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Yoon, Young Hyun, Dong Hyun Hwang, Jun Hyeok Yang, and Seung Eun Lee. "Intellino: Processor for Embedded Artificial Intelligence." Electronics 9, no. 7 (2020): 1169. http://dx.doi.org/10.3390/electronics9071169.
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The development of computation technology and artificial intelligence (AI) field brings about AI to be applied to various system. In addition, the research on hardware-based AI processors leads to the minimization of AI devices. By adapting the AI device to the edge of internet of things (IoT), the system can perform AI operation promptly on the edge and reduce the workload of the system core. As the edge is influenced by the characteristics of the embedded system, implementing hardware which operates with low power in restricted resources on a processor is necessary. In this paper, we propose the intellino, a processor for embedded artificial intelligence. Intellino ensures low power operation based on optimized AI algorithms and reduces the workload of the system core through the hardware implementation of a neural network. In addition, intellino’s dedicated protocol helps the embedded system to enhance the performance. We measure intellino performance, achieving over 95% accuracy, and verify our proposal with an field programmable gate array (FPGA) prototyping.
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Ortmeyer, Cliff. "AI Options for Embedded Systems." New Electronics 52, no. 3 (2019): 26–27. http://dx.doi.org/10.12968/s0047-9624(22)60909-x.
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Zhang, Zhaoyun, and Jingpeng Li. "A Review of Artificial Intelligence in Embedded Systems." Micromachines 14, no. 5 (2023): 897. http://dx.doi.org/10.3390/mi14050897.
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Advancements in artificial intelligence algorithms and models, along with embedded device support, have resulted in the issue of high energy consumption and poor compatibility when deploying artificial intelligence models and networks on embedded devices becoming solvable. In response to these problems, this paper introduces three aspects of methods and applications for deploying artificial intelligence technologies on embedded devices, including artificial intelligence algorithms and models on resource-constrained hardware, acceleration methods for embedded devices, neural network compression, and current application models of embedded AI. This paper compares relevant literature, highlights the strengths and weaknesses, and concludes with future directions for embedded AI and a summary of the article.
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Gusti, Wahyu Ramadhani. "Embedded System Training Kit for Artificial Intelligence." International Journal of Information and Education Technology 14, no. 1 (2024): 72–80. http://dx.doi.org/10.18178/ijiet.2024.14.1.2026.
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Ever-developing science and technology require us always to be ready to adapt. The current challenging era is Society 5.0, which places a strong emphasis on harnessing human potential to overcome diverse challenges, including the development of Artificial Intelligence (AI) technology. Therefore, to improve the quality of human resources, this paper proposes the development of an artificial intelligence training kit based on embedded systems according to industry needs. The development of a training kit utilizing the RnD method was accomplished through the use of the ADDIE (analysis design, development, implementation, and evaluation) model. This model encompasses analysis, design, development, implementation, and evaluation. The technology of the training kit combines fuzzy logic, Artificial Neural Network (ANN), and image processing, consisting of hardware, software, and job sheets. The controller used to process embedded systems is the ESP32 board. Arduino UNO is used to execute the training results of the artificial intelligence system. The training kit performance test results show that all AI programs run optimally, and each component can function according to performance indicators. A group of subject matter and media experts evaluated the feasibility of the project and determined it to be very feasible, with a score of 83.64% and 86.67%. In addition, a feasibility test was conducted with 38 respondents, resulting in a score of 83.35%, and it was categorized as a very feasible tool. The effectiveness of the training kit applied to the experimental class resulted in a post-test mean score of 89.58, while the control class mean score was 76.39, so the AI training kit showed more effectiveness.
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Hwang, Dong Hyun, Chang Yeop Han, Hyun Woo Oh, and Seung Eun Lee. "ASimOV: A Framework for Simulation and Optimization of an Embedded AI Accelerator." Micromachines 12, no. 7 (2021): 838. http://dx.doi.org/10.3390/mi12070838.
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Artificial intelligence algorithms need an external computing device such as a graphics processing unit (GPU) due to computational complexity. For running artificial intelligence algorithms in an embedded device, many studies proposed light-weighted artificial intelligence algorithms and artificial intelligence accelerators. In this paper, we propose the ASimOV framework, which optimizes artificial intelligence algorithms and generates Verilog hardware description language (HDL) code for executing intelligence algorithms in field programmable gate array (FPGA). To verify ASimOV, we explore the performance space of k-NN algorithms and generate Verilog HDL code to demonstrate the k-NN accelerator in FPGA. Our contribution is to provide the artificial intelligence algorithm as an end-to-end pipeline and ensure that it is optimized to a specific dataset through simulation, and an artificial intelligence accelerator is generated in the end.
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Choudhury, Avishek, and Onur Asan. "Human factors: bridging artificial intelligence and patient safety." Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care 9, no. 1 (2020): 211–15. http://dx.doi.org/10.1177/2327857920091007.
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The recent launch of complex artificial intelligence (AI) in the domain of healthcare has embedded perplexities within patients, clinicians, and policymakers. The opaque and complex nature of artificial intelligence makes it challenging for clinicians to interpret its outcome. Incorrect interpretation and poor utilization of AI might hamper patient safety. The principles of human factors and ergonomics (HFE) can assist in simplifying AI design and consecutively optimize human performance ensuring better understanding of AI outcome, their interaction with the clinical workflow. In this paper, we discuss the interactions of providers with AI and how HFE can influence these interacting components to patient safety.
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Yeo, K. K. "Artificial intelligence in cardiology: did it take off?" Russian Journal for Personalized Medicine 2, no. 6 (2023): 16–22. http://dx.doi.org/10.18705/2782-3806-2022-2-6-16-22.
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Artificial intelligence (AI) has been touted as a paradigm shifting, game-changing development in medicine. Did AI in cardiology take off? In this paper, we discuss some areas within cardiology in which there has some been progress in the implementation of AI technologies. Despite the promise of AI, challenges remain including cybersecurity, implementation and change management difficulties. This paper discusses the use of AI embedded as a ‘black box’ technology in existing diagnostic and interventional tools, AI as an adjunct to diagnostic tools such as echo or CT or MRI scans, AI in commercially available wearables, and AI in chatbots and other patient-fronting technologies. Lastly, while there has been some progress, the legal, regulatory, financial and ethical framework remains a work in evolution at national and international levels.
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Azizah, Desi, Aji Wibawa, and Laksono Budiarto. "Hakikat Epistemologi Artificial Intelligence." Jurnal Inovasi Teknologi dan Edukasi Teknik 1, no. 8 (2021): 592–98. http://dx.doi.org/10.17977/um068v1i82021p592-598.
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Artificial Intelligence, commonly abbreviated as AI, is a scientifically intelligent entity created by humans. The entity is embedded into a machine, thus making the machine seem capable of thinking on its own to decide. The definition of AI can be viewed from two approaches, namely a scientific approach (A Scientific Approach) and an engineering approach (An Engineering Approach). The way artificial intelligence works is by combining a large amount of data, with a process that is fast, iterative and has an intelligent algorithm. Artificial intelligence is closely related to philosophy because both use concepts that have the same name and these include intelligence, action, consciousness, epistemology, even free will. Artificial intelligence has advantages and disadvantages.
