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Artykuły w czasopismach na temat "AI-Related technologies"
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Pashkova, Nataliya V., i Maxim V. Vesnyanov. "Ethical issues in using artificial intelligence technologies". Alma mater. Vestnik Vysshey Shkoly, nr 1 (styczeń 2024): 107–10. http://dx.doi.org/10.20339/am.01-24.107.
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The article discusses the ethical aspects associated with the use and development of artificial intelligence technologies. With the development of AI, problems arise that affect various areas of human life, including ethics, economics, medicine, education and others. Analyzes key ethical issues such as the autonomy of AI decision-making; determining who is responsible for damage caused by AI; transparency and data protection, as well as issues of inequality and the potential social consequences of AI. Our task was to identify the main ethical issues related to the use of artificial intelligence and to identify possible solutions for the use and development of AI. To address this, we reviewed and analyzed existing ethical and guiding principles, identified and systematized key ethical issues and different positions on AI. The theoretical sources for our analysis were the results of scientific research in the field of artificial intelligence, considering the ethical aspects of the use and development of such systems.
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Sarwari, Abdul Qahar, i Hamedi Mohd Adnan. "The effectiveness of artificial intelligence (AI) on daily educational activities of undergraduates in a modern and diversified university environment". Advances in Mobile Learning Educational Research 4, nr 1 (27.02.2024): 927–30. http://dx.doi.org/10.25082/amler.2024.01.004.
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This study assessed the effectiveness of AI and AI-related technologies in the daily educational activities of undergraduate students in a modern and diversified university environment. The participants were 13 undergraduate Indonesian students participating in a mobility program for two weeks in Malaysia. A survey questionnaire designed with the help of the existing literature and ChatGPT, which includes ten (10) structured items and seven (7) open-ended questions, was used to collect the data. The relevant SPSS tests were used to analyze the data. Based on the results, of all 13 participants, 12 (92.3%) of them already experienced AI in their daily educational activities, and there were strong positive correlations between the attitudes toward AI and AI experiences, and attitudes toward AI and the effects of AI on education attributes, with correlation scores of .663 and .833 respectively. Based on the participant's answers to the qualitative questions, most of them believed that AI and AI technologies, such as ChatGPT, are helpful in daily educational activities and help them gain information regardless of time and space limitations and do their university-related assignments quickly. Based on the results, AI and AI-related technologies could transform different aspects of modern education.
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Kabashkin, Igor, Boriss Misnevs i Olga Zervina. "Artificial Intelligence in Aviation: New Professionals for New Technologies". Applied Sciences 13, nr 21 (25.10.2023): 11660. http://dx.doi.org/10.3390/app132111660.
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Major aviation organizations have highlighted the need to adopt artificial intelligence (AI) to transform operations and improve efficiency and safety. However, the aviation industry requires qualified graduates with relevant AI competencies to meet this demand. This study analyzed aviation engineering bachelor’s programs at European universities to determine if they are preparing students for AI integration in aviation by incorporating AI-related topics. The analysis focused on program descriptions and syllabi using semantic annotation. The results showed a limited focus on AI and machine learning competencies, with more emphasis on foundational digital skills. Reasons include the newness of aviation AI, its specialized nature, and implementation challenges. As the industry evolves, dedicated AI programs may emerge. But currently, curricula appear misaligned with stated industry goals for AI adoption. The study provides an analytical methodology and competency framework to help educators address this gap. Producing graduates equipped with AI literacy and collaboration skills will be key to aviation’s intelligent future.
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Cetinic, Eva, i James She. "Understanding and Creating Art with AI: Review and Outlook". ACM Transactions on Multimedia Computing, Communications, and Applications 18, nr 2 (31.05.2022): 1–22. http://dx.doi.org/10.1145/3475799.
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Technologies related to artificial intelligence (AI) have a strong impact on the changes of research and creative practices in visual arts. The growing number of research initiatives and creative applications that emerge in the intersection of AI and art motivates us to examine and discuss the creative and explorative potentials of AI technologies in the context of art. This article provides an integrated review of two facets of AI and art: (1) AI is used for art analysis and employed on digitized artwork collections, or (2) AI is used for creative purposes and generating novel artworks. In the context of AI-related research for art understanding, we present a comprehensive overview of artwork datasets and recent works that address a variety of tasks such as classification, object detection, similarity retrieval, multimodal representations, and computational aesthetics, among others. In relation to the role of AI in creating art, we address various practical and theoretical aspects of AI Art and consolidate related works that deal with those topics in detail. Finally, we provide a concise outlook on the future progression and potential impact of AI technologies on our understanding and creation of art.
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Chen, Yinong, i Gennaro De Luca. "Technologies Supporting Artificial Intelligence and Robotics Application Development". Journal of Artificial Intelligence and Technology 1, nr 1 (31.01.2021): 1–8. http://dx.doi.org/10.37965/jait.2020.0065.
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Artificial intelligence (AI) and robotics have gone through three generations of development, from Turing test, logic theory machine, to expert system and self-driving car. In the third-generation today, AI and robotics have collaboratively been used in many areas in our society, including industry, business, manufacture, research, and education. There are many challenging problems in developing AI and robotics applications. We launch this new Journal of Artificial Intelligence and Technology to facilitate the exchange of the latest research and practice in AI and technologies. In this inaugural issue, we first introduce a few key technologies and platforms supporting the third-generation AI and robotics application development based on stacks of technologies and platforms. We present examples of such development environments created by both industry and academia. We also selected eight papers in the related areas to celebrate the foundation of this journal.
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Qian, Zuo Yin, i Wei Sieng Lai. "THE IMPACT OF ARTIFICIAL INTELLIGENCE TECHNOLOGY ON INTERNATIONAL TRADE". Advanced International Journal of Business, Entrepreneurship and SMEs 6, nr 19 (12.03.2024): 153–70. http://dx.doi.org/10.35631/aijbes.619012.
