Relevant bibliographies by topics / AI Data Centers

Contents

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

Academic literature on the topic 'AI Data Centers'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "AI Data Centers"

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Dr.A.Shaji, George. "Redefining Data Centers for the AI Revolution." Partners Universal Multidisciplinary Research Journal (PUMRJ) 02, no. 01 (2025): 13–18. https://doi.org/10.5281/zenodo.14739520.

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Artificial intelligence (AI) is the next big step in technology and changing how businesses work. As businesses use AI more to innovate and stay ahead, their actual infrastructure also needs to change along with it. The rapid increase in AI tasks requires a new way of thinking about traditional data centers to provide better scale, efficiency, reliability, and sustainability. This research analyzes the pressures of reshaping modern data centers and the innovations in compute, storage, networking, resiliency, and sustainability defining the next generation of AI-ready facilities. We also examin
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Sachin Mishra. "The Evolution of Data Centers in the Age of AI." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 5 (2024): 363–68. http://dx.doi.org/10.32628/cseit24105107.

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This article explores the evolving landscape of data centers in the era of artificial intelligence (AI). It examines the exponential growth of the global data center market, driven by increasing data generation and AI adoption. The article discusses key technological developments in data centers, including enhanced operational efficiency through AI-powered systems, specialized hardware for AI workloads, advanced cooling technologies, and sustainability initiatives. It also delves into future prospects, such as increased capacity for complex AI tasks, real-time processing of massive datasets, a
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Madhura, G. K. "Quantum-Inspired Optimization and Resource Allocation in AI-Driven Data Centers and Edge Networks." Journal of Advances in Computational Intelligence Theory 7, no. 2 (2025): 19–25. https://doi.org/10.5281/zenodo.15228558.

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<em>This study presents a comprehensive review and synthesis of recent research advancements integrating quantum-inspired optimization and artificial intelligence (AI) in data centers and edge computing networks. With the exponential growth in data generation and the demand for real-time processing, AI-driven infrastructures face challenges in scalability, latency, and energy efficiency. Through the evaluation of 29 scholarly works authored or co-authored by Vinod Veeramachaneni, Srinivasa Rao Bittla, and Srimaan Yarram, the study highlights innovations across diagnostics in electrical systems
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Kanthed, Surbhi. "Energy-Saving Practices in Data Centers." International Journal of Multidisciplinary Research and Growth Evaluation 4, no. 5 (2023): 1149–55. https://doi.org/10.54660/.ijmrge.2023.4.5.1149-1155.

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Data centers are vital to the digital economy but consume significant amounts of electrical energy, highlighting the need for more sustainable operational practices. This paper examines contemporary energy-saving solutions for data centers, emphasizing empirical strategies rather than theoretical discussions. Key methods include advanced cooling approaches (e.g., hot/cold aisle containment, economizer-based “free cooling,” and liquid immersion cooling), server optimization techniques (such as virtualization, containerization, and intelligent power management), and improvements in power distrib
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Kishor Ingavale, Girish. "Zero-Water Cooling For Modern AI Data Centers." International Journal of Scientific Research and Engineering Trends 11, no. 3 (2025): 1–13. https://doi.org/10.61137/ijsret.vol.11.issue3.121.

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Zou, Jing, Peizhe Xin, Chang Wang, Heli Zhang, Lei Wei, and Ying Wang. "AI Services-Oriented Dynamic Computing Resource Scheduling Algorithm Based on Distributed Data Parallelism in Edge Computing Network of Smart Grid." Future Internet 16, no. 9 (2024): 312. http://dx.doi.org/10.3390/fi16090312.

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Massive computational resources are required by a booming number of artificial intelligence (AI) services in the communication network of the smart grid. To alleviate the computational pressure on data centers, edge computing first network (ECFN) can serve as an effective solution to realize distributed model training based on data parallelism for AI services in smart grid. Due to AI services with diversified types, an edge data center has a changing workload in different time periods. Selfish edge data centers from different edge suppliers are reluctant to share their computing resources with
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Oluwatosin Oladayo Aramide. "Architecting highly resilient AI Fabrics: A Blueprint for Next-Gen Data Centers." World Journal of Advanced Engineering Technology and Sciences 8, no. 1 (2023): 529–39. https://doi.org/10.30574/wjaets.2023.8.1.0049.

