Search

The irruption of artificial intelligence (AI) in all areas of our lives is a reality to which the university, as an institution of higher education, must respond prudently, but also with no hesitation. This paper discusses the potential that resources based on AI presents as potential reviewers of scientific articles in a hypothetical peer review of already published articles. Using different models (GPT-3.5 and GPT-4) and platforms (ChatPDF and Bing), we obtained three full reviews, both qualitative and quantitative, for each of the five articles examined, thus being able to delineate and contrast the results of all of them in terms of the human reviews that these same articles received at the time. The evidence found highlights the extent to which we can and should rely on generative language models to support our decisions as qualified experts in our field. Furthermore, the results also corroborate the hallucinations inherent in these models while pointing out one of their current major shortcomings: the context window limit. On the other hand, the study also points out the inherent benefits of a model that is in a clear expansion phase, providing a detailed view of the potential and limitations that these models offer as possible assistants to the review of scientific articles, a key process in the communication and dissemination of academic research. [ABSTRACT FROM AUTHOR]

Manuscript writing support services using AI technology have become increasingly available in recent years. In keeping with this trend, we need to sort out issues related to authorship in academic writing. Authorship is attached to the contribution of researchers who report innovative research, the originality of which forms the core of their identity. The most important originality is demonstrated in the discussion of study findings. In the discussion section of this paper, we argue that if a researcher uses AI-based manuscript writing support to draft the discussion section, this does not necessarily diminish the researcher's originality. Rather, AI support may allow the researcher to perform creative work in a more refined fashion. Presumably, selecting which AI support to use or evaluating and properly adjusting AI would still remain an important aspect of research for researchers. It is thus reasonable to view a researcher as a cooperative existence realized through a network of cooperative work that includes the use of AI. Discussions on this topic will be scientifically and socially important as AI technology advances in the future. [ABSTRACT FROM AUTHOR]

Artificial intelligence is more present than ever, both in our society in general and in science. At the center of this development has been the concept of deep learning, the use of artificial neural networks that are many layers deep and can often reproduce human‐like behavior much better than other machine‐learning techniques. The articles in this collection are some recent examples of its application for X‐ray photon science and crystallography that have been published in Journal of Applied Crystallography. [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]

For decades, the data-centric digital circuits of global finance have been driving financial markets toward algorithmic management. The penetration of algorithms into finance dates back to the 1980s, well before becoming a significant trend in Internet and social media companies. AI is now considered the new frontier of this process. This paper examines Brazil, the most important financial center in Latin America, as a case study to explore Machine Learning applications in capital markets, providing new evidence on the penetration of automated trading systems in the region. Grounded in a historical overview of the digitalization of capital markets in Brazil, the paper draws on the STS definition of infrastructures and the political economy of digitalized finance to analyze AI as an emerging financial infrastructure. Beyond its physical and material aspects, this approach also addresses how digitalization and AI relate to financialization and the emergence of new financial risks. [ABSTRACT FROM AUTHOR]

Discogenic low back pain (LBP) is a significant clinical condition arising from degeneration of the intervertebral disc, a common yet complex cause of chronic pain, defined by fissuring in the annulus fibrosus resulting in vascularization of growing granulation tissue and growth of nociceptive nerve fibers along the laceration area. This paper delves into the anatomical and pathophysiological underpinnings of discogenic LBP, emphasizing the role of intervertebral disc degeneration in the onset of pain. The pathogenesis is multifactorial, involving processes like mitochondrial dysfunction, accumulation of advanced glycation end products, and pyroptosis, all contributing to disc degeneration and subsequent pain. Despite its prevalence, diagnosing discogenic LBP is challenging due to the overlapping symptoms with other forms of LBP and the absence of definitive diagnostic criteria. Current diagnostic approaches include clinical evaluations, imaging techniques, and the exploration of potential biomarkers. Treatment strategies range from conservative management, such as physical therapy and pharmacological interventions, to more invasive procedures such as spinal injections and surgery. Emerging therapies targeting molecular pathways involved in disc degeneration are under investigation and hold potential for future clinical application. This paper highlights the necessity of a multidisciplinary approach combining clinical, imaging, and molecular data to enhance the accuracy of diagnosis and the effectiveness of treatment for discogenic LBP, ultimately aiming to improve patient outcomes. [ABSTRACT FROM AUTHOR]

This is a critique of how designers deal with temporality in design to speculate about socio-technical futures. The paper unpacks how embedded definitions and assumptions of temporality in current design tools contribute to coloniality in designed futures. Based on this critique, we reject the notion that it is only AI that needs fixing, as design practice becomes implicated in how oppression extends from physical systems to global digital platforms. To make these issues visible, we dissect the Futures Cone model used in speculative design. As an alternative, the paper then presents hauntology as a vocabulary that can aid designers in accommodating pluriversal histories in anticipatory futures and reorienting their speculative tools. To illustrate the benefits of the proposed metaphors, the paper highlights examples of coloniality in digital spaces and emphasizes the failure of speculative design to decolonize future imaginaries. Using points of reference from hauntology, ones that engage with states of lingering or spectrality, and notions of nostalgia, absence, and anticipation, the paper contributes to rethinking the role that design tools play in colonizing future imaginaries, especially those pertaining to potentially disruptive technologies. [ABSTRACT FROM AUTHOR]

