Narrow Artificial Intelligence Research Articles

ШТУЧНИЙ ІНТЕЛЕКТ У ВИЩІЙ ОСВІТІ: МОЖЛИВОСТІ ВИКОРИСТАННЯ

The integration of artificial intelligence into the system of higher education represents a turning point in the process of learning and teaching. The development of artificial intelligence has opened the way to personalized training, automation of administrative tasks and the introduction of innovative training methods. The purpose of the study was to analyze the practical aspects of using artificial intelligence in higher education institutions of Ukraine. It was determined that artificial intelligence is an organized set of information technologies, which makes it possible to perform complex complex tasks. There are three main categories of artificial intelligence: narrow-spectrum artificial intelligence, or Artificial Narrow Intelligence, general artificial intelligence, or Artificial General Intelligence, and artificial superintelligence, or Artificial Super Intelligence. The main educational services provided by artificial intelligence in institutions of higher education are the development and conduct of lectures, seminars and practical classes; teacher counseling; creation of educational programs and electronic courses; development of tasks and simulation of their solution; conducting various educational events; evaluation of the works of education seekers. Some examples of the use of artificial intelligence, in particular chatbots, in the higher education of Ukraine are analyzed and their potential for improving the educational process and forming professional skills is emphasized. An example of the use of GPT-3.5 in the Luhansk Educational and Scientific Institute for teaching foreign languages is presented. Such applications based on artificial intelligence as Thinkster and Duolingo and the main aspects of their use by students of higher education are characterized. Recommendations are provided for the successful implementation of artificial intelligence technologies in higher education.

AI Adoption in Islamic Tourism Stakeholders’ Business Orientation

Despite the expanding worldwide Islamic tourism market, the industry is still largely untapped. As Islamic tourism market comprises of the technology-driven market dominated by the millennial, followed by gen Z and gen Alpha as the next successors, Islamic tourism stakeholders are urged to capitalize cutting-edge technology in their business orientation to gain competitive advantage and business sustainability. As such, this content-analysis paper aims to determine Islamic tourism stakeholders’ adoption of Artificial Intelligence (AI) in their business orientation. Findings indicated that Narrow AI is the most common AI types used by most of the Islamic tourism stakeholders. It was also found that both internal and external factors strongly influenced the AI adoption and it types among the stakeholders. Nevertheless, the organization’s financial health, the business size and scope, and the target market’s needs profoundly affect AI implementation by the business operators. As for the customers, the expected delivery performance, the service facilitating conditions and the expenses they have to pay were found to be significant in influencing them in their AI acceptance. AI adoption positively influences the Islamic tourism stakeholders’ business orientation and the organizations’ sustainability. Capitalizing AI in Islamic tourism business orientation helps to redress Islamic muamalat misconception and other religious related issues and practices, promote understanding about divine religions, allows for greater flexibility in the differences in Fiqh implementation, pooling stronger and sound Muslim experts manpower capitalization and empowerment in various fields as technology and business, ensure Syariah compliance and conformance which results to greater efficiency, increase performance, competitive advantage and Islamic tourism business sanctity and sustainability. As AI is evolving, it is projected that the industry will fully capitalize this technology and establishes legal measures to cope with the industry standard and business sustainability while at the same time ensuring experience and value-driven service to the customers.

Prospective research in the field of teaching creative skills to artificial intelligence

