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This editorial introduces the special issue "The Interplay Between Artificial Intelligence, Production Systems, and Operations Management Resilience.' We selected twelve papers, encompassing many angles that illuminate the advances and challenges dealing with artificial intelligence tools and approaches in the production systems and operations management resilience domains. This editorial presents the papers with a smart view, highlighting the essentials of each article, such as full paper title, background, theory/literature scope, methodology design/analysis approach, and the main findings/contributions. Finally, the conclusions, future pathways, and research directions are presented. [ABSTRACT FROM AUTHOR]

In this short response to the papers appearing in this special issue (Technology, AI Bots and Psychotherapy After Covid), psychotherapist and author Aaron Balick draws on the variety of themes that have arisen within the contributed papers to reflect on the wider issue of computer mediated human relations. In it he makes a distinction between the papers that focus on the therapeutic process mediated by technology and those that look more broadly at the paradigm of therapy practice in this context. Framing technology as a tool, the author pulls together both strands to explore what psychotherapy research may say about the broader issues of societies mediated by technology and how therapeutic research may contribute to these larger social issues. [ABSTRACT FROM AUTHOR]

AbstractOpenAI’s ChatGPT is an advanced large language model AI that has caused both excitement and concern in academia. TurnItIn and similar software programs are widely used in higher education to detect potential plagiarism. However, the extent to which such software can identify papers produced by ChatGPT remains unclear. The current study partially addressed this question by submitting five versions of short essays about criminological topics generated from ChatGPT to the TurnItIn software. Overall, the results indicated that TurnItIn adequately detected that the essays were not original works (mean percent plagiarized score = 31%). The analyses further illustrated that ChatGPT wrote at an exceedingly high level (mean Flesch-Kincaid Grade Level = 15.1) atypical of essays in higher education. Consequently, it appears that detection of AI-generated writing may be easier than initially assumed although the technology is bound to improve. Accordingly, possible strategies for approaching AI in higher education are proposed. [ABSTRACT FROM AUTHOR]

As any field evolves, so do journals' expectations from authors. As Artificial Intelligence (AI) usage in Health Professions Education (HPE) has evolved, Medical Teacher's expectations have changed, and previously-accepted paper types are now routinely rejected. This commentary gives some guidance for authors currently submitting AI in HPE papers to Medical Teacher. [ABSTRACT FROM AUTHOR]

The existence of AI application examples is by itself an admirable achievement of engineers and professionals working on them, and certainly there is an interest to move forward to design AI algorithms to make it write proficiently with originality. Artificial Intelligence (AI) has become a trendy topic through advertisements, notes and even warnings about it, claiming that it can be used to answer almost any kind of question. Generating new content requires that AI conduct research work starting from, in principle, human proposed hypothesis, and then getting analyzable results to formulate conclusions. [Extracted from the article]

This document is an editorial introduction from the Journal of Management Information Systems. It emphasizes the importance of incorporating artificial intelligence (AI) into the field of Information Systems (IS) research. The editorial discusses the potential benefits and risks of AI, including its impact on societal well-being, economic benefits, and long-term threats. It also highlights several research papers published in the journal that explore topics such as trust in AI, AI investments, cybersecurity, value creation through AI systems, and the effects of customization in e-commerce. The editorial encourages the IS community to contribute to the understanding and deployment of AI in order to enhance organizational development and human capabilities. [Extracted from the article]

Though a computer professional, Meredith Broussard feels "that the way people talk about technology is out of touch with what digital technology can actually do", and that seeing the latter as a panacea has "resulted in a tremendous amount of poorly designed technology" (6), which needlessly complicates instead of improving or making life easier. Artificial Unintelligence: How Computers Misunderstand the World: by Meredith Broussard, Cambridge, MA, MIT Press, 2019, 248 pp., $15.95T/£12.99 (paper) Supported by extensive research, I Artificial Unintelligence i cogently challenges the prevailing technophile hype extolling the unlimited ways in which technology supposedly can "change the world for the better" and create a digital utopia with infinite benefits in every area of life. [Extracted from the article]

This paper focuses on the opportunities and challenges associated with the use of artificial intelligence (AI) in academic library operations. In the quest to render fast, effective and efficient services, academic libraries have adopted different technologies in the past. Artificial intelligence technologies is the latest among the technologies currently being introduced in libraries. The technology which is considered an intelligent system, come in the form of robots and expert systems which have natural language processing, machine learning and pattern recognition capabilities. This paper examined the features of AI, the application of AI to library operations, examples of academic libraries with AI technologies in Sub-Saharan Africa, the need for AI in libraries and the challenges associated with the adoption of AI in libraries. The study concluded that AI holds a lot of prospects for the improvement of information services delivery in African academic libraries. Consequently, its adoption is a sinequanon to delivering robust library services in the Fourth Industrial Revolution (4IR). [ABSTRACT FROM AUTHOR]