 Artificial Intelligence yang biasa disingkat dengan AI adalah sebuah entitas cerdas secara ilmiah yang diciptakan oleh manusia. Entitas tersebut di tanamkan ke dalam sebuah mesin, sehingga membuat mesin tersebut seolah-olah mampu berpikir sendiri untuk mengambil sebuah keputusan. Pengertian AI dapat ditinjau dari dua pendekatan yaitu pendekatan ilmiah (A Scientific Approach) dan pendekatan teknik (An Engineering Approach). Cara kerja dari artificial intelligence ini adalah dengan menggabungkan sejumlah data yang terbilang cukup besar, dengan proses yang terbilang cepat, berulang serta memiliki algoritma yang cerdas. Kecerdasan buatan memiliki keterkaitan yang erat dengan filsafat karena keduanya menggunakan konsep yang memiliki nama yang sama dan ini termasuk kecerdasan, tindakan, kesadaran, epistemologi, bahkan kehendak bebas. Kecerdasan buatan memiliki kelebihan dan kekurangan.
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Almusaed, Amjad, Ibrahim Yitmen, and Asaad Almssad. "Enhancing Smart Home Design with AI Models: A Case Study of Living Spaces Implementation Review." Energies 16, no. 6 (2023): 2636. http://dx.doi.org/10.3390/en16062636.
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The normal development of “smart buildings,” which calls for integrating sensors, rich data, and artificial intelligence (AI) simulation models, promises to usher in a new era of architectural concepts. AI simulation models can improve home functions and users’ comfort and significantly cut energy consumption through better control, increased reliability, and automation. This article highlights the potential of using artificial intelligence (AI) models to improve the design and functionality of smart houses, especially in implementing living spaces. This case study provides examples of how artificial intelligence can be embedded in smart homes to improve user experience and optimize energy efficiency. Next, the article will explore and thoroughly analyze the thorough analysis of current research on the use of artificial intelligence (AI) technology in smart homes using a variety of innovative ideas, including smart interior design and a Smart Building System Framework based on digital twins (DT). Finally, the article explores the advantages of using AI models in smart homes, emphasizing living spaces. Through the case study, the theme seeks to provide ideas on how AI can be effectively embedded in smart homes to improve functionality, convenience, and energy efficiency. The overarching goal is to harness the potential of artificial intelligence by transforming how we live in our homes and improving our quality of life. The article concludes by discussing the unresolved issues and potential future research areas on the usage of AI in smart houses. Incorporating AI technology into smart homes benefits homeowners, providing excellent safety and convenience and increased energy efficiency.
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Marcinowski, Maciej. "Artificial Intelligence or the Ultimate Tool for Conservatism." DANUBE 13, no. 1 (2022): 1–12. http://dx.doi.org/10.2478/danb-2022-0001.
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Abstract Artificial intelligence (AI) is foremost viewed as a technologically revolutionary tool, however, the author discusses here whether it is in fact a tool for socio-economic and legal conservatism, because its training data is always embedded in the past. The aim of this paper is to explain, exemplify and predict – whether and how – AI could cause discrimination, stagnation and uniformization by conserving what is relayed even by the most representative data. Furthermore, the author aims to propose possible legal barriers to these phenomena. The presented hypotheses are based upon empirical research and socioeconomic or legal mechanisms, aiming to predict possible results of AI applications under specific conditions. Results indicate that the inherent AI conservatism could indeed cause severe discrimination, stagnation and uniformization, especially if its applications were to remain unquestioned and unregulated. Hopefully, the proposed legal solutions could limit the scope and effectiveness of AI conservatism, encouraging AI-related solutions.
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Antonov, Alexander. "Managing complexity: the EU’s contribution to artificial intelligence governance." Revista CIDOB d'Afers Internacionals, no. 131 (September 22, 2022): 41–65. http://dx.doi.org/10.24241/rcai.2022.131.2.41/en.
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With digital ecosystems being questioned around the world, this paper examines the EU’s role in and contribution to the emerging concept of artificial intelligence (AI) governance. Seen by the EU as the key ingredient for innovation, the adoption of AI systems has altered our understanding of governance. Framing AI as an autonomous digital technology embedded in social structures, this paper argues that EU citizens' trust in AI can be increased if the innovation it entails is grounded in a fundamental rights-based approach. This is assessed based on the work of the High-Level Expert Group on AI (which has developed a framework for trustworthy AI) and the European Commission’s recently approved proposal for an Artificial Intelligence Act (taking a risk-based approach).
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Tarozzi, Martina Elena. "Artificial intelligence for Next generation sequencing data analysis." Science Reviews. Biology 3, no. 1 (2024): 9–15. http://dx.doi.org/10.57098/scirevs.biology.3.1.2.
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In the rapidly evolving field of genomics, our capacity to decipher genetic data encoded in DNA has been transformed by Next Generation Sequencing (NGS) technologies. These advanced technologies produce an enormous volume of data, posing substantial challenges in extracting meaningful biological insights. Artificial intelligence (AI) algorithms offer distinctive possibilities to unravel the biological information embedded in such extensive and intricate datasets. This review offers a synopsis of AI classifications and algorithms, elucidating how these techniques can be employed on sequencing data. Subsequently, a selection of the most typical, promising, or illustrative applications of AI on NGS data to tackle unresolved technical or biological issues are showcased.
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Chu, Charlene, Kathleen Leslie, Shehroz Khan, Rune Nyrup, and Amanda Grenier. "AGEISM IN ARTIFICIAL INTELLIGENCE: A REVIEW." Innovation in Aging 6, Supplement_1 (2022): 663. http://dx.doi.org/10.1093/geroni/igac059.2446.
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Abstract Background Artificial intelligence (AI) has emerged as a major driver of technological development in the 21st century, yet little attention has been paid to algorithmic biases towards older adults. "Digital ageism" is a new form of ageism that is embedded into technology and AI systems. Aim: This review aimed to explore how age-related bias is encoded in AI systems to better understand digital ageism. Methods The scoping review follows a six-stage methodology framework developed by Arksey and O'Malley. The search strategy has been established in six databases and we will investigate grey literature databases, targeted websites, popular search engines. An iterative search strategy was used. Studies meet the inclusion criteria if they are in English, peer-reviewed, available electronically in full-text, and included the concepts ‘bias’ and old age. At least two reviewers independently conducted title/abstract screening and full-text screening. Results Our database searches resulted in 7 595 manuscripts that underwent title and abstract screening. Of these 49 papers, were included in the study. The word "ageism" was explicitly mentioned only in about half of these papers. Approximately half the papers mentioned how age-related bias could be encoded into AI systems. The most commonly used AI applicaiton was computer vision. Conclusions Our preliminary findings contribute foundational knowledge about the age-related biases that were encoded or amplified in AI systems. This work advances how AI can be developed in a manner consistent with ethical values and human rights legislation, particularly as it relates to an older and aging population.
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Tkachev, Andrey N. "About the unconscious and the possibility of creativity in artificial intelligence." Digital Scholar: Philosopher`s Lab 7, no. 1 (2024): 56–61. https://doi.org/10.32326/2618-9267-2024-7-1-56-61.
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The article defines the role of the unconscious when considering the possibility of artificial intelligence (AI) creativity. The author proposes a comparison between the work of the unconscious and artificial intelligence. He suggests understanding the volumes of data embedded in AI as an artificial unconscious. Often critics of the creative abilities of AI point to the need for AI programming as an argument. The author criticizes this position because human creativity also requires a certain program. The article also raises the problem of ontology in connection with AI and the controversial line between natural and artificial. The author believes that based on ontology, the artificial can be interpreted as natural, or as a special type of natural. Probably the main criterion for differentiation between natural and artificial intelligence is the ability to pose new questions, because it is questions that allow one to go beyond the boundaries of the program.
