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
Last updated: 19 July 2025

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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 (2020): 1169. http://dx.doi.org/10.3390/electronics9071169.

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The development of computation technology and artificial intelligence (AI) field brings about AI to be applied to various system. In addition, the research on hardware-based AI processors leads to the minimization of AI devices. By adapting the AI device to the edge of internet of things (IoT), the system can perform AI operation promptly on the edge and reduce the workload of the system core. As the edge is influenced by the characteristics of the embedded system, implementing hardware which operates with low power in restricted resources on a processor is necessary. In this paper, we propose the intellino, a processor for embedded artificial intelligence. Intellino ensures low power operation based on optimized AI algorithms and reduces the workload of the system core through the hardware implementation of a neural network. In addition, intellino’s dedicated protocol helps the embedded system to enhance the performance. We measure intellino performance, achieving over 95% accuracy, and verify our proposal with an field programmable gate array (FPGA) prototyping.
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Ortmeyer, Cliff. "AI Options for Embedded Systems." New Electronics 52, no. 3 (2019): 26–27. http://dx.doi.org/10.12968/s0047-9624(22)60909-x.

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Zhang, Zhaoyun, and Jingpeng Li. "A Review of Artificial Intelligence in Embedded Systems." Micromachines 14, no. 5 (2023): 897. http://dx.doi.org/10.3390/mi14050897.

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Advancements in artificial intelligence algorithms and models, along with embedded device support, have resulted in the issue of high energy consumption and poor compatibility when deploying artificial intelligence models and networks on embedded devices becoming solvable. In response to these problems, this paper introduces three aspects of methods and applications for deploying artificial intelligence technologies on embedded devices, including artificial intelligence algorithms and models on resource-constrained hardware, acceleration methods for embedded devices, neural network compression, and current application models of embedded AI. This paper compares relevant literature, highlights the strengths and weaknesses, and concludes with future directions for embedded AI and a summary of the article.
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Gusti, Wahyu Ramadhani. "Embedded System Training Kit for Artificial Intelligence." International Journal of Information and Education Technology 14, no. 1 (2024): 72–80. http://dx.doi.org/10.18178/ijiet.2024.14.1.2026.

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Ever-developing science and technology require us always to be ready to adapt. The current challenging era is Society 5.0, which places a strong emphasis on harnessing human potential to overcome diverse challenges, including the development of Artificial Intelligence (AI) technology. Therefore, to improve the quality of human resources, this paper proposes the development of an artificial intelligence training kit based on embedded systems according to industry needs. The development of a training kit utilizing the RnD method was accomplished through the use of the ADDIE (analysis design, development, implementation, and evaluation) model. This model encompasses analysis, design, development, implementation, and evaluation. The technology of the training kit combines fuzzy logic, Artificial Neural Network (ANN), and image processing, consisting of hardware, software, and job sheets. The controller used to process embedded systems is the ESP32 board. Arduino UNO is used to execute the training results of the artificial intelligence system. The training kit performance test results show that all AI programs run optimally, and each component can function according to performance indicators. A group of subject matter and media experts evaluated the feasibility of the project and determined it to be very feasible, with a score of 83.64% and 86.67%. In addition, a feasibility test was conducted with 38 respondents, resulting in a score of 83.35%, and it was categorized as a very feasible tool. The effectiveness of the training kit applied to the experimental class resulted in a post-test mean score of 89.58, while the control class mean score was 76.39, so the AI training kit showed more effectiveness.
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Hwang, Dong Hyun, Chang Yeop Han, Hyun Woo Oh, and Seung Eun Lee. "ASimOV: A Framework for Simulation and Optimization of an Embedded AI Accelerator." Micromachines 12, no. 7 (2021): 838. http://dx.doi.org/10.3390/mi12070838.