The Journal of Management Information Systems has published a special issue that focuses on two main topics: the interaction between humans and artificial intelligence (AI), and cybersecurity. The first set of papers explores the impact of generative AI on human accomplishment and productivity, as well as the concerns surrounding employment and the survival of humans in the face of AI. The second set of papers addresses the increasing importance of cybersecurity in the face of sophisticated cyberattacks and the need for a broader understanding of cybersecurity and the use of AI to combat these threats. Other topics covered in the issue include the role of AI in employee performance evaluation, the collaboration between AI and humans in demand forecasting, the effectiveness of fear appeals in shaping employee cybersecurity posture, the relationship between IT innovativeness and the risk of data breaches, the impact of avatars on community identification in virtual social worlds, the performance of foreign IT complementors in mobile app startups, the impact of open-source software communities on cryptocurrency prices, the influence of social relationships on online reviews, the digitalization of loyalty in online reward programs, and the optimization of brand offerings in online marketplaces. [Extracted from the article]

Artificial intelligence (AI) has emerged as an innovative technology with the potential to revolutionize various industries including libraries and information science. Academic libraries are increasingly adopting artificial intelligence (AI) to enhance services, improve efficiency, and enhance user experience. This study utilizes a bibliometric approach to comprehensively analyze current research on AI in academic libraries (AI in ALs). This study employed bibliometric indicators to identify key trends, patterns, and research gaps in the existing literature. A comprehensive dataset of 373 research papers on AI in ALs published between 2002 and 2022 was collected and analyzed using the Scopus database. Various bibliometric tools, such as Biblioshiny, VOSviewer, and BibExcel, have enhanced this analysis. The findings of this study provide important insights. By 2022, there were 64 publications, constituting 17.16% of the total corpus, accompanied by 65 citations. In contrast, 2019 witnessed only 33 publications yet accumulated a substantial number of citations, amounting to 294, representing 8.85% of the overall citations. Conference papers exhibited the highest frequency among different publication types, with 165 publications, whereas journal articles had the highest citation count, accumulating 217 citations. Geographically, China emerged as the leading contributor with 119 publications, and Wuhan University stood out as the most prominent affiliation. Notably, the "Lecture Notes in Computer Science" series emerged as the most prolific source title, publishing 15 articles, of which eight were cited. The authors Wang J., Wang C., and Wang X. from China demonstrated significant contributions, consistently publishing four papers annually from 2010 to 2022. [ABSTRACT FROM AUTHOR]

The orders makespan and resources utilisation are considered as the objective function of the model, and the heterogeneous production resources and logistics resources are integrated to autonomously communicate and interact with each other to bidding for the dynamic production-logistics-integrated operation tasks. The aim of this special issue is to encourage original and latest contributions, and to review and survey research and development on industrial intelligence-driven production and operations management, focusing on state-of-the-art and potential future approaches and technologies and providing a good starting point for researchers entering these research areas. Then, to evaluate the tolerance and persistence capabilities of MSC under supply and demand uncertainties, a graph-based operational robustness analysis method of the IIoT platform for MSC is proposed. The 8th paper entitled 'An integrative decision-making model for the Internet of Things-enabled supply chains of fresh agri-product', by Han et al. proposed a mixed-integer programming model to generate integrative decision-making in the Internet of Things (IoT)-enabled fresh agri-products supply chains. [Extracted from the article]

The article presents a call for papers on artificial intelligence and robots for the library and information professions.

The Journal of Management Information Systems has published a special section on cognitive reapportionment in relation to artificial intelligence (AI) and advances in computing. The section explores the allocation of tasks between humans and machines as AI becomes more capable of cognitive tasks. The limitations of current AI systems are discussed, as well as the potential for collaboration between humans and AI. The journal also includes papers on topics such as knowledge-aware models, crowdsourcing, social media effects, and the impact of government contracting on high-tech firms. [Extracted from the article]

This article examines the potential impact of AI technology on psychotherapy and counseling. It discusses the use of AI chatbots as companions, including for romantic and sexual purposes, and raises questions about the changing nature of therapy in the digital age. The article explores the concept of "technical thinking" and its effects on human psychology and therapeutic practices. It also discusses the history of therapy using communication technologies and the emergence of AI bots in the field. The article concludes by considering the role of traditional therapy in the digital age and the potential benefits and drawbacks of online therapy. Additionally, it highlights a recent study that found face-to-face communication to be more important for mental health during lockdowns, but also acknowledges the positive impact of digital text-based communication. The author emphasizes the need for further research on the effects of the digital age on psychological therapies and invites submissions to their journal. The article provides summaries of several papers included in the journal, covering topics such as online therapy, the impact of technology on the therapeutic relationship, and the experiences of students in remote learning. The author concludes by emphasizing the importance of considering the effects of the digital age on both psychotherapy provision and individuals involved in therapy. [Extracted from the article]