The research relevance is determined by the importance of a thorough study of methods, schemes and models used by artificial intelligence to mechanise creativity in modern conditions of active technological development. The study aims to analyse the main processes taking place in modern art in connection with active technologization of work processes, to identify the leading concepts regarding the possibility of creating machine art in the future, etc. The employed methods are theoretical, such as analysis, systematisation, generalisation, etc., for studying key problems and further development of creativity based on artificial intelligence. The study examines in detail the main developments of Artificial General Intelligence and Artificial Narrow Intelligence, in particular the achievements of Generative adversarial networks and Creative adversarial networks. Artificial intelligence-generated art demonstrates the remarkable capabilities of technologies. The evolving artificial intelligence in the arts introduces “digital art”. Generative Adversarial Networks are used as a foundational tool for artists who use digital methods and texture generation to create unique compositions. Furthermore, sculptors collaborate with artificial intelligence tools to convert drawings into 3D models or transform historical art databases into sculptures. Creative thinking, a hallmark of human intelligence, is determined as artificial intelligence’s ability to generate new and original ideas. The development of emotional intelligence in artificial intelligence enables empathetic responses and the identification of human emotions through voice and facial expressions. The issues of authorised internationality, awareness of the creative process, psychological foundations of artificial empathy and emotional intelligence define the prospects for the development of neuroscience. Challenges persist in defining creativity, authorship, and legal aspects of artificial intelligence-generated art. The study materials may be useful for artists, art educators, technologists, and researchers interested in the intersection of technology and art, legal professionals (especially intellectual property law), and individuals involved in artificial intelligence development may find these findings valuable

Foundations of ANNs: Tolstoy’s Genius Explored using Transformer Architecture

Artificial Narrow Intelligence is in the phase of moving towards the AGN, which will attempt to decide as a human being. We are getting closer to it by each day, but AI actually is indefinite to many, although it is no different than any other set of mathematically defined computer operations in its core. Generating new data from a pre-trained model introduces new challenges to science & technology. In this work, the design of such an architecture from scratch, solving problems, and introducing alternative approaches are what has been conducted. Using a deep thinker, Tolstoy, as an object of study is a source of motivation for the entire research.

A holistic approach to implementing artificial intelligence in radiology

ObjectiveDespite the widespread recognition of the importance of artificial intelligence (AI) in healthcare, its implementation is often limited. This article aims to address this implementation gap by presenting insights from an in-depth case study of an organisation that approached AI implementation with a holistic approach.Materials and methodsWe conducted a longitudinal, qualitative case study of the implementation of AI in radiology at a large academic medical centre in the Netherlands for three years. Collected data consists of 43 days of work observations, 30 meeting observations, 18 interviews and 41 relevant documents. Abductive reasoning was used for systematic data analysis, which revealed three change initiative themes responding to specific AI implementation challenges.ResultsThis study identifies challenges of implementing AI in radiology at different levels and proposes a holistic approach to tackle those challenges. At the technology level, there is the issue of multiple narrow AI applications with no standard use interface; at the workflow level, AI results allow limited interaction with radiologists; at the people and organisational level, there are divergent expectations and limited experience with AI. The case of Southern illustrates that organisations can reap more benefits from AI implementation by investing in long-term initiatives that holistically align both social and technological aspects of clinical practice.ConclusionThis study highlights the importance of a holistic approach to AI implementation that addresses challenges spanning technology, workflow, and organisational levels. Aligning change initiatives between these different levels has proven to be important to facilitate wide-scale implementation of AI in clinical practice.Critical relevance statementAdoption of artificial intelligence is crucial for future-ready radiological care. This case study highlights the importance of a holistic approach that addresses technological, workflow, and organisational aspects, offering practical insights and solutions to facilitate successful AI adoption in clinical practice.Key points1. Practical and actionable insights into successful AI implementation in radiology are lacking.2. Aligning technology, workflow, organisational aspects is crucial for a successful AI implementation3. Holistic approach aids organisations to create sustainable value through AI implementation.Graphical

Integrating ethics in AI development: a qualitative study

BackgroundWhile the theoretical benefits and harms of Artificial Intelligence (AI) have been widely discussed in academic literature, empirical evidence remains elusive regarding the practical ethical challenges of developing AI for healthcare. Bridging the gap between theory and practice is an essential step in understanding how to ethically align AI for healthcare. Therefore, this research examines the concerns and challenges perceived by experts in developing ethical AI that addresses the healthcare context and needs.MethodsWe conducted semi-structured interviews with 41 AI experts and analyzed the data using reflective thematic analysis.ResultsWe developed three themes that expressed the considerations perceived by experts as essential for ensuring AI aligns with ethical practices within healthcare. The first theme explores the ethical significance of introducing AI with a clear and purposeful objective. The second theme focuses on how experts are concerned about the tension that exists between economic incentives and the importance of prioritizing the interests of doctors and patients. The third theme illustrates the need to develop context-sensitive AI for healthcare that is informed by its underlying theoretical foundations.ConclusionsThe three themes collectively emphasized that beyond being innovative, AI must genuinely benefit healthcare and its stakeholders, meaning AI also aligns with intricate and context-specific healthcare practices. Our findings signal that instead of narrow product-specific AI guidance, ethical AI development may need a systemic, proactive perspective that includes the ethical considerations (objectives, actors, and context) and focuses on healthcare applications. Ethically developing AI involves a complex interplay between AI, ethics, healthcare, and multiple stakeholders.

