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This article considers the legacy of astronomer Carl Sagan (1934–1996), whose book The Dragons of Eden presents a complete etiology of the library in the context of human evolution. Essential to Sagan's theory of the origins of the library is the concept of extrasomatic information, or knowledge stored outside the body. Surprising evidence on behalf of Sagan's theory has been presented in recent years by Nobel-Prize-winning neuroscientist Eric Kandel and others: that the human brain has not significantly grown for 50,000 years, presumably not due to evolutionary stagnation, but because cultural evolution has superseded biological evolution in Homo sapiens, an advance which has led to us becoming the dominant species on Earth. [ABSTRACT FROM AUTHOR]

The development of artificial intelligence necessarily implies the anthropological question of the difference between human and artificial intelligence for two reasons: on the one hand artificial intelligence tends to be conceived on the model of human intelligence, on the other hand, a large part of types of artificial intelligence are designed in order to exhibit at least some features of what is conceived as being human intelligence. In this article I address this anthropological question in two parts. First I bring into review and classify some of the main answers that have been proposed until now to this question. I argue that these variety of answers can be broadly classified in three categories, namely a (1) behaviorist, (2) a representational, and (3) a holistic understanding of human intelligence. In a second moment I propose an alternative way of understanding the difference between human and artificial intelligence, which is not essentialist but contextualist and content-related. Contrary to possible answers that I analyse in the first section, this alternative model does not aim at grasping the essence of human intelligence, which could or could not be reproduced in principle by artificial intelligence. It situates rather the fundamental differences between human and artificial intelligence in the context of human existence and the conceptual content of human intelligence, following the phenomenological description of one of its most fundamental features, namely its life-world. Grounding on this approach, it is possible to argue that human and artificial intelligence could be by distinct, even if one could prove that they are eidetically, i.e. by their essence, identical. [ABSTRACT FROM AUTHOR]

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]

The objective of this text is to define the concept that Antonio Damasio develops about man, the human and human intelligence. Antonio Damasio’s main thesis is the hypothesis of the cerebral subject, a neural and materialist image of man based on the brain. For Antonio Damasio, the human is understood in terms of a complex interaction between the body, the mind and the environment. To do this, three key notions that define this idea in Antonio Damasio are presented: emotional reflection, sentimental ethics and the gradualist theory of consciousness. This will converge in Damasio’s personal conception of intelligence, who emphasizes the importance of homeostasis in human intelligence. Our ability to adapt to the environment and make effective decisions is intrinsically linked to our ability to regulate our biological and emotional needs.

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.