Artificial intelligence (AI) has been widely explored across the world over the past decades. A particularly emerging topic is the application of AI in e-learning (AIeL) to improve the effectiveness of teaching and learning in precision education. This study aims to systematically review publication patterns for AIeL research with a focus on leading journals, countries, disciplines, and applications. In addition, a co-citation network analysis was conducted to explore the invisible relationships among the core papers of AIeL to reveal directions for future research. The analysis is based on a total of 86 core AIeL papers accompanied by 1149 citations in follow-up studies obtained from the Web of Science. It was found that a majority of AIeL studies focused on the development and applications of intelligent tutoring systems, followed by using AI to facilitate assessment and evaluation in e-learning contexts. For field researchers, the visualized network diagram serves as a map to explore the invisible relationships among the core AIeL research, providing a structural understanding of AI-supported research in e-learning contexts. A further investigation of the follow-up studies behind the highly co-cited links revealed the extended research directions from the AIeL mainstreams, such as adaptive learning-based evaluation environments. Implications are discussed. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is an important source of competitive advantage as it enables task augmentation and automation. However, while AI can create significant value, it is important to note that AI investments are fraught with risks and uncertainties. Thus, managers are likely to carefully evaluate potential AI investments before committing to investing. However, we know little about how managers' appraisal of AI influences their investment choices. Drawing upon theorization in the areas of business value of AI, agentic information systems (IS) appraisal, and time-situated agency, we extend existing theory in two ways: (1) development of an AI classification (foundational typology) that proposes two dimensions (action autonomy and learning autonomy) for classifying AI by type and level of autonomy; and (2) development of propositions that leverage time-situated agency and the AI classification to explicate how managers' delegation preferences influence their AI investment appraisal. This paper contributes a foundational theoretical platform for furthering AI investment appraisal research. In addition, the paper sets an agenda for future research in this area. [ABSTRACT FROM AUTHOR]

This paper analyzes some details of artificial intelligence algorithms to paper-reviewer assignment problem. In particular, the study on most common algorithms like genetic algorithm (GA), ant colony optimization (ACO) and tabu-search (TS) is made and the performance of these algorithms in paper-reviewer assignment problem is tested. Moreover, two hybrid methods that efficiently combine the above-mentioned algorithms are proposed: the ACO-GA and GA-TS algorithms. To measure the performance of these algorithms, extensive computational experiments are conducted. Evaluation using different data sets shows that the proposed algorithms are effective and achieve good results. [ABSTRACT FROM AUTHOR]

Games have been designed to be challenging and fun for humans to play, so we can test the AI system against the best human players in the world to quantify how good our AI systems are getting to be. We went from there to AlphaGo, which was our AI system to master the ancient and complex game of Go. DeepMind: From Games to Scientific Discovery: This paper is an edited version of Demis Hassabis' 2021 IRI Medal talk. He discussed his personal AI journey - from games to scientific discovery, some of his breakthrough results in complex games of strategy, and some of the exciting ways that lessons from the world of games are helping to accelerate scientific discovery. [Extracted from the article]

New technology refers to the use of computing machines, AI, Big Data, IoT, deep ‎‎learning, IT, MIS, ‎AIS, knowledge management, capture, manipulate, ‎and retrieve shared knowledge. Therefore, the integration of modern technology, ‎entrepreneurship, and business should be well ‎managed to provide a wide range of high-quality ‎and competitive products and services in societies. The aim of this special issue is to highlight the latest features that blend AI, Big Data, and IoT facilitated and employ them to support the successful growth of businesses. The target of this special issue is to accept high-quality scientific articles that express theory and practical conceptualizations of ideas and critical surveys that cover all aspects pertaining to IoT, AI, and Big Data and their relationship to business success. The special issue received 36 papers, some of which were presented in ICBT'2021 and CBF'2022. All of them were desk evaluated by the editors, followed by at least two blind reviews. [ABSTRACT FROM AUTHOR]

When it comes to computation in design, architecture, and the built environment, practices, methods, and tools frequently offer "neutral" and "optimized" techno-solutions to (social) design problems. We welcome contributions that seek to understand and uncover the power relations between (commercial) CAD systems, computational design practices, technology infrastructures, knowledge, education, and their reproductions of bias at multiple scales. This narrative of neutrality conceals power that computer-aided design (CAD) software monopolies and technology providers hold (Cardoso Llach [3]). [Extracted from the article]

The next paper is titled I "Adding Interpretability to Neural Knowledge DNA". i It addresses in a novel way one of the biggest challenges of Artificial Intelligence - explainability. It is an alternative conceptualization of artificial intelligence (AI) that focuses on AI's assistive role, emphasizing the fact that cognitive technology is designed to enhance human intelligence rather than simply replacing it. The Authors introduce the decision tree-based generative method for knowledge extraction and representation to make the NK-DNA explainable, which is a big step toward trust in Artificial Intelligence (AI). [Extracted from the article]