Buddhist Transformation in the Digital Age: AI (Artificial Intelligence) and Humanistic Buddhism

Humanistic Buddhism is one of the mainstreams of modern Buddhism, with special emphasis on the humanistic dimension. With the development of artificial intelligence (AI) technology, Humanistic Buddhism is also at an important stage of modernization and transformation, thus facing a continuous negotiation between religious values and technological innovations. This paper first argues that AI is technically beneficial to the propagation of Buddhism by citing several cases in which AI technology has been used in Buddhism. Then, by comparing Master Hsing Yun’s Buddhist ethics to “Posthuman” ethics, it points out that the theories of Humanistic Buddhism share similarities with AI and Posthuman ethics. Among them, Master Hsing Yun’s theory of “the nature of insentient beings” provides an important theoretical reference for the question of “whether AI can become a Buddha”. From the technical and ethical dimensions, it points out that the interaction between Humanistic Buddhism and AI can promote original uses or implementations of AI technology. However, it should also be noted that compared to the cases of “Artificial Narrow Intelligence”discussed in the paper, the “Strong AI” could lead to much more ethical crises. It is also likely to cause the cult of science and technology, and thus subvert the humanistic tradition of Buddhism with a new instrumental rationality. In addition, there are some potential pitfalls that Humanistic Buddhism may encounter when using AI. Hence, while it is necessary to encourage the use of technologies such as AI in contemporary Buddhism, it is also important for Buddhism to keep a critical distance from digital technologies.

Artificial Intelligence: The Future

Artificial intelligence is the intelligence of machines or software, as opposed to the intelligence of humans or animals. It is also the field of study in computer science that develops and studies intelligent machines. "AI" may also refer to the machines themselves. AI is not a new for the scientist, it was introduce in 1943 with artificial neurons model and get popular in 1950 due to “Turting test” the test was done to get answer that machine can think?, purposed by Alan Turing. Basically AI is categorized into three types, Artificial Narrow Intelligence, Artificial General Intelligence and Artificial Super Intelligence. Deep learning and machine learning is major subfield of Artificial intelligence. DL as a subset of ML, which is also another subset of AI. Therefore, AI is the all-encompassing concept that initially erupted. Application of AI fields are Healthcare, Business, Education, Agriculture, Finance, Law, Entertainment and media, Software coding and IT processes, Security, Manufacturing, Banking and Transportation. In reference to Job creation or distraction, Artificial Intelligence is not job killer but a job category killer”. In the latest report (May 2023) on The Future of Jobs, the World Economic Forum (WEF) predicts the creation of 69 million jobs by 2027 thanks to AI, but also the destruction of 89 million jobs. In context to intelligence level, Jan. 2022, Age of IQ level of AI was as 7 year Children and in Dec. 2022, Age of IQ level of AI was as 9 year Children. India is emerging market in global and it has around 12 % of work could be automated by AI. In India more than 2000 startup are related to AI and 90000 plus AI Professional work in India.The economic impact of AI, for select G20 countries and estimates AI to boost India’s annual growth rate by 1.3 percentage points by 2035. AI has potential to add 1 trillion to India’s economy in 2035. We are going to enter into new technological world, it’s may be our fortune or misfortune. Key Words: - Artificial Intelligence, Machine learning, Job, India

Editorial: The Artificial Intelligence in Translational Medicine and Biomedical Research, How Future Can be Shaped?!!

