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We stand at the threshold of a revolution in science. It is possible that soon scientific writing as we know it will undergo a rapid change, all thanks to the ChatGPT 3.5 language model. The appearance of this tool on the technology market in November 2022 caused a massive stir among Internet users and the academic world. It turned out that chat has the potential to generate completely new and unique scientific texts. In the wake of ChatGPT, similar tools by technology giants came and are coming. Given the above, the scientific community has only a brief moment to try to answer the following questions: whether the texts generated in this way have real scientific value, whether it is ethical for researchers to use them, how to regulate copyright on the use of AI, what are the potential capabilities of artificial intelligence for writing scientific papers. The indicated themes are reflected in the presented text. [ABSTRACT FROM AUTHOR]

This paper investigates the complex interplay between generative artificial intelligence (AI) and human intellect in academic writing and publishing. It examines the 'organic versus synthetic' paradox, emphasizing the implications of using generative AI tools in educational and academic integrity contexts. The paper critiques the prevalent 'publish or perish' culture in academia, highlighting the need for systemic reevaluation due to generative AI's emerging role in academic writing and reporting. It delves into the legal and ethical challenges of authorship and ownership, especially in relation to copyright laws and AI-generated content. The paper discusses generative AI's diverse roles and advocates for transparent reporting to uphold academic integrity. Additionally, it calls for a broader examination of generative AI tools and stresses the need for new mechanisms to identify generative AI use and ensure adherence to academic integrity and ethics. The implications of generative AI are also explored, suggesting the need for innovative AI-inclusive strategies in academia. The paper concludes by emphasizing the significance of generative AI in various information-processing domains, highlighting the urgency to adapt and transform academic practices in an era of rapid generative AI-driven change. [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]

There has been a rise in scepticism regarding the use of Artificial Intelligence (AI) in qualitative research tasks such as critical reviews, conceptualization, thematic and content analysis, and potentially theory development. Concerns have been raised over the possibility that researchers intentionally avoid discussing or even mentioning the use of AI in their studies for a variety of reasons, including the "fear" of criticism and rejection of their papers. The purpose of this paper, which is guided by critical perspective principles, is to examine the controversy surrounding the appropriate recognition of AI in theoretical discussions and qualitative research, including conceptual, critical reviews, empirical, and other types of studies of qualitative nature. Prior to a discussion of how to acknowledge the use of AI, the significance of notions of acknowledgment and academic integrity in the context of research are discussed. As the author of this paper, I acknowledge and document the use of both AI and the researcher's cognitive skills in the development of this theoretical critical perspective study through a four-phase process, while giving directions of when and how to acknowledge the use of AI in qualitative studies. [ABSTRACT FROM AUTHOR]

In recent decades, artificial intelligence has undergone transformative advancements, reshaping diverse sectors such as healthcare, transport, agriculture, energy, and the media. Despite the enthusiasm surrounding AI's potential, concerns persist about its potential negative impacts, including substantial energy consumption and ethical challenges. This paper critically reviews the evolving landscape of AI sustainability, addressing economic, social, and environmental dimensions. The literature is systematically categorized into "Sustainability of AI" and "AI for Sustainability", revealing a balanced perspective between the two. The study also identifies a notable trend towards holistic approaches, with a surge in publications and empirical studies since 2019, signaling the field's maturity. Future research directions emphasize delving into the relatively under-explored economic dimension, aligning with the United Nations' Sustainable Development Goals (SDGs), and addressing stakeholders' influence. [ABSTRACT FROM AUTHOR]

Across the world, artificial intelligence (AI) technologies are being more widely employed in public sector decision-making and processes as a supposedly neutral and an efficient method for optimizing delivery of services. However, the deployment of these technologies has also prompted investigation into the potentially unanticipated consequences of their introduction, to both positive and negative ends. This paper chooses to focus specifically on the relationship between gender bias and AI, exploring claims of the neutrality of such technologies and how its understanding of bias could influence policy and outcomes. Building on a rich seam of literature from both technological and sociological fields, this article constructs an original framework through which to analyse both the perpetuation and mitigation of gender biases, choosing to categorize AI technologies based on whether their input is text or images. Through the close analysis and pairing of four case studies, the paper thus unites two often disparate approaches to the investigation of bias in technology, revealing the large and varied potential for AI to echo and even amplify existing human bias, while acknowledging the important role AI itself can play in reducing or reversing these effects. The conclusion calls for further collaboration between scholars from the worlds of technology, gender studies and public policy in fully exploring algorithmic accountability as well as in accurately and transparently exploring the potential consequences of the introduction of AI technologies. [ABSTRACT FROM AUTHOR]