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Krishna, Anirudh. "Will AI Slay the Poverty Dragon?" Current History 123, no. 849 (2024): 37–39. http://dx.doi.org/10.1525/curh.2024.123.849.37.
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The rise of artificial intelligence and other new technologies has raised hopes that they might contribute to reducing poverty. History suggests that they need to be embedded in broader frameworks of institutions, regulations, and training.
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Ebule, Amejuma Emmanuel. "Leveraging Artificial Intelligence in Business Intelligence Systems for Predictive Analytics." International Journal of Scientific Research and Management (IJSRM) 13, no. 01 (2025): 1862–79. https://doi.org/10.18535/ijsrm/v13i01.ec02.
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Artificial Intelligence (AI) and Business Intelligence (BI) are rapidly emerging as the next big things for organizations to analyze data and gain insights. As this article will go on to examine, the concept of using AI for BI is one that has significant implications about the possible integration of AI into various Business Intelligence systems examined in this article will focus on the application of AI for BI in the use of predicting analytics. When integrating Machine learning, natural language processing, and intelligent automation, these AI-Advanced BI systems assist organizations to go beyond data reporting or simple descriptive analytics and gain an insight to use BI systems to discover and pre-empt issues, besides noticing them using proactive decision making. In discussing the elements of AI-embedded BI systems, this article analyzes how organizations across industries use real-time intelligence and predictive models as indispensable resources for the generation of competitive edge. Some of the advantages highlighted includes improved accuracy for predictions, efficiency of cost on data handling, scalability on large data and the shorter delays on decision making. However, alongside these benefits, the article also addresses key challenges, such as data privacy concerns, biases in AI algorithms, and the complexities of integrating AI into legacy BI platforms. These limitations are critical considerations for organizations seeking to implement AI-driven BI systems effectively. Furthermore, this work discusses the issues relating to the implementation of AI for BI, for example, the integration of AI into existing BI platforms, data quality issues, ethical issues, and the skill gaps in specialized AI talents. The article also discusses new developments in AI integration to BI systems including the growing incorporation of deep learning techniques, automation of decision making and BI democratization for small businesses. They suggest that BI must evolve new business strategies to be effective and meet the information demands needed for corporate competitiveness in today’s data-centric economy. The convergence of advanced analytics and operational decision making makes AI driven BI system the tool with tremendous potential to become the lingua franca of business strategy and growth.
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Fadillah, Jihan Trie. "Artificial Intelligence In The Electric Vehicle Ecosystem: Adoption; Impact; And Future Prospect." International Student Conference on Business, Education, Economics, Accounting, and Management (ISC-BEAM) 2, no. 1 (2024): 2573–79. http://dx.doi.org/10.21009/isc-beam.012.187.
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Artificial Intelligence (AI) has been around since 1940, namely the first digital computer called Atanasoff Berry Computer (ABC) which aroused the enthusiasm of scientists to develop the idea of making an "electronic brain" or hiding electronic devices in the human brain . With Artificial Intelligence (AI) that can work efficiently, of course, work can be done more easily. The purpose of writing this scientific paper is to explain the use of Artificial Intelligence (AI) in the automotive industry which can make it easier for humans in the future. The method taken by the author is through observations obtained from browsing (searching) through the internet, quoting from various written sources and books that match the theme. Artificial Intelligence (AI) is created in machines and made capable of applying them in real life. Artificial Intelligence (AI) embedded in the steering wheel for self-driving cars complements the driver's abilities when it comes to driving. So that the driver can drive more safely. In the automotive industry, Artificial Intelligence (AI) can be implemented to find a balance point between reactive maintenance (risk of failure) and preventive maintenance (can incur high costs) which uses sensors to track equipment conditions and analyze data on an ongoing basis. An example of the application of Artificial Intelligence (AI) in automotive is in its manufacture by carrying out monitoring processes, errors, downtime and optimizing production operations.
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Nwozor, Blessing U., and Happy Faotu. "Artificial Intelligence and SaaS Embedded System: Enhancing Content Creation Through Contextual Language." International Journal of Research and Innovation in Applied Science IX, no. XI (2024): 161–73. https://doi.org/10.51584/ijrias.2024.911013.
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Artificial Intelligence (AI) has been advancing rapidly, allowing machines to perform tasks commonly done by humans, such as writing, coding, diagnosing diseases, predicting weather patterns, translating languages, providing customer support, etc. As AI becomes more sophisticated, its integration into Software as a Service (SaaS) platforms holds significant potential to enhance productivity for individuals and businesses. The application of AI within SaaS can extend across a wide array of domains, including entertainment, academia, finance, content creation, mathematics, and more. This paper explores a contextual architecture for integrating AI into SaaS, specifically focusing on enhancing content creation. The proposed model, with its robust design and leveraging AI’s capabilities, is poised to support content creators in generating high-quality, relevant, and engaging material more efficiently. Data was collected from 100 content creators active on social media platforms such as X, Youtube, Facebook, and Instagram to develop and refine this model. This diverse dataset helped train the AI to understand and replicate various content creation styles and approaches. The research employs the Rapid Application Development (RAD) methodology, chosen for its effectiveness in facilitating rapid prototyping and iterative improvement. This methodology is particularly well-suited to a fast approach, allowing for continuous refinement of the AI model as new data becomes available. The results of this study suggest that integrating AI into SaaS for content creation can significantly improve the productivity and effectiveness of the content generation process, providing valuable tools for creators in a fast-paced digital landscape.
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Hong, Sungcheol. "Wireless Optogenetic Microsystems Accelerate Artificial Intelligence–Neuroscience Coevolution Through Embedded Closed-Loop System." Micromachines 16, no. 5 (2025): 557. https://doi.org/10.3390/mi16050557.
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Brain-inspired models in artificial intelligence (AI) originated from foundational insights in neuroscience. In recent years, this relationship has been moving toward a mutually reinforcing feedback loop. Currently, AI is significantly contributing to advancing our understanding of neuroscience. In particular, when combined with wireless optogenetics, AI enables experiments without physical constraints. Furthermore, AI-driven real-time analysis facilitates closed-loop control, allowing experimental setups across a diverse range of scenarios. And a deeper understanding of these neural networks may, in turn, contribute to future advances in AI. This work demonstrates the synergy between AI and miniaturized neural technology, particularly through wireless optogenetic systems designed for closed-loop neural control. We highlight how AI is now revolutionizing neuroscience experiments from decoding complex neural signals and quantifying behavior, to enabling closed-loop interventions and high-throughput phenotyping in freely moving subjects. Notably, AI-integrated wireless implants can monitor and modulate biological processes with unprecedented precision. We then recount how neuroscience insights derived from AI-integrated neuroscience experiments can potentially inspire the next generation of machine intelligence. Insights gained from these technologies loop back to inspire more efficient and robust AI systems. We discuss future directions in this positive feedback loop between AI and neuroscience, arguing that the coevolution of the two fields, grounded in technologies like wireless optogenetics and guided by reciprocal insight, will accelerate progress in both, while raising new challenges and opportunities for interdisciplinary collaboration.
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Tyagi, Mukund, Dr Nikhil Sirohi, Dr Pallavi Tyagi, and Kajal Yadav. "Future Trends of B2C Marketing with Artificial Intelligence." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 07 (2025): 1–9. https://doi.org/10.55041/ijsrem51309.