The new era of translational medicine is facing a special and unique transition as the Artificial Intelligence (AI) and Machine Learning (ML) advance. The latest findings in many laboratories around the globes are promising and paved the way toward extraordinary innovative development in the field of medicine and medical research. In medical research, gradual and consistent advancement, from rudimentary origins, were initial key steps toward medical discoveries and innovation. However, with the AI inclusion into these research approaches, the traditional translational medicine is pacing up. Creating a great hope for filling many sophisticated gaps in knowledge that has been a challenge by the traditional methodology. Such advancements in AI and its involvement in biomedicine and healthcare are forging the framework for a flourishing and visionary translational medicine and biomedical research. Lately, following the discoveries made by a collaborative team of scientists from the University of Vermont and Tufts University (The xenobots), enormous research and investigation started to glance in the field. Attempts toward drug discovery, wound recoveries and self-proliferating stem cells were made. Most recently, scientists at Tufts University and Harvard University’s Wyss Institute have designed and created living small robots from cells of human trachea. In an in vitro world, these created cells could induce tissue damage recovery. The AI based medical diagnosis is also a revolutionary step toward proper diagnosis and consequently, medical intervention. The AI and ML are becoming so intelligent to an extent not to only diagnose the disease but also predict disease prognosis in future. The narrow AI (WatsonX) that was introduced by the IBM company can play a major role in assisting clinical decisions and image analysis. The challenge that faced the globe after the COVID-19 outbreak with multiple false negativity and false positivity outcomes in the diagnose necessitated the support of the AI in this field. Additionally, the Neuralink that is introduced by Neuralink Corp. is taking a historical step after gaining the approval from FDA to start human trials. Such dramatical change in the brain interface using AI can be a solution for many diseases that human couldn’t treat. Restoring brain activity and autonomy through integration of an AI to human nervous system is a mind-boggling promise that AI can give to medicine and medical science. Finally, these new approaches and advancement in AI and ML are prone to proper design and supervision, as well as, performing randomized clinical trials prior to the use in real-life medical interventions. Nonetheless, research and experimentations for AI inclusion in medicine are still an ongoing process, yet the opportunities are realistic that AI can be of great benefit in supporting medicine and biomedical research, specifically, where there is gap in knowledge and inability of human capacity to resolve them. There is no doubt that AI will be a significant party of the healthcare system in the future and a core medical support.

Can AI be Evil: The Criminal Capacities of ANI

Artificial Narrow Intelligence (ANI) represents a captivating domain within technological advancement, bearing the potential for profound societal transformations. While ANI holds the promise of enhancing various facets of human existence, it concurrently engenders inquiries into its “darker aspects.” This study delves into the challenges associated with ANI’s conceivable manifestation of harm and injustice, a phenomenon devoid of consciousness, intention, or responsibility akin to that of human entities. A pivotal dimension of ANI’s “dark side” pertains to its susceptibility to malevolent utilization. Despite its lack of awareness, ANI serves as a tool for malicious endeavors, encompassing the propagation of disinformation, compromise of security systems, and consequential decision-making. This prompts contemplation on strategies to mitigate these “precise manifestations of malevolence” arising from ANI’s technological progression. Additionally, ANI’s development introduces profound ethical quandaries. Ensuring ANI’s alignment with moral principles while averting scenarios in which it generates decisions conflicting with human morality becomes a pressing concern. This research underscores the imperative for rigorous regulatory frameworks and ethical directives to curtail potential hazards and unscrupulous utilization of ANI. The fundamental objective of this investigation is to advocate for the responsible deployment of ANI in society. A comprehensive understanding of potential risks, complemented by meticulous consideration of ethical dimensions, emerges as an indispensable prerequisite to harmonizing technological advancement with safeguarding societal and individual interests.

Penyuluhan Mengenai Artificial Intelligence Untuk Siswa-Siswi SMP dan SMA Sekolah Lentera Harapan Medan

Artificial Intelligence (Artificial Intelligence or AI) is a field that is growing rapidly and has great potential in influencing various aspects of human life. AI education is important for the younger generation in facing future challenges. This study aims to teach the basics of AI to junior high school (SMP) students at Sekolah Lentera Harapan (SLH). Learning sessions are carried out using interactive methods and actively involve students in discussions. The material includes an introduction to AI, the history and purpose of AI, how it works, the types of AI, and the advantages and disadvantages of AI. AI needs data to come up with appropriate answers, and with time, it will learn and improve. There are three types of AI, namely Artificial Narrow Intelligence (limited AI), Artificial General Intelligence (general AI), and Artificial Superintelligence (super AI). The advantages of AI include fast data processing, job efficiency and handling of dangerous tasks. However, there are also drawbacks such as reliance on big data, limitations to the specific capabilities of AI, and security concerns. This research provides insight into AI and promotes optimal use of AI in society.