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This paper studies the complex impact mechanism of AI (artificial intelligence) technology on international trade. This provides an answer to how different countries use AI technology to increase their own international trade volume. This article is based on an analysis of cross-sectional data for 139 countries in 2021. This paper uses the Ordinary Least Square (OLS) method to perform a analysis on the cross-sectional data. This paper finds that the membership of World Trade Organization (WTO) has obvious significance for a country to use AI technology to promote exports. In countries with a high Government AI Readiness Index, AI technology has a significant role in promoting the growth of international trade. However, there is a significant negative correlation between the number of patent applications for AI-related technologies and imports and exports across countries. Besides, the number of patent applications for AI-related technologies has a direct heterogeneous impact on the imports of countries with different income groups. AI technology is an emerging technology. To study the impact of this technology on international trade, multiple factors of AI technology must be considered at the same time. For countries that are not members of the WTO, joining the WTO can make better use of AI technology to promote the development of their international trade. Between the complex relationship between the number of patent applications for different AI-related technologies and international trade, countries with different income groups should develop AI technologies that are beneficial to their own international trade.
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Rozenes, Shai, i Yuval Cohen. "Artificial Intelligence Synergetic Opportunities in Services: Conversational Systems Perspective". Applied Sciences 12, nr 16 (21.08.2022): 8363. http://dx.doi.org/10.3390/app12168363.
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The importance of this paper is its discovery of the unused synergetic potential of integration between several AI techniques into an orchestrated effort to improve service. Special emphasis is given to the conversational capabilities of AI systems. The paper shows that the literature related to the use of AI in service is divided into independent knowledge domains (silos) that are either related to the technology under consideration, or to a small group of technologies related to a certain application; it then discusses the reasons for the isolation of these silos, and reveals the barriers and the traps for their integration. Two case studies of service systems are presented to illustrate the importance of synergy. A special focus is given to the conversation part of these service systems: the first case presents an application with high potential for integrating new AI technologies into its AI portfolio, while the second case illustrates the advantages of a mature application that has already integrated many technologies into its AI portfolio. Finally, the paper discusses the two case studies and presents inclusion relationships between AI capabilities to facilitate generating a roadmap for extending AI capabilities with synergetic opportunities.
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Okladnikova, S. V., i A. S. Pankrashov. "Application of artificial intelligence technologies in HR management". Herald of Dagestan State Technical University. Technical Sciences 50, nr 2 (31.07.2023): 117–25. http://dx.doi.org/10.21822/2073-6185-2023-50-2-117-125.
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Objective. The purpose of the study is to analyze the possibility of using AI technologies to solve problems related to the selection of employees in project teams, based on production indicators and HR metrics of personnel.Method. Based on the fact that in the field of AI, methods mean algorithms by which tasks are solved, the following number of methods related to AI theory were identified: neural networks, fuzzy logic, expert systems, evolutionary modeling, Machine Learning.Result. An example of the use of AI is given in a situation where it is necessary to recruit personnel for a project based on the length of service and the degree of workload (where a scale with values from “highly loaded” to “not loaded” is used for workload). For the described example, an explanation is given that reveals the use of AI technologies (such as question-and-answer systems) in order to form HR metrics and production indicators. Additionally, the process of applying the objective function to obtain a numerical coefficient based on individual metrics or a combination of them is described in order to make a decision corresponding to its value based on the resulting indicator of the function.Conclusion. Within the framework of the conducted research, the historical patterns that led the field of personnel management to transformation and which made the use of AI technologies relevant in this area were considered. Continuous development and implementation of intelligent tools in the practice of project management facilitates HR processes and increases the efficiency of employee management. The use of AI technologies considered in the study will help to successfully monitor the state of both the project and the project team, which will have a positive impact on the productivity and profit of the enterprise.
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Grove, Hugh, Mac Clouse i Tracy Xu. "New risks related to emerging technologies and reputation for corporate governance". Journal of Governance and Regulation 9, nr 2 (2020): 64–74. http://dx.doi.org/10.22495/jgrv9i2art4.
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Artificial intelligence (AI) has moved from theory into the global marketplace. The United Nations World Intellectual Property Organization released the first report of its Technology Trends series on January 31, 2019. It considered more than 340,000 AI-related patent applications over the last 70 years. 50 percent of all AI patents have been published in just the last five years. The challenges, potential risks, and opportunities for business and corporate governance from emerging technologies, especially artificial intelligence, have been summarized as whereby machines and software can analyze, optimize, prophesize, customize, digitize and automate just about any job in every industry. Boards of directors and executives need to recognize and understand the new risks associated with these emerging technologies and related reputational risks. The major research question of this paper is how boards of directors and executives can deal with both risk challenges and opportunities to strengthen corporate governance. Accordingly, the following sections of this paper discuss key risk management issues: deep shift risks, global risks, digital risks and opportunities, AI initiatives risks, business risks from millennials, business reputational risks, and conclusions.
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El-Mahrouki, Mayada Moustafa. "LEGISLATIVE INDUSTRY CHALLENGES IN CONFRONTING ARTIFICIAL INTELLIGENCE CRIMES". Journal of Law and Sustainable Development 12, nr 4 (2.04.2024): e3566. http://dx.doi.org/10.55908/sdgs.v12i4.3566.
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Objectives: The objective of this scientific paper is to shed light on the challenges facing the legislative industry in the field of artificial intelligence (AI) technologies. Specifically, it aims to anticipate and predict the difficulties related to creating an independent legal personality for AI tools, enabling them to assume rights and obligations, as well as establishing criminal responsibility and punishment in cases where AI technologies commit crimes punishable by law.
Methods: To achieve the stated objectives, this paper employs a theoretical and analytical approach. It involves a comprehensive review and analysis of existing literature, legal frameworks, and case studies related to the legal implications of AI technologies. Additionally, hypothetical scenarios and potential challenges are discussed to illustrate the complexities involved in creating legal frameworks that address the unique nature of AI tools and their impact on society.