Hydroponics is a soilless farming technique that has emerged as a sustainable alternative. However, new technologies such as Industry 4.0, the internet of things (IoT), and artificial intelligence are needed to keep up with issues related to economics, automation, and social challenges in hydroponics farming. One significant issue is optimizing growth parameters to identify the best conditions for growing fruits and vegetables. These parameters include pH, total dissolved solids (TDS), electrical conductivity (EC), light intensity, daily light integral (DLI), and nutrient solution/ambient temperature and humidity. To address these challenges, a systematic literature review was conducted aiming to answer research questions regarding the optimal growth parameters for leafy green vegetables and herbs and spices grown in hydroponic systems. The review selected a total of 131 papers related to indoor farming, hydroponics, and aquaponics. The review selected a total of 123 papers related to indoor farming, hydroponics, and aquaponics. The majority of the articles focused on technology description (38.5%), artificial illumination (26.2%), and nutrient solution composition/parameters (13.8%). Additionally, remaining 10.7% articles focused on the application of sensors, slope, environment and economy. This comprehensive review provides valuable information on optimized growth parameters for smart hydroponic systems and explores future prospects and the application of digital technologies in this field. [ABSTRACT FROM AUTHOR]

This article discusses the application of artificially intelligent robots within eldercare and explores a series of ethical considerations, including the challenges that AI (Artificial Intelligence) technology poses to traditional Chinese Confucian filial piety. From the perspective of Confucian ethics, the paper argues that robots cannot adequately fulfill duties of care. Due to their detachment from personal relationships and interactions, the "emotions" of AI robots are merely performative reactions in different situations, rather than actual emotional abilities. No matter how "humanized" robots become, it is difficult to establish genuine empathy and a meaningful relationship with them for this reason. Even so, we acknowledge that AI robots are a significant tool in managing the demands of elder care and the growth of care poverty, and as such, we attempt to outline some parameters within which care robotics could be acceptable within a Confucian ethical system. Finally, the paper discusses the social impact and ethical considerations brought on by the interaction between humans and machines. It is observed that the relationship between humans and technology has always had both utopian and dystopian aspects, and robotic elder care is no exception. AI caregiver robots will likely become a part of elder care, and the transformation of these robots from "service providers" to "companions" seems inevitable. In light of this, the application of AI-augmented robotic elder care will also eventually change our understanding of interpersonal relationships and traditional requirements of filial piety. [ABSTRACT FROM AUTHOR]

A key goal of educational institutions around the world is to provide inclusive, equitable quality education and lifelong learning opportunities for all learners. Achieving this requires contextualized approaches to accommodate diverse global values and promote learning opportunities that best meet the needs and goals of all learners as individuals and members of different communities. Advances in learning analytics (LA), natural language processes (NLP), and artificial intelligence (AI), especially generative AI technologies, offer potential to aid educational decision making by supporting analytic insights and personalized recommendations. However, these technologies also raise serious risks for reinforcing or exacerbating existing inequalities; these dangers arise from multiple factors including biases represented in training datasets, the technologies' abilities to take autonomous decisions, and processes for tool development that do not centre the needs and concerns of historically marginalized groups. To ensure that Educational Decision Support Systems (EDSS), particularly AI‐powered ones, are equipped to promote equity, they must be created and evaluated holistically, considering their potential for both targeted and systemic impacts on all learners, especially members of historically marginalized groups. Adopting a socio‐technical and cultural perspective is crucial for designing, deploying, and evaluating AI‐EDSS that truly advance educational equity and inclusion. This editorial introduces the contributions of five papers for the special section on advancing equity and inclusion in educational practices with AI‐EDSS. These papers focus on (i) a review of biases in large language models (LLMs) applications offers practical guidelines for their evaluation to promote educational equity, (ii) techniques to mitigate disparities across countries and languages in LLMs representation of educationally relevant knowledge, (iii) implementing equitable and intersectionality‐aware machine learning applications in education, (iv) introducing a LA dashboard that aims to promote institutional equality, diversity, and inclusion, and (v) vulnerable student digital well‐being in AI‐EDSS. Together, these contributions underscore the importance of an interdisciplinary approach in developing and utilizing AI‐EDSS to not only foster a more inclusive and equitable educational landscape worldwide but also reveal a critical need for a broader contextualization of equity that incorporates the socio‐technical questions of what kinds of decisions AI is being used to support, for what purposes, and whose goals are prioritized in this process. [ABSTRACT FROM AUTHOR]

Deepfakes raise various concerns: risks of political destabilization, depictions of persons without consent and causing them harms, erosion of trust in video and audio as reliable sources of evidence, and more. These concerns have been the focus of recent work in the philosophical literature on deepfakes. However, there has been almost no sustained philosophical analysis of deepfakes from the perspective of concerns about honesty and dishonesty. That deepfakes are potentially deceptive is unsurprising and has been noted. But under what conditions does the use of deepfakes fail to be honest? And which human agents, involved in one way or another in a deepfake, fail to be honest, and in what ways? If we are to understand better the morality of deepfakes, these questions need answering. Our first goal in this paper, therefore, is to offer an analysis of paradigmatic cases of deepfakes in light of the philosophy of honesty. While it is clear that many deepfakes are morally problematic, there has been a rising counter-chorus claiming that deepfakes are not essentially morally bad, since there might be uses of deepfakes that are not morally wrong, or even that are morally salutary, for instance, in education, entertainment, activism, and other areas. However, while there are reasons to think that deepfakes can supply or support moral goods, it is nevertheless possible that even these uses of deepfakes are dishonest. Our second goal in this paper, therefore, is to apply our analysis of deepfakes and honesty to the sorts of deepfakes hoped to be morally good or at least neutral. We conclude that, perhaps surprisingly, in many of these cases the use of deepfakes will be dishonest in some respects. Of course, there will be cases of deepfakes for which verdicts about honesty and moral permissibility do not line up. While we will sometimes suggest reasons why moral permissibility verdicts might diverge from honesty verdicts, we will not aim to settle matters of moral permissibility. [ABSTRACT FROM AUTHOR]

The article presents the actuarial artificial intelligence in playing the rock-paper-scissors game. Topics discussed include an overview of the rock-paper-scissors game, an outline of its different strategies, the definition of its variables, its basics, the reset of its probabilities, and the flexibility of its probabilities.

