최근 건축분야의 인공지능 기계학습 연구동향 - 국내·외 연구논문을 중심으로 -

Artificial Intelligence research is presenting phenomenal progress in two directions: (i) new theories and methodologies, and (ii) applications that expand traditional domains with innovative interventions. As indicated by recent reports, this progress has created a disequilibrium, where demand for scientists with skills in Artificial Intelligence is not fulfilled, a trend that will intensify further in the years to come. A potential solution to this shortage of specialised workforce may come from encouraging more women to get educated and follow a career in one of the Artificial Intelligence areas. This special collection of invited papers is dedicated to all women researchers and practitioners in Artificial Intelligence and coincides with the March 8, 2023 International Women’s Day. Moreover, it has two specific goals: (i) to inspire more women to study and practice Artificial Intelligence through presentation of recognized women researchers who can act as role models, and (ii) to highlight some streamlined research areas of Artificial Intelligence.

The research aimed to identify the reality of using artificial intelligence in educational research from the point of view of graduate students at the College of Education at Taif University. The study sample consisted of 32.8% of the study population consisting of (128 male and female students) enrolled in the master’s degree- at the College of Education- Taif University In the specializations of (educational leadership, pedagogy, educational policies, special education, curricula, teaching methods and educational techniques) in the second semester of the academic year 1444-1445 AH, and using the questionnaire tool, the research concluded the following: 1- That students use artificial intelligence to a high degree in all stages of research. 2- One of the most important problems in using artificial intelligence in research is related to reliability and security, and the lack of a clear policy for applying artificial intelligence in educational research. 3- One of the most important controls that students believed should be available (formulation The Artificial Intelligence Ethics Document, the establishment of the University Center for Artificial Intelligence Ethics, and the enhancement of cyber security skills for the educational researcher, while there are no statistically significant differences between the sample members in their responses to the questionnaire axes due to specialization and level While the most widely used artificial intelligence program in educational research was Chat GPT, it affected the reality axis without the rest of the axes. Among the most important recommendations reached by this study: establishing a university center for the ethics of artificial intelligence, drafting a document on the ethics of artificial intelligence in educational research, and adding it as a course for students. Undergraduate and postgraduate studies

<p indent="0mm">The development of sexual reproductive organism embryos begins with the fertilization process, where sperm and egg unite, followed by a series of intricate and orchestrated changes, leading to continuous embryo development and tissue differentiation, culminating in the formation of a complete biological individual. Conducting in-depth and systematic exploration of this intricate and complex process represents a monumental and challenging undertaking. Artificial intelligence, is aimed at emulating the human brain and assisting in addressing more complex problems. With the continuous updating of computing devices and the successive ascent of computing capabilities, artificial intelligence has experienced rapid development, achieving groundbreaking advancements in various fields. In the field of life sciences, artificial intelligence has a wide range of applications, especially in the study of gamete and embryo development, where it was applied in the relatively early days. From gamete selection to embryo quality assessment, and from investigating molecular mechanisms underlying embryo development to elucidating the interconnectedness among various modules during embryo development, including time, phenotype, and molecular features, artificial intelligence has been effectively utilized, demonstrating significant advantages. Hence, exploring how to better leverage artificial intelligence in the research process of gamete and embryo development is a question worthy of deeper investigation at this stage. This paper provides a comprehensive review of the current applications of artificial intelligence in gamete and embryo development research, along with an analysis of the challenges and directions for its future development.

Hazardous waste capacity assurance planning: Volumes for one‐time remediation

Recent advancements in artificial intelligence (AI) applications in medicine have been significant over the past 30 years. To monitor current research developments, it is crucial to examine the latest trends in AI adoption across various medical fields. This bibliometric analysis focuses on AI applications in cardiology. Unlike existing literature reviews, this study specifically examines journal articles published in the last decade, sourced from both Scopus and Web of Science databases, to illustrate the recent trends in AI within cardiology. The bibliometric analysis involves a statistical and quantitative evaluation of the literature on AI application in cardiovascular medicine over a defined period. A comprehensive global literature review is conducted to identify key research areas, authors, and their interrelationships through published works. The leading institutions and most influential authors in research on the role of AI in cardiology were located in the United States, the United Kingdom, and China. This study also provides researchers with an overview of the evolution of research in AI and cardiology. The main contribution of this study is to highlight the prominent authors, countries, journals, institutions, keywords, and trends in the development of AI in cardiology.