Artificial Intelligence (AI) in Nursing

Artificial Intelligence (AI) is a broad field of computer science focused on creating systems that can perform tasks that typically require human intelligence. These tasks include learning, reasoning, problem-solving, perception, language understanding, and speech recognition. AI can be categorized into two main types: Narrow AI (or Weak AI) and General AI (or Strong AI).While the integration of AI in nursing brings numerous benefits, it is essential to address ethical considerations, data privacy, and the need for ongoing education and training for healthcare professionals. The collaboration between AI systems and human expertise is crucial for ensuring the highest standards of patient care.

Personal Data Privacy in the Age of Artificial Intelligence

The article discusses the relationship between Artificial Intelligence (AI) and personal data privacy. Artificial Intelligence which emerged in the 1950s with Alan Turing's question "Can Machines Think?" has achieved significant successes in recent years, particularly in the development of human-like machine designs. The advancement of AI is closely related to the collection of big data, which in turn involves gathering more personal data. In today's context, marked by increased philosophical discussions about freedom and heightened emphasis on the individual, the issue of personal data privacy has become considerably important, accompanied by numerous challenges. Despite the existence of many national and international legal regulations regarding data privacy, there lacks a regulation overseeing the development processes of related technologies. Artificial Intelligence is often classified into categories based on its technical capabilities, namely "Narrow AI," "General AI," and "Superintelligence." AI has attracted significant attention in philosophy and has brought about interdisciplinary research in fields ranging from logic and aesthetics to law, ethics, medicine, and even art. In the face of emerging technologies, it seems inevitable that philosophy will initiate new research trajectories. The paper examines these three concepts related to AI, their connection with the concept of privacy, the issues arising and potentially arising in the realm of personal privacy, and the measures to mitigate these risks. The methodology employed includes a literature review and qualitative data analysis. The research reveals that the notion of privacy is mainly linked to ensuring the security of personal data related to technological advancements and the changing perception of privacy in individuals. However, it's observed that these studies often do not encompass current developments. The paper endeavors to answer questions such as: What do the terms "AI, Narrow AI, General AI, Superintelligence, and Personal Data Privacy" refer to? Have studies been conducted on the relationship between AI concepts and personal data privacy? What are the risks associated with personal data privacy, and how can these risks be mitigated? The study aims to highlight the risks in the development processes of AI as increasingly vocalized by scientists working in this field. It delves into issues such as the collection and storage of personal data, often unbeknownst to individuals, through cloud technologies and the potential problems arising from the use of this data in AI training. The article also discusses potential solutions to these issues. Drawing attention to recent developments in this scope is of paramount importance for social scientists, and disseminating information about these advancements is crucial. Based on the examinations and analyses conducted, it's determined that AI, now possessing new competencies, requires an interdisciplinary approach to address the areas it influences. It's found that leaving this field solely to engineers would result in numerous problems in the near future. Safeguarding privacy in the face of intelligent machines that encompass every aspect of our lives is a formidable challenge. Reducing potential problems necessitates individual awareness, legal regulations concerning AI development processes, and recognizing that AI technologies causing issues can also be part of the solution.