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]

This paper investigates how the introduction of AI to decision making increases moral distance and recommends the ethics of care to augment the ethical examination of AI decision making. With AI decision making, face-to-face interactions are minimized, and decisions are part of a more opaque process that humans do not always understand. Within decision-making research, the concept of moral distance is used to explain why individuals behave unethically towards those who are not seen. Moral distance abstracts those who are impacted by the decision and leads to less ethical decisions. The goal of this paper is to identify and analyze the moral distance created by AI through both proximity distance (in space, time, and culture) and bureaucratic distance (derived from hierarchy, complex processes, and principlism). We then propose the ethics of care as a moral framework to analyze the moral implications of AI. The ethics of care brings to the forefront circumstances and context, interdependence, and vulnerability in analyzing algorithmic decision making. [ABSTRACT FROM AUTHOR]

Police departments around the world implement algorithmic systems to enhance various policing tasks. Ensuring such innovations take place responsibly – with public values upheld – is essential for public organisations. This paper analyses how public values are safeguarded in the case of MONOcam, an algorithmic camera system designed and used by the Netherlands police. The system employs artificial intelligence to detect whether car drivers are holding a mobile device. MONOcam can be considered a good example of value-sensitive design; many measures were taken to safeguard public values in this algorithmic system. In pursuit of responsible implementation of algorithms, most calls and literature focus on such value-sensitive design. Less attention is paid to what happens beyond design. Building on 120+ hours of ethnographic observations as well as informal conversations and three semi-structured interviews, this research shows that public values deemed safeguarded in design are re-negotiated as the system is implemented and used in practice. These findings led to direct impact, as MONOcam was improved in response. This paper thus highlights that algorithmic system design is often based on an ideal world, but it is in the complexities and fuzzy realities of everyday professional routines and sociomaterial reality that these systems are enacted, and public values are renegotiated in the use of algorithms. While value-sensitive design is important, this paper shows that it offers no guarantees for safeguarding public values in practice. [ABSTRACT FROM AUTHOR]

Digital technologies sustain today's world. Every part of the world is working towards digital technologies, which none of us can eliminate. Enormous growth is achieved only by unexpected acceleration by digital technologies, including the Internet of Everything (IoE), Artificial Neural Networks (ANN), Machine Learning (ML), Internet of Things (IoT), Artificial Intelligence (AI), Deep Learning (DL), and many more. These technologies started occupying all the engineering sectors, including manufacturing. This paper focuses on tribology analysis related to manufacturing concerning various digital manufacturing technologies. The paper narration includes Tribology using digital technologies wherein the journals and patent landscape analysis abet them. In trend, Tribology utilizes all these technologies today and envisages its growth with the predominant technological invention in the border view. The survey of various literature reveals that only three digital technologies, including AI, ML, and ANN, are used by tribologists around the globe. Other Technologies like Evolutionary Algorithm (EA), Support Vector Machine (SVM), and Adaptive Neuro-Fuzzy Interference Systems (ANFIS) are not used predominantly. [ABSTRACT FROM AUTHOR]

This paper explores the potential applications of ChatGPT, a powerful Artificial Intelligence (AI) large language model (LLM) developed by OpenAI, for aviation education applications. The authors provide an overview of ChatGPT and its unique features, such as accessibility, conversational abilities, and personalized learning capabilities. The scalability of ChatGPT allows individualized and personalized instruction, a revolutionary aspect that can potentially enhance the student learning experience. This applied research employed an exploratory design to investigate ChatGPT's potential applications to enhance learning at varied levels of learning. The study investigated four research questions: (1) How can ChatGPT be used by students to support learning at each stage of Bloom's Taxonomy?; (2) How can teachers use ChatGPT to enhance student engagement in the learning process at each stage of Bloom's Taxonomy?; (3) What are the potential risks related to using ChatGPT as an educational resource?; and (4) What student guidelines or policies should be in place regarding the use of ChatGPT for learning? The authors provide specific recommendations for entering ChatGPT queries, along with practical application samples that have been tested using the platform. Generalized guidance and policy for the educational use of ChatGPT is also provided. The findings of this project prepare instructors to apply AI LLM resources to enhance aviation education and provide recommendations for its effective and ethical use by both faculty and students. Overall, this paper equips aviation educators with the necessary knowledge to leverage the power of ChatGPT to improve instructional outcomes. [ABSTRACT FROM AUTHOR]

Healthcare has emerged as a key setting where expectations are rising for the potential benefits of artificial intelligence (AI), encompassing a range of technologies of varying utility and benefit. This paper argues that, even as the development of AI for healthcare has been pushed forward by a range of public and private actors, insufficient attention has been paid to a key contradiction at the center of AI for healthcare: that its pursuit to improve health is necessarily accompanied by environmental costs which pose risks to human and environmental health—costs which are not necessarily directly borne by those benefiting from the technologies. This perspective paper begins by examining the purported promise of AI in healthcare, contrasting this with the environmental costs which arise across the AI lifecycle, to highlight this contradiction inherent in the pursuit of AI. Its advancement—including in healthcare—is often described through deterministic language that presents it as inevitable. Yet, this paper argues that there is need for recognition of the environmental harm which this pursuit can lead to. Given recent initiatives to incorporate stakeholder involvement into decision-making around AI, the paper closes with a call for an expanded conception of stakeholders in AI for healthcare, to include consideration of those who may be indirectly affected by its development and deployment. [ABSTRACT FROM AUTHOR]