Artificial intelligence value alignment is a cutting-edge topic discussed in the academic community in recent years. In terms of recent relevant research results, the academic community still needs to deepen its research on the goals and paths of artificial intelligence. In view of this, this article mainly uses the method of literature research, implementing the principles of unifying theory and practice, logic and history as well as truth and value scales to study the core connotation, fundamental goals and practical paths of artificial intelligence value alignment. The core connotations of aligning the value of artificial intelligence is to ensure that the research and application of artificial intelligence meet the practical needs of humans survival and development, thereby promoting better humans survival and development. The fundamental goals of aligning the value of artificial intelligence should mainly include following two points. Firstly, the research and application of artificial intelligence should conform to the scientifically reasonable value goals set by humans. Secondly, enabling the research and application of artificial intelligence to serve the survival and development of humans. The realistic dilemmas of aligning the value of artificial intelligence mainly include mainly include following three points. First, the regulatory system for ethical governance of artificial intelligence still needs to be improved. Second, the ethical governance of artificial intelligence still needs to be strengthened. Third, the synergy between artificial intelligence and humans still needs to be strengthened. The practical path for aligning the value of artificial intelligence should mainly include three points as follows. Firstly, developing and improving a normative system for ethical governance of artificial intelligence. Secondly, strengthening the ethical governance of artificial intelligence. Thirdly, based on the language communication between humans and machine, achieve collaboration between humans and artificial intelligence in value judgment and practical activities.

Urgency of the research of artificial intelligence research in a digital society presents numerous causal changes, among them: space research, biotechnology, nanotechnology, digital technology, etc. The implications for human beings and human civilisation are significant as they affect a number of already existing contemporary issues such as economics, politics, ethics, law, psychology, conflictology, sociology, and ecology. Anthropology has to return to the existence of human beings in a society of artificial intelligence, consciousness and even the physical body. The study conceptualises artificial intelligence as a factor of ecosystem growth in the context of technological, ontological, and anthropological dimensions. The research objectives: 1) to theorise artificial intelligence as a potential and resource for use in all sectors of the socioeconomic sphere in the context of technological dimensions; 2) to clarify the convergence of artificial intelligence and its main components in the context of ontological dimensions; 3) to reveal the role of artificial intelligence in enhancing the digitalisation of society and humans in the context of anthropological dimensions. In the research we are guided by the works of scholars who have studied and are studying the problems of artificial intelligence as a complex social, economic, and cultural phenomenon: R. Andriukaitienė, N. Boström, A. Vance, P. Diamandis, S. Kotler, P. Dickson, L. Kai-Fu, and K. Kell, K. Kell, and others. Research methodology. The theory of artificial intelligence is a complex multidisciplinary science, which is located at the junction of economics, sociology, computer science, mathematics, psychology. Summarizing the processes of interdisciplinary interaction of artificial intelligence, we note that its study requires an interdisciplinary approach based on the synthesis of scientific knowledge, as revealing the content and principles of functioning of a complex invariant system of artificial intelligence. To analyze the modern model of artificial intelligence, it is necessary to apply a systematic institutional-evolutionary approach. By analysing different methodological approaches, it is necessary to acknowledge the presence of an anthropogenic component, as the methodology of scientific research is based on the method of information-anthropogenic analysis based on the fundamental position that no artificial intelligence processes are possible without information/data as an object and human as a subject of cognition. In addition, the following methods were used: induction, deduction, sublimation, synergetics, evolutionary historicism, modelling, and forecasting. The result of the research. Artificial Intelligence (AI) is a broad term used to define technologies/ engineering systems that emulate human intelligence, linking to neurophysiology, robotics, psychology (pattern recognition, modelling of psychological processes), transhumanism, cybernetics (computing power to find patterns in large data sets). Artificial Intelligence has been defined as a software-engineered system that is capable of influencing the environment with varying degrees of autonomy, producing results (predictions, recommendations, decisions) for a specific set of goals for the digitalisation of society. On the other hand, artificial intelligence causes many problems that are seen as cyber/virtual addiction, loss of reality, increased surveillance, loss of protocol control, privacy issues, cybersecurity, etc. The convergence of large data with artificial intelligence has become the single most important development shaping the future of social institutions in the digital society and the prospects for realising potential opportunities. Thanks to the latest digital technologies, advances in software engineering, and evolving artificial intelligence, there are both new perspectives for solving the most complex problems of modern civilization and new challenges in the technological, ontological, and anthropological dimensions.

Artificial intelligence (AI) is quickly transforming business operations and society, as AI capabilities are incorporated into applications ranging from mobile personal assistants to self-driving cars. The potentially disruptive nature of AI calls for an extensive investigation into all aspects of AI-human interactions at individual, group, organizational and market levels. However, there is paucity of academic information systems (IS) research in this area that goes beyond the development and testing of specific narrow AI capabilities. AI represents an important opportunity for organizational and behavioral IS researchers, but also presents challenges associated with the underlying complexity of AI technologies and the diversity of AI applications. Understanding how existing AI research and business practice relate to traditional areas of IS research is an important step towards creating a comprehensive behavioral and organizational AI research agenda. This dissertation seeks to achieve a dual purpose in a series of three essays. Essay 1 seeks to understand the current state of business AI research and practice in business through a quantitative literature review, relate the findings to traditional IS research areas, and identify potentially fruitful research areas for AI-focused IS research. Essays 2 and 3 seek to address specific research questions related to one of such research areas, namely, human interactions with AI enabled applications. Essay 2 focus on user experience with a chatbot, a popular AI application, and Essay 3 explores how user experiences with AI assistant apps differ from their interactions with more traditional IT artifacts.