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Artificial Intelligence (AI) is fundamentally transforming business-to-consumer (B2C) marketing by enhancing the ability of brands to understand, engage, and retain customers. This paper investigates the emerging trends shaping the future of B2C marketing through the integration of advanced AI technologies. Key developments such as generative AI, conversational interfaces, emotion AI, and ethical personalization are enabling hyper-personalized content, real-time customer interactions, and deeper emotional engagement. These innovations are not only optimizing marketing strategies but also reshaping consumer expectations for seamless, intelligent, and value-driven experiences. As AI systems become more embedded in marketing operations, businesses must navigate critical challenges, including data privacy concerns, algorithmic bias, and the evolving landscape of AI regulation. This paper explores how marketers can leverage AI to deliver ethically responsible and consumer-centric campaigns while maintaining transparency and trust. It also highlights the strategic importance of aligning AI-driven marketing initiatives with consumer values and societal norms. By examining current practices and projecting future developments, this study provides actionable insights and strategic directions for marketers aiming to remain competitive in an AI-driven marketplace. Ultimately, the paper emphasizes the need for continuous innovation, ethical consideration, and human-AI collaboration to shape the future of B2C marketing effectively. Keywords: Artificial Intelligence, B2C Marketing, Hyper-Personalization, Generative AI, Ethical Personalization, Consumer Behaviour
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Davenport, Thomas, Abhijit Guha, Dhruv Grewal, and Timna Bressgott. "How artificial intelligence will change the future of marketing." Journal of the Academy of Marketing Science 48, no. 1 (2019): 24–42. http://dx.doi.org/10.1007/s11747-019-00696-0.
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Abstract In the future, artificial intelligence (AI) is likely to substantially change both marketing strategies and customer behaviors. Building from not only extant research but also extensive interactions with practice, the authors propose a multidimensional framework for understanding the impact of AI involving intelligence levels, task types, and whether AI is embedded in a robot. Prior research typically addresses a subset of these dimensions; this paper integrates all three into a single framework. Next, the authors propose a research agenda that addresses not only how marketing strategies and customer behaviors will change in the future, but also highlights important policy questions relating to privacy, bias and ethics. Finally, the authors suggest AI will be more effective if it augments (rather than replaces) human managers.
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Karampelas, Konstantinos. "Integration of artificial intelligence in national science curricula." Contemporary Mathematics and Science Education 6, no. 1 (2025): ep25002. https://doi.org/10.30935/conmaths/15883.
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This study investigates the integration of artificial intelligence (AI) in national science curricula across 21 countries, including Australia, Cyprus, Estonia, France, Finland, Greece, Hong Kong, India, Iceland, Ireland, Nepal, New Zealand, Norway, Ontario (Canada), Poland, Singapore, South Africa, South Korea, Sweden, the United Kingdom, and the United States. By analyzing these curricula, the research identifies the presence of AI-related knowledge, skills, and attitudes, providing a comprehensive understanding of how AI is embedded in educational frameworks. The findings reveal a strong emphasis on practical AI skills, interdisciplinary knowledge, ethical considerations, and societal impacts, preparing students to thrive in an AI-driven future. This comprehensive approach highlights AI’s transformative potential in education. The study emphasizes AI’s role in fostering problem-solving skills and active learning, underscoring the need for practical AI applications and comprehensive teacher training in AI concepts. The analysis also identifies gaps in the explicit mention of “artificial intelligence” itself, suggesting a broader focus on related concepts. Notably, AI is not frequently mentioned explicitly in the curricula but is often approached under the umbrella of information and communication technology in relation to science. Recommendations for enhancing AI integration include comprehensive teacher training, continuous curriculum evaluation, and the inclusion of the ethical and societal implications of AI. This research provides valuable insights for educators and policymakers, highlighting the need for a well-rounded curriculum that prepares students for the future challenges and opportunities presented by AI technologies.
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Adams, BS, James, Mahmud Hasan, PhD, MS, MEng, and Jacob Thorp, BS. "AI (artificial intelligence)-assisted planning within emergency management operations." Journal of Emergency Management 20, no. 1 (2022): 41–52. http://dx.doi.org/10.5055/jem.0622.
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There is a demand for future technologies to be embedded within emergency management operations. Artificial intelligence (AI) can help save lives and create more efficient systems for emergency management operators to prepare, design, develop, and execute responses to disasters and catastrophes. This study intends to provide insight into how AI can integrate with climate modeling and traffic management systems in response to natural disasters. Research with supporting evidence implies that current technology and frameworks can coexist inside the existing infrastructure and emergency management operations. A growing population with an increase in anthropogenic emissions and the inability to predict future disasters and catastrophes suggests that AI can help address these challenges.
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Zhang, Xiaojuan, Yongheng Zhang, Feng Zhang, and Xiuyun Yang. "Research of Schema Evolution and Implementation Scheme Optimization in AI-Enabled Embedded Systems." Wireless Communications and Mobile Computing 2021 (August 14, 2021): 1–10. http://dx.doi.org/10.1155/2021/3591427.
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The demand of embedded artificial intelligence system for powerful computing power and diversified application scenarios will inevitably bring some new problems. This paper builds the system dynamics model of embedded system based on artificial intelligence (AI). By analyzing the causal relationship between the elements of the system dynamics model, the state equation is established, and the parameters are estimated and tested. At the same time, the influence of the model simulation experiment on the relevant factors is evaluated. The simulation results show that the proposed model is effective and efficient.
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Helanski Cardoso, Juliane Cristina. "Digital images generated by Artificial Intelligence as ethnographic experimentation." Desde el Sur 16, no. 2 (2024): e0023. http://dx.doi.org/10.21142/des-1602-2024-0023.
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The article discusses the role of images produced by artificial intelligence (AI) in visual anthropology, highlighting their ability to represent identity and experience. It also addresses the technical and ethical challenges of AI data classification and its interaction with socio-technical networks, questioning technological neutrality. Technical implications include data categorization that can perpetuate biases and power relations. The simplification and distortion of representations by AI is highlighted, requiring a critical analysis of the stories embedded in the categorizations. It is proposed that anthropologists examine the relationship between image, label, and referent, recognizing differences and similarities in their roles and those of AI labelers in knowledge production. In addition, it discusses how AI images can influence anthropological interpretation and analysis by blending reality and emotion. It is argued that a critical engagement with the ethical and technical implications of the generation and use of these images is necessary.
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Jinglu, Zhang, and Wang Linna. "Research on Ai Technology of Intelligent Information Visualization Based on Rtos System Service." Journal of Physics: Conference Series 2717, no. 1 (2024): 012018. http://dx.doi.org/10.1088/1742-6596/2717/1/012018.
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Abstract In order to improve the visual interaction ability of intelligent information, an intelligent information visualization artificial intelligence technology based on RTOS system service is proposed in this paper. Based on embedded integrated control, this paper constructs the system design scheme, client and host system of intelligent information visualization artificial intelligence RTOS service system. The information monitoring module, artificial intelligence programmable logic control module, artificial intelligence processing module and human-computer interaction module of intelligent information visualization artificial intelligence RTOS service system are established to realize the label identification and identification in the process of intelligent information visualization artificial intelligence RTOS service system based on unified resource locator sipur, The communication system model of intelligent information visualization artificial intelligence RTOS service system under the transmission mode of multimedia communication channel is established to realize the automatic information processing of intelligent information visualization RTOS system. The test results show that the design has good stability, strong intelligent information visualization and interaction ability, low memory and time cost.
