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Journal articles on the topic 'Embedded artificial intelligence (AI)'
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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 (July 18, 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 (February 12, 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 (April 22, 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 (July 19, 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 (September 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 (January 21, 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 (August 25, 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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Marcinowski, Maciej. "Artificial Intelligence or the Ultimate Tool for Conservatism." DANUBE 13, no. 1 (March 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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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 (March 10, 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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Chu, Charlene, Kathleen Leslie, Shehroz Khan, Rune Nyrup, and Amanda Grenier. "AGEISM IN ARTIFICIAL INTELLIGENCE: A REVIEW." Innovation in Aging 6, Supplement_1 (November 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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Tarozzi, Martina Elena. "Artificial intelligence for Next generation sequencing data analysis." Science Reviews. Biology 3, no. 1 (April 10, 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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Krishna, Anirudh. "Will AI Slay the Poverty Dragon?" Current History 123, no. 849 (January 1, 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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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 (October 10, 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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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 (January 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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Helanski Cardoso, Juliane Cristina. "Digital images generated by Artificial Intelligence as ethnographic experimentation." Desde el Sur 16, no. 2 (April 30, 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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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 (September 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> </p>
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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.
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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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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 (August 7, 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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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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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 (November 29, 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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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 (March 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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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 (July 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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Blackledge, Jonathan, and Napo Mosola. "Applications of Artificial Intelligence to Cryptography." Transactions on Machine Learning and Artificial Intelligence 8, no. 3 (June 30, 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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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 (September 1, 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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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 (September 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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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 (March 9, 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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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 (December 1, 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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Moodley, K. "Artificial intelligence (AI) or augmented intelligence? How big data and AI are transforming healthcare: Challenges and opportunities." South African Medical Journal 114, no. 1 (December 31, 2023): 22–26. http://dx.doi.org/10.7196/samj.2024.v114i1.1631.
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The sanctity of the doctor-patient relationship is deeply embedded in tradition – the Hippocratic oath, medical ethics, professional codes of conduct, and legislation – all of which are being disrupted by big data and ‘artificial’ intelligence (AI). The transition from paper-based records to electronic health records, wearables, mobile health applications and mobile phone data has created new opportunities to scale up data collection. Databases of unimaginable magnitude can be harnessed to develop algorithms for AI and to refine machine learning. Complex neural networks now lie at the core of ubiquitous AI systems in healthcare. A transformed healthcare environment enhanced by innovation, robotics, digital technology, and improved diagnostics and therapeutics is plagued by ethical, legal and social challenges. Global guidelines are emerging to ensure governance in AI, but many low- and middle-income countries have yet to develop context- specific frameworks. Legislation must be developed to frame liability and account for negligence due to robotics in the same way human healthcare providers are held accountable. The digital divide between high- and low-income settings is significant and has the potential to exacerbate health inequities globally.
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Boutekkouk, Fateh. "AI-Based Methods to Resolve Real-Time Scheduling for Embedded Systems." International Journal of Cognitive Informatics and Natural Intelligence 15, no. 4 (October 2021): 1–44. http://dx.doi.org/10.4018/ijcini.290308.
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Artificial Intelligence is becoming more attractive to resolve nontrivial problems including the well known real time scheduling (RTS) problem for Embedded Systems (ES). The latter is considered as a hard multi-objective optimization problem because it must optimize at the same time three key conflictual objectives that are tasks deadlines guarantee, energy consumption reduction and reliability enhancement. In this paper, we firstly present the necessary background to well understand the problematic of RTS in the context of ES, then we present our enriched taxonomies for real time, energy and faults tolerance aware scheduling algorithms for ES. After that, we survey the most pertinent existing works of literature targeting the application of AI methods to resolve the RTS problem for ES notably Constraint Programming, Game theory, Machine learning, Fuzzy logic, Artificial Immune Systems, Cellular Automata, Evolutionary algorithms, Multi-agent Systems and Swarm Intelligence. We end this survey by a discussion putting the light on the main challenges and the future directions.
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Arifin, Saru. "Artificial intelligence in the workplace : How should moral and legal issues be addressed?" Pro Publico Bono - Magyar Közigazgatás 9, no. 4 (2021): 94–109. http://dx.doi.org/10.32575/ppb.2021.4.6.