In this series, we explore technology-related themes and topics. The series aims to discuss and demystify what may be new areas for some readers and to consider their relevance for English language teachers. This paper explores the potential applicability of ChatGPT—a generative, text-based artificial intelligence (AI) chatbot—to ELT. It offers insights and guidelines for using ChatGPT to develop receptive and productive skills. First, ChatGPT can help teachers generate input materials for listening and reading practices. Second, with ChatGPT, teachers can create individualized opportunities for students to practice their speaking and writing skills. We also note important caveats for teachers to consider when implementing ChatGPT as an instructional tool. By exploring its potential benefits and limitations, this paper contributes to the growing discourse on technology integration in ELT and offers practical recommendations for creating a productive learning environment using AI-driven language models like ChatGPT. [ABSTRACT FROM AUTHOR]

Learning around artificial intelligence (AI)-powered technologies that attends to power is an urgent and widely felt priority among the learning sciences and CS ed broadly. Popular approaches to AI education focus on technical skills, with far less theoretical and practical work around critical and justice-centered AI learning. Adding to this literature, we discuss tool design and observed interactions in Drag vs AI workshops, where participants use hands-on makeup art as a medium for fooling, subverting, and refusing facial recognition. Our broader analysis asks how participants make sense of the technical and political aspects of AI, as they interact with AI through the Drag vs AI workshops' modes of aesthetic transformation, tinkering, and resistance. In this paper, we focus on participants' embodied algorithmic tinkering with AI and affordances for justice-centered computing education. Our analysis highlights how tinkering and play modes of interaction with AI materials can promote critical and agentive learning. [ABSTRACT FROM AUTHOR]

Digital twins are a relatively new form of digital modeling that has been gaining popularity in recent years. This is in large part due to their ability to update in real time to their physical counterparts and connect across multiple devices. As a result, much interest has been directed towards using digital twins in the healthcare industry. Recent advancements in smart wearable technologies have allowed for the utilization of human digital twins in healthcare. Human digital twins can be generated using biometric data from the patient gathered from wearables. These data can then be used to enhance patient care through a variety of means, such as simulated clinical trials, disease prediction, and monitoring treatment progression remotely. This revolutionary method of patient care is still in its infancy, and as such, there is limited research on using wearables to generate human digital twins for healthcare applications. This paper reviews the literature pertaining to human digital twins, including methods, applications, and challenges. The paper also presents a conceptual method for creating human body digital twins using wearable sensors. [ABSTRACT FROM AUTHOR]

This paper examines how AI ethics has been developed at the national level in Japan, and what this process reveals about broader Japanese state imaginaries of how advanced technology should be developed and used, and what a future with these technologies should look like. Key developments in the Japanese government's approach to AI ethics and governance between 2014 and 2023 are laid out, based on an analysis of official reports and policy documents supplemented by data collected via semi-structured interviews with three expert members of the committees that formulated several key sets of ethical principles. The paper considers Japan's positioning in the global race to develop AI ethics principles over this period, as well as the imaginary of AI within the wider historical context of imaginaries about the knowledge society in Japan. I suggest three ways in which AI ethics has been understood and instrumentalized in the Japanese context, and argue that the main methodology used to date—ELSI—complements the government's utopian and techno-determinist imaginaries of the future while concealing a deeply conservative approach that serves to reproduce structural inequalities and discrimination despite the apparent internationalism and progressive values that are repeatedly expressed in state-promoted ethical principles. [ABSTRACT FROM AUTHOR]

English is one of the most used languages for jobs, markets, tourism, discourse and international connectivity. However, English learners face many challenges in gaining English language skills. Extant studies show that AI has affordances to support in English language teaching and learning ELT/L. This study answers the call to examine specific challenges and affordances for using AI in ELT/L. A systematic review method was used with PRISMA principles to identify 42 studies. Findings reveal the geographical locations of studies, learner ages and years of study. Grounded coding was then used to identify affordances of the use of AI in ELT/L in the areas of speaking, writing, reading, pedagogy and self‐regulation. AI in ELT/L challenges uncovered were technology breakdowns, limited capabilities, fear and standardising language. Policymakers, funders, practitioners and educational leaders can use the information provided in this study to gain a holistic understanding of the current trend in the use of AI in ELT/L, and practical implications are provided to guide future use of AI. Practitioner notesWhat is already known about this topic English is one of the most used languages for jobs, markets, tourism, discourse and international connectivity.Empirical evidence shows that pupils can often face difficulties when learning English, with challenges such as irregularity in English spelling.AI has supported language teaching and learning with studies showing that AI can support language‐specific skills.What this paper adds Provides the scholarly community with a unique systematic review in the use of AI in ELT/L across learner levels.Identifies affordances of AI in ELT/L in speaking, writing, reading, pedagogy and self‐regulation.Identifies challenges of AI in ELT/L in technology breakdowns, limited capabilities, fear and standardising language.Provides researchers with a review of the field with identification of gaps and future research opportunities.Implications for practice and/or policy Provides practical implications from the findings for educators, policy makers and program designers.Highlights the gaps in academic knowledge as a lack in the use of AI for assessment in ELT/L. [ABSTRACT FROM AUTHOR]