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Artificial intelligence algorithms need an external computing device such as a graphics processing unit (GPU) due to computational complexity. For running artificial intelligence algorithms in an embedded device, many studies proposed light-weighted artificial intelligence algorithms and artificial intelligence accelerators. In this paper, we propose the ASimOV framework, which optimizes artificial intelligence algorithms and generates Verilog hardware description language (HDL) code for executing intelligence algorithms in field programmable gate array (FPGA). To verify ASimOV, we explore the performance space of k-NN algorithms and generate Verilog HDL code to demonstrate the k-NN accelerator in FPGA. Our contribution is to provide the artificial intelligence algorithm as an end-to-end pipeline and ensure that it is optimized to a specific dataset through simulation, and an artificial intelligence accelerator is generated in the end.
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Choudhury, Avishek, and Onur Asan. "Human factors: bridging artificial intelligence and patient safety." Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care 9, no. 1 (2020): 211–15. http://dx.doi.org/10.1177/2327857920091007.

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The recent launch of complex artificial intelligence (AI) in the domain of healthcare has embedded perplexities within patients, clinicians, and policymakers. The opaque and complex nature of artificial intelligence makes it challenging for clinicians to interpret its outcome. Incorrect interpretation and poor utilization of AI might hamper patient safety. The principles of human factors and ergonomics (HFE) can assist in simplifying AI design and consecutively optimize human performance ensuring better understanding of AI outcome, their interaction with the clinical workflow. In this paper, we discuss the interactions of providers with AI and how HFE can influence these interacting components to patient safety.
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Yeo, K. K. "Artificial intelligence in cardiology: did it take off?" Russian Journal for Personalized Medicine 2, no. 6 (2023): 16–22. http://dx.doi.org/10.18705/2782-3806-2022-2-6-16-22.

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Artificial intelligence (AI) has been touted as a paradigm shifting, game-changing development in medicine. Did AI in cardiology take off? In this paper, we discuss some areas within cardiology in which there has some been progress in the implementation of AI technologies. Despite the promise of AI, challenges remain including cybersecurity, implementation and change management difficulties. This paper discusses the use of AI embedded as a ‘black box’ technology in existing diagnostic and interventional tools, AI as an adjunct to diagnostic tools such as echo or CT or MRI scans, AI in commercially available wearables, and AI in chatbots and other patient-fronting technologies. Lastly, while there has been some progress, the legal, regulatory, financial and ethical framework remains a work in evolution at national and international levels.
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Azizah, Desi, Aji Wibawa, and Laksono Budiarto. "Hakikat Epistemologi Artificial Intelligence." Jurnal Inovasi Teknologi dan Edukasi Teknik 1, no. 8 (2021): 592–98. http://dx.doi.org/10.17977/um068v1i82021p592-598.

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Artificial Intelligence, commonly abbreviated as AI, is a scientifically intelligent entity created by humans. The entity is embedded into a machine, thus making the machine seem capable of thinking on its own to decide. The definition of AI can be viewed from two approaches, namely a scientific approach (A Scientific Approach) and an engineering approach (An Engineering Approach). The way artificial intelligence works is by combining a large amount of data, with a process that is fast, iterative and has an intelligent algorithm. Artificial intelligence is closely related to philosophy because both use concepts that have the same name and these include intelligence, action, consciousness, epistemology, even free will. Artificial intelligence has advantages and disadvantages.
 Artificial Intelligence yang biasa disingkat dengan AI adalah sebuah entitas cerdas secara ilmiah yang diciptakan oleh manusia. Entitas tersebut di tanamkan ke dalam sebuah mesin, sehingga membuat mesin tersebut seolah-olah mampu berpikir sendiri untuk mengambil sebuah keputusan. Pengertian AI dapat ditinjau dari dua pendekatan yaitu pendekatan ilmiah (A Scientific Approach) dan pendekatan teknik (An Engineering Approach). Cara kerja dari artificial intelligence ini adalah dengan menggabungkan sejumlah data yang terbilang cukup besar, dengan proses yang terbilang cepat, berulang serta memiliki algoritma yang cerdas. Kecerdasan buatan memiliki keterkaitan yang erat dengan filsafat karena keduanya menggunakan konsep yang memiliki nama yang sama dan ini termasuk kecerdasan, tindakan, kesadaran, epistemologi, bahkan kehendak bebas. Kecerdasan buatan memiliki kelebihan dan kekurangan.
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Almusaed, Amjad, Ibrahim Yitmen, and Asaad Almssad. "Enhancing Smart Home Design with AI Models: A Case Study of Living Spaces Implementation Review." Energies 16, no. 6 (2023): 2636. http://dx.doi.org/10.3390/en16062636.

