Bibliographies thématiques / Generative Artificial Intelligence

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Littérature scientifique sur le sujet « Generative Artificial Intelligence »

Auteur : Grafiati
Publié le 2 juin 2025
Mis à jour le 31 juillet 2025

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Articles de revues sur le sujet "Generative Artificial Intelligence"

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Rios-Campos, Carlos, Jessica Del Consuelo Luzuriaga Viteri, Elixer Alexandra Palma Batalla, et al. "Generative Artificial Intelligence." South Florida Journal of Development 4, no. 6 (2023): 2305–20. http://dx.doi.org/10.46932/sfjdv4n6-008.

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The general objective of the research is to determine the advances related to Generative Artificial Intelligence. Methodology, in this research, 47 documents have been selected, carried out in the period 2014 - 2023; including: scientific articles, review articles and information from websites of recognized organizations. Results, Generative Artificial Intelligence is demonstrating its importance in various human activities, making it necessary to use it ethically and responsibly. Conclusions, the general objective of the research is to determine the advances related to Generative Artificial I
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Warankar, Mayuri. "Generative Artificial Intelligence." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem31146.

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Generative AI is a cool tech that helps machines create new stuff that looks a lot like what humans make. Think of it like a computer artist who can paint pictures, write stories, compose music, and even make videos. This paper is all about explaining generative AI: where it came from, what it's doing now, and how it's used. It works by using fancy computer programs and smart algorithms to understand patterns in data and then make new things that fit those patterns. It's super important because it's changing how we do things in fields like healthcare, entertainment, and education. But, it's no
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Barradas-Palmeros, Jesús-Arnulfo, and Efrén Mezura-Montes. "Exploring Generative Artificial Intelligence." REVISTA PARAGUAYA DE EDUCACIÓN A DISTANCIA (REPED) 5, no. 4 (2024): 21–28. http://dx.doi.org/10.56152/reped2024-dossieria2-art3.

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This work reviews the concept of Generative Artificial Intelligence (GAI), its main characteristics, and its classification inside the Artificial Intelligence (AI) field. In this way, the main GAI foundational algorithmic architectures for data generation are presented; each considers a previously gathered data set and generates novel and synthetic data. Subsequently, use cases for generative models reported in the literature are presented, ranging from applications in healthcare, education, and customer experiences. The deployment of GAI models still needs to overcome a series of challenges i
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Corchado, Juan M., Sebastian López F., Juan M. Núñez V., Raul Garcia S., and Pablo Chamoso. "Generative Artificial Intelligence: Fundamentals." ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal 12, no. 1 (2023): e31704. http://dx.doi.org/10.14201/adcaij.31704.

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Generative language models have witnessed substantial traction, notably with the introduction of refined models aimed at more coherent user-AI interactions—principally conversational models. The epitome of this public attention has arguably been the refinement of the GPT-3 model into ChatGPT and its subsequent integration with auxiliary capabilities such as search features in Microsoft Bing. Despite voluminous prior research devoted to its developmental trajectory, the model’s performance, and applicability to a myriad of quotidian tasks remained nebulous and task specific. In terms of technol
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Denning, Peter J. "Artificial Intelligence: Generative AI." Ubiquity 2025, July (2025): 1–19. https://doi.org/10.1145/3747356.

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Large language models (LLMs) are the first neural network machines capable of carrying on conversations with humans. They are trained on billions of words of text scraped from the internet. They generate text responses to text inputs. They have transformed the public awareness of artificial intelligence, bringing on reactions ranging from astonishment and awe to trepidation and horror. They have spurred massive investments in new tools for drafting texts, summarizing conversations, summarizing literature, generating images, coding simple programs, supporting education, and amusing humans. Expe
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Wang, Shuheng. "Research on Generative Artificial Intelligence Management." International Journal of Computer Science and Information Technology 2, no. 2 (2024): 350–56. https://doi.org/10.62051/ijcsit.v2n2.41.

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As a new technology, generative artificial intelligence technology not only promotes technological change, but also brings many governance risks because of its unique technical logic. For example, the introduction of massive data sets in the training process of generative artificial intelligence has the risk of data infringement, and the reinforcement learning model based on human feedback may exacerbate the generation and dissemination of false information, thus bringing a series of risks. Although the current law of our country has formed a preliminary framework for the regulation of generat
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Huang, David. "Using generative artificial intelligence in text generation." IET Conference Proceedings 2024, no. 24 (2025): 264–67. https://doi.org/10.1049/icp.2024.4493.

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Harrington, Linda. "Comparison of Generative Artificial Intelligence and Predictive Artificial Intelligence." AACN Advanced Critical Care 35, no. 2 (2024): 93–96. http://dx.doi.org/10.4037/aacnacc2024225.

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Amofa, Birago, Xebiso Blessing Kamudyariwa, Fatima Araujo Pereira Fernandes, Oluyomi Abayomi Osobajo, Faith Jeremiah, and Adekunle Oke. "Navigating the Complexity of Generative Artificial Intelligence in Higher Education: A Systematic Literature Review." Education Sciences 15, no. 7 (2025): 826. https://doi.org/10.3390/educsci15070826.