This paper examines Australia's recent AI governance efforts through the lens of sociotechnical imaginaries. Using the example of Robodebt, it demonstrates how a more holistic and contextual examination of AI governance can help shed light on the social impacts and responsibilities associated with AI technologies. It argues that, despite the recent discursive shift to 'safe and responsible AI', a sociotechnical imaginary of AI as 'economic good' has been a persistent undercurrent in the past two governments' efforts at AI governance. Understanding how such sociotechnical imaginaries are embedded in AI governance can help us better predict how these governance efforts will impact society. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) can significantly contribute to the implementation of the United Nations Sustainable Development Goals (SDGs) by offering innovative solutions and enhancing the efficiency of processes aimed at achieving these goals. There is a perceived need for studies which may look at these connections. Against this background, this paper reports on a study that investigated the connections between artificial intelligence and the implementation of the UN Sustainable Development Goals (SDGs) at higher education institutions. The paper deployed a multi-methods approach. The first one was a bibliometric analysis of publications in the topic. The second method used was an assessment of a set of case studies, that illustrate how artificial intelligence is being deployed among a sample of universities in support of efforts to implement the SDGs and a survey aimed at identifying current and future trends. The data gathered allow some trends to be identified. For instance, that there is a wide range of applications of AI to sustainability in High Education Institutions (HEI), to be chosen in terms of campus operations and greening, outreach and community engagement, research, teaching and learning, and university management. Also, the paper has identified successful examples of the deployment of AI in various sustainability contexts, illustrating what are the success factors for them. Moreover, the survey identified the fact that the use of AI is quite widely spread, and is likely to increase in coming years, due to a greater demand. Finally, AI also poses several challenges, such as authenticity and ethics in assessment (case studies), 'lack of access to software/materials', and 'lack of information technology training for myself/my colleagues' (survey). Overall, AI offers a powerful toolset to accelerate and enhance the implementation of the UN SDGs. By analysing vast datasets, predicting outcomes, optimising processes, and providing new insights, AI has the potential to address complex sustainability challenges across various sectors. HIGHLIGHTS: Artificial intelligence (AI) is fast becoming a component of modern life, being used in many areas. AI has a growing impact on achieving the Sustainable Development Goals. It can catalyse innovations in areas as varied as circular economy and smart cities. AI offers a vital nexus between sustainable development and effective climate change adaptation. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) is predicted to have a deep impact on the future of work and employment. The paper outlines a normative framework to understand and protect human freedom and justice in this transition. The proposed framework is based on four main ideas: going beyond the idea of a Basic Income to compensate the losers in the transition towards AI-driven work, towards a Responsible Innovation approach, in which the development of AI technologies is governed by an inclusive and deliberate societal judgment; going beyond a philosophical conceptualisation of social justice only focused on the distribution of 'primary goods', towards one focused on the different goals, values, and virtues of various social practices (Walzer's 'spheres of justice') and the different individual capabilities of persons (Sen's 'capabilities'); going beyond a classical understanding of capital, towards one explicitly including mental capacities as a source of value for AI-driven activities. In an effort to promote an interdisciplinary approach, the paper combines political and economic theories of freedom, justice and capital with recent approaches in applied ethics of technology, and starts applying its normative framework to some concrete example of AI-based systems: healthcare robotics, 'citizen science', social media and platform economy. [ABSTRACT FROM AUTHOR]

In this paper, the author introduced a sentiment analysis-based customer loyalty prediction model in mobile applications using word embedding models, deep learning algorithms, and deep contextualized word representations. This method showed better performances than usual state-of-the-art methods in separate data sets along with the combined data set on both gender and region classification. Intelligent Systems can be thought of as a concept with a very broad scope. [Extracted from the article]

To optimize anthropogenic material streams, the production process, as well as the quality of the products, must be known. With knowledge of these requirements, it is possible to use extra applied algorithms—in this case evolutionary algorithms as part of artificial intelligence—for the optimization of these secondary material streams. The benefit of this application is the fast and precise calculation of the local and global optima of the optimizing problem. This calculation method uses the benefits of the biological reproduction by applications of mutation, selection, and recombination to find one of the best results in a huge amount of possible and potential results. For the use of secondary materials in the paper production it could be proven that in spite of high quotes of secondary materials in different paper classes, there are some paper classes in which the amount of secondary material could be raised without losing any quality. [ABSTRACT FROM AUTHOR]

This collection on 'Artificial intelligence, big data, algorithms and Industry 4.0 in firms and clusters' is introduced exploring the themes discussed by the nine papers and grouped into three categories to uncover new dynamics and identify future research opportunities for clusters and organizations in these transformative times. The first group explores theoretical aspects of AI and its evolution in social sciences, focusing on industry 4.0, smart cities, big data, and other related topics. The second group examines the role of industrial robots in employment, productivity, and knowledge absorption in industrial districts. The third group discusses innovation in the context of local production systems, AI ecosystems, and the growth and potential of the Metaverse. [ABSTRACT FROM AUTHOR]

