Relevant bibliographies by topics / Embedded artificial intelligence (AI)

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Embedded artificial intelligence (AI)'

Author: Grafiati
Published: 25 May 2024

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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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Dissertations / Theses on the topic "Embedded artificial intelligence (AI)"

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Antonini, Mattia. "From Edge Computing to Edge Intelligence: exploring novel design approaches to intelligent IoT applications." Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/308630.

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The Internet of Things (IoT) has deeply changed how we interact with our world. Today, smart homes, self-driving cars, connected industries, and wearables are just a few mainstream applications where IoT plays the role of enabling technology. When IoT became popular, Cloud Computing was already a mature technology able to deliver the computing resources necessary to execute heavy tasks (e.g., data analytic, storage, AI tasks, etc.) on data coming from IoT devices, thus practitioners started to design and implement their applications exploiting this approach. However, after a hype that lasted for a few years, cloud-centric approaches have started showing some of their main limitations when dealing with the connectivity of many devices with remote endpoints, like high latency, bandwidth usage, big data volumes, reliability, privacy, and so on. At the same time, a few new distributed computing paradigms emerged and gained attention. Among all, Edge Computing allows to shift the execution of applications at the edge of the network (a partition of the network physically close to data-sources) and provides improvement over the Cloud Computing paradigm. Its success has been fostered by new powerful embedded computing devices able to satisfy the everyday-increasing computing requirements of many IoT applications. Given this context, how can next-generation IoT applications take advantage of the opportunity offered by Edge Computing to shift the processing from the cloud toward the data sources and exploit everyday-more-powerful devices? This thesis provides the ingredients and the guidelines for practitioners to foster the migration from cloud-centric to novel distributed design approaches for IoT applications at the edge of the network, addressing the issues of the original approach. This requires the design of the processing pipeline of applications by considering the system requirements and constraints imposed by embedded devices. To make this process smoother, the transition is split into different steps starting with the off-loading of the processing (including the Artificial Intelligence algorithms) at the edge of the network, then the distribution of computation across multiple edge devices and even closer to data-sources based on system constraints, and, finally, the optimization of the processing pipeline and AI models to efficiently run on target IoT edge devices. Each step has been validated by delivering a real-world IoT application that fully exploits the novel approach. This paradigm shift leads the way toward the design of Edge Intelligence IoT applications that efficiently and reliably execute Artificial Intelligence models at the edge of the network.
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Antonini, Mattia. "From Edge Computing to Edge Intelligence: exploring novel design approaches to intelligent IoT applications." Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/308630.

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The Internet of Things (IoT) has deeply changed how we interact with our world. Today, smart homes, self-driving cars, connected industries, and wearables are just a few mainstream applications where IoT plays the role of enabling technology. When IoT became popular, Cloud Computing was already a mature technology able to deliver the computing resources necessary to execute heavy tasks (e.g., data analytic, storage, AI tasks, etc.) on data coming from IoT devices, thus practitioners started to design and implement their applications exploiting this approach. However, after a hype that lasted for a few years, cloud-centric approaches have started showing some of their main limitations when dealing with the connectivity of many devices with remote endpoints, like high latency, bandwidth usage, big data volumes, reliability, privacy, and so on. At the same time, a few new distributed computing paradigms emerged and gained attention. Among all, Edge Computing allows to shift the execution of applications at the edge of the network (a partition of the network physically close to data-sources) and provides improvement over the Cloud Computing paradigm. Its success has been fostered by new powerful embedded computing devices able to satisfy the everyday-increasing computing requirements of many IoT applications. Given this context, how can next-generation IoT applications take advantage of the opportunity offered by Edge Computing to shift the processing from the cloud toward the data sources and exploit everyday-more-powerful devices? This thesis provides the ingredients and the guidelines for practitioners to foster the migration from cloud-centric to novel distributed design approaches for IoT applications at the edge of the network, addressing the issues of the original approach. This requires the design of the processing pipeline of applications by considering the system requirements and constraints imposed by embedded devices. To make this process smoother, the transition is split into different steps starting with the off-loading of the processing (including the Artificial Intelligence algorithms) at the edge of the network, then the distribution of computation across multiple edge devices and even closer to data-sources based on system constraints, and, finally, the optimization of the processing pipeline and AI models to efficiently run on target IoT edge devices. Each step has been validated by delivering a real-world IoT application that fully exploits the novel approach. This paradigm shift leads the way toward the design of Edge Intelligence IoT applications that efficiently and reliably execute Artificial Intelligence models at the edge of the network.
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Chollet, Nicolas. "Embedded-AI-enabled semantic IoT platform for agroecology." Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASG078.