Results: The analysis reveals several key challenges facing the legislative industry in the field of AI technologies. These challenges include the conceptualization of an independent legal personality for AI tools, determining the rights and obligations associated with such personality, and establishing mechanisms for holding AI technologies accountable for their actions. Furthermore, the paper explores the difficulties in imposing criminal responsibility and punishment on AI technologies in cases where they commit crimes.
Conclusion: In conclusion, this scientific paper highlights the complexities and challenges associated with creating legal frameworks for AI technologies. By anticipating and predicting these challenges, it provides valuable insights for legislators, policymakers, and legal professionals involved in shaping the regulatory landscape for AI. Moving forward, it is essential to address these challenges proactively and develop legal frameworks that strike a balance between promoting innovation and safeguarding societal interests in the era of artificial intelligence.
Rozprawy doktorskie na temat "AI-Related technologies"
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Wei, Tian. "Une exploration micro-fondatrice des antécédents et des conséquences de la catégorisation compétitive". Electronic Thesis or Diss., Reims, 2024. http://www.theses.fr/2024REIME003.
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Cette recherche vise à explorer les micro-fondations de la catégorisation compétitive en examinant ses antécédents, à savoir la flexibilité cognitive et les émotions, ainsi que ses conséquences, c’est-à-dire la confiance cognitive. Deux études ont utilisé des simulations informatiques pour montrer comment la flexibilité cognitive et les émotions des managers affectent leur capacité à effectuer de manière efficace et flexible la catégorisation compétitive dans des paysages de marché changeants. Une troisième étude a intégré des études expérimentales et des méthodes d’apprentissage automatique pour démontrer comment les résultats de catégorisation des managers influencent leur confiance cognitive dans les technologies liées à l’IA. Les résultats de ce travail contribuent à la littérature sur les capacités cognitives managériales. Ils suggèrent également des implications pratiques sur la manière dont les entreprises devraient s’adapter aux environnements compétitifs dynamiquesThis work aims to explore the micro-foundations of competitive categorization by examining its antecedents—namely, cognitive flexibility and emotions—and consequences—i.e., cognitive trust. Two studies used computer simulations to show how managers’ cognitive flexibility and emotions affect their ability to effectively and flexibly perform competitive categorization under changing market landscapes. A third study integrated survey experiments and machine-learning methods to demonstrate how managers’ categorization results influence their cognitive trust in AI-related technologies. This work’s findings contribute to the literature on managerial cognitive capabilities. They also suggest practical implications on how firms should adapt to dynamic competitive environments
Książki na temat "AI-Related technologies"
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Nikolinakos, Nikos Th. EU Policy and Legal Framework for Artificial Intelligence, Robotics and Related Technologies - The AI Act. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-27953-9.
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Odincov, Boris. Models and intelligent systems. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1060845.
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The monograph consists of three chapters, the first of which outlines the theoretical foundations of intelligent information systems. Special attention is paid to the disclosure of the term "model" as the intended meaning depends on the understanding of the material. Introduces and examines the new concepts such as the associative and intuitive knowledge while in the creation of intellectual information systems are not used.
The second Chapter contains the analysis of problems of development of artificial intelligence (AI), developed in two directions: classical and statistical. Discusses difficulties in the development of the classical approach, associated with identifying the meaning of words, phrases, text, and formulating thoughts. The analysis of problems arising in the play of imagination and insight, machine understanding of natural language texts, play, verbalization and reflection.
The third Chapter contains examples of the development of intelligent information systems and technologies in practice of management of economic objects. Theoretical bases of construction of information robots designed to support the task hierarchy of the knowledge base and generating control regulations. The technology of their creation and application in the management of the business efficiency of enterprise business processes and its investment activities.
Focused on researchers and developers, AI and intelligent information systems, as well as graduate students and faculty in related academic disciplines.
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EU Policy and Legal Framework for Artificial Intelligence, Robotics and Related Technologies - the AI Act. Springer International Publishing AG, 2023.
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Nisbet, Robert, Gary Miner, Mitchell Goldstein, Scott Burk i Linda A. Miner. Practical Data Analytics for Innovation in Medicine: Building Real Predictive and Prescriptive Models in Healthcare and Medical Research Using AI, ML, and Related Technologies. Elsevier Science & Technology Books, 2022.
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Nisbet, Robert, Gary D. Miner, Mitchell Goldstein, Scott Burk i Linda A. Miner. Practical Data Analytics for Innovation in Medicine: Building Real Predictive and Prescriptive Models in Healthcare and Medical Research Using AI, ML, and Related Technologies. Elsevier Science & Technology, 2022.
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Jotterand, Fabrice, Marcello Ienca, Tenzin Wangmo i Bernice Elger, red. Intelligent Assistive Technologies for Dementia. Oxford University Press, 2019. http://dx.doi.org/10.1093/med/9780190459802.001.0001.
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The development and implementation of intelligent assistive technologies (IATs) to compensate for the specific physical and cognitive deficits of older adults with dementia have been recognized by many as one of the most promising approaches to this emerging financial and caregiving burden. In the past 15 years, advancements in artificial intelligence (AI), pervasive and ubiquitous computing (PUC), and other advanced trends in software and hardware technology have led to the development and design of a wide range of IATs to help older people compensate for the physical and sensory deficits that may accompany dementia and age-related cognitive decline. These technologies are designed to support impaired older adults in the completion of activities of daily living, assist them in the prevention or management of risk, and/or maintain their recreational and social environment. The widespread implementation and use of assistive technologies is a very rapid process, which is reshaping dementia care and producing constantly changing strategies. This volume aims at providing an up-to-date overview of the current state of the art of assistive technologies for dementia care and an examination of their implications at the medical level, including psychological and clinical issues and their ethical and regulatory challenges. The overall goal of this book is to raise societal awareness on the use of IATs for dementia care and take a first step into developing an international regulatory and policy framework.
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Bhushan, Megha, Sailesh Iyer, Ashok Kumar, Tanupriya Choudhury i Arun Negi, red. Artificial Intelligence for Smart Cities and Villages: Advanced Technologies, Development, and Challenges. BENTHAM SCIENCE PUBLISHERS, 2022. http://dx.doi.org/10.2174/97898150492511220101.
