Bibliografías temáticas / Embedded artificial intelligence (AI)

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Literatura académica sobre el tema "Embedded artificial intelligence (AI)"

Autor: Grafiati
Publicado: 25 de mayo de 2024
Última modificación: 31 de julio de 2025

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Artículos de revistas sobre el tema "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
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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
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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, dev
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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
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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, w
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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 commerci
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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
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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 art
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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 empir
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Tesis sobre el tema "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 f
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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 f
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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 numériq
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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 h
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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 compl
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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 i
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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
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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 affordan
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Libros sobre el tema "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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Capítulos de libros sobre el tema "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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Actas de conferencias sobre el tema "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 lo
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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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Informes sobre el tema "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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<div class="section abstract"><div class="htmlview paragraph">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 ind
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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 challe
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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 characteristic
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6

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 enhancement
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7

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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9

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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10

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 & Data Foundation in empowering innovators and
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