Prospective research in the field of teaching creative skills to artificial intelligence

The research relevance is determined by the importance of a thorough study of methods, schemes and models used by artificial intelligence to mechanise creativity in modern conditions of active technological development. The study aims to analyse the main processes taking place in modern art in connection with active technologization of work processes, to identify the leading concepts regarding the possibility of creating machine art in the future, etc. The employed methods are theoretical, such as analysis, systematisation, generalisation, etc., for studying key problems and further development of creativity based on artificial intelligence. The study examines in detail the main developments of Artificial General Intelligence and Artificial Narrow Intelligence, in particular the achievements of Generative adversarial networks and Creative adversarial networks. Artificial intelligence-generated art demonstrates the remarkable capabilities of technologies. The evolving artificial intelligence in the arts introduces “digital art”. Generative Adversarial Networks are used as a foundational tool for artists who use digital methods and texture generation to create unique compositions. Furthermore, sculptors collaborate with artificial intelligence tools to convert drawings into 3D models or transform historical art databases into sculptures. Creative thinking, a hallmark of human intelligence, is determined as artificial intelligence’s ability to generate new and original ideas. The development of emotional intelligence in artificial intelligence enables empathetic responses and the identification of human emotions through voice and facial expressions. The issues of authorised internationality, awareness of the creative process, psychological foundations of artificial empathy and emotional intelligence define the prospects for the development of neuroscience. Challenges persist in defining creativity, authorship, and legal aspects of artificial intelligence-generated art. The study materials may be useful for artists, art educators, technologists, and researchers interested in the intersection of technology and art, legal professionals (especially intellectual property law), and individuals involved in artificial intelligence development may find these findings valuable

Law in the New Era of Emerging Technologies: Can Creation Made by an Artificial Intelligence be Protected by Copyright?

Despite the fact that Artificial Intelligence (AI) has a long history in the twentieth and twenty-first centuries, there are many unsolved challenges. AI is a rapidly growing, promising area of research that is reaching its prime. Contemporarily, AI is used in a wide range of tasks, from virus detection, e-commerce chat boxes, to law-making. Subsequently, AI is not only designed to perform a particular and limited task (Narrow AI), but also to generate new creations based on cognitive skills and advancements in that spectrum (General AI). The topic discussed further in this paper is the second type of AI. This paper will attempt to answer the following questions. First, can creation made by an AI be protected by copyright? Second, who is the creator of the creations made by an AI? Third, how do we determine the holders of moral rights and economic rights for creations made by an AI? This paper used legal research, emphasizing literature study. In so doing, the data were analyzed with the deductive method to construct conclusions. This paper shows that creations made by an AI can be protected with copyright, with the creator being the person or people who contribute the most to the basic code of the AI, as well as the training data used, so that the creation can be produced. Furthermore, the person as creators also become moral rights holders and economic rights holders.

Finding a safe zone in the highlands: Exploring evaluator competencies in the world of AI

AbstractSince the public launch of ChatGPT in November 2022, disciplines across the globe have grappled with questions about how emerging artificial intelligence will impact their fields. In this article I explore a set of foundational concepts in artificial intelligence (AI), then apply them to the field of evaluation broadly, and the American Evaluation Association's evaluator competencies more specifically. Given recent developments in narrow AI, I then explore two potential frameworks for considering which evaluation competencies are most likely to be impacted—and potentially replaced—by emerging AI tools. Building on Moravec's Landscape of Human Competencies and Lee's Risk of Replacement Matrix I create an exploratory Landscape of Evaluator Competencies and an Evaluation‐Specific Risk of Replacement Matrix to help conceptualize which evaluator competencies may be more likely to contribute to long‐term sustainability for the field. Overall, I argue that the interpersonal, and contextually‐responsive aspects of evaluation work—in contrast to the more technical, program management, or methodological aspects of the field—may be the competencies least likely to be impacted or replaced by AI. As such, these may be the competencies we continue to emphasize, both in the day‐to‐day aspects of our operations, and in the training of new and emerging evaluators. This article is intended to be a starting point for discussions that continue throughout the remainder of this issue.