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Labayen, Mikel, Laura Medina, Fernando Eizaguirre, José Flich, and Naiara Aginako. "HPC Platform for Railway Safety-Critical Functionalities Based on Artificial Intelligence." Applied Sciences 13, no. 15 (2023): 9017. http://dx.doi.org/10.3390/app13159017.
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The automation of railroad operations is a rapidly growing industry. In 2023, a new European standard for the automated Grade of Automation (GoA) 2 over European Train Control System (ETCS) driving is anticipated. Meanwhile, railway stakeholders are already planning their research initiatives for driverless and unattended autonomous driving systems. As a result, the industry is particularly active in research regarding perception technologies based on Computer Vision (CV) and Artificial Intelligence (AI), with outstanding results at the application level. However, executing high-performance and safety-critical applications on embedded systems and in real-time is a challenge. There are not many commercially available solutions, since High-Performance Computing (HPC) platforms are typically seen as being beyond the business of safety-critical systems. This work proposes a novel safety-critical and high-performance computing platform for CV- and AI-enhanced technology execution used for automatic accurate stopping and safe passenger transfer railway functionalities. The resulting computing platform is compatible with the majority of widely-used AI inference methodologies, AI model architectures, and AI model formats thanks to its design, which enables process separation, redundant execution, and HW acceleration in a transparent manner. The proposed technology increases the portability of railway applications into embedded systems, isolates crucial operations, and effectively and securely maintains system resources.
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Shadbolt, Nigel. "“From So Simple a Beginning”: Species of Artificial Intelligence." Daedalus 151, no. 2 (2022): 28–42. http://dx.doi.org/10.1162/daed_a_01898.
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Abstract Artificial intelligence has a decades-long history that exhibits alternating enthusiasm and disillusionment for the field's scientific insights, technical accomplishments, and socioeconomic impact. Recent achievements have seen renewed claims for the transformative and disruptive effects of AI. Reviewing the history and current state of the art reveals a broad repertoire of methods and techniques developed by AI researchers. In particular, modern machine learning methods have enabled a series of AI systems to achieve superhuman performance. The exponential increases in computing power, open-source software, available data, and embedded services have been crucial to this success. At the same time, there is growing unease around whether the behavior of these systems can be rendered transparent, explainable, unbiased, and accountable. One consequence of recent AI accomplishments is a renaissance of interest around the ethics of such systems. More generally, our AI systems remain singular task-achieving architectures, often termed narrow AI. I will argue that artificial general intelligence-able to range across widely differing tasks and contexts-is unlikely to be developed, or emerge, any time soon.
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Shadbolt, Nigel. "“From So Simple a Beginning”: Species of Artificial Intelligence." Daedalus 151, no. 2 (2022): 28–42. http://dx.doi.org/10.1162/daed_a_01898.
Full textAbstract:
Abstract Artificial intelligence has a decades-long history that exhibits alternating enthusiasm and disillusionment for the field's scientific insights, technical accomplishments, and socioeconomic impact. Recent achievements have seen renewed claims for the transformative and disruptive effects of AI. Reviewing the history and current state of the art reveals a broad repertoire of methods and techniques developed by AI researchers. In particular, modern machine learning methods have enabled a series of AI systems to achieve superhuman performance. The exponential increases in computing power, open-source software, available data, and embedded services have been crucial to this success. At the same time, there is growing unease around whether the behavior of these systems can be rendered transparent, explainable, unbiased, and accountable. One consequence of recent AI accomplishments is a renaissance of interest around the ethics of such systems. More generally, our AI systems remain singular task-achieving architectures, often termed narrow AI. I will argue that artificial general intelligence-able to range across widely differing tasks and contexts-is unlikely to be developed, or emerge, any time soon.
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Garcia-Perez, Asier, Raúl Miñón, Ana I. Torre-Bastida, and Ekaitz Zulueta-Guerrero. "Analysing Edge Computing Devices for the Deployment of Embedded AI." Sensors 23, no. 23 (2023): 9495. http://dx.doi.org/10.3390/s23239495.
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In recent years, more and more devices are connected to the network, generating an overwhelming amount of data. This term that is booming today is known as the Internet of Things. In order to deal with these data close to the source, the term Edge Computing arises. The main objective is to address the limitations of cloud processing and satisfy the growing demand for applications and services that require low latency, greater efficiency and real-time response capabilities. Furthermore, it is essential to underscore the intrinsic connection between artificial intelligence and edge computing within the context of our study. This integral relationship not only addresses the challenges posed by data proliferation but also propels a transformative wave of innovation, shaping a new era of data processing capabilities at the network’s edge. Edge devices can perform real-time data analysis and make autonomous decisions without relying on constant connectivity to the cloud. This article aims at analysing and comparing Edge Computing devices when artificial intelligence algorithms are deployed on them. To this end, a detailed experiment involving various edge devices, models and metrics is conducted. In addition, we will observe how artificial intelligence accelerators such as Tensor Processing Unit behave. This analysis seeks to respond to the choice of a device that best suits the necessary AI requirements. As a summary, in general terms, the Jetson Nano provides the best performance when only CPU is used. Nevertheless the utilisation of a TPU drastically enhances the results.
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Ko, Ga Eun. "Artificial Intelligence and Your Life: The Problems We Must Face." Science Insights 46, no. 4 (2025): 1799–803. https://doi.org/10.15354/si.25.pe211.
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Artificial intelligence (AI) is transforming human life at an unprecedented pace. From revolutionizing industries and improving health outcomes to shaping how we interact with the world and each other, AI technologies are deeply embedded in modern life. While these systems bring powerful benefits, they also pose serious problems that demand urgent attention. This essay explores key issues related to algorithmic bias, loss of privacy, job displacement, psychological impacts, social inequality, environmental concerns, and inadequate governance. It highlights that as AI continues to evolve, societies must establish ethical frameworks, regulatory policies, and inclusive design processes to ensure AI technologies serve the collective good. Failing to face these problems now may result in long-term consequences that compromise human dignity, autonomy, and societal cohesion.
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Songlin Chen, Songlin Chen, Hong Wen Songlin Chen, and Jinsong Wu Hong Wen. "Artificial Intelligence Based Traffic Control for Edge Computing Assisted Vehicle Networks." 網際網路技術學刊 23, no. 5 (2022): 989–96. http://dx.doi.org/10.53106/160792642022092305007.
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<p>Edge computing supported vehicle networks have attracted considerable attention in recent years both from industry and academia due to their extensive applications in urban traffic control systems. We present a general overview of Artificial Intelligence (AI)-based traffic control approaches which focuses mainly on dynamic traffic control via edge computing devices. A collaborative edge computing network embedded in the AI-based traffic control system is proposed to process the massive data from roadside sensors to shorten the real-time response time, which supports efficient traffic control and maximizes the utilization of computing resources in terms of incident levels associated with different rescue schemes. Furthermore, several open research issues and indicated future directions are discussed.</p> <p>&nbsp;</p>
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Ihor, HUNKO. "Adaptive Approaches to Software Testing with Embedded Artificial Intelligence in Dynamic Environments." International Journal of Current Science Research and Review 08, no. 05 (2025): 2036–51. https://doi.org/10.5281/zenodo.15344813.