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L'agriculture moderne nécessite une profonde transformation pour répondre aux défis du développement durable tout en nourrissant qualitativement et quantitativement la population mondiale croissante. Dans cette optique, les agriculteurs adoptent le "Smart Farming" ou agriculture intelligente. C'est une méthode agricole qui utilise la technologie pour améliorer l'efficacité, la productivité et la durabilité de la production agricole. Elle englobe l'usage de capteurs, l'internet des objets (IoT), l'Intelligence Artificielle (IA), l'analyse de données, la robotique et divers autres outils numériques optimisant des aspects tels que la gestion des sols, l'irrigation, la lutte antiparasitaire ou encore la gestion de l'élevage. L'objectif est d'augmenter la production tout en réduisant la consommation de ressources, minimisant les déchets et améliorant la qualité des produits. Toutefois, malgré ses avantages et son déploiement réussi dans divers projets, l'agriculture intelligente rencontre des limites notamment dans le cadre de l'IoT. Premièrement, les plateformes doivent être capables de percevoir des données dans l'environnement, de les interpréter et de prendre des décisions pour aider à la gestion des fermes. Le volume, la variété et la vélocité de ces données, conjuguées à la grande diversité d'objets ainsi qu'à l'avènement de l'IA embarquée dans les capteurs, rendent difficile les communications sur les réseaux agricoles sans fils. Deuxièmement, les recherches tendent à se focaliser sur des projets répondant aux problématiques de l'agriculture conventionnelle non durable et les projets concernant les petites exploitations axées sur l'agroécologie sont rares. Dans ce contexte, cette thèse explore la création d'une plateforme IoT composée d'un réseau de capteurs intelligents sémantiques, visant à guider les agriculteurs dans la transition et la gestion de leur ferme en agriculture durable tout en minimisant l'intervention humaine
Modern agriculture requires a profound transformation to address the challenges of sustainable development while qualitatively and quantitatively feeding the growing global population. In this light, farmers are adopting "Smart Farming" also called precision agriculture. It is an agricultural method that leverages technology to enhance the efficiency, productivity, and sustainability of agricultural production. This approach encompasses the use of sensors, the Internet of Things (IoT), Artificial Intelligence (AI), data analysis, robotics, and various other digital tools optimizing aspects such as soil management, irrigation, pest control, and livestock management. The goal is to increase production while reducing resource consumption, minimizing waste, and improving product quality. However, despite its benefits and successful deployment in various projects, smart agriculture encounters limitations, especially within the context of IoT. Firstly, platforms must be capable of perceiving data in the environment, interpreting it, and making decisions to assist in farm management. The volume, variety, and velocity of those data, combined with a wide diversity of objects and the advent of AI embedded in sensors, make communication challenging on wireless agricultural networks. Secondly, research tends to focus on projects addressing the issues of non-sustainable conventional agriculture, and projects related to small-scale farms focused on agroecology are rare. In this context, this thesis explores the creation of an IoT platform comprised of a network of semantic smart sensors, aiming to guide farmers in transitioning and managing their farm sustainably while minimizing human intervention
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MAZZIA, VITTORIO. "Machine Learning Algorithms and their Embedded Implementation for Service Robotics Applications." Doctoral thesis, Politecnico di Torino, 2022. http://hdl.handle.net/11583/2968456.

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Hasanzadeh, Mujtaba, and Alexandra Hengl. "Real-Time Pupillary Analysis By An Intelligent Embedded System." Thesis, Mälardalens högskola, Inbyggda system, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-44352.

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With no online pupillary analysis methods today, both the medical and the research fields are left to carry out a lengthy, manual and often faulty examination. A real-time, intelligent, embedded systems solution to pupillary analysis would help reduce faulty diagnosis, speed-up the analysis procedure by eliminating the human expert operator and in general, provide a versatile and highly adaptable research tool. Therefore, this thesis has sought to investigate, develop and test possible system designs for pupillary analysis, with the aim for caffeine detection. A pair of LED manipulator glasses have been designed to standardize the illumination method across testing. A data analysis method of the raw pupillary data has been established offline and then adapted to a real-time platform. ANN was chosen as classification algorithm. The accuracy of the ANN from the offline analysis was 94% while for the online classification the obtained accuracy was 17%. A realtime data communication and synchronization method has been developed. The resulting system showed reliable and fast execution times. Data analysis and classification took no longer than 2ms, faulty data detection showed consistent results. Data communication suffered no message loss. In conclusion, it is reported that a real-time, intelligent, embedded solution is feasible for pupillary analysis.
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Karlsson, Marcus. "Developing services based on Artificial Intelligence." Thesis, Karlstads universitet, Fakulteten för hälsa, natur- och teknikvetenskap (from 2013), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-73090.