Past Myocardial Infarctions and Gender Predict the LVEF Regardless of the Status of Coronary Collaterals: An AI-Informed Research

BACKGROUND: The degree of the development of coronary collaterals is long considered an alternate – that is, a collateral – source of blood supply to an area of the myocardium threatened with vascular ischemia or insufficiency. Hence, the coronary collaterals are beneficial but can also promote harmful (adverse) effects. For instance, the coronary steal effect during the myocardial hyperemia phase and that of restenosis following coronary angioplasty. OBJECTIVES: Our study explores the contribution of coronary collaterals – if any exist – while considering other potential predictors, including demographics and medical history, toward the left ventricular (LV) dysfunction measured through the LV ejection fraction (LVEF). METHODS: Our cross-sectional design study used convenience sampling of 100 patients (n = 100; a male-to-female ratio of 4:1). We conducted frequentist inference statistics using IBM-SPSS version 24 and Microsoft Office Excel 2016 with the analysis ToolPak plugin; we ran parallel neural networks (supervised machine learning (ML)) and a two-step clustering (non-supervised ML) for robust conjoint inference with frequentist statistics. RESULTS: The past incidents of myocardial infarction (p = 0.036) and gender (p = 0.072) influenced the LVEF; both are significant predictors at a 90% confidence interval. We found that gender and past incidents of MI influenced the LVEF regardless of the status of coronary collaterals. Our study did not yield any positive or significant findings concerning the status of coronary collaterals or the coronary circulation dominance patterns. CONCLUSION: Regardless of the status of coronary collaterals, we verified that the female gender is protective of the LV function, contrary to the past infarction incidents that predispose to a deteriorated LV function. Our study’s innovation relates to its status as the first study from India to explore the coronary collaterals and the ejection fraction while incorporating frequentist statistics and narrow artificial intelligence to infer reliable results.

Technology And Globalisation: How Social Media (Narrow Artificial Intelligence) Algorithms and Large Language Models by Internet Companies Have Replaced the Main Stream Media as Agenda Setters For the Global Culture

Technology And Globalisation: How Social Media (Narrow Artificial Intelligence) Algorithms and Large Language Models by Internet Companies Have Replaced the Main Stream Media as Agenda Setters For the Global Culture

Artificial Narrow Intelligence to Strengthen the Vertical Education System: A Review

Artificial Narrow Intelligence to Strengthen the Vertical Education System: A Review

Innovative Analysis Ready Data (ARD) product and process requirements, software system design, algorithms and implementation at the midstream as necessary-but-not-sufficient precondition of the downstream in a new notion of Space Economy 4.0 - Part 1: Problem background in Artificial General Intelligence (AGI)

ABSTRACT Aiming at the convergence between Earth observation (EO) Big Data and Artificial General Intelligence (AGI), this two-part paper identifies an innovative, but realistic EO optical sensory image-derived semantics-enriched Analysis Ready Data (ARD) product-pair and process gold standard as linchpin for success of a new notion of Space Economy 4.0. To be implemented in operational mode at the space segment and/or midstream segment by both public and private EO big data providers, it is regarded as necessary-but-not-sufficient “horizontal” (enabling) precondition for: (I) Transforming existing EO big raster-based data cubes at the midstream segment, typically affected by the so-called data-rich information-poor syndrome, into a new generation of semantics-enabled EO big raster-based numerical data and vector-based categorical (symbolic, semi-symbolic or subsymbolic) information cube management systems, eligible for semantic content-based image retrieval and semantics-enabled information/knowledge discovery. (II) Boosting the downstream segment in the development of an ever-increasing ensemble of “vertical” (deep and narrow, user-specific and domain-dependent) value–adding information products and services, suitable for a potentially huge worldwide market of institutional and private end-users of space technology. For the sake of readability, this paper consists of two parts. In the present Part 1, first, background notions in the remote sensing metascience domain are critically revised for harmonization across the multi-disciplinary domain of cognitive science. In short, keyword “information” is disambiguated into the two complementary notions of quantitative/unequivocal information-as-thing and qualitative/equivocal/inherently ill-posed information-as-data-interpretation. Moreover, buzzword “artificial intelligence” is disambiguated into the two better-constrained notions of Artificial Narrow Intelligence as part-without-inheritance-of AGI. Second, based on a better-defined and better-understood vocabulary of multidisciplinary terms, existing EO optical sensory image-derived Level 2/ARD products and processes are investigated at the Marr five levels of understanding of an information processing system. To overcome their drawbacks, an innovative, but realistic EO optical sensory image-derived semantics-enriched ARD product-pair and process gold standard is proposed in the subsequent Part 2.