Traditional health systems mostly rely on rules created by experts to offer adaptive interventions to patients. However, with recent advances in artificial intelligence (AI) and machine learning (ML) techniques, health-related systems are becoming more sophisticated with higher accuracy in providing more personalized interventions or treatments to individual patients. In this paper, we present an extensive literature review to explore the current trends in ML-based adaptive systems for health and well-being. We conduct a systematic search for articles published between January 2011 and April 2022 and selected 87 articles that met our inclusion criteria for review. The selected articles target 18 health and wellness domains including disease management, assistive healthcare, medical diagnosis, mental health, physical activity, dietary management, health monitoring, substance use, smoking cessation, homeopathy remedy finding, patient privacy, mobile health (mHealth) apps finder, clinician knowledge representation for neonatal emergency care, dental and oral health, medication management, disease surveillance, medical specialty recommendation, and health awareness. Our review focuses on five key areas across the target domains: data collection strategies, model development process, ML techniques utilized, model evaluation techniques, as well as adaptive or personalization strategies for health and wellness interventions. We also identified various technical and methodological challenges including data volume constraints, data quality issues, data diversity or variability issues, infrastructure-related issues, and suitability of interventions which offer directions for future work in this area. Finally, we offer recommendations for tackling these challenges, leveraging on technological advances such as multimodality, Cloud technology, online learning, edge computing, automatic re-calibration, Bluetooth auto-reconnection, feedback pipeline, federated learning, explainable AI, and co-creation of health and wellness interventions. [ABSTRACT FROM AUTHOR]

This editorial aims to present the papers accepted for the special issue (SI) 'Industry experiences of Artificial Intelligence (AI): benefits and challenges in operations and supply chain management.' First, we provide a brief introduction considering the relationship between AI and operations and supply chain management (OSCM) by highlighting some companies already using and practical insights. In sequence, we introduce the papers selected for the SI. The last section gives some intriguing and challenging research directions for scholars and industry practitioners by highlighting potential topics, research opportunities, and possible benefits. Through this SI, we look forward to helping industry practitioners, policy-makers, scholars, and all interested in this field to gain more knowledge about AI applications and insights in relation to OSCM. [ABSTRACT FROM AUTHOR]

The present study analyzes the research output of India in the field of artificial intelligence using scientometric analysis techniques. The data were collected manually using the Scopus database at the end of July 2015. Publications in the field of artificial intelligence research in India from 1968 to 2014 were retrieved. It was found that a total of 6,529 papers were published in the field of artificial intelligence in India during that time period. The research output has grown considerably since 2004, and the last 2 years have witnessed a large publications output from the field. The average citation per paper of this data set is 3.06. The average number of authors per paper is three. “Artificial Intelligence” was found to be the most popular keyword, followed by “Algorithms.” A total of 12.64% of the papers have been published with international collaboration. Anna University was found to be the leader in research productivity. It was found that the IITs played a major role in the field of artificial intelligence research in India. [ABSTRACT FROM AUTHOR]

Delayed graft function (DGF) is an early complication after kidney transplantation. The literature on DGF has experienced substantial growth. However, there is a lack of bibliometric analysis of DGF. This study aimed to analyze the scientific outputs of DGF and explore its hotspots from 2013 to 2023 by using CiteSpace and VOSviewer. The 2058 pieces of literature collected in the Web of Science Core Collection (WOSCC) from 1 January 2013 to 31 December 2023 were visually analyzed in terms of the annual number of publications, authors, countries, journals, literature co-citations, and keyword clustering by using CiteSpace and VOSviewer. We found that the number of papers published in the past ten years showed a trend of first increasing and then decreasing; 2021 was the year with the most posts. The largest number of papers was published by the University of California System, and the largest number of papers was published by the United States. The top five keyword frequency rankings are: 'delayed graft function', 'kidney transplantation', 'renal transplantation', 'survival', and 'recipients'. These emerging trends include 'brain death donors', 'blood absence re-injection injuries', 'tacrolimus', 'older donors and recipients', and 'artificial intelligence and DGF'. In summary, this study reveals the authors and institutions that could be cooperated with and discusses the research hotspots in the past ten years. It provides a reference and direction for future research and application of DGF. [ABSTRACT FROM AUTHOR]

With the rapid development of multimedia signal processing and digital communication technology, a number of information security issues have also emerged correspondingly, such as content tampering, privacy leakage, copy-move forgery in digital images and videos, and biometric spoofing. This special issue on AI Oriented Information Hiding and Multimedia Forensics focuses on the new methods of information hiding and forensics for multimedia data based on intelligent techniques. AI Security The paper, entitled "High-Capacity Information Hiding Based on Residual Network", aims to solve the problem of low capacity of conventional information hiding methods, and utilizes the deep neural network of ResNet to build a hidden network and a decoding network for secret image hiding. [Extracted from the article]