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Smart cities and villages have enhanced the quality of lives of residents. Various computer-assisted technologies have been harnessed for the development of smart cities and villages in order to provide solutions for common and niche urban problems. The development of smart environments has been possible due to advances in computing power and artificial intelligence (AI) that have allowed the deployment of scalable technologies. Artificial Intelligence for Smart Cities and Villages: Advanced Technologies, Development, and Challenges summarizes the role of AI in planning and designing smart solutions for urban and rural environments. This book is divided into three sections to impart a better understanding of the topics to readers. These sections are: 1) Demystifying smart cities and villages: A traditional perspective, 2) Smart innovations for rural lifestyle management solutions, and 3) Case studies. Through this book, readers will be able to understand various advanced technologies that are vital to the development of smart cities and villages. The book presents 15 chapters that present effective solutions to urban and rural challenges. Concepts highlighted in chapters include smart farms, indoor object classification systems, smart transportation, blockchains for medical information, humanoid robots for rural education, IoT devices for farming, and much more. This book is intended for undergraduate and graduate engineering students across all disciplines, security providers in the IT and related fields, and trainees working for infrastructure management companies. Researchers and consultants at all levels working in the areas of artificial intelligence, machine learning, IoT, blockchain, network security, and cloud computing will also find the contents beneficial for planning projects involving smart environments.
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Achermann, Simone, i Stephan Sigrist. The Future of Clothing. Bloomsbury Publishing Plc, 2023. http://dx.doi.org/10.5040/9781350138629.
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Disappearing dress codes, customers as designers and wearable technology; in recent years the production and function of clothing has undergone massive change. New manufacturing technologies have brought bespoke design within reach of many consumers for the first time. Miniature sensors can turn ordinary garments into smart devices. And blurring gender roles and class distinctions point the way towards a more fluid approach to clothing, fashion and design. The Future of Clothing offers a critical introduction to these developments from an interdisciplinary perspective, engaging with their implications for the clothing industry and related fields. You'll learn how mass-personalization impacts the luxury market, the effects of automation on craftsmanship and how AI design may affect individual style choices. Contributors include fashion historian, author and broadcaster Amber Butchart, adventurer, ecologist and head of the Sculpt the Future Foundation David de Rothschild, and best-selling author Yuval Noah Harari. There are also 8 exclusive illustrations by Salvador Dali, depicting the surrealist master’s extraordinary vision of how fine-tailored clothing might evolve in the 21st century. Together they form a truly unique guide to the future of this most creative industry.
Części książek na temat "AI-Related technologies"
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Dokku, Prathyusha, i Vrushali Kamalakar. "Exploring AI and Related Technologies in Understanding Cultural Heritage". W RILEM Bookseries, 985–95. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-39450-8_80.
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Illési, Zsolt, i Vid S. Honfi. "A Security Assessment of AI, Related to the Financial Institutions". W Security-Related Advanced Technologies in Critical Infrastructure Protection, 85–94. Dordrecht: Springer Netherlands, 2022. http://dx.doi.org/10.1007/978-94-024-2174-3_7.
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Wilner, Alex, i Casey Babb. "New Technologies and Deterrence: Artificial Intelligence and Adversarial Behaviour". W NL ARMS, 401–17. The Hague: T.M.C. Asser Press, 2020. http://dx.doi.org/10.1007/978-94-6265-419-8_21.
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AbstractOffering a critical synthesis of extant insights into technological developments in AI and their potential ramifications for international relations and deterrence postures, this chapter argues that AI risks influencing military deterrence and coercion in unique ways: it may alter cost-benefit calculations by removing the fog of war, by superficially imposing rationality on political decisions, and by diminishing the human cost of military engagement. It may recalibrate the balance between offensive and defensive measures, tipping the scales in favour of pre-emption, and undermine existing assumptions imbedded in both conventional and nuclear deterrence. AI might altogether remove human emotions and eliminate other biological limitations from the practice of coercion. It may provide users the ability to collect, synthesize, and act upon real-time intelligence from several disparate sources, augmenting the certainty and severity of punishment strategies, both in theatre and online, compressing the distance between intelligence, political decisions, and coercive action. As a result, AI may quicken the overall pace of action across all domains of coercion, in conflict, crisis, and war, and within the related subfields of national security, counterterrorism, counter-crime, and counter-espionage.
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Berre, Arne J., Aphrodite Tsalgatidou, Chiara Francalanci, Todor Ivanov, Tomas Pariente-Lobo, Ricardo Ruiz-Saiz, Inna Novalija i Marko Grobelnik. "Big Data and AI Pipeline Framework: Technology Analysis from a Benchmarking Perspective". W Technologies and Applications for Big Data Value, 63–88. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-78307-5_4.
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AbstractBig Data and AI Pipeline patterns provide a good foundation for the analysis and selection of technical architectures for Big Data and AI systems. Experiences from many projects in the Big Data PPP program has shown that a number of projects use similar architectural patterns with variations only in the choice of various technology components in the same pattern. The project DataBench has developed a Big Data and AI Pipeline Framework, which is used for the description of pipeline steps in Big Data and AI projects, and supports the classification of benchmarks. This includes the four pipeline steps of Data Acquisition/Collection and Storage, Data Preparation and Curation, Data Analytics with AI/Machine Learning, and Action and Interaction, including Data Visualization and User Interaction as well as API Access. It has also created a toolbox which supports the identification and use of existing benchmarks according to these steps in addition to all of the different technical areas and different data types in the BDV Reference Model. An observatory, which is a tool, accessed via the toolbox, for observing the popularity, importance and the visibility of topic terms related to Artificial Intelligence and Big Data technologies has also been developed and is described in this chapter.
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Kurç, Çağlar. "Enabling Technology of Future Warfare: Turkey’s Approach to Defense AI". W Contributions to Security and Defence Studies, 331–52. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-58649-1_15.
