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Digital twins (DT) have increasingly garnered attention in today's manufacturing industry for their potential to enhance productivity, efficiency, and decision-making processes. This study presents a human-centric digital twin framework (HCDT) specifically designed for a three-axis vertical milling machine (VMC3), following the ISO 23247 standard. Our system seeks to offer a comprehensive virtual depiction of a tangible milling machine, facilitating immediate monitoring, analysis, and enhancement of machining operations. This study targets specifically the prediction of power consumption in three-axis vertical machining using advanced machine learning. Power consumption during modified Kakino toolpath cutting was examined through a series of trials that involved adjusting spindle speed, depth of cut, and feed rate. The analysis was conducted utilizing physical sensors, mathematical modeling, and machine learning. The utilization of machine learning methods, specifically random forest, exhibited encouraging outcomes, as evidenced by a mean absolute error (MAE) of 17.30. In addition, virtual simulations were performed to forecast power usage. The proposed applied digital twin (ADT) incorporates human intelligence and artificial intelligence to effectively integrate physical and virtual environments, offering a unique approach that adheres to the ISO 23247 framework. The cross-system entity of the digital twin showcases that the calculated power has large variations with the experimental power. However, the proposed ADT approach has resulted in higher similarity to the calculated, experimental, and predicted power consumption respectively in the proposed digital twin scenario. Also, the addition of explainability to the result has developed the trustworthiness in the digital twin environment. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this paper is twofold: to explore the significance and implications of the rise of AI technology for the field of tertiary education in general and, in particular, to answer the question of whether teachers can be replaced by intelligent AI systems such as androids, what that requires in terms of human capabilities and what that might mean for teaching and learning in higher education. Design/methodology/approach: Given the interdisciplinary nature of this conceptual paper, a literature review serves as a methodological tool to access data pertaining to the research question posed in the paper. Findings: This exploratory paper gathers a range of evidence from the philosophy of mind (the mind-body problem), Kahneman's (2011) System 1 and System 2 models of the mind, Gödel's (1951) Two Incompleteness Theorems, Polanyi's (1958, 1966) theory of tacit knowing and Searle's (1980) Chinese Room thought experiment to the effect that no AI system can ever fully replace a human being because no machine can replicate the human mind and its capacity for intelligence, consciousness and highly developed social skills such as empathy and cooperation. Practical implications: AI is rising, but there are inherent limits to what machines can achieve when compared to human capabilities. An android can at most attain "weak AI", that is, it can be smart but lack awareness or empathy. Therefore, an analysis of good teaching at the tertiary level shows that learning, knowledge and understanding go far beyond any quantitative processing that an AI machine does so well, helping us to appreciate the qualitative dimension of education and knowledge acquisition. ChatGPT is robotic, being AI-generated, but human beings thrive on the human-to-human interface – that is, human relationships and meaningful connections – and that is where the true qualitative value of educational attainment will be gauged. Social implications: This paper has provided evidence that human beings are irreplaceable due to our unique strengths as meaning-makers and relationship-builders, our capacity for morality and empathy, our creativity, our expertise and adaptability and our capacity to build unity and cooperate in building social structures and civilization for the benefit of all. Furthermore, as society is radically automated, the purpose of human life and its reevaluation will also come into question. For instance, as more and more occupations are replaced by ChatGPT services, more and more people will be freed up to do other things with their time, such as caring for relatives, undertaking creative projects, studying further and having children. Originality/value: The investigation of the scope and limitations of AI is significant for two reasons. First, the question of the nature and functions of a mind becomes critical to the possibility of replication because if the human mind is like a super-sophisticated computer, then the relationship between a brain and mind is similar (if not identical) to the relationship between a computer as machine hardware and its programme or software (Dreyfus, 1979). [ ] If so, it should be theoretically possible to understand its mechanism and reproduce it, and then it is just a matter of time before AI research and development can replicate the human mind and eventually replace a human teacher, especially if an AI machine can teach just as intelligently yet more efficiently and economically. But if AI has inherent limitations that preclude the possibility of ever having a human-like mind and thought processes, then our investigation can at least clarify in what ways AI/AGI – such as ChatGPT – could support teaching and learning at universities. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) has revolutionized various industries by learning from data, mimicking human behavior, and making autonomous decisions. However, despite AI's advancements in data processing and decision-making, it cannot fully replicate human attributes such as emotional understanding and ethical judgment. This paper explores the intersection of AI and Human Intelligence (HI) within the audit profession, focusing on the implications for the auditor’s professional judgment and skepticism. The integration of AI in auditing promises enhanced efficiency, precision, and data processing capabilities beyond human limits. However, it also raises ethical concerns regarding data privacy, algorithmic bias, and accountability. These concerns highlight the importance of maintaining human oversight and ethical standards in audit practices. Through a comprehensive literature review, this study compares the cognitive abilities, functional capabilities, and ethical implications of AI and human auditors. Key findings underscore AI's potential to complement human auditors by improving accuracy and uncovering anomalies, while recognizing the irreplaceable role of human judgment in complex decision-making processes. The study provides insights into the transformative impact of AI on the audit profession, advocating for a balanced approach that harnesses AI's capabilities while preserving the integrity and critical thinking of human auditors. The findings contribute to a deeper understanding of AI's integration into auditing, informing best practices and guiding future research in maintaining the profession's standards amidst technological advancements.