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Artificial Intelligence (AI) has emerged as a new method for efficiently and cost-effectively assisting human activities as science and technology have progressed in the fourth industrial revolution. It has been argued that Artificial Intelligence works in two ways. It can both create and eliminate jobs. Based on present technological capabilities, AI has sparked speculative discussions concerning its implications for morality and law. This article argues that AI is a technological advancement that will help businesses grow in the fourth industrial revolution. The controller determines its effects hence it can be put to either good or bad use. As a result, for AI to benefit the prosperity and well-being of humanity to the greatest degree, morals must be embedded in its use, and the law must be enacted to ensure that human commitment to using AI wisely in business processes is consistent.
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Vemuri, Naveen, Naresh Thaneeru, and Venkata Manoj Tatikonda. "Securing Trust: Ethical Considerations in AI for Cybersecurity." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 2, no. 2 (May 30, 2023): 167–75. http://dx.doi.org/10.60087/jklst.vol2.n2.p175.
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The basic purpose of combining artificial intelligence with cybersecurity is to solve complex issues that emerge in the systems. This paper demonstrates a very distinct AI framework that can be embedded in cyber security, which rests upon this very basic foundation. The decisions that are made using artificial intelligence are not only transparent but are also simple to comprehend in vigorous situations of cyber security. This essay also discusses the ethical issues regarding AI, its risk, and accountability when some failure occurs in the context of cyber security. The efficacy of blending ethical systems into artificial intelligence for the purpose of improving safety and privacy is also demonstrated in this paper. We have suggested the consideration of an ethical system in every step of the development cycle of the system and it should be continuously monitored and updated for the prompt resolution of all the emerging issues in the system. This paper suggests that there should be a mutual collaboration between cyber security experts, system developers, and lawmakers for a successful integration of systems
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Zhang, F., W. Ke, H. Ouyang, and S. Qiu. "INDOOR VISIBLE LIGHT LOCALIZATION METHOD BASED ON EMBEDDED ARTIFICIAL INTELLIGENCE." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVI-3/W1-2022 (April 22, 2022): 255–61. http://dx.doi.org/10.5194/isprs-archives-xlvi-3-w1-2022-255-2022.
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Abstract. This paper proposes an indoor visible light location method based on embedded platform and optical frequency image recognition technology with artificial intelligence, which can effectively improve the location effect in complex indoor environment. By transplanting the artificial intelligence (AI) based image classification algorithm into the embedded platform, this method uses a forward neural network to analyse the position information coming from the coded optical frequency image received by a camera, and then the positioning results can be obtained. In view of the "motion state" and "occlusion state" that are most likely to fail in traditional visible image localization, we specially supplement the training set of relevant characteristic optical frequency images to enhance the robustness of the method. According to the track and positioning results of the moving platform receiver, the proposed method can provide accurate and reliable navigation and positioning and has stronger anti-interference ability compared with the traditional light intensity or light image positioning methods.
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Bushuev, Serhiy, Andriy Ivko, and Yuriy Tikhonovych. "Syncretic project management in the era of artificial intelligence explosion." Environmental safety and natural resources 49, no. 1 (March 29, 2024): 85–98. http://dx.doi.org/10.32347/2411-4049.2024.1.85-98.
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As the technological landscape rapidly evolves, the convergence of innovation and artificial intelligence (AI) presents unprecedented opportunities and challenges for project management. This paper introduces a comprehensive mathematical model for the syncretic management of innovative projects in the age of the AI explosion. Syncretism in this context refers to the seamless integration of diverse elements, including interdisciplinary collaboration, AI technologies, and adaptive methodologies, to optimize project outcomes. The proposed model encompasses various facets of project management, innovation, and AI integration. It delineates stages of project lifecycle management, emphasizing resource allocation, risk assessment, and adaptive strategies. In the innovation management domain, the model incorporates methodologies for idea generation, technology scouting, and open innovation, recognizing AI's role in shaping the innovative landscape. A crucial aspect of the model lies in the integration of AI technologies throughout the project. This includes identifying relevant use cases, managing data effectively, selecting appropriate AI models, and establishing decision support systems. The syncretic approach emphasizes cross-functional collaboration, fostering an environment where different disciplines seamlessly contribute to project success. Resource optimization is a key focus, leveraging AI to allocate resources efficiently, predict maintenance needs, and enhance overall project performance. Ethical and legal considerations are embedded within the model to ensure responsible AI usage, and the paper outlines mechanisms for ongoing training and development to equip teams with the necessary skills. The model's effectiveness is evaluated through the lens of monitoring and evaluation, with defined key performance indicators, continuous monitoring, and feedback loops for iterative improvements. Communication and collaboration are underscored, utilizing modern tools to facilitate stakeholder engagement and effective teamwork. This paper contributes to the evolving discourse on project management by providing a robust framework that adapts to the dynamic nature of AI and innovation. It serves as a guide for project managers, interdisciplinary teams, and decision-makers navigating the challenges and opportunities presented by the syncretic management of innovative projects in the era of the AI explosion.