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AbstractDefense artificial intelligence (AI) development in Turkey focuses primarily on improving the capabilities of autonomous systems, sensors, and decision support systems because Turkey believes autonomous systems are the future of modern warfare. Hence, autonomous systems, mainly uncrewed aerial vehicles (UAVs), have remained a priority in Turkish AI development since 2011 and have expanded to include all types of uncrewed vehicles. Increasing autonomous systems’ capability in intelligence gathering and fighting, as well as enabling swarm operations, are prioritized in developing defense AI. While Turkey enhances the capabilities of autonomous systems, humans will continue to be critical for decision-making in the foreseeable future. However, the fast pace of developing and deploying autonomous systems exacerbates the dilemma of human-machine interaction. Turkey believes that AI enhancements to decision-making systems would help humans make quicker decisions and ease human-machine interactions. Thus, Turkey specifically focuses on AI-related technologies like machine learning, computer vision, and natural language processing with an application focus on autonomous vehicles and robotics. The intertwined development of AI and related technologies form the core of Turkey’s AI ecosystem. Turkey’s AI ecosystem is nascent but growing. Turkey aims to create synergies between its defense and civilian industries, academic institutions, and government through various ecosystem initiatives. Turkey seeks to increase its R&D spending on AI to increase employment and grow the ecosystem. Training and managing a skilled workforce are essential to building the indigenous AI development capacity that Turkey is looking for.
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Trocin, Cristina, Jan Gunnar Skogås, Thomas Langø i Gabriel Hanssen Kiss. "Operating Room of the Future (FOR) Digital Healthcare Transformation in the Age of Artificial Intelligence". W Digital Transformation in Norwegian Enterprises, 151–72. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05276-7_9.
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AbstractNew technologies are emerging under the umbrella of digital transformation in healthcare such as artificial intelligence (AI) and medical analytics to provide insights beyond the abilities of human experts. Because AI is increasingly used to support doctors in decision-making, pattern recognition, and risk assessment, it will most likely transform healthcare services and the way doctors deliver those services. However, little is known about what triggers such transformation and how the European Union (EU) and Norway launch new initiatives to foster the development of such technologies. We present the case of Operating Room of the Future (FOR), a research infrastructure and an integrated university clinic which investigates most modern technologies such as artificial intelligence (AI), machine learning (ML), and deep learning (DL) to support the analysis of medical images. Practitioners can benefit from strategies related to AI development in multiple health fields to best combine medical expertise with AI-enabled computational rationality.
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Nizamis, Alexandros, Georg Schlake, Georgios Siachamis, Vasileios Dimitriadis, Christos Patsonakis, Christian Beecks, Dimosthenis Ioannidis, Konstantinos Votis i Dimitrios Tzovaras. "Designing a Marketplace to Exchange AI Models for Industry 5.0". W Artificial Intelligence in Manufacturing, 27–41. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-46452-2_2.
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AbstractNowadays, the market for AI services is continuously growing and it is expected to exceed 5 trillion euros in the next 5 years. However, the sharing of knowledge is primarily achieved by the sharing of published AI-related papers. The sharing of the trained AI/ML models is still in its infancy stage and in some domains it does not even exist. In this chapter, a marketplace for exchanging AI models related to smart manufacturing and Industry 5.0 domains is introduced. The proposed AI Marketplace consists of a semantic-based repository that manages the AI models, a blockchain-based framework that adds the business logic and web-based user interfaces that enable models’ exploration and sharing, and transactions among the stakeholders. The purpose of this chapter is to present the implementation details of this AI Model Marketplace by highlighting the key concepts and technologies used along with the main supported functionalities. By using such a marketplace, the manufacturing companies are able to capitalize in a large variety of AI models to solve various problems enabling intelligent, flexible, and cost-effective production.
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Tuo, Yanzheng, Lanyu Ning i Aiyuan Zhu. "How Artificial Intelligence Will Change the Future of Tourism Industry: The Practice in China". W Information and Communication Technologies in Tourism 2021, 83–94. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-65785-7_7.
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AbstractIn the future, artificial intelligence (AI) is likely to substantially change both the tourism industry and tourist behavior. At present, research on artificial intelligence and tourism is receiving widespread attention, but most of them focus on a certain subject or a specific aspect of the tourism industry. For example, artificial intelligence influences the behavior of tourists and tourism enterprises. The analysis of the impact of artificial intelligence on the tourism industry as a system is still insufficient. Therefore, this research proposes a multi-dimensional framework from an industry perspective based on the existing definition of artificial intelligence. The framework involves three aspects: the level of intelligence, task types, and whether artificial intelligence is embedded in robots. The authors use a large number of Chinese practice cases to investigate how AI affects the tourism industry, then put forward a research agenda to analyze how destination government, tourism enterprises and tourist experience will change in the future. Finally, they highlight important issues related to privacy, prejudice and ethics.
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Artemova, Ekaterina. "Deep Learning for the Russian Language". W The Palgrave Handbook of Digital Russia Studies, 465–81. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-42855-6_26.
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AbstractDeep learning is a term used to describe artificial intelligence (AI) technologies. AI deals with how computers can be used to solve complex problems in the same way that humans do. Such technologies as computer vision (CV) and natural language processing (NLP) are distinguished as the largest AI areas. To imitate human vision and the ability to express meaning and feelings through language, deep learning exploits artificial neural networks that are trained on real life evidence.While most vision-related tasks are solved using common methods nearly irrespective of target domains, NLP methods strongly depend on the properties of a given language. Linguistic diversity complicates deep learning for NLP. This chapter focuses on deep learning applications to processing the Russian language.
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Donati, Pierpaolo. "Impact of AI/Robotics on Human Relations: Co-evolution Through Hybridisation". W Robotics, AI, and Humanity, 213–27. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-54173-6_18.
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AbstractThis chapter examines how the processes of human enhancement that have been brought about by the digital revolution (including AI and robotics, besides ICTs) have given rise to new social identities and relationships. The central question consists in asking how the Digital Technological Matrix, understood as a cultural code that supports artificial intelligence and related technologies, causes a hybridisation between the human and the non-human, and to what extent such hybridisation promotes or puts human dignity at risk. Hybridisation is defined here as entanglements and interchanges between digital machines, their ways of operating, and human elements in social practices. The issue is not whether AI or robots can assume human-like characteristics, but how they interact with humans and affect their social identities and relationships, thereby generating a new kind of society.