Concerning a posthuman perspective, this paper attempts to provide a new perspective on future changes in teaching and learning in the age of artificial intelligence. With the development of technological civilisation, humans have adapted to the environmental world while at the same time attempting to remould it using technology and tools. Humans have survived by acquiring new skills and abilities to manipulate technology and tools. Human Transformation (HX), updated to respond to technological innovations, is now upcoming human intellectual activities through AI technology. What are the challenges of HX in the age of AI, and what perspectives will be critical in this process? This paper traces back to how machines with computational intelligence or reasoning functions were named ‘artificial intelligence’ that can reproduce human intellectual activities. It examines the wide-ranging social impact of the naming of AI and the growing phenomenon of expectations and anxieties about AI. It then notes two sources behind the posthuman debate. The first is the trend towards an upgraded stage of human intelligence over the current human by enhancing it through medical and even AI-based technology. The second trend seeks a new direction for post-humanity by focusing on its diversity, such as society and culture, through a critical examination of the view that uniformly evaluates all human conditions through a universal model of human beings. Navigating them is an excellent educational challenge. Focusing on the similarities and differences between human intelligence and artificial intelligence, the paper examines the challenges of education to develop the unique characteristics of human intelligence further and achieve freedom from AI technology, considering the legal, ethical and social issues (ELSI) of making wise use of AI.

The Hayes-Wheelwright Product-Process Matrix (PPM) has historically been a pivotal tool in manufacturing, guiding firms to strategically align product volume and variety with the most optimal production process. Delving into its developmental journey, this research thoroughly examines the PPM's evolution through the industrial eras. From the outset of Industry 2.0, marked by the adoption of Flexible Manufacturing Systems prioritizing mass customization, to the inception of Industry 4.0, characterized by a seamless integration of digital and physical systems with a pronounced focus on tailored products, the matrix has consistently adapted. The main findings of this work underline the imperative to reconceptualize the PPM within the context of Industry 5.0. Building on this foundation, a new model is here proposed to represent the PPM matrix in the Industry 5.0 environment that incorporates creative and technological innovation and the concept of sustainability. This entails a fusion of cutting-edge technological developments, urgent sustainability considerations, and the unyielding deep human insights. The proposed matrix (PPM 5.0) maps each product to the most suitable production process and guides companies in selecting processes that align with sustainable objectives. Furthermore, it provides a flexible foundation designed to readily adapt to evolving market demands, technological advancements, and emerging trends in manufacturing, offering substantial benefits to practitioners and business owners in their strategic design decisions and technology investments. To achieve these insights, a qualitative methodology is employed, grounded in an exhaustive review of the extant literature, thereby ensuring a holistic and informed perspective on the future trajectory of industrial practices. [ABSTRACT FROM AUTHOR]

Copyright of Teoría de la Educación. Revista Interuniversitaria is the property of Ediciones Universidad de Salamanca and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The science-religion dialogue, an enduring discourse, delves into the realms of science and religion, examining their respective domains, methodologies, and implications. This dialogue, evolving over centuries, adapts alongside advancements in scientific discoveries and religious interpretations. Artificial intelligence (AI) emerges as a pivotal facilitator in this dialogue, leveraging its capacity to analyze extensive datasets, discern commonalities, and foster respectful exchanges. AI serves as a conduit for translating complex scientific concepts into more accessible forms, thereby fostering understanding and bridging diverse perspectives. However, the intersection of AI and the science-religion dialogue also poses various concerns and challenges. Despite its neutral nature, AI’s applications and interpretations can inadvertently influence the dialogue, potentially leading to perceptions of harm. This article explores potential ways in which AI could impact the science-religion dialogue, examining both its promises and pitfalls. [ABSTRACT FROM AUTHOR]