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Ashfaq, Zarlish, Rafia Mumtaz, Abdur Rafay, Syed Mohammad Hassan Zaidi, Hadia Saleem, Sadaf Mumtaz, Adnan Shahid, Eli De Poorter, and Ingrid Moerman. "Embedded AI-Based Digi-Healthcare." Applied Sciences 12, no. 1 (January 5, 2022): 519. http://dx.doi.org/10.3390/app12010519.
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Healthcare is an indispensable part of human life and chronic illnesses like cardiovascular diseases (CVD) have a deeply negative impact on the healthcare sector. Since the ever-growing population of chronic patients cannot be managed at hospitals, therefore, there is an urgent need for periodic monitoring of vital parameters and apposite treatment of these patients. In this paper, an Internet of Medical Things (IoMT) -based remote patient monitoring system is proposed which is based on Artificial Intelligence (AI) and edge computing. The primary focus of this paper is to develop an embedded prototype that can be used for remote monitoring of cardiovascular patients. The system will continuously monitor physiological parameters like body temperature, heart rate, and blood oxygen saturation, and then report the health status to the authenticated users. The system employs edge computing to perform multiple functionalities including health status inference using a Machine Learning (ML) model which makes predictions on real-time data, alert notifications in case of an emergency, and transferring data between the sensor network and the cloud. A web-based application is developed for the depiction of raw data and ML results and to provide a direct communication channel between the patient and the doctor. The ML module achieved an accuracy of 96.26% on the test set using the K-Nearest Neighbors (KNNs) algorithm. This solution aims to address the sense of emergency due to the alarming statistics that highlight the mortality rate of cardiovascular patients. The project will enable a smart option based on IoT and ML to improve standards of living and prove crucial in saving human lives.
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Owens, Emer, Barry Sheehan, Martin Mullins, Martin Cunneen, Juliane Ressel, and German Castignani. "Explainable Artificial Intelligence (XAI) in Insurance." Risks 10, no. 12 (December 1, 2022): 230. http://dx.doi.org/10.3390/risks10120230.
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Explainable Artificial Intelligence (XAI) models allow for a more transparent and understandable relationship between humans and machines. The insurance industry represents a fundamental opportunity to demonstrate the potential of XAI, with the industry’s vast stores of sensitive data on policyholders and centrality in societal progress and innovation. This paper analyses current Artificial Intelligence (AI) applications in insurance industry practices and insurance research to assess their degree of explainability. Using search terms representative of (X)AI applications in insurance, 419 original research articles were screened from IEEE Xplore, ACM Digital Library, Scopus, Web of Science and Business Source Complete and EconLit. The resulting 103 articles (between the years 2000–2021) representing the current state-of-the-art of XAI in insurance literature are analysed and classified, highlighting the prevalence of XAI methods at the various stages of the insurance value chain. The study finds that XAI methods are particularly prevalent in claims management, underwriting and actuarial pricing practices. Simplification methods, called knowledge distillation and rule extraction, are identified as the primary XAI technique used within the insurance value chain. This is important as the combination of large models to create a smaller, more manageable model with distinct association rules aids in building XAI models which are regularly understandable. XAI is an important evolution of AI to ensure trust, transparency and moral values are embedded within the system’s ecosystem. The assessment of these XAI foci in the context of the insurance industry proves a worthwhile exploration into the unique advantages of XAI, highlighting to industry professionals, regulators and XAI developers where particular focus should be directed in the further development of XAI. This is the first study to analyse XAI’s current applications within the insurance industry, while simultaneously contributing to the interdisciplinary understanding of applied XAI. Advancing the literature on adequate XAI definitions, the authors propose an adapted definition of XAI informed by the systematic review of XAI literature in insurance.