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The normal development of “smart buildings,” which calls for integrating sensors, rich data, and artificial intelligence (AI) simulation models, promises to usher in a new era of architectural concepts. AI simulation models can improve home functions and users’ comfort and significantly cut energy consumption through better control, increased reliability, and automation. This article highlights the potential of using artificial intelligence (AI) models to improve the design and functionality of smart houses, especially in implementing living spaces. This case study provides examples of how artificial intelligence can be embedded in smart homes to improve user experience and optimize energy efficiency. Next, the article will explore and thoroughly analyze the thorough analysis of current research on the use of artificial intelligence (AI) technology in smart homes using a variety of innovative ideas, including smart interior design and a Smart Building System Framework based on digital twins (DT). Finally, the article explores the advantages of using AI models in smart homes, emphasizing living spaces. Through the case study, the theme seeks to provide ideas on how AI can be effectively embedded in smart homes to improve functionality, convenience, and energy efficiency. The overarching goal is to harness the potential of artificial intelligence by transforming how we live in our homes and improving our quality of life. The article concludes by discussing the unresolved issues and potential future research areas on the usage of AI in smart houses. Incorporating AI technology into smart homes benefits homeowners, providing excellent safety and convenience and increased energy efficiency.
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Marcinowski, Maciej. "Artificial Intelligence or the Ultimate Tool for Conservatism." DANUBE 13, no. 1 (2022): 1–12. http://dx.doi.org/10.2478/danb-2022-0001.

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Abstract Artificial intelligence (AI) is foremost viewed as a technologically revolutionary tool, however, the author discusses here whether it is in fact a tool for socio-economic and legal conservatism, because its training data is always embedded in the past. The aim of this paper is to explain, exemplify and predict – whether and how – AI could cause discrimination, stagnation and uniformization by conserving what is relayed even by the most representative data. Furthermore, the author aims to propose possible legal barriers to these phenomena. The presented hypotheses are based upon empirical research and socioeconomic or legal mechanisms, aiming to predict possible results of AI applications under specific conditions. Results indicate that the inherent AI conservatism could indeed cause severe discrimination, stagnation and uniformization, especially if its applications were to remain unquestioned and unregulated. Hopefully, the proposed legal solutions could limit the scope and effectiveness of AI conservatism, encouraging AI-related solutions.
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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<br>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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Shrivastwa, Ritu Ranjan. "Enhancements in Embedded Systems Security using Machine Learning." Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAT051.