The research focus was motivated by the limited understanding of cognitive technologies and the growing gap between artificial intelligence (AI) and human intelligence. The research is a literature review, and its purpose is to simplify the meaning and processes behind cognitive technologies, notably, the fundamentals of machine learning (ML) and computer vision with the intention to briefly address the alleged threat of AI taking over the job market. The research is a review of peer-reviewed articles retrieved from comparative studies, systematic reviews, meta-analysis, service research, reports, conference proceedings, experimental studies, literature reviews, scientometric analyses, books, and multi-case studies, dating from the years of 2018 to 2024. This literature review defines machine learning (ML), artificial intelligence (AI), computer vision, and convolutional neural networks (CNNs). It also compares machine learning to traditional programming and reveals the types of learning in ML models’ training. ML and its correlation with AI are also discussed and details about theory of mind, self-aware AI, reactive machines, and limited memory AI are shared. The literature expounds computer vision, particularly convolutional neural network (CNN) and CNN layers. Recent cutting-edge applications of artificial intelligence including generative AI models and autonomous systems are also incorporated. Finally, the literature briefly addresses the alleged threat of AI taking over the job market. The findings of this literature review reveal that AI is becoming the new way of operating. The conclusion shows that AI models require significant computation to allow computers to learn autonomously. Thus, understanding mathematical models of data and perfecting the process of writing software could be the key to remaining employable as more jobs are expected to be shifted due to AI and tasks automation. Keywords: Cognitive technology, artificial intelligence, machine learning, computer vision, convolutional neural networks

The thriving contemporary form of artificial intelligence (AI) called machine learning is often represented sensationally in popular media as a semi-mystical technology. Machine learning systems are frequently ascribed anthropomorphic capacities for learning, emoting and reasoning which, it is suggested, might lead to the alleviation of humanity’s woes. One critical reaction to such sensational proclamations has been to focus on the mundane reality of contemporary machine learning as mere inductive prediction based on statistical generalizations, albeit with surprisingly powerful abilities (Pasquinelli 2017). While the deflationist reaction is a necessary reply to sensationalist agitation, adequate comprehension of modern AI cannot be achieved while neglecting its material and social context. One does not have to subscribe wholeheartedly to the social construction of technology thesis1 to allow that the development and evolution of technologies are influenced by social factors. For AI, the most important aspect of the current social context is arguably capital, which increasingly dominates AI research and production. One former computer science professor describes a “giant sucking sound of [AI] academics going into industry” (Metz 2017). This paper introduces capital’s theory of AI as utility and initiates a discussion on its social consequences. First, I discuss utilities and their infrastructures and introduce a few critical thoughts on the topic. Second, I situate modern AI by way of a brief history. Third, I detail capital’s view of AI as a utility and the technical details underpinning it. Fourth, I sketch how AI as a utility frames a social problematic beyond the important issues of algorithmic bias and the automation of work. I do so by extrapolating from one consequence of AI as a utility which multiple capitalist firms predict: the curation of human subjectivities.

Background and aim The growing number of publications on the application of artificial intelligence (AI) in medicine underlines the enormous importance and potential of this emerging field of research. In gastrointestinal endoscopy, AI has been applied to all segments of the gastrointestinal tract most importantly in the detection and characterization of colorectal polyps. However, AI research has been published also in the stomach and esophagus for both neoplastic and non-neoplastic disorders.The various technical as well as medical aspects of AI, however, remain confusing especially for non-expert physicians.This physician-engineer co-authored review explains the basic technical aspects of AI and provides a comprehensive overview of recent publications on AI in gastrointestinal endoscopy. Finally, a basic insight is offered into understanding publications on AI in gastrointestinal endoscopy.