As a help to researchers for organizing their thoughts and providing data-driven outcomes, Artificial Intelligence (AI) has the potential to develop scientific writing. AI-powered technologies have been created by businesses like Semantic Scholar and Paper Digest to scan scientific texts and extract pertinent data. By expediting the publishing process and enabling academics to concentrate more on their own work, AI-based writing tools like GPT-3 can produce high-quality papers that closely resemble those of well-known authors. These tools can help with idea organization, creating rough drafts, and enhancing the general caliber of scientific work. For instance, ChatGPT is a useful tool in research and publishing since it may help scientists with material arrangement, draft generation, and proofreading. The drawbacks of AI must be understood, as well as the difficulties posed by prejudice, ethical issues, and the requirement for human innovation. We can improve the scientific writing process and increase scientific research by utilizing AI's potential while adding human knowledge. But there is still room for development, and it is essential to guarantee openness, morality, and reliability in AI-driven technology for academic study. [ABSTRACT FROM AUTHOR]

This paper explores a paradigm shift in work culture in the contemporary art sector due to digital transition and the introduction of AI. New ways of working with AI and digital software are embedded and normalized in everyday Chinese artistic practices. This work includes new forms of creativity and efficiency, yet, simultaneously includes new types of digital labour. This paper conceptualizes this as "digital art work," which draws attention to the often-overlooked aspects of artists' work, particularly their everyday artistic practices that increasingly include digital software and AI. What is the role and position of the artist in an environment where digital software and AI are becoming more central in artistic creation? How do artists creatively (mis)use AI? What does this paradigm shift in work culture mean for the future of the artist's role and the future of the contemporary art sector? This paper draws on 48 semi-structured interviews with visual artists and arts professionals, including painters, sculptors, mixedmedia, and internet artists as well as contemporary art gallery owners, museum project directors, curators, and culture policymakers living and working in China during 2023. The findings show how Chinese artists are mastering AI and opening up new spaces for creativity and how the contemporary art sector in China has already transitioned to a new "digital way" in artistic creation. These findings can help to create policy around AI globally and provide solutions for the sustainability of the artist profession and the future of the contemporary art sector. [ABSTRACT FROM AUTHOR]

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In recent years, with the continuous expansion of artificial intelligence (AI) application forms and fields, users' acceptance of AI applications has attracted increasing attention from scholars and business practitioners. Although extant studies have extensively explored user acceptance of different AI applications, there is still a lack of understanding of the roles played by different AI applications in human–AI interaction, which may limit the understanding of inconsistent findings about user acceptance of AI. This study addresses this issue by conducting a systematic literature review on AI acceptance research in leading journals of Information Systems and Marketing disciplines from 2020 to 2023. Based on a review of 80 papers, this study made contributions by (i) providing an overview of methodologies and theoretical frameworks utilized in AI acceptance research; (ii) summarizing the key factors, potential mechanisms, and theorization of users' acceptance response to AI service providers and AI task substitutes, respectively; and (iii) proposing opinions on the limitations of extant research and providing guidance for future research. [ABSTRACT FROM AUTHOR]

A new solution for fully automated and unmanned ticket pasting verification based on deep convolutional neural networks is designed to address the issues of low efficiency, error-proneness, and wastage of manpower in the supplier service hall. The technology makes full use of machine vision and image processing, AI precise positioning correction algorithm and other methods to build an automatic unattended bill collection, paste and verification platform. Through the technologies of high-speed identification of invoice information, 3D vision-guidance planning, control of the path of robotic arm, detection of invoice pasting and repeating based on ultrasonic sensors, and tidal temporary storage of paper invoices, and so on, the automatic high-speed identification and inspection of bills in the supplier service hall are realized, and the efficiency and accuracy of bill processing in the supplier hall are improved. Experiments show that this research method reinforces ability of identification calibration and order correlation, and improves the efficiency of Invoice filing. [ABSTRACT FROM AUTHOR]