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La liste des appareils connectés (ou IoT) s’allonge avec le temps, de même que leur vulnérabilité face aux attaques ciblées provenant du réseau ou de l’accès physique, communément appelées attaques Cyber Physique (CPS). Alors que les capteurs visant à détecter les attaques, et les techniques d’obscurcissement existent pour contrecarrer et améliorer la sécurité, il est possible de contourner ces contre-mesures avec des équipements et des méthodologies d’attaque sophistiqués, comme le montre la littérature récente. De plus, la conception des systèmes intégrés est soumise aux contraintes de complexité et évolutivité, ce qui rend difficile l’adjonction d’un mécanisme de détection complexe contre les attaques CPS. Une solution pour améliorer la sécurité est d’utiliser l’Intelligence Artificielle (IA) (au niveau logiciel et matériel) pour surveiller le comportement des données en interne à partir de divers capteurs. L’approche IA permettrait d’analyser le comportement général du système à l’aide des capteurs , afin de détecter toute activité aberrante, et de proposer une réaction appropriée en cas d’attaque. L’intelligence artificielle dans le domaine de la sécurité matérielle n’est pas encore très utilisée en raison du comportement probabiliste. Ce travail vise à établir une preuve de concept visant à montrer l’efficacité de l’IA en matière de sécurité.Une partie de l’étude consiste à comparer et choisir différentes techniques d’apprentissage automatique (Machine Learning ML) et leurs cas d’utilisation dans la sécurité matérielle. Plusieurs études de cas seront considérées pour analyser finement l’intérêt et de l’ IA sur les systèmes intégrés. Les applications seront notamment l’utilisation des PUF (Physically Unclonable Function), la fusion de capteurs, les attaques par canal caché (SCA), la détection de chevaux de Troie, l’intégrité du flux de contrôle, etc<br>The list of connected devices (or IoT) is growing longer with time and so is the intense vulnerability to security of the devices against targeted attacks originating from network or physical penetration, popularly known as Cyber Physical Security (CPS) attacks. While security sensors and obfuscation techniques exist to counteract and enhance security, it is possible to fool these classical security countermeasures with sophisticated attack equipment and methodologies as shown in recent literature. Additionally, end node embedded systems design is bound by area and is required to be scalable, thus, making it difficult to adjoin complex sensing mechanism against cyberphysical attacks. The solution may lie in Artificial Intelligence (AI) security core (soft or hard) to monitor data behaviour internally from various components. Additionally the AI core can monitor the overall device behaviour, including attached sensors, to detect any outlier activity and provide a smart sensing approach to attacks. AI in hardware security domain is still not widely acceptable due to the probabilistic behaviour of the advanced deep learning techniques, there have been works showing practical implementations for the same. This work is targeted to establish a proof of concept and build trust of AI in security by detailed analysis of different Machine Learning (ML) techniques and their use cases in hardware security followed by a series of case studies to provide practical framework and guidelines to use AI in various embedded security fronts. Applications can be in PUFpredictability assessment, sensor fusion, Side Channel Attacks (SCA), Hardware Trojan detection, Control flow integrity, Adversarial AI, etc
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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.<br>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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Books on the topic "Embedded artificial intelligence (AI)"

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

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Li, Bin. Embedded Artificial Intelligence. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-5038-2.

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Boruah, Arpita Nath, Mrinal Goswami, Manoj Kumar, and Octavio Loyola-González. Embedded Artificial Intelligence. Chapman and Hall/CRC, 2025. https://doi.org/10.1201/9781003481089.

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Bhateja, Vikrant, Suresh Chandra Satapathy, and Hassan Satori, eds. Embedded Systems and Artificial Intelligence. 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. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-39158-3.

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

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Long, Guodong, Xinghuo Yu, and Sen Wang, eds. AI 2021: Advances in Artificial Intelligence. 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. 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. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63004-5.

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Orgun, Mehmet A., and John Thornton, eds. AI 2007: Advances in Artificial Intelligence. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-76928-6.

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

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Li, Bin. "Embedded AI Development Process." In Embedded Artificial Intelligence. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-5038-2_9.

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Li, Bin. "Embedded AI Accelerator Chips." In Embedded Artificial Intelligence. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-5038-2_7.

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Sungheetha, Akey, and Rajesh Sharma. "AI at the Edge." In Embedded Artificial Intelligence. Chapman and Hall/CRC, 2025. https://doi.org/10.1201/9781003481089-5.

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Li, Bin. "Principle of Embedded AI Chips." In Embedded Artificial Intelligence. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-5038-2_2.