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This thesis explores the development process of services based on artificial intelligence (AI) technology within an industrial setting. There has been a renewed interest in the technology and leading technology companies as well as many start-ups has integrated it into their market offerings. The technology´s general application potential for enhancing products and services along with the task automation possibility for improved operational excellence makes it a valuable asset for companies. However, the implementation rate of AI services is still low for many industrial actors. The research in the area has been technically dominated with little contribution from other disciplines. Therefore, the purpose of this thesis is to identify development challenges of AI services and drawing on service development- and value-theory to propose a process framework promoting implementation. The work will have two main contributions. Firstly, to compare differences in theoretical and practical development challenges and secondly to combine AI with service development and value theory. The empirical research is done through a single case study based on a systematic combining research approach. It moves iteratively between the theory and empirical findings to direct and support the thesis throughout the work process. The data was collected through semi-structured interviews with a purposive sample. It consisted of two groups of interview participants, one AI expert group and one case internal group. This was supported by participant observation of the case environment. The data analysis was done through flexible pattern matching. The results were divided into two sections, practical challenges and development aspect of AI service development. These were combined with the selected theories and a process framework was generated. The study showed a current understudied area of business and organisational aspect regarding AI service development. Several such challenges were identified with limited theoretical research as support. For a wider industrial adoption of AI technology, more research is needed to understand the integration into the organisation. Further, sustainability and ethical aspect were found not to be a primary concern, only mention in one of the interviews. This, despite the plethora of theory and identified risks found in the literature. Lastly, the interdisciplinary research approach was found to be beneficial to the AI field to integrate the technology into an industrial setting. The developed framework could draw from existing service development models to help manage the identified challenges.
Denna uppsats utforskar utvecklingsprocessen av tjänster baserade på artificiell intelligens (AI) i en industriell miljö. Tekniken har fått ett förnyat intresse vilket har lett till att allt fler ledande teknik företag och start-up:s har integrerat AI i deras marknads erbjudande. Teknikens generella applikations möjlighet för att kunna förbättra produkter och tjänster tillsammans med dess automatiserings möjlighet för ökad operationell effektivitet gör den till en värdefull tillgång för företag. Dock så är implementations graden fortfarande låg för majoriteten av industrins aktörer. Forskningen inom AI området har varit mycket teknik dominerat med lite bidrag från andra forskningsdiscipliner. Därför syftar denna uppsats att identifiera utvecklingsutmaningar med AI tjänster och genom att hämta delar från tjänsteutveckling- och värde teori generera ett processramverk som premierar implementation. Uppsatsen har två huvudsakliga forskningsbidrag. Först genom att jämföra skillnader mellan teoretiska och praktiska utvecklingsutmaningar, sedan bidra genom att kombinera AI med tjänsteutveckling- och värdeteori. Den empiriska forskningen utfördes genom en fallstudie baserad på ett systematic combining tillvägagångsätt. På så sätt rör sig forskning iterativt mellan teori och empiri för att forma och stödja uppsatsen genom arbetet. Datat var insamlad genom semi strukturerade intervjuer med två separata, medvetet valda intervjugrupper där ena utgjorde en AI expert grupp och andra en intern grupp för fallstudien. Detta stöttades av deltagande observationer inom fallstudiens miljö. Dataanalysen utfördes med metoden flexible pattern matching. Resultatet var uppdelat i två olika sektioner, den första med praktiska utmaningar och den andra med utvecklingsaspekter av AI tjänsteutveckling. Dessa kombinerades med de utvalda teorierna för att skapa ett processramverk. Uppsatsen visar ett under studerat område angående affär och organisation i relation till AI tjänsteutveckling. Ett flertal av sådana utmaningar identifierades med begränsat stöd i existerande forskningslitteratur. För en mer utbredd adoption av AI tekniken behövs mer forskning för att förstå hur AI ska integreras med organisationer. Vidare, hållbarhet och etiska aspekter var inte en primär aspekt i resultatet, endast bemött i en av intervjuerna trots samlingen av artiklar och identifierade risker i litteraturen. Till sist, det tvärvetenskapliga angreppsättet var givande för AI området för att bättre integrera tekniken till en industriell miljö. Det utvecklade processramverket kunde bygga på existerande tjänsteutvecklings modeller för att hantera de identifierade utmaningarna.
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ALAMEH, MOHAMAD. "Embedded Artificial Intelligence for Tactile Sensing." Doctoral thesis, Università degli studi di Genova, 2021. http://hdl.handle.net/11567/1039756.