Streszczenia konferencji na temat "AI-Related technologies"
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Wu, Xu, Daguo Qin i Yang Li. "Challenges and Inspirations of AI and Related Technologies in Intelligence Analysis". W 2022 IEEE 10th Joint International Information Technology and Artificial Intelligence Conference (ITAIC). IEEE, 2022. http://dx.doi.org/10.1109/itaic54216.2022.9836509.
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G. Filho, Arlindo R., Rodrigo Z. Fanucchi, Ingrid Winkler i Alexandre Gomes de Siqueira. "The Generative AI Revolution in Immersive Technologies". W Anais Estendidos do Simpósio de Realidade Virtual e Aumentada, 82–84. Sociedade Brasileira de Computação, 2024. http://dx.doi.org/10.5753/svr_estendido.2024.243198.
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Generative Artificial Intelligence has revolutionized various aspects of the modern world, ranging from individual users leveraging tools to answer queries to companies developing advanced chatbots. However, it may still be premature to assert that all the potential applications of Generative AI have been fully explored. Among these unexplored possibilities, the integration with immersive technologies, such as virtual reality, augmented reality, and mixed reality, remains a domain with substantial room for development. This paper explores the opportunities related to Generative AI in the context of immersive technologies, as well as the challenges associated with its broader adoption in virtual worlds.
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RALEA, Cristina, Octavian-Cosmin DOBRIN i Andrei BURCEA. "THE IMPORTANCE OF QUALITY ASSESSMENTS WITHIN THE IMPLEMENTATION OF AI TECHNOLOGIES". W INTERNATIONAL MANAGEMENT CONFERENCE. Editura ASE, 2024. http://dx.doi.org/10.24818/imc/2023/03.12.
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Implementation of AI Technology represents the most recent challenge that companies face, while trying to increase business performance. This study covers various aspects related to AI implementation challenges, together with the importance of performing quality assessments to ensure the reliability, fairness, and effectiveness of AI systems.
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Ziakkas, Dimitrios, Julius Keller i Sudip Vhaduri. "The Role of Artificial Intelligence in the Fatigue Risk Management System". W Human Interaction and Emerging Technologies (IHIET-AI 2024). AHFE International, 2024. http://dx.doi.org/10.54941/ahfe1004594.
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Fatigue in aviation is defined as “a physiological state of reduced mental or physical performance capability resulting from sleep loss, extended wakefulness, circadian phase, and/or workload (mental and/or physical activity) that can impair a person’s alertness and ability to perform safety-related operational duties”. Fatigue compromises human performance and is considered an identified threat to flight safety. Fatigue is inevitable within the aviation operations context. Therefore, fatigue cannot be eliminated; it must be managed. Fatigue Risk Management System (FRMS) has recently been implemented in airline operations. FRMS is “a data-driven means of continuously monitoring and managing fatigue-related safety risks, based upon scientific principles, knowledge, and operational experience that aims to ensure relevant personnel are performing at adequate levels of alertness”. Nevertheless, studies reported prominent levels of fatigue for 68.5% to 93% among professional pilots. This implies that current FRMS strategies need to be strengthened. The ever-growing and constantly changing aviation industry mandates novel complementary approaches to enhance the established FRMS. EASA has already acknowledged the potential benefits of artificial intelligence (AI) in aviation. Implementing AI applications in FRMS could benefit both flight safety and cost efficiency. AI can analyze various sources of data to enhance real-time detection and prediction. Countermeasures could, then, be employed to address pilots’ drowsiness and mental impairments and, thus, prevent fatigue-related incidents. This study provides a holistic human-centric approach regarding AI utilization in FRMS by outlining the potential benefits and challenges. Implementing AI applications in FRMS could benefit both flight safety and cost efficiency. AI can be integrated with existing FRMS and provide a more comprehensive approach to fatigue management. Given AI’s computational capacity, it can analyze vast amounts of various sourced data to enhance real-time detection and prediction. Countermeasures could, then, be employed to address pilots’ drowsiness and mental impairment and, thus, prevent fatigue-related incidents. AI applications are promising in using pilot’s data sourced through wearable devices (e.g., smartwatches, fitness, and eye trackers) for the detection of fatigue in domains such as a) Monitoring/analyzing electroencephalogram (EEG) signal; b) Yawing detection; c) Facial muscle detection; d) Drowsiness detection; e) Pupil detection and monitor pilot fatigue levels in real-time. Additionally, AI could analyze natural language during the pilot’s communication to detect signs of fatigue. Also, AI could integrate and contribute to fatigue flight data. Moreover, AI can contribute to FRMS by identifying high-risk periods for fatigue and developing customized mitigation strategies based on individual characteristics such as circadian rhythms, sleep patterns, and workload. AI can be used to develop predictive models that can forecast the likelihood of fatigue for individual crew members based on numerous factors, such as sleep patterns and workload. These models can inform scheduling decisions and other fatigue risk management strategies. AI algorithms could also optimize crew rostering to avoid last-minute unfit-for-duty reports and prevent additional costs. On the contrary, there are considerable challenges to overcome. The reliability and validity of data used by AI systems are critical to their effectiveness in predicting and managing fatigue. Methods to measure the accuracy and consistency of data must be established and continually monitored. Additionally, there is a risk that AI systems can perpetuate or even amplify existing biases and inequalities based on training data. Regulations and legal frameworks must be established to guide the use of AI in fatigue risk management. EASA is working on a structured AI integration within the aviation industry. Personalized and sensitive data should be collected for the intended purpose. Data governance should ensure privacy and security to protect individuals. Also, some ethical considerations could be raised (e.g., the responsibility for decision-making and accountability in case of errors or incidents. Moreover, it is essential to understand how AI systems can integrate with human factors and not become a source of stress or distraction for pilots. Non-intrusive wearable AI systems’ sensor integration is of utmost importance. Implementing AI systems can be costly, and the return on investment must be carefully considered. Determining the cost-effectiveness of AI systems in fatigue risk management is essential. Lastly, resistance to change by the users could be anticipated. The Purdue research case study connects the ongoing measurement of Fatigue in aviation training (Purdue SATT student pilots) using AI applications with the commercial aviation FRMS
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Pomsar, L., M. Zorkovsky, B. Rusinakova i I. Zolotova. "Exposing students to AI and industry related skills via OpenLab High School – Industry – University partnership". W 2019 17th International Conference on Emerging eLearning Technologies and Applications (ICETA). IEEE, 2019. http://dx.doi.org/10.1109/iceta48886.2019.9039971.