In this article, we explore the potential of enhancing academic prose and idea generation by fine-tuning a large language model (here, GPT-3) on one's own previously published writings: AUTOGEN ("AI Unique Tailored Output GENerator"). We develop, test, and describe three distinct AUTOGEN models trained on the prior scholarly output of three of the current authors (SBM, BDE, JS), with a fourth model trained on the combined works of all three. Our AUTOGEN models demonstrate greater variance in quality than the base GPT-3 model, with many outputs outperforming the base model in format, style, overall quality, and novel idea generation. As proof of principle, we present and discuss examples of AUTOGEN-written sections of existing and hypothetical research papers. We further discuss ethical opportunities, concerns, and open questions associated with personalized academic prose and idea generators. Ethical opportunities for personalized LLMs such as AUTOGEN include increased productivity, preservation of writing styles and cultural traditions, and aiding consensus building. However, ethical concerns arise due to the potential for personalized LLMs to reduce output diversity, violate privacy and intellectual property rights, and facilitate plagiarism or fraud. The use of coauthored or multiple-source trained models further complicates issues surrounding ownership and attribution. Open questions concern a potential credit-blame asymmetry for LLM outputs, the legitimacy of licensing agreements in authorship ascription, and the ethical implications of coauthorship attribution for data contributors. Ensuring the output is sufficiently distinct from the source material is crucial to maintaining ethical standards in academic writing. These opportunities, risks, and open issues highlight the intricate ethical landscape surrounding the use of personalized LLMs in academia. We also discuss open technical questions concerning the integration of AUTOGEN-style personalized LLMs with other LLMs, such as GPT-4, for iterative refinement and improvement of generated text. In conclusion, we argue that AUTOGEN-style personalized LLMs offer significant potential benefits in terms of both prose generation and, to a lesser extent, idea generation. If associated ethical issues are appropriately addressed, AUTOGEN alone or in combination with other LLMs can be seen as a potent form of academic enhancement. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) policy, innovation, and practice are moving ahead in rapid fashion. There is a growing mismatch between technological innovations in AI, which are advancing at a rapid rate, and normative and regulatory frameworks, which are lagging, particularly when it comes to protecting democratic values and human rights principles. National governments and multilateral institutions are attempting to catch up. At least 175 countries, firms and other organizations have produced documents listing ethical principles for AI. These efforts have proceeded in a somewhat fragmented manner, yet there are emerging signs of consolidation as the United States, Europe, and other countries begin to coalesce around shared principles. Europe, in particular, has raced ahead to draft comprehensive legislation, the Artificial Intelligence Act (AIA), to oversee these technologies and systems. What has motivated the European Union to pursue this approach? And how will this effort influence AI norms globally? This paper describes how Europe's AI norm-building process represents an effort to ensure EU priorities are reflected in the AI governance landscape. Europe's approach faces uncertainty. While it is likely that the AIA will meaningfully influence global AI norms, several factors may hinder its global diffusion and adoption. [ABSTRACT FROM AUTHOR]

Technology and computation in 2024 are dominated by applications of artificial intelligence (AI) and large language models (LLMs). The domains of marketing and advertising have already been transformed over the past 30 years by the internet, data, and digital technologies. Practitioners and scholars are focused on AI and LLMs as the drivers of the next paradigm shift in communication and advertising. Both constituencies are aligned on the primary goal of advertising to create economic growth by building brands with differentiation and meaning that contribute to corporate financial valuations. This paper notes the failure of digital advertising to build brands over the past three decades, while economic value in markets has been usurped by digital platforms. Further, scholars question the wider impact of digital advertising on society and the environment. This paper presents a positive vision for marketing and advertising enabled by LLMs. It envisions advertising's goals redefined for a "triple bottom line" of profit, people, and planet. It proposes a systems dynamics framework to model how advertising can contribute to business innovation and profit within an equitable society and sustainable economy. Finally, the paper calls for research to investigate this positive future. [ABSTRACT FROM AUTHOR]

This paper offers a novel perspective on trust in artificial intelligence (AI) systems, focusing on the transfer of user trust in AI creators to trust in AI systems. Using the agentic information systems (IS) framework, we investigate the role of AI alignment and steerability in trust transference. Through four randomized experiments, we probe three key alignment-related attributes of AI systems: creator-based steerability, user-based steerability, and autonomy. Results indicate that creator-based steerability amplifies trust transference from AI creator to AI system, while user-based steerability and autonomy diminish it. Our findings suggest that AI alignment efforts should consider the entity with which the AI goals and values should be aligned and highlight the need for research to theorize from a triadic view encompassing the user, the AI system, and its creator. Given the diversity in individual goals and values, we recommend that developers move beyond the prevailing "one-size-fits-all" alignment strategy. Our findings contribute to trust transference theory by highlighting the boundary conditions under which trust transference breaks down or holds in the emerging human-AI environment. [ABSTRACT FROM AUTHOR]

Reports on the 1st European Workshop on Multi-Agent Systems, held at St. Catherine's College in Oxford University in Oxford, England. Increase in the number of European researchers in the area of multi-agent systems; Aim of the workshop; Sessions in the workshop; Details of the second Workshop on Multi-Agent Systems.