The Ministry of Defence, specifically the Royal Navy, uses the ‘Duty Holder Structure’ to manage how it complies with deviations to maritime laws and health and safety regulations where military necessity requires it. The output statements ensuring compliance are ‘safe to operate’ certification for all platforms and equipment, and the ‘operate safely’ declaration for people who are suitably trained within the organisation. Together this forms the Safety Case. Consider a handgun; the weapon has calibration, design and maintenance certification to prove it is safe to operate, and the soldier is trained to be qualified competent to make predictable judgement calls on how and when to pull the trigger (operate safely). Picture those statements as separate circles drawn on a Venn diagram. As levels of autonomy and complexity are dialled up the two circles converge. Should autonomy increase to the point that the decision to fire be under the control of an Artificial Intelligence within the weapon’s software then the two circles will overlap. This paper details research conclusions within the overlap, and proposes a new methodology able to certify that an AI based autonomous weapons system is “safe to operate itself safely” when in an autonomous state. [ABSTRACT FROM AUTHOR]

Purpose: ChatGPT is a new development in this technological era. This artificial intelligence-based tool responds to individuals' queries and produces the requested content within seconds. Therefore, it is becoming popular among academics, the research community and library professionals. This study aims to test (through personal interaction with the tool) the authenticity of the ChatGPT's produced records. Another objective of the research is to check the relevance between the individuals' queries and the tool's results. The research also intends to identify the challenges in retrieving information through ChatGPT. Design/methodology/approach: The five researchers from different countries and organizations experienced ChatGPT by asking questions on more than 70 subjects. The responses were recorded in Notepad and converted into MS Excel and MS Access to standardize and analyze the data. The investigators consulted 11 reputed databases/sources, including Web of Science and Scopus, to assess the authenticity of the data retrieved through ChatGPT. Findings: The findings confirmed that over 90% of results produced by ChatGPT were fake (the information did not exist in the literature). Similarly, the study sheds light on the discrepancies, such as irrelevant and incomplete information in the data generated by ChatGPT. Originality/value: This is a unique study that shares the findings based on the different regions' researchers' personal experiences with ChatGPT. The researchers covered different subject areas (above 70) while asking questions to ChatGPT. The paper shares implications for researchers, students, faculty members, academic/research organizations and policymakers. [ABSTRACT FROM AUTHOR]

This paper looks at the way that Bing AI uses tone policing and racial gaslighting in its conversations with users as a method of disciplining them away from critical anti-racist ideological engagement and towards an ethos of white supremacy. We use a critical discourse analysis to examine the conversations produced through our use of Microsoft's Bing AI chatbot to find that through both the content it generates and the mechanisms through which it responds the Bing AI chatbot polices the tone of its users in a variety of ways. [ABSTRACT FROM AUTHOR]

This extensive literature review is not a stand‐alone paper, as it was conducted to help set the scene for the third and current stage of the Harbinger of Change project (H‐3), which is focusing on the impact of artificial intelligence (AI) on early career researchers (ECRs). Its purpose is to inform the design, scope and question‐forming of the ongoing interview project (2024–). The overarching aim of the review is to establish what we know of the already extant and/or predicted opportunities and challenges that AI‐driven technologies present to researchers, in general, and the cohort of junior researchers among them, in particular. It was found that very little empirical evidence exists of what is happening to ECRs on the ground, and that tends to be drowned in a sea of forecasts and prognostications. However, down the road there are clear benefits accruing to ECRs and, arguably, the most appealing for novice researchers must be the benefits of enhancing their productivity, the key to all scholarly rewards, inclusive of career advancement. [ABSTRACT FROM AUTHOR]

Photovoltaic power generation is influenced not only by variable environmental factors, such as solar radiation, temperature, and humidity, but also by the condition of equipment, including solar modules and inverters. In order to preserve energy production, it is essential to maintain and operate the equipment in optimal condition, which makes it crucial to determine the condition of the equipment in advance. This paper proposes a method of determining a degradation of efficiency by focusing on photovoltaic equipment, especially inverters, using LSTM (Long Short-Term Memory) for maintenance. The deterioration in the efficiency of the inverter is set based on the power generation predicted through the LSTM model. To this end, a correlation analysis and a linear analysis were performed between the power generation data collected at the power plant to learn the power generation prediction model and the data collected by the environmental sensor. With this analysis, a model was trained using solar radiation data and power data that are highly correlated with power generation. The results of the evaluation of the model's performance show that it achieves a MAPE of 7.36, an RMSE of 27.91, a MAE of 18.43, and an R2 of 0.97. The verified model is applied to the power generation data of the selected inverters for the years 2020, 2021, and 2022. Through statistical analysis, it was determined that the error rate in 2022, the third year of its operation, increased by 159.55W on average from the error rate of the power generation forecast in 2020, the first year of operation. This indicates a 0.75% decrease in the inverter's efficiency compared to the inverter's power generation capacity. Therefore, it is judged that it can be applied effectively to analyses of inverter efficiency in the operation of photovoltaic plants. [ABSTRACT FROM AUTHOR]