The recent prominence of advanced chatbots that greatly mimic human intelligence and conversation appears to have set a new stage in the rapidly developing field of Artificial Intelligence. Chatbots such as ChatGTP and Bard have risen to global popularity among internet users who interface with the chatbots in a nearly humanlike manner through a question-and-answer format. But such great technological developments also give rise to questions regarding theology and spirituality. Thus, this paper asks: what does it mean to be human in an increasingly AI-driven world? How can Christian communities around the globe respond to the ongoing developments in the field of AI? Based on missional anthropology, this paper argues for an understanding of humans as embodied agents of God as central in a missional strategy to respond to the proliferation of advanced AI chatbots today. [ABSTRACT FROM AUTHOR]

Academic writing is a contested area, even more so in times of large language models and artificial intelligence (AI). This writing is tricky to navigate and master especially for newcomers – staff and students. Learning Developers almost uniquely play with writing as a practice of emergence and discovery. Academic writing is a process: we write to become academic. Students write to join their epistemic communities, and Learning Developers write to give birth to an emergent field. Drawing on recent work by Syska and Buckley (2022) and Abegglen, Burns and Sinfield (2022; 2023), we argue that academic writing is an initiation into and participation in wider professional and academic discourses. We ‘write to learn’ rather than ‘learn to write’. In our practice with students, we know that we need to move beyond the ‘mechanics’ of writing and make the process meaningful, engaging, interactive, and fun. Similarly, Syska and Buckley (2022) have explored what makes Learning Developers ‘tick’ with respect to academic writing – revealing how, counterintuitively perhaps, academic writing can become an inclusive Learning Development space: our ‘happy place’. With this presentation, we opened the discussion on academic writing for building the Learning Development community.

Copyright of RED - Revista de Educación a Distancia is the property of Universidad de Murcia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Militärgeschichtliche Zeitschrift is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Italian Journal of Health Education, Sport & Inclusive Didactics is the property of Edizioni Universitarie Romane and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Strategic Universe Journal / Univers Strategic is the property of Dimitrie Cantemir Christian University, Institute for Security Studies and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Similar to the field of human intelligence, artificial intelligence (AI) has experienced a long history of advances and controversies regarding its definition, assessment, and application. Starting over 70 years ago, AI set out to achieve a single, general-purpose technology that could overcome many tasks in a similar fashion to humans. However, until recently, implementations were based on narrowly defined tasks, making the systems inapplicable to even slight variations of the same task. With recent advances towards more generality, the contemplation of artificial general intelligence (AGI) akin to human general intelligence (HGI) can no longer be easily dismissed. We follow this line of inquiry and outline some of the key questions and conceptual challenges that must be addressed in order to integrate AGI and HGI and to enable future progress towards a unified field of general intelligence. [ABSTRACT FROM AUTHOR]

Academics and practitioners are paying increasing attention to service automation and taking advantage of the tremendous opportunities offered by artificial intelligence (AI). The requirement to deliver effective services has greatly increased interest in this topic. In the current study, we examined how AI is being used in healthcare. We searched the Scopus database for articles to obtain a thorough understanding of current research on AI in healthcare. After excluding articles not closely related to the topic, 190 articles (from 1992 to May 2022) were selected for in-depth analysis. This study provided a bibliometric analysis of the co-occurrence of keywords, citations, co-citations, bibliographic links, and number of publications. We identified under-researched areas in the field of AI in healthcare and highlighted important directions for future research. [ABSTRACT FROM AUTHOR]

The emphasis on artificial intelligence (AI) is rapidly increasing across many diverse aspects of society. This manuscript discusses some of the key topics related to the expansion of AI. These include a comparison of the unique cognitive capabilities of human intelligence with AI, and the potential risks of using AI in clinical medicine. The general public attitudes towards AI are also discussed, including patient perspectives. As the promotion of AI in high-risk situations such as clinical medicine expands, the limitations, risks and benefits of AI need to be better understood.