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The fast-growing advancement in AI technologies has resulted in huge loads on the data center architecture resulting in the need to create extremely resistant, and fault-tolerant AI fabrics. This paper looks at AI design principles and technologies necessitated in the construction of fault-tolerant AI infrastructures that can support complex, data-heavy workloads. The major technologies of VXLAN EVPN, RDMA and ultra-low latency interconnect like RoCEv2, NV Link and PCIe Gen5 are paramount to high availability, low latency and high throughput. This article reviews industrial best practice by ob
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Researcher. "LEVERAGING AI AND ML TO REVOLUTIONIZE ENERGY EFFICIENCY IN DATA CENTERS." International Journal of Computer Engineering and Technology (IJCET) 15, no. 4 (2024): 370–83. https://doi.org/10.5281/zenodo.13269832.

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This article explores the transformative impact of Artificial Intelligence (AI) and Machine Learning (ML) on energy efficiency in data centers. It examines various areas where these technologies drive significant improvements, including predictive analytics, dynamic cooling management, smart workload scheduling, automated peak shaving, real-time optimization, enhanced maintenance strategies, and holistic system integration. Implementing AI/ML solutions optimizes operational costs and contributes to sustainability efforts by reducing the overall carbon footprint of data center facilities. Throu
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Dhruvitkumar V Talati. "AI for self-adaptive cloud systems: Towards fully autonomous data centers." World Journal of Advanced Research and Reviews 25, no. 30 (2025): 333–40. https://doi.org/10.30574/wjarr.2025.25.3.0727.

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The increasing complexity of modern computing systems, coupled with growing demands for energy-efficient and cost-effective data centers, has driven the need for self-adaptive cloud systems. Advancements in artificial intelligence hold the promise of enabling fully autonomous data centers that can adapt to dynamic workloads, optimize resource utilization, and reduce environmental impact. This paper explores the applications of AI techniques in the context of self-adaptive cloud systems, highlighting the potential for AI-powered solutions to address key challenges in the design, operation, and
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Janardhanan, Harish. "AI-Driven Load Balancing for Energy-Efficient Data Centers." International Journal of Computer Trends and Technology 72, no. 8 (2024): 13–18. http://dx.doi.org/10.14445/22312803/ijctt-v72i8p103.

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Dissertations / Theses on the topic "AI Data Centers"

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Lundin, Lowe. "Artificial Intelligence for Data Center Power Consumption Optimisation." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-447627.

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The aim of the project was to implement a machine learning model to optimise the power consumption of Ericsson’s Kista data center. The approach taken was to use a Reinforcement Learning agent trained in a simulation environment based on data specific to the data center. In this manner, the machine learning model could find interactions between parameters, both general and site specific in ways that a sophisticated algorithm designed by a human never could. In this work it was found that a neural network can effectively mimic a real data center and that the Reinforcement Learning policy "TD3"
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Mohapatra, Avantika. "Designing for AI : A collaborative framework to bridge the gap between designers and data scientists, and enabling designers to create human-centered AI products and services." Thesis, KTH, Integrerad produktutveckling, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-286016.

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Emerging advances in the realm of Artificial Intelligence (AI) have had a tremendous impact on various fields around us and society as a whole. As technologies continue to evolve, so will the role of designers when it comes to using AI. It has the potential to be the next tool designers use to create human-centered products and services. To truly understand AI and harness its capabilities, it is crucial to demystify the term and its inner workings. This thesis is explorative research to shed light on collaborative intelligence and how designers can harness the capabilities of AI. It further ex
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Books on the topic "AI Data Centers"

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Naidoo, Mahandra, Sylvain Baffi, and Nada Aridhi. Developing effective digital case management systems for labour dispute prevention and resolution institutions. ILO, 2025. https://doi.org/10.54394/ruqt8770.