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Abstract : Artificial intelligence (AI) is rapidly being integrated into application domains such as autonomous vehicles, health care, and cybersecurity; therefore, the requirements for dependable and robust AI-embedded systems are more pressing in these dynamic environments characterized by unpredictable variations in operational conditions. The traditional software testing methodologies that depend on static test cases and a predetermined set of scenarios usually fail to tackle the complexity of modern AI applications, resulting in undetected defects and security vulnerabilities. This study will evaluate adaptive test methods based on reinforcement learning (RL), fuzz testing, and other hybrid strategies for their application in software reliability assurance across environments such as stable, low-resource, high-load, and adversarial. The research is built upon a series of experiments on conversational chatbots, fraud detection systems, and autonomous navigation modules, demonstrating that RL-adaptive testing methods improve defect detection by 35-47% in dynamic environments compared to static testing methods and achieve 40-50% greater stability against stress (concerning the system itself). For the traditional testing methods, RL-based methods reduced failure rates by 75%; fuzz testing proved effective in detecting edge cases but was less stable when the same edge cases were instantiated in adversarial conditions. Furthermore, the paper identifies prominent challenges in AI Software Testing, like environmental drifts and non-deterministic outputs, which are seen to be better adapted through RL-based methods. Although there is a trade-off regarding explainability and computational overhead, the data demonstrates that adaptive testing can transform safety-critical applications and highlights hybrid approaches combining the dynamic optimization of RL with the anomaly detection of fuzz testing. The description of the application areas presented in this document offers concrete recommendations to developers and engineers, enabling safer and more dependable AI in real systems.
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Jovanović, Sonja, Lazar Dražeta, Bogdan Dražeta, and Aleksandar Petrović. "Business perspective on exploring the intriguing vistas of artificial intelligence." European Journal of Applied Economics 22, no. 1 (2025): 1–17. https://doi.org/10.5937/ejae22-55443.
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Although the use of artificial intelligence (AI) in everyday life is increasing, the complex nature of AI still prevents its full adoption. The potential of AI is boundless, and it will reshape the reality. How humans perceive the world will forever change with AI tools implemented in embedded devices. Apart from the possibility of changing human perception through augmented reality (AR), AI is also applied in 3D-printing, art, and decoding of ancient communication systems. Moreover, the most important role AI has today is in healthcare. The use of AI tools can contribute to improving the way patients' conditions are monitored, diagnosing specific diseases with a high percentage of accuracy. With the rapid advancements in this field, regulations regarding the safe uses of such technologies remain uncertain. This paper focuses on current AI applications in different social and business fields; it aims to shed light on the immense possibilities of AI-based predictions while inspiring curiosity about its transformative force across industries. The journey that ventures into the depths of the enigma surrounding AI is a testament to the curiosity of the human species, guidance, a drive for imagination, and the relentless pursuit of knowledge.
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Nugroho, Oktian Fajar, Lisna Hikmawaty, and Silvia Ratna Juwita. "Artificial Intelligence Technology Embedded in High School Science Learning: A Study of Teacher Perception." Pedagonal : Jurnal Ilmiah Pendidikan 8, no. 2 (2024): 132–43. http://dx.doi.org/10.55215/pedagonal.v8i2.16.
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This study examines teachers' perceptions and acceptance of using artificial intelligence (AI) technology in the teaching of Sciences (IPA) in Vocational High Schools (SMK). Based on quantitative and qualitative data, the results indicate that the understanding and use of AI in teaching are still limited. Quantitative data were collected through a structured questionnaire to assess teachers' knowledge, confidence, and AI usage frequency, while qualitative data were obtained through in-depth interviews exploring their views and challenges. Although some teachers see the great potential of AI in enhancing student engagement and learning outcomes, various obstacles such as lack of training and technological infrastructure hinder optimal implementation. This articles also offers strategic recommendations for integrating AI into the science curriculum in vocational schools. Although AI has significant potential in vocational science education, its effective implementation is hindered by limited teacher training and infrastructure, necessitating strategic improvements to fully leverage AI's benefits in schools.
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Podile, Dr Venkateswararao, Kovvuri Priyanka Reddy, Vemareddy Nikhil Sai Reddy, Mekala Surendra Babu, and Adusumalli Karthik Phani. "Artificial Intelligence and Corporate Social Responsibility: Synergies, Challenges, and Future Directions." International Journal of Advanced Multidisciplinary Research and Studies 4, no. 6 (2024): 95–99. http://dx.doi.org/10.62225/2583049x.2024.4.6.3399.
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Corporate social responsibility, or CSR, has been the subject of an increasing amount of interest with the infusion of artificial intelligence or AI. AI may open up much-needed improvements in CSR practice, including better decision-making, transparency, and sustainable benefits, but it also poses challenges related to the ethical concerns of data privacy, bias, and accountability. This paper focuses on how an AI-based approach enhances CSR activities and identifies risks embedded in AI adoption in approaches to CSR. The study combines qualitative interviews with industry experts and quantitative analysis of CSR reports from AI-adopting firms and thus sheds lights on the upside and downside of AI-enhanced CSR. Finally, this article ends with recommendations in developing a framework that would align AI capabilities with CSR objectives and indicates how that may potentially challenge future research directions in order to mitigate the ethical challenges involved.
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Thi Minh, Pham Trang. "The Protection of Human Rights Under the Artificial Intelligence Act." Közigazgatási és Infokommunikációs Jogi PhD Tanulmányok 6, no. 1 (2025): 22–37. https://doi.org/10.47272/kikphd.2025.1.2.
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Artificial intelligence (AI) has developed rapidly and is having a profound impact on society as a whole. AI-driven technologies are now present in sectors such as healthcare, agriculture, food safety, education, media, sports, and culture, where they have the potential to optimize human time and enhance work efficiency. However, the social prospects of AI are both alluring and alarming; its promises and perils are difficult to disentangle. The risks to users have become an increasing concern as AI technologies are embedded in everyday products and services. Furthermore, AI can influence human behavior in new and unexpected ways, potentially undermining human dignity. To ensure better conditions for the application and use of AI in the development of social and economic sectors—while placing human rights at the center—the European Union enacted the AI Act, which entered into force in August 2024. This is the world’s first comprehensive AI legislation, establishing a legal framework for both users and developers of AI systems in Europe. It aims to create a safe, transparent, and trustworthy environment for the deployment of AI technologies. This paper will examine AI systems and explore the current challenges related to human rights in the context of rapid AI advancement. In addition, it will analyze the provisions of the AI Act to shed light on how it addresses the protection of human rights.
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Das, Sumit, Manas Kumar Sanyal, and Debamoy Datta. "Artificial Intelligent Embedded Doctor (AIEDr.)." International Journal of Big Data and Analytics in Healthcare 4, no. 2 (2019): 34–56. http://dx.doi.org/10.4018/ijbdah.2019070103.
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This article focuses on the development of a diagnostic model for low back pain management, a mathematical model describing the cause of the disease and an inclusive hardware implementation with artificial intelligence (AI). It has been observed that the greater part of the people in developing countries cannot afford the cost of this treatment due to low financial status. Moreover, a continuous assessment is not made for continuous monitoring of the patient's status. The problem of back pain develops slowly and if some early assessments can be made, then the treatment becomes effective. The proposed method developed in this article is based on galvanic skin response (GSR). GSR is used to monitor the pain of the patients and a modified back-pain management algorithm is used for tackling the correlation between stress and pain. The system continuously monitors the condition of a patient and if any symptoms of low back pain (LBP) develop, it immediately diagnoses diseases and chronic pains, and it recommends going to a doctor.