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Nair, Rekha R., Tina Babu, and P. M. Ebin. "Developing Edge AI for Embedded Systems." In Embedded Artificial Intelligence. Chapman and Hall/CRC, 2025. https://doi.org/10.1201/9781003481089-4.

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Kalaiselvi, S., M. Senbagavalli, and T. Jesudas. "Embedded AI-Based Approaches for Skin Cancer Detection." In Embedded Artificial Intelligence. Chapman and Hall/CRC, 2025. https://doi.org/10.1201/9781003481089-9.

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

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Banshal, Sumit Kumar. "Data Security and Ethical Considerations in Embedded AI Systems." In Embedded Artificial Intelligence. Chapman and Hall/CRC, 2025. https://doi.org/10.1201/9781003481089-20.

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Aishwarya, R., and G. Mathivanan. "Fusion of Edge Computing in AI-Enabled Embedded Technologies." In Embedded Artificial Intelligence. Chapman and Hall/CRC, 2025. https://doi.org/10.1201/9781003481089-3.

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Debnath, Saswati, M. Senbagavalli, K. Ramalakshmi, Shradha Naik, and Nazmin Begum. "Security of Social Media Content for AI-Embedded Systems." In Embedded Artificial Intelligence. Chapman and Hall/CRC, 2025. https://doi.org/10.1201/9781003481089-21.

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

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Deé-Lukács, András Gergely, András Földvári, and András Pataricza. "Evaluation of Embedded AI Through Model Difference Analysis." In 32nd Minisymposium of the Department of Artificial Intelligence and Systems Engineering. Budapest University of Technology and Economics, Department of Artificial Intelligence and Systems Engineering, 2025. https://doi.org/10.3311/minisy2025-011.

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Jamal, Abdelfattah, Karima Aissaoui, and Sanae Kassal. "Enhancing Recruitment Transparency and Efficiency with Explainable AI (XAI)." In 2024 3rd International Conference on Embedded Systems and Artificial Intelligence (ESAI). IEEE, 2024. https://doi.org/10.1109/esai62891.2024.10913744.

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Elidrissi, Asmae, and My Abdelouahed Sabri. "Addressing Unhappiness in Elderly Care: Challenges in Causal AI and Span Categorization Solutions." In 2024 3rd International Conference on Embedded Systems and Artificial Intelligence (ESAI). IEEE, 2024. https://doi.org/10.1109/esai62891.2024.10913526.

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Hachi, Soufiane, and Abdelouahed Sabri. "Development of an Explainable AI (XAI) system for the interpretation of ViT in the diagnosis of medical images." In 2024 3rd International Conference on Embedded Systems and Artificial Intelligence (ESAI). IEEE, 2024. https://doi.org/10.1109/esai62891.2024.10913833.

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Rani, Dolly, Amandeep Kaur, Ruchi Mittal, Amanpreet Kaur, and Neha Garg. "Exploring Arduino Board Applications in Embedded Systems: The Role of AI, Cloud Computing, and Edge Computing." In 2025 3rd International Conference on Communication, Security, and Artificial Intelligence (ICCSAI). IEEE, 2025. https://doi.org/10.1109/iccsai64074.2025.11063918.

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P, Chozha Rajan, A. Sarfaraz Ahmed, Vyshnava Divya, Y. Nagendar, Ashwala Mohan, and Athiraja Atheeswaran. "Real-Time Signal Processing in IoT-Based Embedded Systems Using Hybrid AI-Enhanced Edge Computing." In 2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA). IEEE, 2024. https://doi.org/10.1109/icaiqsa64000.2024.10882219.

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Pichler, Alexander, and Nicolas Hueber. "Training embedded DNN-based military vehicle detectors for aerial applications with few images using multisource vehicle signatures, data augmentation, and generative AI." In Artificial Intelligence for Security and Defence Applications II, edited by Henri Bouma, Yitzhak Yitzhaky, Radhakrishna Prabhu, and Hugo J. Kuijf. SPIE, 2024. http://dx.doi.org/10.1117/12.3031777.