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Sekkay, Firdaous. "Prevention of Work-Related Musculoskeletal Disorders supported by Artificial Intelligence". W 9th International Conference on Human Interaction and Emerging Technologies - Artificial Intelligence and Future Applications. AHFE International, 2023. http://dx.doi.org/10.54941/ahfe1002945.
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In the industry 4.0 era, industrial production is designed to be more efficient, more flexible, and with higher quality. Besides, it is characterized by greater automation and computerization. However, in the industrial field, workers are still involved in many jobs requiring them to lift and move heavy items and other production activities that expose them to the associated risk factors for developing work-related musculoskeletal disorders (WMSDs). In physical ergonomics, studies have shown potential for preventing WMSDs through artificial intelligence (AI). In this regard, this literature review aims to establish the current state of art regarding the use of AI to reduce the risk of developing WMSDs. A literature review was carried out in two databases, and through the combination of keywords, 188 articles were found. Twenty-eight papers were retrieved and analyzed based on dimensions related to WMSDs risk factors, ergonomic criteria, and AI applications.
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Chen, Xi, i Wei Yu. "The Application of AI Generation Tools in the Graduation Projects (Theses) of Animation-Related Majors". W 2024 6th International Conference on Computer Science and Technologies in Education (CSTE). IEEE, 2024. http://dx.doi.org/10.1109/cste62025.2024.00025.
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Musazade, Nurlan. "Tools and Technologies Utilized in Data-Related Positions: An Empirical Study of Job Advertisements". W 36th Bled eConference – Digital Economy and Society: The Balancing Act for Digital Innovation in Times of Instability. University of Maribor Press, 2023. http://dx.doi.org/10.18690/um.fov.6.2023.10.
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Role, value and amount of data and related tools, technologies, companies and professions in society is rising. Since the required skills for data-related professions are predicted to experience changes, and labor market mismatches create challenges for stakeholders, this research focuses on changes in the required tools and technologies for data-related positions. The presented research defines trends and changes in frequencies of the tools utilized in the data-related professions by applying quantitative content analysis on collected data from job advertisements of Finland, Denmark and Poland. The research findings show that tools used in data-related professions experience significant changes over time. For example, AI and cloud computing-related skills, and SQL started to be required more, whereas Excel, SPSS and similar tools are less expected from the candidates. Furthermore, while R programming language utilization rises in analytics related positions, Python is more common in positions related to data science.
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Ho, Amic. "Redefining Creativity and Artistic Endeavours: Exploring the Impact of AI-Generated Digital Art on Human Society". W 12th International Conference on Human Interaction and Emerging Technologies (IHIET 2024). AHFE International, 2024. http://dx.doi.org/10.54941/ahfe1005476.
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Artificial intelligence has transformed the field of creative work, challenging existing ideas of creativity and creative work. AI-generated digital art has emerged at the crossroads of innovative technology and human creativity and prompted an extensive investigation into its potential consequences on a wide range of human activity. Students also consider whether AI-generated digital art influences individuals’ willingness to engage in art-related activities and produce their work. The use of AI in art evokes components of self-consciousness and identity. Furthermore, the researchers examined the colours provoked by AI-generated digital art, revealing useful information on how AI art generates new emotions and interactions. This article seeks to identify ways in which AI technologies and methodologies open up new possibilities for creators and artists, investigating through case studies, surveys, and qualitative and quantitative measures the transformative potential of AI-generated digital art and its influence on cultural and social dynamics.
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Rif`a-Pous, Helena, Victor Garcia-Font, Carlos N´u˜nez-G´omez i Julian Salas. "Security, Trust and Privacy Challenges in AI-Driven 6G Networks". W 11th International Conference on Computer Science, Engineering and Information Technology, 95–113. Academy & Industry Research Collaboration Center, 2024. http://dx.doi.org/10.5121/csit.2024.141408.
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The advent of 6G networks promises unprecedented advancements in wireless communication, offering wider bandwidth and lower latency compared to its predecessors. This article explores the evolving infrastructure of 6G networks, emphasizing the transition towards a more disaggregated structure and the integration of artificial intelligence (AI) technologies. Furthermore, it explores the security, trust and privacy challenges and attacks in 6G networks, particularly those related to the use of AI. It presents a classification of network attacks stemming from its AI-centric architecture and explores technologies designed to detect or mitigate these emerging threats. The paper concludes by examining the implications and risks linked to the utilization of AI in ensuring a robust network.
Raporty organizacyjne na temat "AI-Related technologies"
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Gluckman, Peter, i Hema Sridhar. A framework for evaluating rapidly developing digital and related technologies: AI, Large Language Models and beyond. International Science Council, październik 2023. http://dx.doi.org/10.24948/2023.11.
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Quimba, Francis Mark, Ramonette Serafica, Connie Bayudan-Dacuycuy, Abigail Andrada i Neil Irwin Moreno. Green and Digital: Managing the Twin Transition toward Sustainable Development. Philippine Institute for Development Studies, grudzień 2023. http://dx.doi.org/10.62986/dp2023.21.
