatGPT to personalize learning experiences, catering to individual student needs and preferences. While the findings highlight ChatGPT's transformative potential in high school education, they also emphasize the need to address related challenges. Ethical concerns, such as data privacy and algorithmic bias, demand careful consideration. Moreover, to prevent an over-reliance on technology, a balanced curriculum integration is crucial. This research adds to the literature on AI's educational integration, offering evidence of ChatGPT's impact and guiding educators, policymakers, and stakeholders in navigating AI's classroom opportunities and challenges. It underlines the importance of continuous research and collaboration to fully leverage AI in education, preparing students for future challenges.

AI in knowledge sharing, which ethical challenges are raised in decision-making processes for organisations?

PurposeThis study aims to identify and assess the key ethical challenges associated with integrating artificial intelligence (AI) in knowledge-sharing (KS) practices and their implications for decision-making (DM) processes within organisations.Design/methodology/approachThe study employs a mixed-methods approach, beginning with a comprehensive literature review to extract background information on AI and KS and to identify potential ethical challenges. Subsequently, a confirmatory factor analysis (CFA) is conducted using data collected from individuals employed in business settings to validate the challenges identified in the literature and assess their impact on DM processes.FindingsThe findings reveal that challenges related to privacy and data protection, bias and fairness and transparency and explainability are particularly significant in DM. Moreover, challenges related to accountability and responsibility and the impact of AI on employment also show relatively high coefficients, highlighting their importance in the DM process. In contrast, challenges such as intellectual property and ownership, algorithmic manipulation and global governance and regulation are found to be less central to the DM process.Originality/valueThis research contributes to the ongoing discourse on the ethical challenges of AI in knowledge management (KM) and DM within organisations. By providing insights and recommendations for researchers, managers and policymakers, the study emphasises the need for a holistic and collaborative approach to harness the benefits of AI technologies whilst mitigating their associated risks.

Applications and Challenges of Artificial Intelligence in Personalized Marketing

With the rapid development of artificial intelligence technology, its application in personalized marketing is more and more extensive. This paper aims to explore the application status, advantages and challenges of AI in personalized marketing, and put forward corresponding suggestions. Through literature review and case analysis, this paper finds that AI can significantly improve the marketing effect in personalized marketing, but there are also some technical and ethical challenges.

Balancing the scale: navigating ethical and practical challenges of artificial intelligence (AI) integration in legal practices

AbstractThe paper explores the integration of artificial intelligence in legal practice, discussing the ethical and practical issues that arise and how it affects customary legal procedures. It emphasises the shift from labour-intensive legal practice to technology-enhanced methods, with a focus on artificial intelligence's potential to improve access to legal services and streamline legal procedures. This discussion importantly highlights the ethical challenges introduced by the integration of Artificial Intelligence, with a specific focus on issues of bias and transparency. These ethical concerns become particularly paramount in the context of sensitive legal areas, including but not limited to, child custody disputes, criminal justice, and divorce settlements. It underscores the critical need for maintaining ethical vigilance, advocating for developing and implementing AI systems characterised by a profound commitment to ethical integrity. This approach is vital to guarantee fairness and uphold transparency across all judicial proceedings. The study advocates for a "human in the loop" strategy that combines human knowledge and AI techniques to mitigate biases and guarantee individualised legal results to ensure AI functions as a complement rather than a replacement, the paper concludes by emphasising the necessity of preserving the human element in legal practices.

Democracy out-of-the-box: analysis of compliance with constitutional principles in tax policies that use Artificial Intelligence

The Democratic State of Law emerged with the objective of improving everyone’s life, restricting the power of tyrants, who, among other arbitrary acts, unfairly taxed the people. While taxes remain crucial for the State’s upkeep, modern rules prevent individuals from enduring excessive burdens. Although new technologies leveraging Artificial Intelligence (AI) hold the promise of enhancing lives, the extent of this improvement raises questions. This study delves into the relationship between individuals and the State, specifically exploring the use of AI in tax-related scenarios. Conducting a comprehensive three-stage investigation, the first stage involved surveying the reasons behind State-imposed limitations on taxation. The second stage identified criteria from computer literature addressing potential AI challenges and strategies for achieving explainability. Additionally, a discussion emphasized the importance of technological models aligning with principles that underpin our society. Then, to understand how actions have been carried out in Public Administration, a Systematic Mapping Study (SMS) was carried out, analyzing works from the ACM Digital Library, SBC OpenLib (SOL) and the ENAP (National School of Public Administration)’s repository (a Gray Literature repository). On the ENAP website, works relating to the Creativity and Innovation Award from the Federal Revenue of Brazil were specifically consulted. In the end, 10 works that met the inclusion criteria were selected. It was concluded that none of them has an AI explainable model, considering the interpretability criteria of models (Rules and logic, Data visualization, and Model documentation). In light of this, it is recommended that studies addressing the use of AI in Public Administration incorporate a dedicated section discussing the explainability of models.

Exploring the Prospects and Challenges of Artificial Intelligence in Shaping the Future of Web 3.0

Abstract: In the era of digital innovation, Artificial Intelligence (AI) has emerged as a pivotal catalyst, unlocking new avenues for the evolution of Web 3.0. Web 3.0 signifies the next phase of the internet, characterized by decentralized structures, peer-topeer networks, and cutting-edge technologies like blockchain and smart contracts. This research provides an in-depth exploration of the role played by AI in shaping Web 3.0, delving into both its opportunities and challenges. AI proves instrumental in processing and analysing vast datasets with increased efficiency, fostering intelligent decision-making and insightful outcomes. The paper extensively covers essential Web 3.0 concepts and technologies, encompassing the Semantic Web and ontologies, and underscores AI's transformative potential across diverse industries such as healthcare, finance, and education. An analysis of the challenges posed by AI in the Web 3.0 landscape, including issues of data privacy, bias, trust, and ethics, is presented. Furthermore, the research examines the broader societal implications of AI in Web 3.0. Conclusively, the paper outlines future directions and implications of AI within the Web 3.0 framework, proposing potential areas for subsequent research. By contributing to a comprehensive understanding of AI's impact on web development and its broader societal implications, this research aims to guide future endeavours in this dynamic field.