Large format IoT displays support a range of exciting visual experiences which are encountered in our day‐to‐day lives. Four processor roles, media player, display controller, touch controller and CV/AI module, leverage four processor types, scalar, vector, spatial and matrix, to power these experiences. Algorithm characteristics such as arithmetic, memory access and data‐dependence complexity influence the suitability of an architecture to a workload. Including, GPUs to rendering, CPUs to interactive application, and FPGAs to custom video pipelines. Programmatic advertising, touch‐less kiosks and CODEC enhancements to minimize bitrate of 8K video streams can require inference (AI) workloads. Matrix (I PU) type processors are designed to address this workload type. The narrow pitch LED wall display market is growing at 10x the overall video wall display market rate and leverages spatial FPGA processing in LED controllers, also requiring 8K resolution at smaller surface area sizes as pixel pitch reduces. Requirements on each heterogenous processor type is concluded to be growing with the combination of market trends. [ABSTRACT FROM AUTHOR]

Scaled agile development approaches are now used widely in modern software engineering, allowing businesses to improve teamwork, productivity, and product quality. The incorporation of artificial intelligence (AI) into scaled agile development methods (SADMs) has emerged as a potential strategy in response to the ongoing demand for simplified procedures and the increasing complexity of software projects. This paper explores the intersection of AI-driven assistants within the context of the scaled agile framework (SAFe) for large-scale software development, as it stands out as the most widely adopted framework. Our paper pursues three principal objectives: (1) an evaluation of the challenges and impediments encountered by organizations during the implementation of SADMs, (2) an assessment of the potential advantages stemming from the incorporation of AI in large-scale contexts, and (3) the compilation of aspects of SADMs that AI-driven assistants enhance. Through a comprehensive systematic literature review, we identified and described 18 distinct challenges that organizations confront. In the course of our research, we pinpointed seven benefits and five challenges associated with the implementation of AI in SADMs. These findings were systematically categorized based on their occurrence either within the development phase or the phases encompassing planning and control. Furthermore, we compiled a list of 15 different AI-driven assistants and tools, subjecting them to a more detailed examination, and employing them to address the challenges we uncovered during our research. One of the key takeaways from this paper is the exceptional versatility and effectiveness of AI-driven assistants, demonstrating their capability to tackle a broader spectrum of problems. In conclusion, this paper not only sheds light on the transformative potential of AI, but also provides invaluable insights for organizations aiming to enhance their agility and management capabilities. [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]

Large format IoT displays support a range of exciting visual experiences which are encountered in our day‐to‐day lives. Four processor roles, media player, display controller, touch controller and CV/AI module, leverage four processor types, scalar, vector, spatial and matrix, to power these experiences. Algorithm characteristics such as arithmetic, memory access and data‐dependence complexity influence the suitability of an architecture to a workload. Including, GPUs to rendering, CPUs to interactive application, and FPGAs to custom video pipelines. Programmatic advertising, touch‐less kiosks and CODEC enhancements to minimize bitrate of 8K video streams can require inference (AI) workloads. Matrix (VPU) type processors are designed to address this workload type. The narrow pitch LED wall display market is growing at 10x the overall video wall display market rate and leverages spatial FPGA processing in LED controllers, also requiring 8K resolution at smaller surface area sizes as pixel pitch reduces. Requirements on each heterogenous processor type is concluded to be growing with the combination of market trends. [ABSTRACT FROM AUTHOR]

Gravitational wave research presents a range of intriguing challenges, each of which has driven significant progress in the field. Key research problems include glitch classification, glitch cancellation, gravitational wave denoising, binary black hole signal detection, gravitational wave bursts, and minor issues that contribute to the overall understanding of gravitational wave phenomena. This paper explores the applications of artificial intelligence, deep learning, and machine learning techniques in addressing these challenges. The main goal of the paper is to provide an effective view of AI and deep learning usage for gravitational wave analysis. Thanks to the advancements in artificial intelligence and machine learning techniques, aided by GPUs and specialized software frameworks, these techniques have played a key role over the last decade in the identification, classification, and cancellation of gravitational wave signals, as presented in our results. This paper provides a comprehensive exploration of the adoption rate of these techniques, with reference to the software and hardware involved, their effectiveness, and potential limitations, offering insights into the advancements in the analysis of gravitational wave data. [ABSTRACT FROM AUTHOR]