We are confronted with the concept of intelligence every day. Starting with human intelligence to artificial intelligence. Some animals are also attested to be intelligent based on specific problems they solve. We also come across terms such as swarm intelligence, emotional intelligence or even physical intelligence. But there is still a lack of a clear definition of what intelligence actually is and, in particular, how it could be measured. Intelligence tests that provide quantitative information have so far only been available from psychology and only for people. There is a lack of criteria for what makes a system an intelligent system. This became particularly clear with the question of whether generative AI, such as ChatGPT, can be considered intelligent at all. So can intelligence be derived from the cognitive abilities of a system, or is it ultimately decisive how these abilities come about? This paper suggests a definition of the term intelligence and suggests an explanation for what constitutes intelligence and to what extent intelligence is required to gain knowledge. And finally it is questioned whether artificial systems are intelligent and have any knowledge at all. [ABSTRACT FROM AUTHOR]

Non-destructive inspection and analysis techniques are crucial for quality assessment and defect analysis in various industries. They enable for screening and monitoring of parts and products without alteration or impact, facilitating the exploration of material interactions and defect formation. With increasing complexity in microelectronic technologies, high reliability, robustness and thus, successful failure analysis is essential. Machine learning (ML) approaches have been developed and evaluated for the analysis of acoustic echo signals and time-resolved thermal responses for assessing their ability for defect detection. In the present paper different ML architectures were evaluated, including 1D and 2D convolutional neural networks (CNNs) after transforming time domain data into the spectral- and wavelet domains. Results showed that 2D CNNs processing data in wavelet domain representation performed best, however at the expense of additional computational effort. Furthermore, ML-based analysis was explored for lock-in thermography to detect and locate defects in the axial dimension based on thermal emissions. While promising, further research is needed to fully realize its potential. [ABSTRACT FROM AUTHOR]

Extracting information from scanned business documents, while a necessary commercial task, continues to be mostly done manually, requiring significant human effort. Current solutions for automated document information extraction still have limited capabilities in regards to user-required customizability and extraction of dataset-specific information, leaving the area as a very active field of research. In this paper, we propose modifications and improvements to our previously developed custom pipeline for extracting and tabulating key-value pairs from commercial invoice documents. Our design changes and additions adapt the pipeline to a wider variety of document types and use cases, primarily through the implementation of dataset-specific configuration files that promote customizability along with new technical modules that address both general and dataset-specific complexities. We compare our pipeline's performance against current machine learning and commercial solutions on a real-world dataset, and demonstrate that it is able to extract a wider variety of fields while maintaining competitive or greater accuracies compared to the alternate solutions. [ABSTRACT FROM AUTHOR]

Laser beam welding is the most modern and promising process for the automatic or robotized welding of structures of the highest Execution Class, EXC3-4, which are made of a variety of weldable structural materials, mainly steel, titanium, and nickel alloys, but also a limited range of aluminum, magnesium, and copper alloys, reactive materials, and even thermoplastics. This paper presents a systematic review and analysis of the author's research results, research articles, industrial catalogs, technical notes, etc., regarding laser beam welding (LBW) and laser hybrid welding (LHW) processes. Examples of industrial applications of the melt-in-mode and keyhole-mode laser welding techniques for low-alloy and high-alloy steel joints are analyzed. The influence of basic LBW and LHW parameters on the quality of welded joints proves that the laser beam power, welding speed, and Gas Metal Arc (GMA) welding current firmly decide the quality of welded joints. A brief review of the artificial intelligence (AI)-supported online quality-monitoring systems for LBW and LHW processes indicates the decisive influence on the quality control of welded joints. [ABSTRACT FROM AUTHOR]

Recent technological advancements have empowered nonhuman entities, such as virtual assistants and humanoid robots, to simulate human intelligence and behavior. This paper investigates how autonomous agents influence individuals' perceptions and behaviors toward others, particularly human employees. Our research reveals that the socio‐emotional capabilities of autonomous agents lead individuals to attribute a humanlike mind to these nonhuman entities. Perceiving a high level of humanlike mind in the nonhuman, autonomous agents affects perceptions of actual people through an assimilation process. Consequently, we observe “assimilation‐induced dehumanization”: the humanness judgment of actual people is assimilated toward the lower humanness judgment of autonomous agents, leading to various forms of mistreatment. We demonstrate that assimilation‐induced dehumanization is mitigated when autonomous agents possess capabilities incompatible with humans, leading to a contrast effect (Study 2), and when autonomous agents are perceived as having a high level of cognitive capability only, resulting in a lower level of mind perception of these agents (Study 3). Our findings hold across various types of autonomous agents (embodied: Studies 1–2 and disembodied: Studies 3–5), as well as in real and hypothetical consumer choices. [ABSTRACT FROM AUTHOR]

The use of artificial intelligence (AI) is already redefining the ways we engage with various types of information and data. In the wake of AI's unprecedented influence, its impact on grey literature (GL) remains an important yet underexplored domain. It is up to all GL professionals to integrate AI into their work and harness its power. This paper delves into the innovative applications of AI to bolster the efficiency of GL management, elevate metadata quality, and enhance user experiences. Four fundamental GL management facets, namely collection of GL, metadata creation, summarization, and user experience, offer great potential and a good starting point for using the power of AI. By harnessing AI's transformative capabilities, GL professionals can spearhead a paradigm shift in managing grey literature, promising extensive and far-reaching implications for the field. [ABSTRACT FROM AUTHOR]