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Giorgio, Agostino. "No Pain Device: Empowering Personal Safety with an Artificial Intelligence-Based Nonviolence Embedded System." Electronics 13, no. 9 (May 2, 2024): 1766. http://dx.doi.org/10.3390/electronics13091766.
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This paper presents the development of a novel anti-violence device titled “no pAIn” (an acronym for Never Oppressed Protected by Artificial Intelligence Nonviolence system), which harnesses the power of artificial intelligence (AI). Primarily designed to combat violence against women, the device offers personal safety benefits for individuals across diverse demographics. Operating autonomously, it necessitates no user interaction post-activation. The AI engine conducts real-time speech recognition and effectively discerns genuine instances of aggression from non-violent disputes or conversations. Facilitated by its Internet connectivity, in the event of detected aggression, the device promptly issues assistance requests with real-time precise geolocation tracking to predetermined recipients for immediate assistance. Its compact size enables discreet concealment within commonplace items like candy wrappers, purpose-built casings, or wearable accessories. The device is battery-operated. The prototype was developed using a microcontroller board (Arduino Nano RP2040 Connect), incorporating an omnidirectional microphone and Wi-Fi module, all at a remarkably low cost. Subsequent functionality testing, performed in debug mode using the Arduino IDE serial monitor, yielded successful results. The AI engine exhibited exceptional accuracy in word recognition, complemented by a robust logic implementation, rendering the device highly reliable in discerning genuine instances of aggression from non-violent scenarios.
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Chang, Deng-Lin, Sheng-Hsueh Yang, Sheau-Ling Hsieh, Hui-Jung Wang, and Keh-Chia Yeh. "Artificial Intelligence Methodologies Applied to Prompt Pluvial Flood Estimation and Prediction." Water 12, no. 12 (December 17, 2020): 3552. http://dx.doi.org/10.3390/w12123552.
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Regarding urban flooding issues, applying Artificial Intelligence (AI) methodologies can provide a timely prediction of imminent incidences of flash floods. The study aims to develop and deploy an effective real-time pluvial flood forecasting AI platform. The platform integrates rainfall hyetographs embedded with uncertainty analyses as well as hydrological and hydraulic modeling. It establishes a large number synthetic of torrential rainfall events and their simulated flooding datasets. The obtained data contain 6000 sets of color-classified rainfall hyetograph maps and 300,000 simulated flooding maps (water depth) in an urban district. The generated datasets are utilized for AI image processing. Through the AI deep learning classifications, the rainfall hyetograph map feature parameters are detected and extracted. The trained features are applied to predict potential rainfall events, recognize their potential inundated water depths as well as display flooding maps in real-time. The performance assessments of the platform are evaluated by Root Means Square Error (RMSE), Nash Sutcliffe Efficiency Coefficient (NSCE) and Mean Absolute Percentage Error (MAPE). The results of RMSE and NSCE indicators illustrate that the methodologies and approaches of the AI platform are reliable and acceptable. However, the values of MAPE show inconsistency. Ultimately, the platform can perform and be utilized promptly in real-time and ensure sufficient lead time in order to prevent possible flooding hazards.
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Bajpai, Pragati. "Artificial Intelligence and its Use in the Field of Education." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (May 31, 2024): 1952–57. http://dx.doi.org/10.22214/ijraset.2024.61986.