Analytical exploration of integration of AI in Information Systems

The integration of Artificial Intelligence (AI) into Information Systems (IS) is ushering in a transformative era of data-driven decision-making. This research paper presents a comprehensive exploration of AI's applications, benefits, challenges, and future directions within IS. AI is revolutionizing data management through techniques such as automated data integration, natural language processing, and enhanced data quality, while also providing sophisticated decision support systems with predictive analytics and recommendation engines. Businesses benefit from streamlined processes, real-time analytics, and improved cybersecurity measures. However, challenges such as data quality, AI skill shortages, ethical concerns, and integration complexities must be addressed. The paper envisions future directions where Explainable AI (XAI) offers transparent decision rationales, ethics and governance frameworks ensure responsible AI adoption, augmented intelligence fosters human-AI collaboration, AI extends to edge computing for real-time processing, and AI fortifies cybersecurity measures. As AI technologies continue to mature, organizations must invest in research and development while formulating robust AI adoption strategies to harness the potential of AI in IS. The fusion of AI and IS is poised to redefine information management, facilitating more intelligent, efficient, and secure operations in the evolving digital landscape. Key Words: Artificial Intelligence, Information Systems, Machine Learning, Data Analytics, Natural Language Processing, Automation, Decision Support, Big Data.

Promoting sustainability in finance with AI: A review of current practices and future potential

This study explores the transformative integration of Artificial Intelligence (AI) into sustainable finance, highlighting its potential to redefine financial practices in alignment with Environmental, Social, and Governance (ESG) criteria. Through a systematic review of current practices and an analysis of AI's applications, challenges, and strategic frameworks, the research elucidates AI's role in enhancing financial operations' efficiency, accuracy, and sustainability. Findings indicate that AI technologies, such as the Financial Maximally Filtered Graph (FMFG) algorithm, significantly improve the processing and analysis of vast datasets, facilitating sustainable investment decisions. However, the integration of AI into sustainable finance is accompanied by ethical, regulatory, and technological challenges. The study proposes strategic recommendations for overcoming these barriers, emphasizing the development of robust policy frameworks, industry best practices, and a balanced approach to AI integration. The conclusion underscores the promise of AI in advancing sustainable finance, offering insights for stakeholders on navigating the complexities of this integration to achieve a more sustainable and resilient financial system.

A scholarly dialogue: writing scholarship, authorship, academic integrity and the challenges of AI

ABSTRACT Concerns about the role of technology and the quality of student writing in higher education are not new. Historically, writing scholars have been at the forefront of initiatives that scrutinise and integrate new technologies in higher education. This article contends that writing scholars are again uniquely equipped to assist students, teachers in all disciplines, and institutions of higher education in navigating the challenges created by the public availability of generative artificial intelligence (AI). Adopting a dialogical approach, the article brings together six scholars from diverse disciplinary backgrounds within the broad umbrella of writing studies. Through the lens of writing scholarship, this chorus of critical voices illuminates the important questions posed by, and possible responses to, AI in higher education. Although AI complicates key issues in higher education such as academic integrity, assessment, and authorship, writing scholarship provides an essential framework for educators to respond to these challenges. Collectively, these scholars map the history of writing scholars’ responses to technological change in higher education and suggest how writing scholars can contribute to the debates and discussions concerning the impact of AI on higher education.

From Statistical Relational to Neuro-Symbolic Artificial Intelligence

The integration of learning and reasoning is one of the key challenges in artificial intelligence and machine learning today. The area of Neuro-Symbolic AI (NeSy) tackles this challenge by integrating symbolic reasoning with neural networks. In our recent work, we provided an introduction to NeSy by drawing several parallels to another field that has a rich tradition in integrating learning and reasoning, namely Statistical Relational Artificial Intelligence (StarAI).

MetaDiff: Meta-Learning with Conditional Diffusion for Few-Shot Learning

Equipping a deep model the ability of few-shot learning (FSL) is a core challenge for artificial intelligence. Gradient-based meta-learning effectively addresses the challenge by learning how to learn novel tasks. Its key idea is learning a deep model in a bi-level optimization manner, where the outer-loop process learns a shared gradient descent algorithm (called meta-optimizer), while the inner-loop process leverages it to optimize a task-specific base learner with few examples. Although these methods have shown superior performance on FSL, the outer-loop process requires calculating second-order derivatives along the inner-loop path, which imposes considerable memory burdens and the risk of vanishing gradients. This degrades meta-learning performance. Inspired by recent diffusion models, we find that the inner-loop gradient descent process can be viewed as a reverse process (i.e., denoising) of diffusion where the target of denoising is the weight of base learner but origin data. Based on this fact, we propose to model the gradient descent algorithm as a diffusion model and then present a novel conditional diffusion-based meta-learning, called MetaDiff, that effectively models the optimization process of base learner weights from Gaussian initialization to target weights in a denoising manner. Thanks to the training efficiency of diffusion models, our MetaDiff does not need to differentiate through the inner-loop path such that the memory burdens and the risk of vanishing gradients can be effectively alleviated for improving FSL. Experimental results show that our MetaDiff outperforms state-of-the-art gradient-based meta-learning family on FSL tasks.

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.

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.