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Technological innovation has transformed educational settings, enabling artificial intelligence (AI)-driven teaching and learning processes. While AI is still in its embryonic stage in education, generative artificial intelligence has evolved rapidly, significantly shifting the teaching and learning context. With no clarity about the impacts of generative artificial intelligence on education, there is a need to synthesise research findings to demystify generative artificial intelligence and address concerns regarding its application in the teaching and learning process. This paper systematical
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Kaur Sidhu, Brahmaleen. "Generative Artificial Intelligence: Unveiling the Potential and Challenges." International Journal of Science and Research (IJSR) 13, no. 4 (2024): 1127–35. http://dx.doi.org/10.21275/sr24414234432.

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Thèses sur le sujet "Generative Artificial Intelligence"

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Benedetti, Riccardo. "From Artificial Intelligence to Artificial Art: Deep Learning with Generative Adversarial Networks." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/18167/.

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Neural Network had a great impact on Artificial Intelligence and nowadays the Deep Learning algorithms are widely used to extract knowledge from huge amount of data. This thesis aims to revisit the evolution of Deep Learning from the origins till the current state-of-art by focusing on a particular prospective. The main question we try to answer is: can AI exhibit artistic abilities comparable to the human ones? Recovering the definition of the Turing Test, we propose a similar formulation of the concept, indeed, we would like to test the machine's ability to exhibit artistic behaviour equiv
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ABUKMEIL, MOHANAD. "UNSUPERVISED GENERATIVE MODELS FOR DATA ANALYSIS AND EXPLAINABLE ARTIFICIAL INTELLIGENCE." Doctoral thesis, Università degli Studi di Milano, 2022. http://hdl.handle.net/2434/889159.

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For more than a century, the methods of learning representation and the exploration of the intrinsic structures of data have developed remarkably and currently include supervised, semi-supervised, and unsupervised methods. However, recent years have witnessed the flourishing of big data, where typical dataset dimensions are high, and the data can come in messy, missing, incomplete, unlabeled, or corrupted forms. Consequently, discovering and learning the hidden structure buried inside such data becomes highly challenging. From this perspective, latent data analysis and dimensionality reductio
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Konuko, Goluck. "Low Bitrate Face Video Compression with Generative Animation Models." Electronic Thesis or Diss., université Paris-Saclay, 2025. http://www.theses.fr/2025UPAST014.

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Cette thèse aborde le défi de réaliser une compression vidéo à ultra-faible débit pour la vidéoconférence, en se concentrant sur la préservation d'une haute qualité visuelle tout en minimisant la bande passante de transmission. Les codecs traditionnels comme HEVC et VVC éprouvent des difficultés à très bas débits, particulièrement pour représenter avec précision les expressions faciales dynamiques, les mouvements de tête et les occlusions, qui sont essentiels pour le réalisme et la précision dans la communication en face à face. Pour surmonter ces limitations, cette recherche développe une mét
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Griffith, Todd W. "A computational theory of generative modeling in scientific reasoning." Diss., Georgia Institute of Technology, 1999. http://hdl.handle.net/1853/8177.

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Navaroli, Nicholas Martin. "Generative Probabilistic Models for Analysis of Communication Event Data with Applications to Email Behavior." Thesis, University of California, Irvine, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3668831.

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<p> Our daily lives increasingly involve interactions with others via different communication channels, such as email, text messaging, and social media. In this context, the ability to analyze and understand our communication patterns is becoming increasingly important. This dissertation focuses on generative probabilistic models for describing different characteristics of communication behavior, focusing primarily on email communication. </p><p> First, we present a two-parameter kernel density estimator for estimating the probability density over recipients of an email (or, more generally,
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Mallik, Mohammed Tariqul Hassan. "Electromagnetic Field Exposure Reconstruction by Artificial Intelligence." Electronic Thesis or Diss., Université de Lille (2022-....), 2023. https://pepite-depot.univ-lille.fr/ToutIDP/EDENGSYS/2023/2023ULILN052.pdf.

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Le sujet de l'exposition aux champs électromagnétiques a fait l'objetd'une grande attention à la lumière du déploiement actuel du réseaucellulaire de cinquième génération (5G). Malgré cela, il reste difficilede reconstituer avec précision le champ électromagnétique dans unerégion donnée, faute de données suffisantes. Les mesures in situ sontd'un grand intérêt, mais leur viabilité est limitée, ce qui renddifficile la compréhension complète de la dynamique du champ. Malgré legrand intérêt des mesures localisées, il existe encore des régions nontestées qui les empêchent de fournir une carte d'exp
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Goodman, Genghis. "A Machine Learning Approach to Artificial Floorplan Generation." UKnowledge, 2019. https://uknowledge.uky.edu/cs_etds/89.