Human error estimating food intake is a major source of bias in nutrition research. Artificial intelligence (AI) methods may reduce bias, but the overall accuracy of AI estimates is unknown. This study was a systematic review of peer-reviewed journal articles comparing fully automated AI-based (e.g. deep learning) methods of dietary assessment from digital images to human assessors and ground truth (e.g. doubly labelled water). Literature was searched through May 2023 in four electronic databases plus reference mining. Eligible articles reported AI estimated volume, energy, or nutrients. Independent investigators screened articles and extracted data. Potential sources of bias were documented in absence of an applicable risk of bias assessment tool. Database and hand searches identified 14,059 unique publications; fifty-two papers (studies) published from 2010 to 2023 were retained. For food detection and classification, 79% of papers used a convolutional neural network. Common ground truth sources were calculation using nutrient tables (51%) and weighed food (27%). Included papers varied widely in food image databases and results reported, so meta-analytic synthesis could not be conducted. Relative errors were extracted or calculated from 69% of papers. Average overall relative errors (AI vs. ground truth) ranged from 0.10% to 38.3% for calories and 0.09% to 33% for volume, suggesting similar performance. Ranges of relative error were lower when images had single/simple foods. Relative errors for volume and calorie estimations suggest that AI methods align with – and have the potential to exceed – accuracy of human estimations. However, variability in food image databases and results reported prevented meta-analytic synthesis. The field can advance by testing AI architectures on a limited number of large-scale food image and nutrition databases that the field determines to be adequate for training and testing and by reporting accuracy of at least absolute and relative error for volume or calorie estimations. These results suggest that AI methods are in line with – and have the potential to exceed – accuracy of human estimations of nutrient content based on digital food images. Variability in food image databases used and results reported prevented meta-analytic synthesis. The field can advance by testing AI architectures on a limited number of large-scale food image and nutrition databases that the field determines to be accurate and by reporting accuracy of at least absolute and relative error for volume or calorie estimations. Overall, the tools currently available need more development before deployment as stand-alone dietary assessment methods in nutrition research or clinical practice. [ABSTRACT FROM AUTHOR]

Based on the panel data of 285 cities in China from 2000 to 2019, this paper searches the number of patent applications related to urban artificial intelligence from five dimensions: algorithm, data, computing power, application scenario and related technology. Combining the two perspectives of industrial upgrading and rationalization, we analyze the internal influence theory of the research topic from the theoretical and empirical perspectives. The results show that artificial intelligence is not only conducive to industrial upgrading, but also significantly inhibit the deviation of industrial structure from equilibrium, which is conducive to industrial rationalization. In addition, the conclusion of this paper is still valid after a series of robustness tests, such as eliminating the samples of central cities, winsorize treatment and instrumental variables method. Through the heterogeneity test, it is found that the promoting effect of artificial intelligence on industrial upgrading is more obvious in big cities and cities with high level of industrial upgrading. The internal mechanism test results show that artificial intelligence promotes industrial upgrading by promoting technological innovation. In cities with a high degree of marketization and Internet development, the role of artificial intelligence in promoting industrial upgrading can be strengthened. The research conclusions of this paper will be conducive to accelerating the development of artificial intelligence to promote industrial upgrading, and provide a useful reference for realizing high-quality development. [ABSTRACT FROM AUTHOR]

This paper highlights the importance of involving academic librarians in the development of ethical AI policies. The Academic Librarian Framework for Ethical AI Policy Development (ALF Framework) is introduced, recognizing librarians' unique skills and expertise. The paper discusses the benefits of their involvement, including expertise in information ethics and privacy, practical experience with AI tools, and collaborations. It also addresses challenges, such as limited awareness, institutional resistance, resource constraints, interdisciplinary collaboration, and evolving AI technologies, offering practical solutions. By actively involving librarians, institutions can develop comprehensive and ethical AI policies that prioritize social responsibility and respect for human rights. [ABSTRACT FROM AUTHOR]