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This working paper presents insights and lessons learned from the ILO's 2025 South-South and Triangular Cooperation (SSTC) workshop on digital case management systems. Convened in Bangkok, the workshop gathered representatives from South Asia, South-East Asia, and Southern Africa to address the persistent challenges of manual, paper-based labour dispute management systems, particularly inefficiencies and barriers to access to justice. Through a comparative analysis of digital case management systems from Australia, South Africa, the United Kingdom, and the Philippines, participants explored ke
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Geraci, Robert M. Futures of Artificial Intelligence. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9788194831679.001.0001.

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Twenty-first-century life is increasingly governed by artificial intelligence (AI) technologies such as machine learning, big data analysis, facial recognition, and robotics. For decades, an ideology of apocalyptic progress and cosmic transformation has accompanied the advancement of AI in the U.S.; that vision is intimately connected to transhumanism—the idea that humanity can transcend its limits, even mortality, using technology. Based on contributions from science and science fiction, advocates of such Apocalyptic AI suggest that the world will soon see godlike machine intelligence and tha
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Reed, Stephen K. Cognitive Skills You Need for the 21st Century. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780197529003.001.0001.

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Cognitive Skills You Need for the 21st Century begins with the Future of Jobs Report 2018 of the World Economic Forum that describes trending skills through the year 2022. To assist with the development of these skills, the book describes techniques that should benefit everyone. The 20 chapters occupy 6 sections on acquiring knowledge (comprehension, action, categorization, abstraction), organizing knowledge (matrices, networks, hierarchies), reasoning (visuospatial reasoning, imperfect knowledge, strategies), problem-solving (problems, design, dynamics), artificial intelligence (data sciences
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Vázquez Zamudio, Miriam Roxana. Desarrollo de proyectos empresariales en la industria 4.0. Universidad Tecnocientífica del Pacífico, 2024. https://doi.org/10.58299/utp.237.

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“Developing Business Projects Industry 4.0” is a book tht explores the digital transformation of companies through the adoption of projects in companies and technologies such as the Internet of Things (Iot), big data, artificial intelligence (AI). It focuses on the application of agile methodologies and user-centered desing for the development of innovative projects, highlighting the importance of logistics, maintenance, ICT ́s and sustainability. The book offers case studies and practical and sustainability. The book ofters case studies and practical advice for project management, team buildi
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Divissenko, Nikita. Regulating Innovation in the Digital Age. Hart Publishing, 2025. https://doi.org/10.5040/9781509978366.

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This book examines the role, impact, and limitations of regulation as a tool for shaping innovative markets. It contends that the current supply-centred approach is suboptimal in the context of digital innovation and proposes a blueprint for a more demand-conscious approach to regulation. The focus on the demand-side is prompted by the evolving role of consumers within the innovation process in the digital and data-driven economy, the regulatory implications of which are underexplored in legal scholarship. The book features in-depth case studies of the most recent regulatory initiatives in the
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Потапенко, В. Г., І. А. Павленко, В. М. Нагірний, Є. В. Маляревський та А. А. Головка. Аналіз загроз національній безпеці у сфері внутрішньої політики. Національний інститут стратегічних досліджень, 2025. https://doi.org/10.53679/niss-analytrep.2025.06.

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В аналітичній доповіді «Аналіз загроз національній безпеці у сфері внутрішньої політики» представлено результати третього щорічного експертного опитування, проведеного центром внутрішньополітичних досліджень НІСД. Учасники проєкту оцінювали дестабілізаційний потенціал загроз та ймовірність його реалізації. Оцінки експертів, назагал, корелюють з даними соціологічних опитувань. Уперше за час реалізації проєкту було проведене окреме опитування чат-ботів, що використовують штучний інтелект. Отримані результати можуть використати органи державної влади при розробці стратегічних документів та практи
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Zhai, Kevin, and Hong Zhai. Rising AI Mind from Globally Correlated Big Data Center Entity : Artificial Intelligence, Autonomous Vehicles, Spooky Actions at Distance & Behind the Net: The Good, the Bad, and the Ugly. Survival Guide for Working in Amazon, Facebook, Google... Independently Published, 2020.