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Abstract: The purpose of this study was to assess the impact of Artificial Intelligence (AI) on education. Premised on a narrative and framework for assessing AI identified from a preliminary analysis, the scope of the study was limited to the application and effects of AI in administration, instruction, and learning. A qualitative research approach, leveraging the use of literature review as a research design and approach was used and effectively facilitated the realization of the study purpose. Artificial intelligence is a field of study and the resulting innovations and developments that have culminated in computers, machines, and other artifacts having human-like intelligence characterized by cognitive abilities, learning, adaptability, and decision-making capabilities. The study ascertained that AI has extensively been adopted and used in education, particularly by education institutions, in different forms. AI initially took the form of computer and computer related technologies, transitioning to webbased and online intelligent education systems, and ultimately with the use of embedded computer systems, together with other technologies, the use of humanoid robots and web-based chatbots to perform instructors' duties and functions independently or with instructors. Using these platforms, instructors have been able to perform different administrative functions, such as reviewing and grading students' assignments more effectively and efficiently, and achieve higher quality in their teaching activities. On the other hand, because the systems leverage machine learning and adaptability, curriculum and content has been customized and personalized in line with students' needs, which has fostered uptake and retention, thereby improving learners experience and overall quality of learning.
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Ktari, Jalel, Tarek Frikha, Monia Hamdi, Hela Elmannai, and Habib Hmam. "Lightweight AI Framework for Industry 4.0 Case Study: Water Meter Recognition." Big Data and Cognitive Computing 6, no. 3 (July 1, 2022): 72. http://dx.doi.org/10.3390/bdcc6030072.
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The evolution of applications in telecommunication, network, computing, and embedded systems has led to the emergence of the Internet of Things and Artificial Intelligence. The combination of these technologies enabled improving productivity by optimizing consumption and facilitating access to real-time information. In this work, there is a focus on Industry 4.0 and Smart City paradigms and a proposal of a new approach to monitor and track water consumption using an OCR, as well as the artificial intelligence algorithm and, in particular the YoLo 4 machine learning model. The goal of this work is to provide optimized results in real time. The recognition rate obtained with the proposed algorithms is around 98%.
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Buarque, Bernardo S., Ronald B. Davies, Ryan M. Hynes, and Dieter F. Kogler. "OK Computer: the creation and integration of AI in Europe." Cambridge Journal of Regions, Economy and Society 13, no. 1 (February 1, 2020): 175–92. http://dx.doi.org/10.1093/cjres/rsz023.
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Abstract This article investigates the creation and integration of artificial intelligence (AI) patents in Europe. We create a panel of AI patents over time, mapping them into regions at the NUTS2 level. We then proceed by examining how AI is integrated into the knowledge space of each region. In particular, we find that those regions where AI is most embedded into the innovation landscape are also those where the number of AI patents is largest. This suggests that, to increase AI innovation, it may be necessary to integrate it with industrial development, a feature central to many recent AI-promoting policies.
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Yeh, Shin-Cheng, Ai-Wei Wu, Hui-Ching Yu, Homer C. Wu, Yi-Ping Kuo, and Pei-Xuan Chen. "Public Perception of Artificial Intelligence and Its Connections to the Sustainable Development Goals." Sustainability 13, no. 16 (August 16, 2021): 9165. http://dx.doi.org/10.3390/su13169165.
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Artificial Intelligence (AI) will not just change our lives but bring about revolutionary transformation. AI can augment efficiencies of good and bad things and thus has been considered both an opportunity and risk for the sustainable development of humans. This study designed a survey to collect 1018 samples of educated people with access to the internet in Taiwan regarding their perceptions of AI and its connections to the Sustainable Development Goals (SDGs). The respondents showed high confidence in their AI knowledge. They had a very positive attitude toward AI but at the same time thought AI was risky. In general, people in Taiwan could be “rational optimists” regarding AI. We also examined how people think of the linkages between AI and the SDGs and found that SDG 4, SDG 9, and SDG 3 had the highest “synergy” and lowest rates of “trade-off”. Significant differences for some key questions were also identified concerning the demographic variables such as gender, age, education, and college major. According to the data analysis, education played as the base to construct a sustainable AI-aided town with an embedded innovative circular economy and high-quality water and energy services, making the residents live healthier lives. The findings of this study can be referred to when the perceptions of AI and sustainability issues are of interest for an emerging high-tech economy such as Taiwan and other Asian countries.
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Emanuilov, Ivo, and Orian Dheu. "Flying High for AI? Perspectives on EASA’s Roadmap for AI in Aviation." Air and Space Law 46, Issue 1 (January 1, 2021): 1–28. http://dx.doi.org/10.54648/aila2021001.