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Humr, Scott, and Mustafa Canan. "Intermediate Judgments and Trust in Artificial Intelligence-Supported Decision-Making." Entropy 26, no. 6 (2024): 500. http://dx.doi.org/10.3390/e26060500.
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Human decision-making is increasingly supported by artificial intelligence (AI) systems. From medical imaging analysis to self-driving vehicles, AI systems are becoming organically embedded in a host of different technologies. However, incorporating such advice into decision-making entails a human rationalization of AI outputs for supporting beneficial outcomes. Recent research suggests intermediate judgments in the first stage of a decision process can interfere with decisions in subsequent stages. For this reason, we extend this research to AI-supported decision-making to investigate how intermediate judgments on AI-provided advice may influence subsequent decisions. In an online experiment (N = 192), we found a consistent bolstering effect in trust for those who made intermediate judgments and over those who did not. Furthermore, violations of total probability were observed at all timing intervals throughout the study. We further analyzed the results by demonstrating how quantum probability theory can model these types of behaviors in human–AI decision-making and ameliorate the understanding of the interaction dynamics at the confluence of human factors and information features.
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Garg, Puru, and Binayak Dutta. "Impact of Artificial Intelligence on Everyday Life." International Journal of Innovative Research in Science,Engineering and Technology 13, no. 05 (2024): 9972–79. http://dx.doi.org/10.15680/ijirset.2024.1305567.
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This research paper will tell you about how the AI is helping us in our daily lives. How it is making not only our lives. Have you ever thought about how artificial intelligence (AI) is changing the game in our daily lives? From smart home appliances to virtual assistants, artificial intelligence tools are used in many areas of our daily lives. This what I mean is a game changer. That's the beauty of artificial intelligence; It makes life easier by taking control of all those annoying tasks and programs. Plus, isn't it amazing how chatbots and translation tools are changing the way we communicate? Autonomous cars and artificial intelligence-powered transportation promise to make roads safer and smoother. In healthcare, AI is improving diagnosis, self-healing, and even robotic surgery; Let's talk about a big improvement, right? Artificial intelligence is developing the way to learn to adapt and simplify the management of schools. It's like having your own virtual teacher guiding you through the course! Privacy concerns, algorithmic biases; These are real problems we have to deal with as we delve deeper into the technological world. seen? What risks should we be aware of? With so much happening around us, it's important to understand how embedded AI is in everyday life. . It is the force that shapes our daily lives in ways we could only dream of in the past. Fasten your seatbelts as we ride this wave of change together!
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Luo, Man, and Xuan Xiao. "THE IMPACT OF AI-EMBEDDED ENTERPRISE SOCIAL MEDIA USAGE ON EMPLOYEES' KNOWLEDGE SHARING BEHAVIOR." EUrASEANs: journal on global socio-economic dynamics, no. 6(49) (December 1, 2024): 589–602. https://doi.org/10.35678/2539-5645.6(49).2024.589-602.
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With the rapid advancement of artificial intelligence (AI) technology, embedding AI into enterprise social media has become a common practice for employees in online work, exerting a profound influence on their psychology and behavior. However, how AI-embedded enterprise social media (AI-embedded ESM) affects employees' knowledge sharing remains unclear. Based on the theory of psychological empowerment (PE), this paper constructs two types of AI-embedded enterprise social media usage—Task-Oriented and Social-Oriented -and examines their impact on employees' willingness to engage in knowledge-sharing behavior. A two-stage survey was conducted with 575 employees using structural equation modeling. The results reveal that both task-oriented and social-oriented AI-embedded enterprise social media usage enhance employees' sense of Work Meaningfulness, Job Autonomy, Self-Efficacy, and Influence, thereby promoting knowledge-sharing behavior.
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Blackledge, Jonathan, and Napo Mosola. "Applications of Artificial Intelligence to Cryptography." Transactions on Machine Learning and Artificial Intelligence 8, no. 3 (2020): 21–60. http://dx.doi.org/10.14738/tmlai.83.8219.
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This paper considers some recent advances in the field of Cryptography using Artificial Intelligence (AI) It specifically considers the applications of Machine Learning (ML) and Evolutionary Computing (EC) concepts used to generate ciphers. A short overview is given on Artificial Neural Networks (ANNs) and the principles of Deep Learning (DL) using Deep ANNs. In this context, the paper considers: (i) the implementation of EC and ANNs to generate unique and unclonable ciphers; (ii) ML strategies for detecting the genuine randomness (or otherwise) of binary streams for applications in Cryptanalysis. The paper aims to provide an overview on how AI can be applied for encrypting data and undertaking cryptanalysis of such data and other encrypted data classes in order to assess the cryptographic strength of an encryption algorithm. For example, to detect patterns of intercepted data streams that are signatures of encrypted data. An application is presented which includes authentication of high-value documents such as bank notes, using smartphones. Using an antenna of a smartphone to read (in the near field) an embedded flexible integrate circuit with a non-programmable coprocessor, ultra-strong encrypted information can be used on-line for validation.
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Gnacy-Gajdzik, Anna, and Piotr Przystałka. "Automating the Analysis of Negative Test Verdicts: A Future-Forward Approach Supported by Augmented Intelligence Algorithms." Applied Sciences 14, no. 6 (2024): 2304. http://dx.doi.org/10.3390/app14062304.
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In the epoch characterized by the anticipation of autonomous vehicles, the quality of the embedded system software, its reliability, safety, and security is significant. The testing of embedded software is an increasingly significant element of the development process. The application of artificial intelligence (AI) algorithms in the process of testing embedded software in vehicles constitutes a significant area of both research and practical consideration, arising from the escalating complexity of these systems. This paper presents the preliminary development of the AVESYS framework which facilitates the application of open-source artificial intelligence algorithms in the embedded system testing process. The aim of this work is to evaluate its effectiveness in identifying anomalies in the test environment that could potentially affect testing results. The raw data from the test environment, mainly communication signals and readings from temperature, as well as current and voltage sensors are pre-processed and used to train machine learning models. A verification study is carried out, proving the high practical potential of the application of AI algorithms in embedded software testing.
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Gembaczka, Pierre, Burkhard Heidemann, Bernhard Bennertz, Wolfgang Groeting, Thomas Norgall, and Karsten Seidl. "Combination of sensor-embedded and secure server-distributed artificial intelligence for healthcare applications." Current Directions in Biomedical Engineering 5, no. 1 (2019): 29–32. http://dx.doi.org/10.1515/cdbme-2019-0008.
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AbstractThe application of artificial intelligence (AI) in the areas of health, care and social participation offers great opportunities but also involves great challenges. Extensive regulatory, ethical and data-security related requirements exist for data recording, storage and processing of respective personalized and patient-related data. “Artificial Intelligence as a Service” (AIaaS) is pushed for consumer applications by global players, which implies data storage on external database server. However, the available solutions do not meet the requirements. Moreover, small and medium-sized enterprises (SMEs) in the field of healthcare fear the loss of data sovereignty and information outflow. In this paper, we propose a secure and resource-efficient approach by embedding AI directly close to the sensor in combination with secure and distributed data processing on local server or certified “Trusted Data Center”. For this purpose, we have developed the Artificial Intelligence for Embedded Systems (AIfES) platform-independent machine learning library in C programming language. It contains a fully configurable deep artificial neural network with feedforward structure. The library can be run directly on a microcontroller and even allows to train the neural network. Possible healthcare applications include direct (pre-) processing of sensor data, sensor calibration, pattern recognition and classification.