The associability of artificial intelligence (AI) as a dual-use technology for nuclear material production (NMP) within the academic and practitioner communities remains widely neglected, and so a widening opportunity for AI to aid in illicit and covert non-peaceful applications exists. To address this emerging gap, this paper investigates the evolving and applicable uses of AI and finds broad evidence of its use to optimize performance, promote innovation, reduce costs, and enhance safety associated with the development and production of nuclear material. AI's use in this arena will, thereby, facilitate broader accessibility of peaceful uses of nuclear science and technology, while at the same time cause concerns that said improvements can aid the illicit development of nuclear weapons. As such, this paper advocates for a three-dimensional solution to manage the evolving dual-use concern of AI that involves advancing states-centric monitoring and regulation, promoting intellectual exchange between the nonproliferation sector and the AI industry, and encouraging AI industrial contributions. [ABSTRACT FROM AUTHOR]

The objective of this paper is to examine the use and applications of reinforcement learning (RL) techniques in the production planning and control (PPC) field addressing the following PPC areas: facility resource planning, capacity planning, purchase and supply management, production scheduling and inventory management. The main RL characteristics, such as method, context, states, actions, reward and highlights, were analysed. The considered number of agents, applications and RL software tools, specifically, programming language, platforms, application programming interfaces and RL frameworks, among others, were identified, and 181 articles were sreviewed. The results showed that RL was applied mainly to production scheduling problems, followed by purchase and supply management. The most revised RL algorithms were model-free and single-agent and were applied to simplified PPC environments. Nevertheless, their results seem to be promising compared to traditional mathematical programming and heuristics/metaheuristics solution methods, and even more so when they incorporate uncertainty or non-linear properties. Finally, RL value-based approaches are the most widely used, specifically Q-learning and its variants and for deep RL, deep Q-networks. In recent years however, the most widely used approach has been the actor-critic method, such as the advantage actor critic, proximal policy optimisation, deep deterministic policy gradient and trust region policy optimisation. [ABSTRACT FROM AUTHOR]

There are great interests of designing research metrics and indices to measure the research impacts in research institutes. Unfortunately, most of those indices ignore critical design issues, e.g. the disparity between domains, the impact of journals or conferences in which papers are published, normalising the range of the index values to certain intervals, and the scalability of using the index to rank different research entities. In this paper, a new normalised fuzzy index, (NFindex), is proposed as a fuzzy-based research impact metric. The proposed index is a scalable index whose values are normalised to the percentage levels. NFindex achieves both inter-discipline normalisation and intra-discipline consistency. The capability of NFindex to achieve the inter-discipline normalisation enables fair comparison between different research domains regardless their nature in terms of influence and contribution to other research areas, e.g. natural science. Therefore, NFindex gives a universal normalised single-number metric that can be used by research institutes to solve the problem of inter-discipline scholar ranking. Moreover, it can help universal ranking of universities and research institutes according to their research capabilities and impacts. The obtained results, on diverse research areas, prove the potential of NFindex in terms of both intra-discipline consistency and inter-discipline normalisation. [ABSTRACT FROM AUTHOR]

As the core issue for improving manufacturing system performance (such as product quality, production efficiency and cost, etc.), modeling, analysis and optimization of manufacturing systems have always been studied towards smart manufacturing. The paper I KGAssembly: Knowledge graph-driven assembly process generation and evaluation for complex components i , by Bin Zhou, Jinsong Bao, Zhiyu Chen and Yahui Liu, proposes a knowledge graph-driven assembly process generation and evaluation method for complex components. [Extracted from the article]

Can Artificial Intelligence (AI) be used for good in the academic and creative worlds? This is a question that is being asked by educators who are concerned about student learning and academic integrity AI use in higher education. Numerous articles decry the potential of college students using AI to cheat on exam questions, discussion board posts, and research papers. Yet, through the use of language learning models (LLM), AI may be used as a tool to increase efficiency, perform repetitive tasks, and aid with research and analysis. It is through the lens of AI as a tool, not as a concern or replacement of human involvement, that the authors examined Elicit.org, a literature review search tool that uses LLM to aid the research process. [ABSTRACT FROM AUTHOR]

Manufacturing is undergoing a paradigmatic shift as it assimilates and is transformed by machine learning and other cognitive technologies. A new paradigm usually necessitates a new framework to comprehend it fully, organise extant knowledge, identify gaps in knowledge, guide future research and practice, and synthesise new knowledge. Paradoxically, such a framework to guide the research and practice of ML in manufacturing remains absent. This paper attempts to fill this gap by presenting the interpretive model of manufacturing as an integrative framework for ML in manufacturing. A systematic hybrid literature review approach has been adopted to conduct both thematic and conceptual synthesis of the literature. The descriptive literature review method has been used to conduct a thematic synthesis of the literature. The framework synthesis method has been used to complete a conceptual synthesis of the literature. The resultant framework, the interpretive model of manufacturing, is articulated as consisting of scan, store, interpret, execute, and learn as its purposive components. Research questions have been identified for each of these components, as well as at their interfaces, to develop a comprehensive and systematic research agenda. Additional areas for extending research have also been identified. Implications for manufacturing operations, manufacturing strategy, and manufacturing policy have been drawn out for practitioners and policy makers. [ABSTRACT FROM AUTHOR]