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In early 2020, the European Union Aviation Safety Agency (EASA) published its long anticipated ‘Roadmap for Artificial Intelligence in Aviation’. This document builds upon previous European initiatives such as the High-Level Expert Group’s Ethical Guidelines on artificial intelligence (‘AI’), where the concept of ‘trustworthiness’ is embedded as a key pillar and a pre-requisite for developing and deploying AI technologies. The roadmap assesses the associated ethical, safety and regulatory challenges that may arise from the deployment and use of AI applications in aviation. This article provides an overview of the main takeaways, strengths and weaknesses of this roadmap. It critically analyses the main challenges of AI-driven technologies throughout the entire aviation domain. The article argues the roadmap would benefit from considering new regulatory tools and processes, such as regulatory sandboxing and AI-driven certification, and contends any efforts for standardization of AI in aviation must be reconciled with existing standardization of automation and that this may not always be a straightforward process as far as interoperability is concerned. Finally, the article argues that further exploration of the identification and allocation of liability will be indispensable in fostering increased levels of trust in AI-enabled aviation. AI, Innovation, Regulation, Aviation, Autonomous Systems, Regulatory Sandboxing
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Strohm, Lea, Charisma Hehakaya, Erik R. Ranschaert, Wouter P. C. Boon, and Ellen H. M. Moors. "Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors." European Radiology 30, no. 10 (May 26, 2020): 5525–32. http://dx.doi.org/10.1007/s00330-020-06946-y.
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Abstract Objective The objective was to identify barriers and facilitators to the implementation of artificial intelligence (AI) applications in clinical radiology in The Netherlands. Materials and methods Using an embedded multiple case study, an exploratory, qualitative research design was followed. Data collection consisted of 24 semi-structured interviews from seven Dutch hospitals. The analysis of barriers and facilitators was guided by the recently published Non-adoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework for new medical technologies in healthcare organizations. Results Among the most important facilitating factors for implementation were the following: (i) pressure for cost containment in the Dutch healthcare system, (ii) high expectations of AI’s potential added value, (iii) presence of hospital-wide innovation strategies, and (iv) presence of a “local champion.” Among the most prominent hindering factors were the following: (i) inconsistent technical performance of AI applications, (ii) unstructured implementation processes, (iii) uncertain added value for clinical practice of AI applications, and (iv) large variance in acceptance and trust of direct (the radiologists) and indirect (the referring clinicians) adopters. Conclusion In order for AI applications to contribute to the improvement of the quality and efficiency of clinical radiology, implementation processes need to be carried out in a structured manner, thereby providing evidence on the clinical added value of AI applications. Key Points • Successful implementation of AI in radiology requires collaboration between radiologists and referring clinicians. • Implementation of AI in radiology is facilitated by the presence of a local champion. • Evidence on the clinical added value of AI in radiology is needed for successful implementation.
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Chao, Heng-Sheng, Chiao-Yun Tsai, Chung-Wei Chou, Tsu-Hui Shiao, Hsu-Chih Huang, Kun-Chieh Chen, Hao-Hung Tsai, Chin-Yu Lin, and Yuh-Min Chen. "Artificial Intelligence Assisted Computational Tomographic Detection of Lung Nodules for Prognostic Cancer Examination: A Large-Scale Clinical Trial." Biomedicines 11, no. 1 (January 6, 2023): 147. http://dx.doi.org/10.3390/biomedicines11010147.
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Low-dose computed tomography (LDCT) has emerged as a standard method for detecting early-stage lung cancer. However, the tedious computer tomography (CT) slide reading, patient-by-patient check, and lack of standard criteria to determine the vague but possible nodule leads to variable outcomes of CT slide interpretation. To determine the artificial intelligence (AI)-assisted CT examination, AI algorithm-assisted CT screening was embedded in the hospital picture archiving and communication system, and a 200 person-scaled clinical trial was conducted at two medical centers. With AI algorithm-assisted CT screening, the sensitivity of detecting nodules sized 4–5 mm, 6~10 mm, 11~20 mm, and >20 mm increased by 41%, 11.2%, 10.3%, and 18.7%, respectively. Remarkably, the overall sensitivity of detecting varied nodules increased by 20.7% from 67.7% to 88.4%. Furthermore, the sensitivity increased by 18.5% from 72.5% to 91% for detecting ground glass nodules (GGN), which is challenging for radiologists and physicians. The free-response operating characteristic (FROC) AI score was ≥0.4, and the AI algorithm standalone CT screening sensitivity reached >95% with an area under the localization receiver operating characteristic curve (LROC-AUC) of >0.88. Our study demonstrates that AI algorithm-embedded CT screening significantly ameliorates tedious LDCT practices for doctors.