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Fuchs, Thomas. In Defence of the Human Being. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780192898197.001.0001.

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With the progress of artificial intelligence, the digitalization of the lifeworld, and the reduction of the mind to neuronal processes, the human being appears more and more as a product of data and algorithms. Thus, we conceive ourselves “in the image of our machines,” and conversely, we elevate our machines and our brains to new subjects. At the same time, demands for an enhancement of human nature culminate in transhumanist visions of taking human evolution to a new stage. Against this self-reification of the human being, the present book defends a humanism of embodiment: our corporeality,
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Govi, Elisabetta, ed. BIRTH. Archeologia dell’infanzia nell’Italia preromana. Bologna University Press, 2022. http://dx.doi.org/10.30682/disciarche31.

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L’archeologia dell’infanzia è un campo di ricerca molto attuale nel quadro internazionale degli studi dedicati all’antichità e negli anni più recenti ha visto una crescita considerevole anche grazie all’approccio interdisciplinare che arricchisce le indagini archeologiche di prospettive complementari, prime fra tutte quella dell’antropologia fisica e culturale. Il mondo infantile resta tuttavia molto difficile da analizzare per la scarsa visibilità che i bambini hanno in termini archeologici, a causa della fragilità dei resti ossei e della limitata evidenza sul piano sociale e culturale. Occor
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Book chapters on the topic "AI Data Centers"

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Shao, Liyu, Qin Chen, and Min He. "Intelligent Inspection Robot for Commercial Bank Data Centers: Computer Vision Technology." In AI in Banking. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-3837-6_11.

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Shi, Tao, Hui Ma, and Gang Chen. "Multi-objective Container Consolidation in Cloud Data Centers." In AI 2018: Advances in Artificial Intelligence. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03991-2_71.

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Jaroš, Václav. "Geolocation Data as a Research Tool for the Organization of the Settlement System and Mobility Mapping – Case Study of the Spatial Mobility Model in Czechia." In AI, Data, and Digitalization. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-53770-7_1.

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AbstractGeolocation data is a widely used source of the spatial information about the population. Their great potential might be also used for population mobility research to identify spatial interactions forming the hierarchical structure of the settlement system. For this purpose, a model of data acquisition and their preliminary analysis was developed. This model represents an effective tool for mapping the mobility behavior of the population. Using the example of Czechia, primary commuting links are identified, which are subsequently analyzed in detail using GIS tools in both desktop and o
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Munke, Johannes, Mohamad Hayek, Martin Golasowski, et al. "Data System and Data Management in a Federation of HPC/Cloud Centers." In HPC, Big Data, and AI Convergence Towards Exascale. CRC Press, 2022. http://dx.doi.org/10.1201/9781003176664-4.

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Smolka, Mareike, Lennart Stoepel, Jasmin Quill, et al. "Transdisciplinary Development of Neuromorphic Computing Hardware for Artificial Intelligence Applications: Technological, Economic, Societal, and Environmental Dimensions of Transformation in the NeuroSys Cluster4Future." In Transformation Towards Sustainability. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-54700-3_10.

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AbstractArtificial Intelligence (AI) promises economic growth and solutions to global problems but also raises societal concerns. Training AI models has a big carbon footprint due to data processing in fossil-fuel-reliant data centers. If the data centers are outside the European legal space, data processing incurs privacy risks. Besides, reliance on AI aggravates Europe’s dependence on non-European chipmakers, whose supply chains can be disrupted. To address such concerns, NeuroSys develops energy-efficient neuromorphic hardware tailored to AI applications that protect privacy by processing d
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(Mary) Tai, Hsueh-Yung. "Applications of Big Data and Artificial Intelligence." In Digital Health Care in Taiwan. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05160-9_11.