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Electronic tactile sensing becomes an active research field whether for prosthetic applications, robotics, virtual reality or post stroke patients rehabilitation. To achieve such sensing, an array of sensors is used to retrieve human-skin like information, which is called Electronic skin (E-skin). Humans through their skins, are able to collect different types of information e.g. pressure, temperature, texture, etc. which are then passed to the nervous system, and finally to the brain in order to extract high level information from these sensory data. In order to make E-skin capable of such task, data acquired from E-skin should be filtered, processed, and then conveyed to the user (or robot). Processing these sensory information, should occur in real-time, taking in consideration the power limitation in such applications, especially prosthetic applications. The power consumption itself is related to different factors, one factor is the complexity of the algorithm e.g. number of FLOPs, and another is the memory consumption. In this thesis, I will focus on the processing of real tactile information, by 1)exploring different algorithms and methods for tactile data classification, 2)data organization and preprocessing of such tactile data and 3)hardware implementation. More precisely the focus will be on deep learning algorithms for tactile data processing mainly CNNs and RNNs, with energy-efficient embedded implementations. The proposed solution has proved less memory, FLOPs, and latency compared to the state of art (including tensorial SVM), applied to real tactile sensors data. Keywords: E-skin, tactile data processing, deep learning, CNN, RNN, LSTM, GRU, embedded, energy-efficient algorithms, edge computing, artificial intelligence.
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Kim, Jee Won. "How speciesism affects artificial intelligence (AI) adoption intent." Thesis, Queensland University of Technology, 2022. https://eprints.qut.edu.au/228673/1/Jee%20Won_Kim_Thesis.pdf.

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As there have been concerns about the excessive advancement of artificial intelligence (AI) surpassing humans, exploring reactions to AI as challenging human superiority is meaningful. By examining how the hierarchical and discriminative views on animals (speciesism) affects the views on non-living AI, this thesis has significant and novel contributions to AI adoption literature and AI product marketing.
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Fatima, Samar. "Mapping artificial intelligence affordances for the public sector." Thesis, Queensland University of Technology, 2022. https://eprints.qut.edu.au/235926/1/Samar%2BFatime%2BThesis.pdf.

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This thesis explores the affordances of artificial intelligence (AI) for the public sector. The thesis consists of three studies that answer what, why and how questions of AI affordance actualization in public sector using a combination of primary and secondary data sources. In this thesis, the affordance theory lens is used to explore AI affordance perception and actualization for the public sector through three related studies. The perception of AI affordance is investigated in the first two studies. The third study designed and evaluated artefact for public agencies to actualize AI affordance.
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Malmborn, Albin, and Linus Sjöberg. "Implementing Artificial intelligence." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20942.

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Den här uppsatsen har som syfte att undersöka huruvida det är möjligt att ta fram riktlinjer för vad privata verksamheter behöver ta i beaktande inför en planerad implementering av artificiell intelligens. Studien kommer belysa faktorer som hjälper företag att förstå vad som krävs inför en sådan omställning, men även de hinder som måste övervinnas för att lyckas. Studiens datainsamling har genomförts med två metoder, först en litteraturstudie sedan kvalitativa, semistrukturerade forskningsintervjuer. Dessa har sedan analyserats med vars en analysmetod som kompletterar varandra och därefter tolkats för att se mönster som kan besvara studiens frågeställning: Vad måste svenska organisationer inom den privata sektorn beakta för att lyckas implementera Artificiell intelligens i sin verksamhet? Resultatet har tagits fram genom att jämföra vetenskapliga texter och intervjuer, för att undersöka om den akademiska och praktiska synen skiljer sig åt. Studien resulterade i åtta faktorer som företag borde ta i beaktning inför en implementering av artificiell intelligens. Författarna hoppas att med den här studien kunna främja svensk utveckling inom artificiell intelligens och på så vis generera ett större nationellt mervärde och en starkare konkurrenskraft internationell.
The purpose of this paper is to investigate the possibilities to develop guidelines for businesses to take into account before an implementation of artificial intelligence. The study will highlight different factors that will help companies to understand what is required to make this kind of digital transition, it will also highlight the obstacles companies have to overcome in order to succeed. The data collection was conducted in two parts, first a literature study and then qualitative, semi-structured interviews. These were analyzed with their own analysis which supplement each other, and interpreted to identify patterns that could answer the study's main question: What must Swedish organizations in the private sector consider in order to successfully implement Artificial Intelligence in their operations?The result of the study has been produced by comparing scientific texts and interviews, to investigate whether the academic and practical views differ. The study resulted in eight factors that companies should consider before implementing artificial intelligence. The authors hope that the study will promote Swedish development in artificial intelligence and thus generate a greater national value and international competitiveness.
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Books on the topic "Embedded artificial intelligence (AI)"