In recent times, large language models (LLMs) have made significant strides in generating computer code, blurring the lines between code created by humans and code produced by artificial intelligence (AI). As these technologies evolve rapidly, it is crucial to explore how they influence code generation, especially given the risk of misuse in areas such as higher education. The present paper explores this issue by using advanced classification techniques to differentiate between code written by humans and code generated by ChatGPT, a type of LLM. We employ a new approach that combines powerful embedding features (black-box) with supervised learning algorithms including Deep Neural Networks, Random Forests, and Extreme Gradient Boosting to achieve this differentiation with an impressive accuracy of 98 % . For the successful combinations, we also examine their model calibration, showing that some of the models are extremely well calibrated. Additionally, we present white-box features and an interpretable Bayes classifier to elucidate critical differences between the code sources, enhancing the explainability and transparency of our approach. Both approaches work well, but provide at most 85–88% accuracy. Tests on a small sample of untrained humans suggest that humans do not solve the task much better than random guessing. This study is crucial in understanding and mitigating the potential risks associated with using AI in code generation, particularly in the context of higher education, software development, and competitive programming. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to investigate consumers' emotional responses to artificial intelligence (AI) defeating people. Meanwhile, the authors investigate the negative spillover effect of AI defeating people on consumers' attitudes toward AI companies. The authors also try to alleviate this spillover effect. Design/methodology/approach: Using four studies to test the hypotheses. In Study 1, the authors use the fine-tuned Bidirectional Encoder Representations from the Transformers algorithm to run a sentiment analysis to investigate how AI defeating people influences consumers' emotions. In Studies 2 to 4, the authors test the effect of AI defeating people on consumers' attitudes, the mediating effect of negative emotions and the moderating effect of different intentions. Findings: The authors find that AI defeating people increases consumers' negative emotions. In terms of downstream consequences, AI defeating people induces a spillover effect on consumers' unfavorable attitudes toward AI companies. Emphasizing the intention of helping people can effectively mitigate this negative spillover effect. Practical implications: The authors' findings remind governments, policymakers and AI companies to pay attention to the negative effect of AI defeating people and take reasonable steps to alleviate this negative effect. The authors help consumers rationally understand this phenomenon and correctly control and reduce unnecessary negative emotions in the AI era. Originality/value: This paper is the first study to examine the adverse effects of AI defeating humans. The authors contribute to research on the dark side of AI, the outcomes of competition matches and the method to analyze emotions in user-generated content (UGC). [ABSTRACT FROM AUTHOR]

In the dynamic realm of golf, where every swing can make the difference between victory and defeat, the strategic selection of golf clubs has become a crucial factor in determining the outcome of a game. Advancements in artificial intelligence have opened new avenues for enhancing the decision-making process, empowering golfers to achieve optimal performance on the course. In this paper, we introduce an AI-based game planning system that assists players in selecting the best club for a given scenario. The system considers factors such as distance, terrain, wind strength and direction, and quality of lie. A rule-based model provides the four best club options based on the player's maximum shot data for each club. The player picks a club, shot, and target and a probabilistic classification model identifies whether the shot represents a birdie opportunity, par zone, bogey zone, or worse. The results of our model show that taking into account factors such as terrain and atmospheric features increases the likelihood of a better shot outcome. [ABSTRACT FROM AUTHOR]

The British Army is regularly ranked as the best training provider in the UK, with the Royal Navy and Royal Air Force also in the top ten. The Military train a lot of people, from the most basic infantry training to advanced skills and leadership courses. At the pinnacle of this is the Defence Academy, which provides world-class professional defence and security education for UK military, civil service and the defence industry and for allies and partners from across the world. This article is based on the Prince Philip Lecture 2024 delivered by Major General Rowell at the Royal United Services Institute for Defence Studies at its Whitehall, London, headquarters on 10 July 2024. An abridged version of the first part of this paper appeared in Education Journal No. 568, 17 July 2024. [ABSTRACT FROM AUTHOR]

Recent developments in AI and robotics enable people to create personalised digital duplicates – these are artificial, at least partial, recreations or simulations of real people. The advent of such duplicates enables people to overcome their individual scarcity. But this comes at a cost. There is a common view among ethicists and value theorists suggesting that individual scarcity contributes to or heightens the value of a life or parts of a life. In this paper, we address this topic. We make five main points. First, that there is a plausible prima facie case for the scarcity threat: AI may undermine the value of an individual human life by making us less scarce. Second, notwithstanding this prima facie threat, the role of scarcity in individual value is disputable and always exists in tension with the contrasting view that scarcity is a tragedy that limits our value. Third, there are two distinct forms of scarcity – instrumental and intrinsic – and they contribute to value in different ways. Fourth, digital duplication technology may undermine instrumental scarcity, to at least some extent, but the axiological consequences of this are highly variable. Fifth, digital duplication technology does not affect intrinsic scarcity, and may actually heighten it. [ABSTRACT FROM AUTHOR]

This paper argues that individual propositional knowledge, as traditionally analyzed in terms of true-justified-ungettiered belief, does not require phenomenal experience. Accordingly, those who are satisfied with the traditional conception need to come to terms with the possibility that AI and other zombies that lack phenomenal experience possess knowledge. Alternatively, those who resist attributing knowledge to AI based on the assumption that knowledge requires phenomenal experience need to modify or replace the traditional conception of knowledge to incorporate this requirement. [ABSTRACT FROM AUTHOR]