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AbstractThis chapter introduces the application of National Health Insurance (NHI) big data in creating digital claim review tools and artificial intelligence (AI) training to improve review efficacy. By analyzing big data in the NHI medical information system, the National Health Insurance Administration (NHIA) can detect abnormal or unusual claims and efficiently reduce medical waste. AI models are further generated with the NHI big data to identify duplicated medical images and monitor the quality of uploaded images and test results from medical institutions.The NHIA also seeks external res
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Sakaino, Akira. "International Collaboration Between Data Spaces and Carrier Networks." In Designing Data Spaces. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-93975-5_28.

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AbstractNTT, headquartered in Japan, is a global ICT company with data centers, network facilities, R&amp;D centers, and business locations all over the world. It is developing new data infrastructures that utilize next-generation information communications technology and digital twin computing. This section introduces NTT’s R&amp;D activities, services and solutions, and future initiatives to ensure security, compliance, fairness, transparency, and interoperability in the global data space. The details are as follows: Concerns and issues regarding the cross-border sharing and use of industria
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Corneli, Alessandra, and Marianna Rotilio. "Urban Centres Management: A Digital Twin Approach." In CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality. Firenze University Press, 2023. http://dx.doi.org/10.36253/979-12-215-0289-3.122.

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The management of the built environment is a topic that requires reference to the management of complex systems. In fact, the variety of domains involved means that the management of urban centers is not only complicated, and therefore it is not enough to model a set of rules that are representative of phenomena related to the real environment. Not only that, but what is evident is that emergency management lacks the ability to access real-time information that could be decisive. Having tools that provide real-time data, that reprocess it, and that are able to provide an enriched and slightly
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Corneli, Alessandra, and Marianna Rotilio. "Urban Centres Management: A Digital Twin Approach." In CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality. Firenze University Press, 2023. http://dx.doi.org/10.36253/10.36253/979-12-215-0289-3.122.

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The management of the built environment is a topic that requires reference to the management of complex systems. In fact, the variety of domains involved means that the management of urban centers is not only complicated, and therefore it is not enough to model a set of rules that are representative of phenomena related to the real environment. Not only that, but what is evident is that emergency management lacks the ability to access real-time information that could be decisive. Having tools that provide real-time data, that reprocess it, and that are able to provide an enriched and slightly
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Spellman, Frank R. "Data Center Limiting Factors." In The Science of AI in Environmental Engineering. CRC Press, 2025. https://doi.org/10.1201/9781003601562-20.

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Conference papers on the topic "AI Data Centers"

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Eda, Takeharu, Monikka Busto, Takuro Udagawa, et al. "Technical Challenges for AI in Space Data Centers." In IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2024. http://dx.doi.org/10.1109/igarss53475.2024.10642138.

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Tong, Chenxiang, Yimin Yin, Chengtao Jiang, Qinfen Hao, and Mingjun Xia. "On-chip optical frequency comb-based wireless terahertz interconnect system for data centers." In 2024 AI Photonics Technology Symposium, edited by Xiang Zhang. SPIE, 2024. http://dx.doi.org/10.1117/12.3034969.

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Zheng, Ying, Lei Jiao, Yuedong Xu, Bo An, Xin Wang, and Zongpeng Li. "Scheduling Generative-AI Job DAGs with Model Serving in Data Centers." In 2024 IEEE/ACM 32nd International Symposium on Quality of Service (IWQoS). IEEE, 2024. http://dx.doi.org/10.1109/iwqos61813.2024.10682885.

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Krishnaram, Prasanna Chandran Melnatami. "Green AI: Optimizing Energy Efficiency of Workloads for Sustainable Data Centers." In 2025 IEEE Green Technologies Conference (GreenTech). IEEE, 2025. https://doi.org/10.1109/greentech62170.2025.10977613.