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Vermesan, Ovidiu, Mario Diaz Nava, and Björn Debaillie. Embedded Artificial Intelligence. New York: River Publishers, 2023. http://dx.doi.org/10.1201/9781003394440.

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Bhateja, Vikrant, Suresh Chandra Satapathy, and Hassan Satori, eds. Embedded Systems and Artificial Intelligence. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0947-6.

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Alareeni, Bahaaeddin A. M., and Islam Elgedawy, eds. Artificial Intelligence (AI) and Finance. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-39158-3.

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Artificial intelligence. 3rd ed. Reading, Mass: Addison-Wesley Pub. Co., 1992.

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Artificial intelligence. 3rd ed. Reading, Mass: Addison-Wesley, 1992.

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Winston, Patrick Henry. Artificial intelligence. 3rd ed. Reading, MA: Addison-Wesley, 1993.

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Surviving AI. [Place of publication not identified]: Three Cs, 2015.

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Long, Guodong, Xinghuo Yu, and Sen Wang, eds. AI 2021: Advances in Artificial Intelligence. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97546-3.

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Nicholson, Ann, and Xiaodong Li, eds. AI 2009: Advances in Artificial Intelligence. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10439-8.

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Peng, Wei, Damminda Alahakoon, and Xiaodong Li, eds. AI 2017: Advances in Artificial Intelligence. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63004-5.

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Book chapters on the topic "Embedded artificial intelligence (AI)"

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Vermesan, Ovidiu, and Marcello Coppola. "Edge AI Platforms for Predictive Maintenance in Industrial Applications." In Embedded Artificial Intelligence, 89–104. New York: River Publishers, 2023. http://dx.doi.org/10.1201/9781003394440-9.

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Mateu, Loreto, Johannes Leugering, Roland Müller, Yogesh Patil, Maen Mallah, Marco Breiling, and Ferdinand Pscheidl. "Tools and Methodologies for Edge-AI Mixed-Signal Inference Accelerators." In Embedded Artificial Intelligence, 25–34. New York: River Publishers, 2023. http://dx.doi.org/10.1201/9781003394440-3.

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Miro-Panades, Ivan, Inna Kucher, Vincent Lorrain, and Alexandre Valentian. "Meeting the Latency and Energy Constraints on Timing-critical Edge-AI Systems." In Embedded Artificial Intelligence, 61–67. New York: River Publishers, 2023. http://dx.doi.org/10.1201/9781003394440-6.

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Adadi, Amina, and Mohammed Berrada. "Explainable AI for Healthcare: From Black Box to Interpretable Models." In Embedded Systems and Artificial Intelligence, 327–37. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0947-6_31.

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Foulds, James, and Eibe Frank. "Revisiting Multiple-Instance Learning Via Embedded Instance Selection." In AI 2008: Advances in Artificial Intelligence, 300–310. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89378-3_29.

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Khalili, Ali, Massimo Narizzano, and Armando Tacchella. "Learning for Verification in Embedded Systems: A Case Study." In AI*IA 2016 Advances in Artificial Intelligence, 525–38. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-49130-1_38.

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Losavio, Michael. "Forensic Proof and Criminal Liability for Development, Distribution and Use of Artificial Intelligence." In AI Embedded Assurance for Cyber Systems, 37–48. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-42637-7_3.

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Purkayastha, Saptarshi, Rohan Isaac, Sharon Anthony, Shikhar Shukla, Elizabeth A. Krupinski, Joshua A. Danish, and Judy Wawira Gichoya. "A General-Purpose AI Assistant Embedded in an Open-Source Radiology Information System." In Artificial Intelligence in Medicine, 373–77. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-34344-5_46.

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Dignum, Virginia. "Responsible Artificial Intelligence: Recommendations and Lessons Learned." In Social and Cultural Studies of Robots and AI, 195–214. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-08215-3_9.