Translating poetry has always been a challenging issue for some reasons. Firstly, it is accompanied by cultural challenges. Secondly, it is considered untranslatable due to its unique qualities. Thirdly, the poet’s style, if not fully captured in translation, becomes a pale substitute for the original. Fourthly, issues related to Artificial Intelligence (AI) also arise. This article tried to analyze some of these issues in the light of the Persian translations of the literary techniques of Shakespeare's Sonnet 62 in a qualitative-explanatory way and with the help of human and machine intelligence tools. The main goal was to pay attention to the upcoming challenges in the translation of literary techniques. Farzad provided a fluent translation, Moghadam provided a relatively free translation, Shafa provided a translation faithful to the content, and Tabibzadeh provided an accurate and accepted translation of the sonnet. Among the AI tools, Google Translate provided a literal translation close to the original text, Telegram Bot provided a more complete and fluent translation, and OpenAI GPT provided a more accurate and human-like translation of the sonnet. It seemed that literary translation, especially poetry, required human intelligence creativity in order to bring its "alternative" presence closer to the "original". Keywords: Human Intelligence, Artificial Intelligence (AI), Sonnet, Accuracy, Acceptability. IntroductionIt has been over a hundred years since Shakespeare’s works have been translated into Persian. During this time, Shakespeare's sonnets have also been translated and published in periodicals. Translators, such as Masoud Farzad, Majid Movaqqar, Shoja al-Din Shafa, Taghi Tafazzoli, Behnam Moghadam, Amrullah Abjadiyan, and Omid Tabibzadeh, are among those, who have translated Shakespeare's sonnets into Persian. Besides using human intelligence, Shakespeare's sonnets can also be translated into Persian through Artificial Intelligence (AI), which would be challenging and interesting in its own way. In this article, Sonnet 62, which had been translated into Persian by most of the aforementioned translators and AI tools, was examined from the perspectives of the proposed strategies for translating poetry. The discussion of translating the genre of poetry into other languages has always been raised alongside issues related to the original works themselves. In translating conventional texts generally and literary texts, especially poetry, we do not merely deal with translating linguistic items from one language to another. Since literary texts are considered an inseparable part of the cultures of nations and carry specific semantic loads, translation of these texts also involves particular difficulties and problems due to cultural issues. In the process of translating an English sonnet into a Persian ghazal, some issues arise that need attention. Can a sonnet be translated into a sonnet while preserving its formal and structural features or undergoing slight modifications and can it be translated into the form of a Persian ghazal? To what extent is it possible to transfer cultural elements in addition to poetic ones in the translation process? To what extent can AI succeed in conveying the poet’s most intimate states and feelings into another language, considering that AI, due to its structural nature, lacks the emotional intelligence that characterizes human intelligence? Materials & MethodsIn the common and traditional categorization that is theme-based rather than form-based, Shakespeare's sonnets are divided into three general categories: Sonnets 1-126 address the fair youth; Sonnets 127-152 deal with the dark lady; and Sonnets 153 and 154 are known as the Anacreontic sonnets (Anacreon 583 BC-485 BC), which have romantic themes (Abjadiayn, 2008; Tabibzadeh, 2017). Sonnet 62, along with Sonnets 59, 60, and 65, deals with the relationship between time and beauty (Abjadiyan, 2008, p. 205). According to Tabibzadeh, it is the first of the two sonnets about the poet's old age (p. 184). In this sonnet, Shakespeare first speaks of the sin of self-love, which has taken over his eyes and his entire being, rooting deeply in his heart (first quatrain). Then, in the second quatrain, he talks about his beauty, which is unparalleled in the world. However, in the third quatrain, he says that he sees himself as a very worn-out, old, and decrepit man when he looks into a mirror. Finally, in the couplet, he attributes all that splendor and beauty he has thought he has had to the fair youth and imagines his own old age to be beautiful in the light of the fair youth's youthful beauty. This sonnet was first translated by Masoud Farzad. Then, Majid Movaqqar translated it. The third translation was done by Shafa. The fourth translation, a prose translation, was done by Tafazzoli. Then, Behnam provided a rhythmic and rhymed translation of this sonnet. Finally, Tabibzadeh (2017) provided a sufficiently acceptable, though unrhymed and unmetered, translation of it. Additionally, the author translated this same sonnet into Persian using AI tools, such as Google Translate, and, more recently, ChatGPT. Research FindingsThe translations of Shakespeare's Sonnet 62 by various human translators and AI tools revealed interesting