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The global shift toward sustainability and increased digitalization is evident. Nations are integrating renewable energy, carbon emission reduction, and advancements in green technologies into their development plans. Simultaneously, Industry 4.0 has revealed the diverse ways technology influences human life. Rather than separate factors, these dual forces are interconnected elements that countries must navigate for sustainable progress. As countries pursue development strategies, taking a closer look at this twin phenomenon is important. This study assesses how investments, labor, science, technology, innovation, and artificial intelligence contribute to this dual transformation. Through desk reviews and synthesized data, this paper highlights relevant initiatives, technology-related risks, and recommendations for addressing existing gaps. The study acknowledges existing government initiatives aligned with green transition and digitalization. However, it identifies persistent issues, such as inadequate investment in clean climate technologies, shortage of skilled workers in green sectors, and uncertainties regarding the sustainability of AI. To overcome these challenges, the paper proposes directions to bridge gaps, remove barriers to participation, and enhance capabilities, aiming to harness this combined transition's potential fully.
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Fang, Mei Lan, Judith Sixsmith, Jacqui Morris, Chris Lim, Morris Altman, Hannah Loret, Rayna Rogowsky, Andrew Sixsmith, Rebecca White i Taiuani Marquine Raymundo. AgeTech, Ethics and Equity: Towards a Cultural Shift in AgeTech Ethical Responsibility. University of Dundee, 2023. http://dx.doi.org/10.20933/100001292.
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Population ageing is a global phenomenon which presents major challenges for the provision of care at home and in the community (ONS, 2018). Challenges include the human and economic costs associated with increasing numbers of older people with poor physical and mental health, loneliness, and isolation challenges (Mihalopoulos et al., 2020). The global ageing population has led to a growth in the development of technology designed to improve the health, well-being, independence, and quality of life of older people across various settings (Fang, 2022). This emerging field, known as “AgeTech,” refers to “the use of advanced technologies such as information and communications technologies (ICT’s), technologies related to e-health, robotics, mobile technologies, artificial intelligence (AI), ambient systems, and pervasive computing to drive technology-based innovation to benefit older adults” (Sixsmith, et al., 2020 p1; see also Pruchno, 2019; Sixsmith, Sixsmith, Fang, and Horst, 2020). AgeTech has the potential to contribute in positive ways to the everyday life and care of older people by improving access to services and social supports, increasing safety and community inclusion; increasing independence and health, as well as reducing the impact of disability and cognitive decline for older people (Sixsmith et al, 2020). At a societal level, AgeTech can provide opportunities for entrepreneurs and businesses (where funding and appropriate models exist) (Akpan, Udoh and Adebisi, 2022), reduce the human and financial cost of care (Mihalopoulos et al., 2020), and support ageing well in the right place (Golant, 2015).
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Borrett, Veronica, Melissa Hanham, Gunnar Jeremias, Jonathan Forman, James Revill, John Borrie, Crister Åstot i in. Science and Technology for WMD Compliance Monitoring and Investigations. The United Nations Institute for Disarmament Research, grudzień 2020. http://dx.doi.org/10.37559/wmd/20/wmdce11.
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The integration of novel technologies for monitoring and investigating compliance can enhance the effectiveness of regimes related to weapons of mass destruction (WMD). This report looks at the potential role of four novel approaches based on recent technological advances – remote sensing tools; open-source satellite data; open-source trade data; and artificial intelligence (AI) – in monitoring and investigating compliance with WMD treaties. The report consists of short essays from leading experts that introduce particular technologies, discuss their applications in WMD regimes, and consider some of the wider economic and political requirements for their adoption. The growing number of space-based sensors is raising confidence in what open-source satellite systems can observe and record. These systems are being combined with local knowledge and technical expertise through social media platforms, resulting in dramatically improved coverage of the Earth’s surface. These open-source tools can complement and augment existing treaty verification and monitoring capabilities in the nuclear regime. Remote sensing tools, such as uncrewed vehicles, can assist investigators by enabling the remote collection of data and chemical samples. In turn, this data can provide valuable indicators, which, in combination with other data, can inform assessments of compliance with the chemical weapons regime. In addition, remote sensing tools can provide inspectors with real time two- or three-dimensional images of a site prior to entry or at the point of inspection. This can facilitate on-site investigations. In the past, trade data has proven valuable in informing assessments of non-compliance with the biological weapons regime. Today, it is possible to analyse trade data through online, public databases. In combination with other methods, open-source trade data could be used to detect anomalies in the biological weapons regime. AI and the digitization of data create new ways to enhance confidence in compliance with WMD regimes. In the context of the chemical weapons regime, the digitization of the chemical industry as part of a wider shift to Industry 4.0 presents possibilities for streamlining declarations under the Chemical Weapons Convention (CWC) and for facilitating CWC regulatory requirements.
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FEDOTOV, MIKHAIL, red. Digest of Works on Intellectual Property Journal. National Research University Higher School of Economics, październik 2024. http://dx.doi.org/10.17323/tis.2024.v1.
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Quarterlyscientific and analytical magazine named Works on Intellectual Property (www.tis.hse.ru) was founded in 1999 by the UNESCO Chair on Copyright, Neighboring, Cultural and Information Rights. Its origins were such prominent jurists as Illaria Bachilo, Yuriy Baturin, Mikhail Boguslavsky, Adolf Ditz (Germany), Viktor Dozortsev, Mikhail Fedotov, Mihaly Ficsor (Hungary), Eduard Gavrilov, Peter Maggs (USA), Kaarle Nordenstreng (Finland), Al-exander Sergeev, etc.The journal publishes scientific articles and analytics, book reviews, and expert opinions on a wide range of IP problems and the modern information ecosystem. Among the topics that attract the attention of the authors and readers of the journal are topical issues of copyright and related rights, intellectual property rights in the context of digitalization, and cultural and information rights in the context of UNESCO conventions and recommendations. The list embraces human rights in the digital age, legal regulation of cyberspace, mass communications, digital platforms and ecosystems, AI technologies, and transhumanism.The journal is published based on the Agreement between UNESCO and the National Research University High-er School of Economics (HSE University). The latest issue and archive of the journal are on the magazine web page https://tis.hse.ru/.