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AbstractArtificial Intelligence (AI) has huge potential to bring accuracy, efficiency, cost savings and speed to a whole range of human activities and to provide entirely new insights into behaviour and cognition. However, the way AI is developed and deployed for a great part determines how AI will impact our lives and societies. For instance, automated classification systems can deliver prejudiced results and therefore raise questions about privacy and bias; and, the autonomy of intelligent systems, such as, e.g. self-driving vehicles, raises concerns about safety and responsibility. AI’s impact concerns not only the research and development directions for AI, but also how these systems are introduced into society and used in everyday situations. There is a large debate concerning how the use of AI will influence labour, well-being, social interactions, health care, income distribution and other social areas. Dealing with these issues requires that ethical, legal, societal and economic implications are taken into account. In this paper, I will discuss how a responsible approach to the development and use of AI can be achieved, and how current approaches to ensure the ethical alignment of decisions made or supported by AI systems can benefit from the social perspective embedded in non-Western philosophies, in particular the Ubuntu philosophy.
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Beckwith, Amy M., Berthe Y. Choueiry, and Hui Zou. "How the Level of Interchangeability Embedded in a Finite Constraint Satisfaction Problem Affects the Performance of Search." In AI 2001: Advances in Artificial Intelligence, 50–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45656-2_5.

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Conference papers on the topic "Embedded artificial intelligence (AI)"

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Alyoshina, Irina. "Artificial Intelligence for University Marketing." In III International Conference Technology & Entrepreneurship in Digital Society. Real Economy Publishing House, 2021. http://dx.doi.org/10.17747/teds-2020-17-21.

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Artificial intelligence (AI) is embedded in digital marketing technologies and so facilitates the development of university marketing management. The article discusses digital globalization trends and factors influential for AI deployment in higher education.
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Besimi, Nuhi, Adrian Besimi, and Betim Cico. "Artificial Intelligence in Education and Learning (AI in Research)." In 2022 11th Mediterranean Conference on Embedded Computing (MECO). IEEE, 2022. http://dx.doi.org/10.1109/meco55406.2022.9797216.

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Banjanović-Mehmedović, Lejla, and Jasmin Jahić. "Integration of artificial intelligence in Industry 4.0: challenges, paradigms and applications." In Međunarodna naučna konferencija Aplikacija industrije 4.0 – prilika za novi iskorak u svim industrijskim granama. Academy of Sciences and Arts of Bosnia and Herzegovina, 2022. http://dx.doi.org/10.5644/pi2022.202.22.

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Artificial intelligence (AI) is a very disruptive technology, which combined with powerful computational hardware have opened new possibilities for world-wide technological progress in industry. There is an urgent need for systematic development and implementation of AI to see its real impact in the next generation of industrial systems, namely Industry 4.0. Organisations that do not do so will fail to maintain their competitiveness. This paper provides an insight into the main paradigms of AI technologies used in Industry 4.0, by giving emphasis to the key enabling digitalization technologies and their challenges. In addition, we present an overview of AI current state and the most important AI algorithms used in Industry 4.0. Finally, we discuss trends related to adoption of AI in the context of software embedded applications and software architectures for embedded systems.
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Fulton, Richard, Diane Fulton, and Susan Kaplan. "Artificial Intelligence: Framework of Driving Triggers to Past, Present and Future Applications and Influencers of Industry Sector Adoption." In 11th International Conference on Embedded Systems and Applications (EMSA 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.120613.

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To gain a sense of the development of Artificial Intelligence (AI), this research analyzes what has been done in the past, presently in the last decade and what is predicted for the next several decades. The paper will highlight the biggest changes in AI and give examples of how these technologies are applied in several key industry sectors along with influencers that can affect adoption speed. Lastly, the research examines the driving triggers such as cost, speed, accuracy, diversity/inclusion and interdisciplinary research/collaboration that propel AI into an essential transformative technology.
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He, Haofei, Pengjia Huang, Aimin Xiong, Jie Xiao, Fangwu Li, and Weixing Zhou. "Design of Embedded Real-time Video Fire AI Recognition System." In 2021 International Conference on Computer Communication and Artificial Intelligence (CCAI). IEEE, 2021. http://dx.doi.org/10.1109/ccai50917.2021.9447481.

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Wu, Honghan, Aneeta Sylolypavan, Minhong Wang, and Sarah Wild. "Quantifying Health Inequalities Induced by Data and AI Models." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/721.