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Sharma, P., A. Meshaikhis, C. Ennaceur, et al. "CUI Monitoring for Cold Duty Insulation, Evaluation and Use Cases." In MECC 2023. AMPP, 2023. https://doi.org/10.5006/mecc2023-20256.

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Abstract Predictive Corrosion Management have a lot of attractive use cases including Corrosion Under Insulation (CUI) enabled by monitoring and Artificial Intelligence (AI). This paper describes the deployment and evaluation of remote monitoring solution with application in corrosion under insulation management especially for cold duty insulation with intermittent service. A remote Monitoring system using Electro-Magnetic Guided Radar (EMGR) is described in this paper that uses advanced analytics for predictions. This technology involves use of sensors embedded in the insulation and a data logger transmits data wirelessly to a central server. Risk analysis and localization for CUI risk severity are then automatically performed with advanced analytics and accessed by the end user to help optimize CUI inspection plan. This provides important data for corrosion engineers to plan, schedule and target their efforts. This presentation will describe the evaluation and experience of deployment of CUI monitoring in an oil and gas plant. The lessons learnt from field use cases will be presented. The new method of using sensing and Industrial Internet of Things (IOT) to detect and predict corrosion in the field will be a huge impact for the asset integrity industry struggling with the threat of hidden corrosion such as CUI.
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Phong, Cao Thanh, Do Thuy Huong, Duong Quoc Thinh, and Nguyen Thanh Hoang. "AI in Higher Education: Does AI Fear Hinder Learning Outcomes?" In 2024 Artificial Intelligence Revolutions (AIR). IEEE, 2024. https://doi.org/10.1109/air63653.2024.00013.

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Cao, Hui. "AI photonics." In Emerging Topics in Artificial Intelligence (ETAI) 2024, edited by Giovanni Volpe, Joana B. Pereira, Daniel Brunner, and Aydogan Ozcan. SPIE, 2024. http://dx.doi.org/10.1117/12.3032303.

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Reports on the topic "Embedded artificial intelligence (AI)"

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Doo, Johnny. Beyond Aviation: Embedded Gaming, Artificial Intelligence, Training, and Recruitment for the Advanced Air Mobility Industry. SAE International, 2024. https://doi.org/10.4271/epr2024028.

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&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Recent advancements in electric vertical take-off and landing (eVTOL) aircraft and the broader advanced air mobility (AAM) movement have generated significant interest within and beyond the traditional aviation industry. Many new applications have been identified and are under development, with considerable potential for market growth and exciting potential. However, talent resources are the most critical parameters to make or break the AAM vision, and significantly more talent is needed than the traditional aviation industry is able to currently generate. One possible solution—leverage rapid advancements of artificial intelligence (AI) technology and the gaming industry to help attract, identify, educate, and encourage current and future generations to engage in various aspects of the AAM industry.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;&lt;b&gt;Beyond Aviation: Embedded Gaming, Artificial Intelligence, Training, and Recruitment for the Advanced Air Mobility Industry&lt;/b&gt; discusses how the modern gaming population of 3.3 million individuals could be engaged through embedded AAM-based scenarios and AI-enhanced grading systems for concept creation, engineering, manufacturing, air space design and management, piloting, remote operations, infrastructure planning, vehicle operations.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;&lt;a href="https://www.sae.org/publications/edge-research-reports" target="_blank"&gt;Click here to access the full SAE EDGE&lt;/a&gt;&lt;sup&gt;TM&lt;/sup&gt;&lt;a href="https://www.sae.org/publications/edge-research-reports" target="_blank"&gt; Research Report portfolio.&lt;/a&gt;&lt;/div&gt;&lt;/div&gt;
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Azzutti, Alessio, Mark Cummins, Iain MacNeil, and Chuks Otioma. Simplifying Compliance: The Role of AI and RegTech. University of Glasgow and University of Strathclyde, 2025. https://doi.org/10.36399/gla.pubs.351604.