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Anand, Karanveer. "AI-Driven Optimization of Cloud Data Centers: Enhancing Reliability and Cost Efficiency." In 2025 International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI). IEEE, 2025. https://doi.org/10.1109/icdsaai65575.2025.11011873.

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Ebadinezhad, Sahar, Alwaled Khalid Shaalan Shaalan, Noor Khattab Omar Algailani, Mohammed Al-Hussein, and Charles Zulu Yonmah. "Designing and Analyzing Green Networking Solutions in Data Centers Using AI for Energy Optimization." In 2025 International Conference on Machine Learning and Autonomous Systems (ICMLAS). IEEE, 2025. https://doi.org/10.1109/icmlas64557.2025.10967765.

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Evans, Scott C., Sachini Piyoni Ekanayake, Alexander Duncan, et al. "Sustainable Grid through Distributed Data Centers : Spinning AI Demand for Grid Stabilization and Optimization." In 2025 IEEE Conference on Technologies for Sustainability (SusTech). IEEE, 2025. https://doi.org/10.1109/sustech63138.2025.11025705.

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Santos, José, Pavlos Maniotis, Chen Wang, et al. "Evaluating the Network Effects of Orchestration Strategies for AI Workloads in Modern Data Centers." In 2025 IEEE 11th International Conference on Network Softwarization (NetSoft). IEEE, 2025. https://doi.org/10.1109/netsoft64993.2025.11080575.

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Chiang, Flore. "Overview of Safety Requirements for Liquid Cooling Technologies in Data Centers Supporting AI and ML Workloads." In 2025 IEEE International Symposium on Product Compliance Engineering (ISPCE). IEEE, 2025. https://doi.org/10.1109/ispce64260.2025.11044900.

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Biadene, D., J. Huber, J. W. Kolar, and P. Mattavelli. "Extended Smart-Link Quasi-Single-Stage 3-Phase AC-DC Power Supply Module for AI-Driving Data Centers." In 2025 IEEE Applied Power Electronics Conference and Exposition (APEC). IEEE, 2025. https://doi.org/10.1109/apec48143.2025.10977101.

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Reports on the topic "AI Data Centers"

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Prakash, Giri, Nicki Hickmon, Adam Theisen, et al. AI-Based Upgrades to Observational Data Centers to Facilitate Data Interoperability. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1769667.

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Miller, Kyle, and Andrew Lohn. Onboard AI: Constraints and Limitations. Center for Security and Emerging Technology, 2023. http://dx.doi.org/10.51593/2022ca008.

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Artificial intelligence that makes news headlines, such as ChatGPT, typically runs in well-maintained data centers with an abundant supply of compute and power. However, these resources are more limited on many systems in the real world, such as drones, satellites, or ground vehicles. As a result, the AI that can run onboard these devices will often be inferior to state of the art models. That can affect their usability and the need for additional safeguards in high-risk contexts. This issue brief contextualizes these challenges and provides policymakers with recommendations on how to engage w
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Shengjergji, Sofije, Anna Luzai, Stephanie Mills, Parker Van Nostrand, Anna Lindroos Cermakova, and Natalia Ingebretsen Kucirkova. Environmental impact of EdTech: The hidden costs of digital learing. University in Stavanger, 2024. http://dx.doi.org/10.31265/usps.285.

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The report "Environmental Impact of EdTech: The Hidden Costs of Digital Learning" highlights the often-overlooked environmental consequences of the expanding Educational Technology (EdTech) sector. While EdTech is frequently associated with reducing paper usage and promoting remote learning, the report emphasizes the significant environmental costs linked to the production, consumption, and disposal of digital devices. Key challenges include the energy consumption of data centers, the carbon footprint of AI-driven technologies, and the growing issue of electronic waste, which disproportionatel
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Hick, Jason, Mark Izzo, Mateo Aragon, et al. Scalable Data Center Capacity for DOE's AI Prototype A Rapidly Available Gigawatt Data Center for DOE. Office of Scientific and Technical Information (OSTI), 2025. https://doi.org/10.2172/2545780.