In 1998, Fields medallist Stephen Smale [Smale (1998) [1]] proposed his famous eighteen problems to the mathematicians of this century. The statement of his eighteenth problem is simple but very important. The statement of his problem is, “What are the limits of intelligence, both artificial and human?”. To answer the limit of human intelligence, in this paper, we introduce cognitive-consequence space and cognitive-consequence topology, and mainly prove that deductive and non-deductive parts of a human mind will never be empty. It proves a human being will continue to think and solve problems using both deductive and non-deductive inferences as long as they are alive. Hence, we conclude that human intelligence is limitless. We also introduce cognitive closure, cognitive similarity distance, cognitive limit point, cognitive-continuous function, consequence ideal, consequence filter, Gödel's incompleteness black hole, and study related properties. We also provide suitable justifications to show that cognitive consequence topological space is not similar to that of any existing topological space because it connects cognitive space and consequence operator in one frame to find the limit of human intelligence. Moreover, we also provide justifications to state that artificial intelligence has limitations. Thus, we conclude that human intelligence will always remain superior to artificial intelligence.

Human-machine systems, especially those involving reinforcement learning (RL), are becoming increasingly common across application domains. Designing these systems to be effective and reliable requires a task-oriented understanding of both human learning (HL) and RL. In particular, how does the structure of a learning task affect the learning performance of humans and RL algorithms? Games and other learning environments can serve as important tools in this line of research. While a trend toward increasingly complex environments has led to improved RL capabilities, such environments are difficult to use for the dedicated study of task structure for humans and algorithms. To address this gap we present a novel learning environment called the Game of Hidden Rules (GOHR), built to support rigorous study of the impact of task structure on HL and RL. We demonstrate the GOHR’s utility for such study through example experiments where humans and learning algorithms display opposite responses in performance to tested variations in task structure.

Artificial intelligence (AI) driven solutions have the potential to significantly impact individuals with disabilities by providing assistance in their daily activities and facilitating the acquisition of new abilities. The utilisation of AI technology in assisting individuals with disabilities has novel prospects for enhancing accessibility, fostering inclusivity throughout society, and enabling autonomous living, which would otherwise pose considerable challenges or remain unattainable. As the field of AI continues to progress, it holds the potential to facilitate the development of increasingly sophisticated and groundbreaking approaches to tackle the multifaceted obstacles encountered by individuals with disabilities. Consequently, AI has the capacity to foster greater inclusivity for this population. AI driven solutions have the potential to significantly impact individuals with disabilities by providing assistance in their daily activities and facilitating the acquisition of new abilities. Several solutions are being currently developed by numerous startups as well as corporate conglomerates like Google, Microsoft and Apple. An independent and self-sustaining life will be the future normal for persons with disability (PwD) with these rapidly progressing developments. [ABSTRACT FROM AUTHOR]

The present rate of growth of powerful AI systems motivates an accurate comparison between the notion of computers and the workings of natural sciences. Statements such as "intelligence doesn't require flesh, blood or carbon atoms" or "it's computation all the way down" incite a substrate‐independent view, providing shortcuts for Darwinian evolution and the possible appearance of sentient machines. This view is discussed and contrasted from a quantum chemical perspective. The qualitative difference between the developed AI and the evolved HI is recognized and the importance of a material constituent, formulated in terms of energy‐temperature, conjugate to an immaterial ingredient, in the context of time‐entropy, is pointed out as a necessary feature. The popular dictum "it from bit" does not appear valid unless amended with its obverse "bit from it." [ABSTRACT FROM AUTHOR]

This paper discusses two opposing views about the relation between artificial intelligence (AI) and human intelligence: on the one hand, a worry that heavy reliance on AI technologies might make people less intelligent and, on the other, a hope that AI technologies might serve as a form of cognitive enhancement. The worry relates to the notion that if we hand over too many intelligence-requiring tasks to AI technologies, we might end up with fewer opportunities to train our own intelligence. Concerning AI as a potential form of cognitive enhancement, the paper explores two possibilities: (1) AI as extending—and thereby enhancing—people's minds, and (2) AI as enabling people to behave in artificially intelligent ways. That is, using AI technologies might enable people to behave as if they have been cognitively enhanced. The paper considers such enhancements both on the level of individuals and on the level of groups. [ABSTRACT FROM AUTHOR]