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The Financial Regulation Innovation Lab (FRIL) is dedicated to simplifying compliance through emerging technologies, with Artificial Intelligence (AI) representing the latest evolution in regulatory technology (RegTech). Building on previous research and industry engagement—including workshops, blogs, webinars, and a micro-credential course—this White Paper presents key considerations for the conceptualisation, design, and implementation of AI-driven compliance systems. We begin by examining the nature of regulatory rules and the compliance process before exploring the complexities that challenge AI deployment. The discussion then shifts to Generative AI (GenAI) as a cutting-edge innovation, analysing its capabilities and relevance to compliance functions. A focused use case on GenAI in robo-advisory services illustrates AI’s potential in asset management, where conventional AI is already well-established. Finally, we consider the broader organisational implications of AI adoption, emphasising the opportunity to view compliance as an embedded and adaptive function able to evolve and respond to changing stakeholder expectations and regulatory frameworks.
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Bhatnagar, Ansh, and Devyani Gajjar. Policy implications of artificial intelligence (AI). Parliamentary Office of Science and Technology, 2024. http://dx.doi.org/10.58248/pn708.

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Latorre, Lucia, Valentín Muro, Eduardo Rego, Mariana Gutierrez, Ignacio Cerrato, and Jose Daniel Zarate. Tech Report Artificial Intelligence. Inter-American Development Bank, 2024. http://dx.doi.org/10.18235/0013015.

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This report provides a comprehensive overview of AI, from its fundamentals to its practical applications, covering topics such as its definition, evolution, and implementation. It also delves into various applications, such as machine learning, natural language processing, computer vision, and generative AI, providing specific examples and use cases across sectors like healthcare, logistics, environment, and security.
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Blanchard, Alexander, and Laura Bruun. Bias in Military Artificial Intelligence. Stockholm International Peace Research Institute, 2024. https://doi.org/10.55163/cjft9557.

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To support states involved in the policy debate on military artificial intelligence (AI), this background paper provides a deeper examination of the issue of bias in military AI. Three insights arise. First, policymakers could usefully develop an account of bias in military AI that captures shared concern around unfairness. If so, ‘bias in military AI’ might be taken to refer to the systemically skewed performance of a military AI system that leads to unjustifiably different behaviours—which may perpetuate or exacerbate harmful or discriminatory outcomes—depending on such social characteristics as race, gender and class. Second, among the many sources of bias in military AI, three broad categories are prominent: bias in society; bias in data processing and algorithm development; and bias in use. Third, bias in military AI can have various humanitarian consequences depending on context and use. These range from misidentifying people and objects in targeting decisions to generating flawed assessments of humanitarian needs.
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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.), 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, 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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Brione, Patrick, and Devyani Gajjar. Artificial intelligence: ethics, governance and regulation. Parliamentary Office of Science and Technology, 2024. http://dx.doi.org/10.58248/hs51.

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Diaz-Herrera, Jorge L. Artificial Intelligence (AI) and Ada: Integrating AI with Mainstream Software Engineering. Defense Technical Information Center, 1994. http://dx.doi.org/10.21236/ada286093.

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Haddad, Ibrahim. Artificial Intelligence and Data in Open Source. The Linux Foundation, 2022. https://doi.org/10.70828/zaow8899.

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Artificial intelligence (AI) is no different from any other technology domain where OSS dominates. As with other industries, OSS adoption in the AI field has increased the use of open source in products and services, contributions to existing projects, the creation of projects fostering collaboration, and the development of new technologies. Artificial Intelligence and Data in Open Source reviews critical challenges in the open source AI ecosystem, discusses common characteristics across AI and data projects, and presents the role of the LF AI &amp; Data Foundation in empowering innovators and accelerating open source development.
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