A brief summary of a lengthy work of writing, such as a book, research paper, or thesis, is called an abstract. It describes the key concepts, methods, findings, and conclusions, and is usually positioned at the beginning of the document. start of academic or professional documents, abstracts help an abstract serve as a tool that allows readers can rapidly comprehend a work’s primary goal and focus without the requirement for to read the entire document. It enables readers to effectively understand the content’s goals and relevance. is to provide a preview of the information, helping readers decide whether the full document is relevant to their research or interests. It summarizes the main problem or question addressed, the methods or approach used to investigate it, and the significant outcomes or conclusions drawn. It typically contains a brief introduction, methodology, results. Abstracts are commonly used in academic journals, conferences, and other scholarly publications, as well as in research proposals and grant applications. A range of technologies collectively referred to as artificial intelligence (AI) enable computers to do complex tasks, such as data analysis, spoken or written language comprehension and translation, visual perception, and more. making suggestions. A well-written abstract can improve the visibility and accessibility of a work, as it allows researchers and professionals to quickly assess its relevance. Clear, concise, and accurate representation of the core ideas is essential for a successful abstract. Research significance: Artificial intelligence (AI) research is transformative, driving advancements in technology, society, and the economy. It fosters innovation in areas like automation, robotics, and data analysis, enabling more efficient systems and unlocking new possibilities. In healthcare, AI enhances diagnostics, personalizes treatments, and accelerates drug discovery, while in education, it provides adaptive learning tailored to individual needs. Economically, AI boosts productivity, creates new markets, and improves decision-making through data-driven insights. It also discusses global concerns including food security, public health, and climate change. can be addressed by enhancing and streamlining solutions and predictive modeling. AI research raises important ethical and philosophical questions, ensuring fairness, transparency, and responsible use. Additionally, it strengthens national and global security through advancements in defense and cybersecurity. Beyond practical applications, AI deepens our understanding of cognition and expands exploration into uncharted territories. AI research is a cornerstone of progress, reshaping society and empowering humanity to address the future. Methodology: The Demetal Methodology It is a structured method employed to study and represent intricate systems. by examining interrelationships among various factors. It combines qualitative and quantitative techniques to identify cause-effect relationships, prioritize issues, and facilitate decision-making. This methodology is particularly effective in fields like systems engineering, supply chain management, and social sciences, where interdependencies play a significant role. It begins with identifying key factors or components of a system, followed by expert input to establish connections and weightages between them. Using tools like pairwise comparison matrices, it quantifies these relationships, enabling visualization through causal diagrams or directed graphs. Alternative: Water quality, spa amenities, affordability, ease of access, and reputation. Evalution parameter: Water Quality, SPA Facilities, Cost, Accessibility, Reputation. Result: Water Quality (1): The first rank which may imply that basic standards are assumed or that it is less influential in decision-making compared to other factors. SPA Facilities (5): The last rank, emphasizing the importance of high-quality amenities and treatment options for a superior customer experience.

Ever since the concept of artificial intelligence (AI) has been suggested, it has undergone years of research and development. Under the current condition of rapid development of information and data technology, AI has shown significant effective value and application capability in multiple fields, especially in medical treatment. AI has become essential for routine medical treatment. This review summarizes the current clinical application of AI in orthopedics, in reference to the basic principle of AI, AI supported in clinical diagnosis, AI supported in clinical decision-making, AI supported clinical surgery, and the combination of AI and telemedicine. At the same time, this review also specifies the advantages, disadvantages, and capability of AI in the current clinical application, to provide some understanding for further research of AI.

In reaction to the growing attention paid to big data and artificial intelligence in hospitality and tourism research, we systematically reviewed 270 relevant studies to identify topical themes and trends. We first briefly reviewed the emergence definition of big data. Next, we introduced the methodology of literature collection and presented results of bibliometric analysis. Then, we identified types of big data used and the application of artificial intelligence in big data usage in hospitality and tourism research, followed by unveiling major themes of big data and artificial intelligence research in extant literature such as forecasting, industry development, marketing, performance analysis, consumer behaviors, attitudes, and so on. In addition, implications and challenges of applying big data and artificial intelligence in hospitality and tourism research and new directions for future research are identified. Finally, we discuss limitations of our review, proposing future research directions for scholars.

In this final set of explorations/meditations (of three), we examine the requirements for developing artificial general intelligence (AGI) through the lens of human cognitive architecture, with particular emphasis on the role of narrative selfhood and social cognition. Drawing on perspectives from cognitive science, philosophy of mind, and artificial intelligence research, we critically evaluate current claims about the capabilities of large language models, particularly regarding their purported achievements of theory of mind and self-awareness. We argue that genuinely human-like artificial intelligence may require more than sophisticated pattern recognition and language modeling, potentially necessitating the development of coherent narrative self-models and rich causal understanding. Special attention is given to the relationship between consciousness, conscience, and trustworthy AI systems, suggesting that meaningful artificial intelligence may require forms of richly-embodied and socially-embedded development to achieve robust and reliable functionality. We conclude by proposing that the path to artificial general intelligence may require recapitulating aspects of human cognitive development, particularly regarding the construction of narrative identity and social-moral reasoning capabilities. This analysis has implications for both the technical development of AI systems and the ethical frameworks through which we evaluate artificial minds.