There has been tremendous progress in artificial neural networks (ANNs) over the past decade; however, the gap between ANNs and the biological brain as a learning device remains large. With the goal of closing this gap, this paper reviews learning mechanisms in the brain by focusing on three important issues in ANN research: efficiency, continuity, and generalization. We first discuss the method by which the brain utilizes a variety of self-organizing mechanisms to maximize learning efficiency, with a focus on the role of spontaneous activity of the brain in shaping synaptic connections to facilitate spatiotemporal learning and numerical processing. Then, we examined the neuronal mechanisms that enable lifelong continual learning, with a focus on memory replay during sleep and its implementation in brain-inspired ANNs. Finally, we explored the method by which the brain generalizes learned knowledge in new situations, particularly from the mathematical generalization perspective of topology. Besides a systematic comparison in learning mechanisms between the brain and ANNs, we propose "Mental Schema 2.0," a new computational property underlying the brain's unique learning ability that can be implemented in ANNs. [ABSTRACT FROM AUTHOR]

In human intelligence, a verbal statement from a source is seldom 100% true or false, and not very often is the source a total liar or a truth teller. From this standing point, a simple dichotomy of a liar or a truth teller might not offer an adequate diagnostic value for the purposes of human intelligence. A more diagnostic approach would be to assess which parts of the predominantly truthful verbal statement are likely to be true and which parts are assessed to be doubtful. In addition, the use of two parallel methods to detect deceit should improve the diagnostic value of the results. A pilot study in laboratory conditions (n = 8, yielding 190 assessment points) utilising an applied mock crime scenario was conducted. Correlation calculations showed that a dual-method approach slightly improved the within-statement truth accuracy, and it was achieved mainly by decreasing the number of false positives. As the truth accuracy was increased, the lie accuracy within the test group slightly decreased. The results confirmed that by applying parallel orienting response (EDA) and cognitive load (speech-related indices)-based assessment methods, it is possible to detect embedded lies successfully in an information-gathering interview setup. [ABSTRACT FROM AUTHOR]

The recent prominence of advanced chatbots that greatly mimic human intelligence and conversation appears to have set a new stage in the rapidly developing field of Artificial Intelligence. Chatbots such as ChatGTP and Bard have risen to global popularity among internet users who interface with the chatbots in a nearly humanlike manner through a question-and-answer format. But such great technological developments also give rise to questions regarding theology and spirituality. Thus, this paper asks: what does it mean to be human in an increasingly AI driven world? How can Christian communities around the globe respond to the ongoing developments in the field of AI? Basing on missional anthropology, this paper argues for an understanding of humans as embodied agents of God as central in a missional strategy to respond to the proliferation of advanced AI chatbots today.

Abstract Background With the increasing demand brought on by the beginning of the fourth industrial revolution in the period of post-digital education and bio-digital technology, artificial intelligence (AI) has played a pivotal role in supporting human intelligence and contributing to intellectuals within science, technology, science, and mathematics (STEM) and in the broader field of higher education. Thus, this study examines how writers for mainstream STEM journals and higher education magazines perceive the impact of ChatGPT, a powerful AI chatbot, on STEM research and higher education. ChatGPT can generate realistic texts based on user prompts. However, this platform also poses ethical challenges for academic integrity, authorship, and publication. Results Using a comparative media discourse analysis approach, this study analyzes 72 articles from four media outlets: (a) Springer Nature; (b) The Chronicle of Higher Education; (c) Inside Higher Ed; and (d) Times Higher Education. The results show that the writers expressed various concerns and opinions about the potential conflicts and crises caused by ChatGPT in three areas: (a) academic research and publication; (b) teaching and learning; and (c) human resources management. Conclusions This study concludes with some policy implications and suggestions for future research on ChatGPT and AI ethics in academia by reilluminating the most overarching policy concerns related to ethical writing in STEM research and higher education and limitations to the blindness to authorship and academic integrity among diverse stakeholders.