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Gruber, Peter. Using ChatGPT and Copilot for Efficient Data Analysis in R. Instats Inc., 2023. http://dx.doi.org/10.61700/0lgrq7wtqjztt469.

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This workshop provides a 21st-century introduction to [b]Statistical Analysis with R,[/b] focusing on the efficient use of [b]AI assistants[/b] such as Github Copilot and ChatGPT. Participants will learn step by step how to install AI tools and how to harness their power. They will be able to create R code in the blink of an eye and with unprecedented ease of use. They will learn the principles of the R language, an important fundament for competently assessing and using AI-generated code. An official Instats certificate of completion is provided at the conclusion of the seminar along with 2 E
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6

Gruber, Peter H. Using ChatGPT for Efficient Data Analysis with R. Instats Inc., 2023. http://dx.doi.org/10.61700/hx2nyqcwog614469.

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This workshop provides a 21st-century introduction to Statistical Analysis with R and ChatGPT. Participants will learn how to harness the power of AI and how to optimally collaborate with ChatGPT for greatly simplifying the process of working with and analyzing data in R. Write R programs faster and find errors more efficiently. An official Instats certificate of completion is provided at the conclusion of the seminar along with 2 ECTS Equivalent points.
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Gruber, Peter. Using ChatGPT for Efficient Data Analysis with R. Instats Inc., 2023. http://dx.doi.org/10.61700/xwwiqwcmk4x0r469.

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Abstract:
This workshop provides a 21st-century introduction to Statistical Analysis with R and ChatGPT. Participants will learn how to harness the power of AI and how to optimally collaborate with ChatGPT for greatly simplifying the process of working with and analyzing data in R. Write R programs faster and find errors more efficiently. An official Instats certificate of completion is provided at the conclusion of the seminar along with 2 ECTS Equivalent points.
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8

Pasupuleti, Murali Krishna. AI-Driven Automation: Transforming Industry 5.0 withMachine Learning and Advanced Technologies. National Education Services, 2025. https://doi.org/10.62311/nesx/rr225.

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Abstract: This article delves into the transformative role of artificial intelligence (AI) and machine learning (ML) in shaping Industry 5.0, a paradigm centered on human- machine collaboration, sustainability, and resilient industrial ecosystems. Beginning with the evolution from Industry 4.0 to Industry 5.0, it examines core AI technologies, including predictive analytics, natural language processing, and computer vision, which drive advancements in manufacturing, quality control, and adaptive logistics. Key discussions include the integration of collaborative robots (cobots) that enhance hu
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Fearns, Joshua, and Lydia Harriss. Data science skills in the UK workforce. Parliamentary Office of Science and Technology, 2023. http://dx.doi.org/10.58248/pn697.

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This POSTnote looks at specialist data skills in the UK, including for artificial intelligence. It considers demand and supply, workforce demographics, challenges, and initiatives to increase supply. Key points: • Collecting and analysing data offers potential economic and social benefits. Analysis by the McKinsey Global Institute estimated that, by 2030, UK GDP could increase by up to 22% as a result of AI. • Potential societal benefits could range from climate change mitigation, to improving early detection and diagnosis of cancers by using AI to identify patterns from imaging (MRI) scans th
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Christopher, Lauren, Stanley Chien, Yaobin Chen, Mei Qiu, William Reindl, and Liya Koshy. Anomaly Detection in Traffic Patterns Using the INDOT Camera System. Purdue University, 2025. https://doi.org/10.5703/1288284317778.

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The Transportation and Autonomous Systems Institute (TASI) of Purdue University Indianapolis (PUI) and the INDOT Traffic Management Center worked together to develop a system that monitors traffic conditions using INDOT CCTV video feeds. Computer vision-based traffic anomaly detection has been studied for the past 20 years, and a thorough state-of-the-art analysis was produced in a recent survey paper. Although AI has contributed to improving anomaly detection, several major challenges remain, such as tracking errors, illumination, weather, occlusion handling, camera pose, and perspective. In
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