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Suleiman, Dawud Adaviruku, Tahir Mumtaz Awan, and Maria Javed. "Enhancing digital marketing performance through usage intention of AI-powered websites." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 4 (2021): 810. http://dx.doi.org/10.11591/ijai.v10.i4.pp810-817.
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<p><span>Digital and wireless technology are a crucial part of today’s modern life. Artificial intelligence (AI) uses different technologies and systems for speech recognition, visual perception and decision making to mimic human functions. This study explores the impact of AI on website interactivity and the ease of use for enhancing digital marketing performance. The methodology used is qualitative with structured interviews, using three artificial intelligence-powered websites (Amazon, Alibaba, and Uber) as reference. The participants' structured interview responses were grouped into different thematic heading for coding and were subsequently analyzed by NVivo. The result found that artificial intelligence empowered websites were interactive, participants don’t feel safe and secure, easy to use, and tend to boost digital marketing performances. This study implies that more digital marketing companies should consider integrating artificial intelligence capabilities in their business operations. More security features should be embedded to help customers calm the fears of web insecurities.</span></p>
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Rodrigues, Leonel Cezar, Reziere Dagobi da Silva, Simone Maria Espinosa, and Valeria Riscarolli. "Artificial Intelligence, Ethics and Speed Processing in the Law System." Journal of Law and Corruption Review 6 (August 30, 2024): e084. http://dx.doi.org/10.37497/corruptionreview.6.2024.84.
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Objective: This study aims to demonstrate how the use of generative Artificial Intelligence (AI) fosters innovation within the Judiciary by enhancing the operational performance of the legal system. Methodology: The research adopts an explanatory qualitative approach with a theoretical foundation. It relies on secondary data and documentary evidence sourced from specialized literature. Results: The findings suggest that generative AI significantly expands the operational capacity of judges and legal professionals by automating repetitive tasks and facilitating the generation of legal sentences. This leads to improved decision-making and more effective legal strategies, thus enhancing the overall efficiency of the judiciary. Conclusions: The integration of generative AI in the legal system has the potential to revolutionize the practice of law, making it more accessible and less discriminatory. The ethical considerations embedded in AI systems are crucial for ensuring that justice is administered fairly and in alignment with fundamental human rights. As AI continues to evolve, its role in supporting judicial processes will likely increase, contributing to a more efficient and ethical legal system.
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Rodrigues, Leonel Cezar, da Silva Reziere Dagobi, Simone Maria Espinosa, and Valeria Riscarolli. "Artificial Intelligence, Ethics and Speed Processing in the Law System." Journal of Law and Corruption Review 6 (October 16, 2024): 01–19. https://doi.org/10.37497/CorruptionReview.6.2024.84.
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<strong>Objective:</strong> This study aims to demonstrate how the use of generative Artificial Intelligence (AI) fosters innovation within the Judiciary by enhancing the operational performance of the legal system. <strong>Methodology:</strong> The research adopts an explanatory qualitative approach with a theoretical foundation. It relies on secondary data and documentary evidence sourced from specialized literature. <strong>Results:</strong> The findings suggest that generative AI significantly expands the operational capacity of judges and legal professionals by automating repetitive tasks and facilitating the generation of legal sentences. This leads to improved decision-making and more effective legal strategies, thus enhancing the overall efficiency of the judiciary. <strong>Conclusions:</strong> The integration of generative AI in the legal system has the potential to revolutionize the practice of law, making it more accessible and less discriminatory. The ethical considerations embedded in AI systems are crucial for ensuring that justice is administered fairly and in alignment with fundamental human rights. As AI continues to evolve, its role in supporting judicial processes will likely increase, contributing to a more efficient and ethical legal system.
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Schwabe, Nils, Yexu Zhou, Leon Hielscher, Tobias Röddiger, Till Riedel, and Sebastian Reiter. "Tools and methods for Edge-AI-systems." at - Automatisierungstechnik 70, no. 9 (2022): 767–76. http://dx.doi.org/10.1515/auto-2022-0023.
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Abstract The enormous potential of artificial intelligence, especially artificial neural networks, when used for edge computing applications in cars, traffic lights or smart watches, has not yet been fully exploited today. The reasons for this are the computing, energy and memory requirements of modern neural networks, which typically cannot be met by embedded devices. This article provides a detailed summary of today’s challenges and gives a deeper insight into existing solutions that enable neural network performance with modern HW/SW co-design techniques.
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Bavli, Itai, and Sandro Galea. "Key considerations in the adoption of Artificial Intelligence in public health." PLOS Digital Health 3, no. 7 (2024): e0000540. http://dx.doi.org/10.1371/journal.pdig.0000540.
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The integration of Artificial Intelligence (AI) into public health has the potential to transform the field, influencing healthcare at the population level. AI can aid in disease surveillance, diagnosis, and treatment decisions, impacting how healthcare professionals deliver care. However, it raises critical questions about inputs, values, and biases that must be addressed to ensure its effectiveness. This article investigates the factors influencing the values guiding AI technology and the potential consequences for public health. It outlines four key considerations that should shape discussions regarding the role of AI in the future of public health. These include the potential omission of vital factors due to incomplete data inputs, the challenge of balancing trade-offs in public health decisions, managing conflicting inputs between public health objectives and community preferences, and the importance of acknowledging the values and biases embedded in AI systems, which could influence public health policy-making.
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National, Press Associates. "ARTIFICIAL INTELLIGENCE (AI) AND ITS THREATS ON HUMAN SOCIETY." Research & Reviews in Biotechnology & Biosciences, no. 2 (January 6, 2025): 1–6. https://doi.org/10.5281/zenodo.14605855.
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Artificial Intelligence (AI) has rapidly become an integral part of contemporary society, promising innovation and efficiency across diverse domains. However, as AI technologies continue to advance, concerns regarding their potential threats to human society have garnered increasing attention. This abstract provides a succinct overview of the multifaceted challenges posed by AI, encompassing ethical, social, and economic dimensions.The evolution of AI has led to transformative changes in the way individuals interact with technology, from personalized recommendation systems to autonomous vehicles. Despite these advancements, a critical examination reveals a spectrum of threats that necessitate careful consideration. Privacy breaches, stemming from the extensive data collection inherent in AI systems, raise profound ethical questions. Job displacement due to automation and the potential erosion of certain employment sectors further underscore the societal impact of AI.Biases embedded in AI algorithms present another significant challenge, as they can perpetuate and even exacerbate existing societal inequalities. Moreover, the deployment of autonomous systems raises ethical concerns surrounding accountability and decision-making. This abstract explores these issues by drawing upon a systematic literature review, analyzing case studies, and incorporating expert perspectives.As society grapples with the implications of AI, it is imperative to engage in a comprehensive discussion regarding the responsible development and deployment of these technologies. The abstract concludes by emphasizing the urgent need for collaborative efforts among policymakers, industry stakeholders, and the public to address these threats, ensuring that AI contributes positively to the well-being and equitable advancement of human society.Keywords:Automation, Machine Learning, Neural Networks, Deep Learning, Robotics, Ethical Concerns, JobDisplacement, Privacy Issues, Bias in AI, Autonomous Weapons, Singularity, Superintelligence, Surveillance and e - Social Impact.