insights into the strengths and limitations of each approach. The human translators, including Farzad, Movaqqar, Shafa, Tafazzoli, Moghadam, and Tabibzadeh, had produced translations with varying degrees of fluency, faithfulness, and literary quality. Farzad and Movaqqar had prioritized fluency over fidelity, resulting in translations that had strayed from the original text. In contrast, Shafa and Tafazzoli had focused more on faithfulness, with Tafazzoli's prose translation staying very close to the original content. Moghadam had offered a relatively free but rhythmic and rhymed translation, while Tabibzadeh had presented a sufficiently accurate and acceptable unrhymed and unmetered version. When examining the translations produced by AI tools, some interesting patterns emerged. Google Translate, despite advancements in the field, generated a largely literal and close-to-the-original translation, focusing more on primary word meanings rather than secondary connotative meanings. On the other hand, the Telegram bot and OpenAI's GPT produced more accurate and human-like translations. The Telegram bot's translation was more complete and fluent than Google's but less comprehensive and precise than OpenAI's. Notably, OpenAI's translation was, in some cases, even more literary than human translations. Still, the assumption that AI cannot effectively translate literary texts due to the presence of emotion and feeling could be challenged. OpenAI's more complex algorithms enabled it to find more appropriate literary equivalents for certain words and provide a more coherent translation, particularly in the final lines of the sonnet. However, the issue of accurately translating certain literary qualities, such as the speaker’s voice and tone, remained problematic for AI tools. The melancholic and regretful tone of the poem's speaker lamenting the loss of youth was not adequately recreated in the machine translations. These findings suggested that human intelligence still played a crucial role in capturing the nuanced, emotional, and cultural aspects of literary works while AI tools could make significant strides in literary translation. The interplay between human and artificial intelligence in the translation of poetry and other literary genres warrants further exploration as it may lead to innovative approaches that combine the strengths of both. Discussion of Results & ConclusionFarzad, Movaqqar, Shafa, Tafazzoli, Moghadam, and Tabibzadeh as translators possessing human intelligence were found to have produced translations with various literary qualities of the mentioned Shakespearean sonnet: Farzad and Movaqqar had strived for a fluent but unfaithful translation to the original text; Shafa had provided a fluent and somewhat faithful translation; Tafazzoli had delivered a prose translation so faithful to the original poem in terms of content; Moghadam had offered a relatively free but rhythmic and rhymed translation; and Tabibzadeh had presented a sufficiently acceptable translation. However, this question arose: What similarities and distinctions may occur in the overall process of translating texts, particularly literary texts, and more specifically, translation of poetry, including the studied sample of Shakespearean sonnet when translation tools shift from human intelligence to artificial intelligence? Google, although said to have made significant advancements, was not very successful in translating the sonnet and produced a nearly literal and close-to-the-original translation, focusing more on the primary and dictionary meanings of words and phrases rather than their secondary and connotative meanings. On the other hand, the two other tools provided a more accurate and human-like translation of the sonnet. The translation offered by the Telegram bot was more complete and fluent than Google's but less complete and accurate than OpenAI's translation. Despite the fact that the Telegram bot and OpenAI tools apparently had the same origin, they did not translate the sonnet identically due to their different programming. Among these, OpenAI's translation was, overall, closer to human intelligence translation and, in some cases, even more literary. Perhaps, this could challenge the initial assumption that AI cannot translate literary texts effectively due to the presence of emotion and feeling and it might even be argued that this assumption was somewhat falsifiable. OpenAI showed that, due to its more complex algorithms, it had been programmed in a way that it could find more literary equivalents for some words. Furthermore, this tool demonstrated a more accurate understanding of the last lines of the sonnet compared to the other two tools and was able to provide a more coherent translation. However, the issue of translating certain qualities of literary texts, such as the speaker’s voice and tone, still remained problematic. A general look at the translations of the sonnet showed that machine translations were not able to adequately recreate the melancholic and regretful tone of the speaker of the poem, someone who lamented the lost days of youth. Nevertheless, addressing the challenges of human and artificial intelligence translations requires more space, which the author hopes to deal with in the future.