Background: With the increasing demand brought on by the beginning of the fourth industrial revolution in the period of post-digital education and bio-digital technology, artificial intelligence (AI) has played a pivotal role in supporting human intelligence and contributing to intellectuals within science, technology, science, and mathematics (STEM) and in the broader field of higher education. Thus, this study examines how writers for mainstream STEM journals and higher education magazines perceive the impact of ChatGPT, a powerful AI chatbot, on STEM research and higher education. ChatGPT can generate realistic texts based on user prompts. However, this platform also poses ethical challenges for academic integrity, authorship, and publication. Results: Using a comparative media discourse analysis approach, this study analyzes 72 articles from four media outlets: (a) Springer Nature; (b) The Chronicle of Higher Education; (c) Inside Higher Ed; and (d) Times Higher Education. The results show that the writers expressed various concerns and opinions about the potential conflicts and crises caused by ChatGPT in three areas: (a) academic research and publication; (b) teaching and learning; and (c) human resources management. Conclusions: This study concludes with some policy implications and suggestions for future research on ChatGPT and AI ethics in academia by reilluminating the most overarching policy concerns related to ethical writing in STEM research and higher education and limitations to the blindness to authorship and academic integrity among diverse stakeholders. [ABSTRACT FROM AUTHOR]

Copyright of Socialno Delo is the property of Socialno Delo and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Like any military power, violent non-state actors operate intelligence, including human intelligence (HUMINT), to suit their needs. The article aims to explore the primary methods used by Hezbollah and Hamas in recruiting and handling human sources since the mid-2000s, illustrating each method with several case studies. An examination of these methods may shed light on a significant aspect of the intelligence war waged by Hezbollah and Hamas against Israel. In addition, analysis of these organizations' HUMINT activity demonstrates how violent non-state actors, which are inherently the weaker side in their asymmetric warfare against advanced state entities, can compensate for their disadvantages and conduct effective intelligence activity. The article reveals how the clever and creative use of HUMINT maximizes its benefits for violent non-state actors. [ABSTRACT FROM AUTHOR]

Similar to the field of human intelligence, artificial intelligence (AI) has experienced a long history of advances and controversies regarding its definition, assessment, and application. Starting over 70 years ago, AI set out to achieve a single, general-purpose technology that could overcome many tasks in a similar fashion to humans. However, until recently, implementations were based on narrowly defined tasks, making the systems inapplicable to even slight variations of the same task. With recent advances towards more generality, the contemplation of artificial general intelligence (AGI) akin to human general intelligence (HGI) can no longer be easily dismissed. We follow this line of inquiry and outline some of the key questions and conceptual challenges that must be addressed in order to integrate AGI and HGI and to enable future progress towards a unified field of general intelligence.

This paper aims to address the divergences and contradictions in the definition of intelligence across different areas of knowledge, particularly in computational intelligence and psychology, where the concept is of significant interest. Despite the differences in motivation and approach, both fields have contributed to the rise of cognitive science. However, the lack of a standardized definition, empirical evidence, or measurement strategy for intelligence is a hindrance to cross-fertilization between these areas, particularly for semantic-based applications. This paper seeks to equalize the definitions of intelligence from the perspectives of computational intelligence and psychology, and offer an overview of themethods used tomeasure intelligence. We argue that there is no consensus for intelligence, and the term is interchangeably used with similar, opposed, or even contradictory definitions in many fields. This paper concludes with a summary of its central considerations and contributions, where we state intelligence is an agent's ability to process external and internal information to find an optimum adaptation (decision-making) to the environment according to its ontology and then decode this information as an output action. [ABSTRACT FROM AUTHOR]

Employee turnover (ET) is a major issue faced by firms in all business sectors. Artificial intelligence (AI) machine learning (ML) prediction models can help to classify the likelihood of employees voluntarily departing from employment using historical employee datasets. However, output responses generated by these AI-based ML models lack transparency and interpretability, making it difficult for HR managers to understand the rationale behind the AI predictions. If managers do not understand how and why responses are generated by AI models based on the input datasets, it is unlikely to augment data-driven decision-making and bring value to the organisations. The main purpose of this article is to demonstrate the capability of Local Interpretable Model-Agnostic Explanations (LIME) technique to intuitively explain the ET predictions generated by AI-based ML models for a given employee dataset to HR managers. From a theoretical perspective, we contribute to the International Human Resource Management literature by presenting a conceptual review of AI algorithmic transparency and then discussing its significance to sustain competitive advantage by using the principles of resource-based view theory. We also offer a transparent AI implementation framework using LIME which will provide a useful guide for HR managers to increase the explainability of the AI-based ML models, and therefore mitigate trust issues in data-driven decision-making. [ABSTRACT FROM AUTHOR]

An editorial discusses the integration of artificial intelligence (AI) in health-related scientific publishing. It emphasizes the promises and pitfalls of AI in health systems, treatments, and public health. The need for transparency and ethical considerations in AI applications is highlighted, with concerns about potential societal inequalities.