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AI technologies are being increasingly tested and applied in critical environments including healthcare. Without an effective way to detect and mitigate AI induced inequalities, AI might do more harm than good, potentially leading to the widening of underlying inequalities. This paper proposes a generic allocation-deterioration framework for detecting and quantifying AI induced inequality. Specifically, AI induced inequalities are quantified as the area between two allocation-deterioration curves. To assess the framework’s performance, experiments were conducted on ten synthetic datasets (N>33,000) generated from HiRID - a real-world Intensive Care Unit (ICU) dataset, showing its ability to accurately detect and quantify inequality proportionally to controlled inequalities. Extensive analyses were carried out to quantify health inequalities (a) embedded in two real-world ICU datasets; (b) induced by AI models trained for two resource allocation scenarios. Results showed that compared to men, women had up to 33% poorer deterioration in markers of prognosis when admitted to HiRID ICUs. All four AI models assessed were shown to induce significant inequalities (2.45% to 43.2%) for non-White compared to White patients. The models exacerbated data embedded inequalities significantly in 3 out of 8 assessments, one of which was >9 times worse.
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Sclavounos, Paul D., and Yu Ma. "Artificial Intelligence Machine Learning in Marine Hydrodynamics." In ASME 2018 37th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/omae2018-77599.

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Artificial Intelligence (AI) Support Vector Machine (SVM) learning algorithms have enjoyed rapid growth in recent years with applications in a wide range of disciplines often with impressive results. The present paper introduces this machine learning technology to the field of marine hydrodynamics for the study of complex potential and viscous flow problems. Examples considered include the forecasting of the seastate elevations and vessel responses using their past time records as “explanatory variables” or “features” and the development of a nonlinear model for the roll restoring, added moment of inertia and viscous damping using the vessel response kinematics from free decay tests as “features”. A key innovation of AI-SVM kernel algorithms is that the nonlinear dependence of the dependent variable on the “features” is embedded into the SVM kernel and its selection plays a key role in the performance of the algorithms. The kernel selection is discussed and its relation to the physics of the marine hydrodynamic flows considered in the present paper is addressed.
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Ahn, Byungmin, Jaehun Jang, Hanbyeul Na, Mankeun Seo, Hongrak Son, and Yong Ho Song. "AI Accelerator Embedded Computational Storage for Large-Scale DNN Models." In 2022 IEEE 4th International Conference on Artificial Intelligence Circuits and Systems (AICAS). IEEE, 2022. http://dx.doi.org/10.1109/aicas54282.2022.9869991.

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Flogie, Andrej, and Maja Vičič Krabonja. "Artificial Intelligence in Education: Developing Competencies and Supporting Teachers in Implementing AI in School Learning Environments." In 2023 12th Mediterranean Conference on Embedded Computing (MECO). IEEE, 2023. http://dx.doi.org/10.1109/meco58584.2023.10155054.

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Safley, Nick. "Artificial Connections: Finding the Architect’s Role in Text-to-Image Tectonics." In 112th ACSA Annual Meeting. ACSA Press, 2024. http://dx.doi.org/10.35483/acsa.am.112.30.

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Architects create images and representations of buildings to communicate construction and fabrication information. Construction necessitates utilizing mediating technologies to compress and communicate design information about material organization across space and time.1 Successful architectural design images embed material and construction knowledge needed to translate an idea into material. Whether joint lines in hand-drafted elevations imagining stone blocks or walls in a Revit model with embedded construction layers, the images architects produce for building require knowledge of complex material construction cultures and an idea of construction’s aesthetic implication. Images of this sort maintain the professional and disciplinary position of the architect as an organizer of the materials, processes, and labor needed to actualize building in the contingencies of the physical world. Artificial intelligence, especially text-to-image generation platforms, enables the rapid creation of images depicting forms, scenes, and objects with an unprecedented speed and a no-table absence of embedded knowledge from the architect. The rapidly emerging capacity of AI posits a unique and changing role for the architect in the design and construction process. AI allows anyone to generate compelling images with high resolution and photorealism. However, current AI-generated images lack embedded knowledge related to construction, often featuring illogical or impractical material connections or assemblies. This project researches how architects can execute material connections as depicted by undisciplined artificial intelligence and explores how apparent failures or errors in AI-generated material connections can challenge and expand the tectonic boundaries of architectural thinking.
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Reports on the topic "Embedded artificial intelligence (AI)"

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Bhatnagar, Ansh, and Devyani Gajjar. Policy implications of artificial intelligence (AI). Parliamentary Office of Science and Technology, January 2024. http://dx.doi.org/10.58248/pn708.