Artificial intelligence is one of the most basic and serious topics of research and investigation in the world today. Artificial intelligence is progressing and developing in a lightning-fast and amazing manner in the current era. Very soon, we will see huge changes and developments in this type of new technology. This new technique and technology has been widely used internationally in various professions and industries. The profession of law, law, and distributive justice is not exempt from this issue; now this question is raised, which of the tools will be more useful and appropriate to achieve distributive justice; Human intelligence or artificial intelligence? Is it possible to bring society closer to real justice and fairness with this new technology in this modern era? What is certain is that artificial intelligence with the behavior of human intelligence strengthens capabilities and increases efficiency. Therefore, the findings using descriptive, analytical, and argumentative methods show that artificial intelligence in terms of speed, accuracy, and decision-making power in analysis and problem analysis is more effective and efficient compared to human intelligence. Thus, the use of this new technology helps a lot in the fair distribution of incomes, opportunities, and important economic issues in society. Although the negative effects caused by the expansion of this new technology cause economic, educational, moral, and other discriminations and inequalities; however, it is possible to reduce the negative effects of these inequalities caused by the development of this new technology by exercising caution, carefully managing and monitoring data, creating programs and supporting laws, and using artificial intelligence technology based on the principles of distributive justice.

Translating poetry has always been a challenging issue for some reasons. Firstly, it is accompanied by cultural challenges. Secondly, it is considered untranslatable due to its unique qualities. Thirdly, the poet’s style, if not fully captured in translation, becomes a pale substitute for the original. Fourthly, issues related to Artificial Intelligence (AI) also arise. This article tried to analyze some of these issues in the light of the Persian translations of the literary techniques of Shakespeare's Sonnet 62 in a qualitative-explanatory way and with the help of human and machine intelligence tools. The main goal was to pay attention to the upcoming challenges in the translation of literary techniques. Farzad provided a fluent translation, Moghadam provided a relatively free translation, Shafa provided a translation faithful to the content, and Tabibzadeh provided an accurate and accepted translation of the sonnet. Among the AI tools, Google Translate provided a literal translation close to the original text, Telegram Bot provided a more complete and fluent translation, and OpenAI GPT provided a more accurate and human-like translation of the sonnet. It seemed that literary translation, especially poetry, required human intelligence creativity in order to bring its "alternative" presence closer to the "original". [ABSTRACT FROM AUTHOR]

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]

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 core of our research is consciousness, in multiple forms in which it can metamorphose into the inner "temple" of the human being and into the age of AI. This interdisciplinary research captures the "dialogue" between philosophy and law. And no, it is not about AI engineering and its forms of manifestation, but rather it is an "encapsulation" of the risks and benefits that lie behind the AI "label", so that we together make a "leap" towards the awareness of the need to resize or regulate new rights, rights that will preserve our identity in the absolute subtly outlined by AI. Our "mystery" of being, still shrouded "in a cloak of the unknown", seems to become ephemeral in our searches through the "journey of life", the place of this "divine spark" being taken today by the mystery of being of artificial intelligence (AI). Humanity almost feels "small and powerless" in the face of the creation and greatness of the universe of infinite possibilities, gradually "unveiled" by AI. After all, what is a man in infinity? Has AI become capable of reproducing the essence of intelligence: human thinking and wisdom? No matter how great the tendency to reproduce human behaviour and intellect, we must realize, however, that AI is not endowed with emotion, love, critical thinking or morality, and the need for ethics in regulating AI remains a key landmark open to research, in order to reach a safe and reliable area. The definition of human intelligence remains a book open to reflection and analysis, intelligence being an extremely complex attribute, fluid, difficult to measure and especially almost impossible to copy, since it is constantly shaped by factors such as personal motivation, social context, education, personality, attributes related to human intimacy. Without aspiring to reach the "high notes" of knowledge, we ask ourselves: what about the essence of humanity, the inner ego, elements untouched by the legislative area, but only by that of science, given that AI manages to decode and optimize human emotions? What about artificial intelligence that make vulnerable brain and mental integrity through the impermissible alteration of thoughts, which can alter, remove or recover people's memories, as well as manipulate their thoughts? What about the moral and spiritual existence of an individual? Can AI be conscious? It is hard to believe that robots will climb a "ladder" of conscience and consciousness, since AI is confined to the field of possibilities built by man, its creator. [ABSTRACT FROM AUTHOR]

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]

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 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]

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.)

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.

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.

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]

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.)

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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.)

Since the term 'Artificial Intelligence' was coined, the respective research field has frequently emulated human mental faculties. Despite diverging viewpoints regarding the feasibility of achieving human-like cognition in machines, the very use of the word intelligence for complex computer systems evokes human consciousness. Likewise, there have been attempts to understand the human mind in terms of computers, exemplified by the computational theory of mind. By contrast, my article underscores the categorical difference between the mind and machines. Partly building upon arguments by David Gelernter and Bert Olivier, I focus on literary examples spanning from Shakespeare to T.S. Eliot that accentuate subjective experience, the intricate relationship between body and mind, and the anticipation of death as human characteristics beyond the reach of computational systems. [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. [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]

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.

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.

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]

Artificial Intelligence refers the technology that parades individualities allied with a human mind such as knowledge. Artificial Intelligence is a technology that qualifies machines to think, grasp, and accomplish tasks previously carried out by humans. It is a capacity to rationalize and take activities that have the best chance of accomplishing a precise goal. The influence of AI is expanding to a greater extent in human resource management. It starts from the recruitment process to exit process of an employee. Especially the Artificial Intelligence technologies that are focused on the reproduction of principles of human intelligence functioning. HR is the area which serves the entire organisation and need extra efforts have used these companies. HR professionals have started to realize the advantages of a data-driven decision. AI using workforce data will help HR professional to better understand their workforce and to foresee problems and trends in advance. AI will also help HR executives to formulate and implement strategies effectively. The usage of AI will help HR to cut person-hours in tasks that can now be completed by the machine. AI has made their jobs run more smoothly and effectively. Considering how fast the area of AI in HR is growing, it must provide a significant advantage to companies. [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]

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]