Marcus and Davis's Rebooting AI: Building Artificial Intelligence We Can Trust (2019) describes the narrowness of current artificial intelligence, posits constituents of human intelligence it contends artificial intelligence (AI) must reproduce to achieve a general (broad) formulation, and envisages the contributions of general AI to human living. This article (1) reviews Marcus and Davis's argument, (2) phenomenologically evaluates their interpretation of human intelligence, and (3) discloses the moral inconsistencies implied by their vision of the relation between general AI and human being. Transcendental and hermeneutic phenomenological perspectives are employed to address the range of phenomena the authors affiliate with human intelligence. [ABSTRACT FROM AUTHOR]

Law is a global phenomenon and one of the highest revenue-generating industries. Due to its slow pace of development, it becomes difficult to adopt new technologies and tools for the better administration of the law. In the legal profession, researching is a requisite skill for lawyers. Even though legal research skills vary from lawyer to lawyer, even in the same case, every lawyer must engage in legal research to solve legal problems. Artificial Intelligence (AI) is a computer system that does tasks effectively and efficiently without any need for human intelligence. Much has been propounded on (AI) and the law in recent times, this paper focuses on elucidating AI and its relation to the practice and administration of the law, by addressing key issues on these topics. The paper aims to showcase that although the law is rigid, there is a very real possibility that it might change shortly with the help of artificial intelligence interference, which would change the working of the law in the country. It demonstrates the effects of artificial intelligence, both advantages and disadvantages and examines how they affect other areas of the law while using doctrinal methods of research. In India, there is no specific legislation governing artificial intelligence; the authors have looked into the laws of the US, and UK governing AI and have examined the Personal Data Protection Bill, 2019, and stated challenges that will be faced with the takeover of artificial intelligence. [ABSTRACT FROM AUTHOR]

Modern management requires the highest level of analytics and the optimisation of business processes with a low risk of poor management decisions. These are essential since rapid changes in the financial world and the external environment can have critical effects. The direction of a company's growth and the effectiveness of its management systems depend directly on the quality of intellectualisation. This study aims to develop a new methodology for studying the criteria and results of the intellectualisation processes to achieve the highest efficiency in company management systems. This study used sociological and empirical methods to find intellectualisation efficiency criteria. These criteria were then used to analyse the intellectualisation process in ten Russian companies. The correlation analysis method revealed a close relationship between the intellectualisation integral indicator and company performance over time. The results showed that the intellectualisation efficiency criteria are intellectualisation indicators in human resource management systems as well as computer-aided and automated management systems. In addition, it was found that company performance depends on the intellectualisation integral indicator, the human intelligence and artificial intelligence synergy, as well as on the efficiency of using artificial intelligence in business processes. [ABSTRACT FROM AUTHOR]

This paper proposes a Human Intelligence (HI)-based Computational Intelligence (CI) and Artificial Intelligence (AI) Fuzzy Markup Language (CI&AI-FML) Metaverse as an educational environment for co-learning of students and machines. The HI-based CI&AI-FML Metaverse is based on the spirit of the Heart Sutra that equips the environment with teaching principles and cognitive intelligence of ancient words of wisdom. There are four stages of the Metaverse: preparation and collection of learning data, data preprocessing, data analysis, and data evaluation. During the data preparation stage, the domain experts construct a learning dictionary with fuzzy concept sets describing different terms and concepts related to the course domains. Then, the students and teachers use the developed CI&AI-FML learning tools to interact with machines and learn together. Once the teachers prepare relevant material, students provide their inputs/texts representing their levels of understanding of the learned concepts. A Natural Language Processing (NLP) tool, Chinese Knowledge Information Processing (CKIP), is used to process data/text generated by students. A focus is put on speech tagging, word sense disambiguation, and named entity recognition. Following that, the quantitative and qualitative data analysis is performed. Finally, the students' learning progress, measured using progress metrics, is evaluated and analyzed. The experimental results reveal that the proposed HI-based CI&AI-FML Metaverse can foster students' motivation to learn and improve their performance. It has been shown in the case of young students studying Software Engineering and learning English. [ABSTRACT FROM AUTHOR]