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Rinuado, Christina, William Leonard, Christopher Morey, Theresa Coumbe, Jaylen Hopson, and Robert Hilborn. Artificial intelligence (AI)–enabled wargaming agent training. Engineer Research and Development Center (U.S.), April 2024. http://dx.doi.org/10.21079/11681/48419.

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Fiscal Year 2021 (FY21) work from the Engineer Research and Development Center Institute for Systems Engineering Research lever-aged deep reinforcement learning to develop intelligent systems (red team agents) capable of exhibiting credible behavior within a military course of action wargaming maritime framework infrastructure. Building from the FY21 research, this research effort sought to explore options to improve upon the wargaming framework infrastructure and to investigate opportunities to improve artificial intelligence (AI) agent behavior. Wargaming framework infrastructure enhancements included updates related to supporting agent training, leveraging high-performance computing resources, and developing infrastructure to support AI versus AI agent training and gameplay. After evaluating agent training across different algorithm options, Deep Q-Network–trained agents performed better compared to those trained with Advantage Actor Critic or Proximal Policy Optimization algorithms. Experimentation in varying scenarios revealed acceptable performance from agents trained in the original baseline scenario. By training a blue agent against a previously trained red agent, researchers successfully demonstrated the AI versus AI training and gameplay capability. Observing results from agent gameplay revealed the emergence of behavior indicative of two principles of war, which were economy of force and mass.
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Arnold, Zachary, and Ngor Luong. China’s Artificial Intelligence Industry Alliance. Center for Security and Emerging Technology, May 2021. http://dx.doi.org/10.51593/20200094.

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As part of its strategy to achieve global leadership in AI, the Chinese government brings together local governments, academic institutions, and companies to establish collaboration platforms. This data brief examines the role of China’s Artificial Intelligence Industry Alliance in advancing its AI strategy, and the key players in the Chinese AI industry.
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Diaz-Herrera, Jorge L. Artificial Intelligence (AI) and Ada: Integrating AI with Mainstream Software Engineering. Fort Belvoir, VA: Defense Technical Information Center, September 1994. http://dx.doi.org/10.21236/ada286093.

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Vilalta, Ricardo. A Gentle Introduction to Artificial Intelligence. Instats Inc., 2023. http://dx.doi.org/10.61700/ws1767apt7i86469.

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This workshop offers a comprehensive overview of AI, hands-on applications, and its relevance in academic research, with discussions on ethical considerations and future trends in AI. The workshop is designed for PhD students, professors, and professional researchers. An official Instats certificate of completion and 2 ECTS equivalent points are provided at the conclusion.
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Gillespie, Nicole, Caitlin Curtis, Rossana Bianchi, Ali Akbari, and Rita Fentener van Vlissingen. Achieving Trustworthy AI: A Model for Trustworthy Artificial Intelligence. Australia: The University of Queensland and KPMG, November 2020. http://dx.doi.org/10.14264/ca0819d.

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Waugh, Gordon W., and Deirdre J. Knapp. Development of an Army Civilian Artificial Intelligence (AI) Specialty. Fort Belvoir, VA: Defense Technical Information Center, November 1997. http://dx.doi.org/10.21236/ada343149.

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Bhatnagar, Ansh, and Devyani Gajjar. How is artificial intelligence affecting society? Parliamentary Office of Science and Technology, May 2024. http://dx.doi.org/10.58248/rr15.

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Kim, Kyungmee, and Boulanin Vincent. Artificial Intelligence for Climate Security: Possibilities and Challenges. Stockholm International Peace Research Institute, December 2023. http://dx.doi.org/10.55163/qdse8934.

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Recent advances in artificial intelligence (AI)—largely based on machine learning—offer possibilities for addressing climate-related security risks. AI can, for example, make disaster early-warning systems and long-term climate hazard modelling more efficient, reducing the risk that the impacts of climate change will lead to insecurity and conflict. This SIPRI Policy Report outlines the opportunities that AI presents for managing climate-related security risks. It gives examples of the use of AI in the field and delves into the problems—notably methodological and ethical—associated with the use of AI for climate security. The report concludes with recommendations for policymakers and researchers who are active in the area of climate security or who use AI for sustainability.
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Murdick, Dewey, and Patrick Thomas. Patents and Artificial Intelligence: A Primer. Center for Security and Emerging Technology, September 2020. http://dx.doi.org/10.51593/20200038.

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Patent data can provide insights into the most active countries, fields and organizations in artificial intelligence research. This data brief analyzes worldwide trends in AI patenting to offer metrics on inventive activity.
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