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Joyce, Kelly, Laurel Smith-Doerr, Sharla Alegria, Susan Bell, Taylor Cruz, Steve G. Hoffman, Safiya Umoja Noble, and Benjamin Shestakofsky. "Toward a Sociology of Artificial Intelligence: A Call for Research on Inequalities and Structural Change." Socius: Sociological Research for a Dynamic World 7 (January 2021): 237802312199958. http://dx.doi.org/10.1177/2378023121999581.
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This article outlines a research agenda for a sociology of artificial intelligence (AI). The authors review two areas in which sociological theories and methods have made significant contributions to the study of inequalities and AI: (1) the politics of algorithms, data, and code and (2) the social shaping of AI in practice. The authors contrast sociological approaches that emphasize intersectional inequalities and social structure with other disciplines’ approaches to the social dimensions of AI, which often have a thin understanding of the social and emphasize individual-level interventions. This scoping article invites sociologists to use the discipline’s theoretical and methodological tools to analyze when and how inequalities are made more durable by AI systems. Sociologists have an ability to identify how inequalities are embedded in all aspects of society and to point toward avenues for structural social change. Therefore, sociologists should play a leading role in the imagining and shaping of AI futures.
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Chen, Kexin. "Crossing the Achilles Heel of Algorithms: Identifying the Developmental Dilemma of Artificial Intelligence-Assisted Judicial Decision-Making." Journal of Electronic Research and Application 8, no. 1 (January 23, 2024): 69–72. http://dx.doi.org/10.26689/jera.v8i1.5991.
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In the developmental dilemma of artificial intelligence (AI)-assisted judicial decision-making, the technicalarchitecture of AI determines its inherent lack of transparency and interpretability, which is challenging to fundamentallyimprove. This can be considered a true challenge in the realm of AI-assisted judicial decision-making. By examining thecourt’s acceptance, integration, and trade-offs of AI technology embedded in the judicial field, the exploration of potentialconflicts, interactions, and even mutual shaping between the two will not only reshape their conceptual connotations andintellectual boundaries but also strengthen the cognition and re-interpretation of the basic principles and core values of thejudicial trial system.
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McRae, Michael P., Kritika S. Rajsri, Timothy M. Alcorn, and John T. McDevitt. "Smart Diagnostics: Combining Artificial Intelligence and In Vitro Diagnostics." Sensors 22, no. 17 (August 24, 2022): 6355. http://dx.doi.org/10.3390/s22176355.
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We are beginning a new era of Smart Diagnostics—integrated biosensors powered by recent innovations in embedded electronics, cloud computing, and artificial intelligence (AI). Universal and AI-based in vitro diagnostics (IVDs) have the potential to exponentially improve healthcare decision making in the coming years. This perspective covers current trends and challenges in translating Smart Diagnostics. We identify essential elements of Smart Diagnostics platforms through the lens of a clinically validated platform for digitizing biology and its ability to learn disease signatures. This platform for biochemical analyses uses a compact instrument to perform multiclass and multiplex measurements using fully integrated microfluidic cartridges compatible with the point of care. Image analysis digitizes biology by transforming fluorescence signals into inputs for learning disease/health signatures. The result is an intuitive Score reported to the patients and/or providers. This AI-linked universal diagnostic system has been validated through a series of large clinical studies and used to identify signatures for early disease detection and disease severity in several applications, including cardiovascular diseases, COVID-19, and oral cancer. The utility of this Smart Diagnostics platform may extend to multiple cell-based oncology tests via cross-reactive biomarkers spanning oral, colorectal, lung, bladder, esophageal, and cervical cancers, and is well-positioned to improve patient care, management, and outcomes through deployment of this resilient and scalable technology. Lastly, we provide a future perspective on the direction and trajectory of Smart Diagnostics and the transformative effects they will have on health care.