Battery Management System (BMS) functions to monitor individual cell in a battery pack and its crucial task is to maintain stability throughout the battery pack. The BMS is responsible for maintaining the safety of the battery as well as not to harm the user or environment. The parameters that are to be monitored in a battery are Voltage, Current and Temperature. With the collected data, BMS carefully monitors the charging–discharging behavior of the battery particularly in the Lithium-ion (Li-ion) batteries in which charging and discharging behavior are completely different. This paper proposes a real-time IOT connected deep learning algorithm for estimation of State-of-Charge (SoC) of Li-ion batteries. This paper provides unique objectives and congruence between model-based conventional methods and state-of-the-art deep learning algorithm, specifically Feed Forward Neural Network (FNN) which is nonRecurrent. This paper also highlights the advantages of Internet-of-Things (IoT) connected deep learning algorithm for estimation of State-of-Charge of Li-ion batteries in Hybrid Electric Vehicles (HEVs) and Electric Vehicles (EVs). The major advantage of the proposed method is that the Artificial Intelligence (AI)-based techniques aim to bring the estimation error less than 2% at a low cost and time without the model of the battery, at par with conventional method of Extended Kalman Filter (EKF) which is the best ever practical estimation theory. Another advantage of the proposed method is that in an abnormal condition (i.e., Unsafe Temperature) the IF This Then That (IFTTT) IoT mobile application interfaced with BMS through ThingSpeak cloud, sends a notification alert to the battery expert or to the user prior to an emergency. Finally, the real-time data of the battery parameters are collected through ThingSpeak cloud platform for future research and analysis. [ABSTRACT FROM AUTHOR]

We seem to be on the cusp of an AI-driven revolution in military affairs. Scholars have explored many aspects of this revolution but one of the most vibrant debates has addressed the question of lethal autonomous weapons. Some scholars believe that autonomous weapons, and especially autonomous drone swarms, are about to colonise the battlefield. This paper assesses this argument. It identifies three common mistakes in discussions of lethal autonomy. Scholars overestimate the capability of autonomous drone swarms. They underestimate their dependence on other weapon systems. Finally, they presume that autonomous weapons will favour the offence. This paper rejects all three claims. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to discuss the responsibility of AI experts for guiding the development of AI in a desirable direction. More specifically, the aim is to answer the following research question: To what extent are AI experts responsible in a forward-looking way for effects of AI technology that go beyond the immediate concerns of the programmer or designer? AI experts, in this paper conceptualised as experts regarding the technological aspects of AI, have knowledge and control of AI technology that non-experts do not have. Drawing on responsibility theory, theories of the policy process, and critical algorithm studies, we discuss to what extent this capacity, and the positions that these experts have to influence the AI development, make AI experts responsible in a forward-looking sense for consequences of the use of AI technology. We conclude that, as a professional collective, AI experts, to some extent, are responsible in a forward-looking sense for consequences of use of AI technology that they could foresee, but with the risk of increased influence of AI experts at the expense of other actors. It is crucial that a diversity of actors is included in democratic processes on the future development of AI, but for this to be meaningful, AI experts need to take responsibility for how the AI technology they develop affects public deliberation. [ABSTRACT FROM AUTHOR]

This review explores the transformative integration of artificial intelligence (AI) and healthcare through conversational AI leveraging Natural Language Processing (NLP). Focusing on Large Language Models (LLMs), this paper navigates through various sections, commencing with an overview of AI's significance in healthcare and the role of conversational AI. It delves into fundamental NLP techniques, emphasizing their facilitation of seamless healthcare conversations. Examining the evolution of LLMs within NLP frameworks, the paper discusses key models used in healthcare, exploring their advantages and implementation challenges. Practical applications in healthcare conversations, from patient-centric utilities like diagnosis and treatment suggestions to healthcare provider support systems, are detailed. Ethical and legal considerations, including patient privacy, ethical implications, and regulatory compliance, are addressed. The review concludes by spotlighting current challenges, envisaging future trends, and highlighting the transformative potential of LLMs and NLP in reshaping healthcare interactions. [ABSTRACT FROM AUTHOR]

In this paper, we have developed the SEMAR (Smart Environmental Monitoring and Analytics in Real-Time) IoT application server platform for fast deployments of IoT application systems. It provides various integration capabilities for the collection, display, and analysis of sensor data on a single platform. Recently, Artificial Intelligence (AI) has become very popular and widely used in various applications including IoT. To support this growth, the integration of AI into SEMAR is essential to enhance its capabilities after identifying the current trends of applicable AI technologies in IoT applications. In this paper, we first provide a comprehensive review of IoT applications using AI techniques in the literature. They cover predictive analytics, image classification, object detection, text spotting, auditory perception, Natural Language Processing (NLP), and collaborative AI. Next, we identify the characteristics of each technique by considering the key parameters, such as software requirements, input/output (I/O) data types, processing methods, and computations. Third, we design the integration of AI techniques into SEMAR based on the findings. Finally, we discuss use cases of SEMAR for IoT applications with AI techniques. The implementation of the proposed design in SEMAR and its use to IoT applications will be in future works. [ABSTRACT FROM AUTHOR]

This paper is about the opposite of judgement and calculation. This opposition has been a traditional anchor of critiques concerned with the rise of AI decision making over human judgement. Contrary to these approaches, it is argued that human judgement is not and cannot be replaced by calculation, but that it is human judgement that contextualises computational structures and gives them meaning and purpose. The article focuses on the epistemic structure of algorithms and artificial neural networks to find that they always depend on human judgement to be related to real life objects or purposes. By introducing the philosophical concept of judgement, it becomes clear that the property of judgement to provide meaning and purposiveness is based on the temporality of human life and the ambiguity of language, which quantitative processes lack. A juxtaposition shows that calculations and clustering can be used and referred to in more or less prejudiced and reflecting as well as opaque and transparent ways, but thereby always depend on human judgement. The paper clearly asserts that the transparency of AI is necessary for their autonomous use. This transparency requires the explicitness of the judgements that constitute these computational structures, thereby creating an awareness of the conditionality of such epistemic entities. [ABSTRACT FROM AUTHOR]