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The process of designing a floorplan is highly iterative and requires extensive human labor. Currently, there are a number of computer programs that aid humans in floorplan design. These programs, however, are limited in their inability to fully automate the creative process. Such automation would allow a professional to quickly generate many possible floorplan solutions, greatly expediting the process. However, automating this creative process is very difficult because of the many implicit and explicit rules a model must learn in order create viable floorplans. In this paper, we propose a met
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Bartocci, John Timothy. "Generating a synthetic dataset for kidney transplantation using generative adversarial networks and categorical logit encoding." Bowling Green State University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1617104572023027.

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Franceschelli, Giorgio. "Generative Deep Learning and Creativity." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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“Non ha la presunzione di originare nulla; può solo fare ciò che noi sappiamo ordinarle di fare”. Così, oltre 150 anni fa, Lady Lovelace commentava la Macchina Analitica di Babbage, l’antenato dei nostri computer. Una frase che, a distanza di tanti anni, suona quasi come una sfida: grazie alla diffusione delle tecniche di Generative Deep Learning e alle ricerche nell’ambito della Computational Creativity, sempre più sforzi sono stati destinati allo smentire l’ormai celebre Obiezione della Lovelace. Proprio a partire da questa, quattro domande formano i capisaldi della Computational Creativity:
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Kalantari, John I. "A general purpose artificial intelligence framework for the analysis of complex biological systems." Diss., University of Iowa, 2017. https://ir.uiowa.edu/etd/5953.

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This thesis encompasses research on Artificial Intelligence in support of automating scientific discovery in the fields of biology and medicine. At the core of this research is the ongoing development of a general-purpose artificial intelligence framework emulating various facets of human-level intelligence necessary for building cross-domain knowledge that may lead to new insights and discoveries. To learn and build models in a data-driven manner, we develop a general-purpose learning framework called Syntactic Nonparametric Analysis of Complex Systems (SYNACX), which uses tools from Bayesian
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Livres sur le sujet "Generative Artificial Intelligence"

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Solanki, Shivam R., and Drupad K. Khublani. Generative Artificial Intelligence. Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0403-8.

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Kovač, Mitja. Generative Artificial Intelligence. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-65514-2.

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Paliszkiewicz, Joanna, Ireneusz Dąbrowski, and Leila Halawi. Trust in Generative Artificial Intelligence. Routledge, 2025. https://doi.org/10.4324/9781003586937.

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Elkhodr, Mahmoud, and Ergun Gide. Generative Artificial Intelligence Empowered Learning. Chapman and Hall/CRC, 2025. https://doi.org/10.1201/9781003422433.

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Kumar, Rajendra, Shankar Ramamoorthy, Vishal Jain, Utku Köse, and Ong Eng Tek. Generative Artificial Intelligence in Healthcare. CRC Press, 2025. https://doi.org/10.1201/9781003488255.

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Fan, Yizhou. Learning with Generative Artificial Intelligence. Routledge, 2025. https://doi.org/10.4324/9781003632146.

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Marino, Domenico, and Melchiorre Alberto Monaca, eds. Generative Artificial Intelligence and Fifth Industrial Revolution. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-73880-7.

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Vajjhala, Narasimha Rao, Sanjiban Sekhar Roy, Burak Taşcı, and Muhammad Enamul Hoque Chowdhury, eds. Generative Artificial Intelligence (AI) Approaches for Industrial Applications. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-76710-4.

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Tu, Yiliu Paul, and Maomao Chi, eds. E-Business. Generative Artificial Intelligence and Management Transformation. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-94187-0.

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Tu, Yiliu Paul, and Maomao Chi, eds. E-Business. Generative Artificial Intelligence and Management Transformation. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-94190-0.

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Chapitres de livres sur le sujet "Generative Artificial Intelligence"

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van der Zant, Tijn, Matthijs Kouw, and Lambert Schomaker. "Generative Artificial Intelligence." In Studies in Applied Philosophy, Epistemology and Rational Ethics. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-31674-6_8.

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Carnahan, David. "Generative Artificial Intelligence." In Digital Health, AI and Generative AI in Healthcare. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-83526-1_5.

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Solanki, Shivam R., and Drupad K. Khublani. "Building Enterprise-Grade Applications Using LLMs." In Generative Artificial Intelligence. Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0403-8_9.

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Solanki, Shivam R., and Drupad K. Khublani. "Introduction to Generative AI." In Generative Artificial Intelligence. Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0403-8_1.

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Solanki, Shivam R., and Drupad K. Khublani. "Generative Large Language Models." In Generative Artificial Intelligence. Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0403-8_6.

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Solanki, Shivam R., and Drupad K. Khublani. "Building Demo Applications Using LLMs." In Generative Artificial Intelligence. Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0403-8_8.

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Solanki, Shivam R., and Drupad K. Khublani. "From Script to Screen: Unveiling Text-to-Video Generation." In Generative Artificial Intelligence. Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0403-8_3.

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Solanki, Shivam R., and Drupad K. Khublani. "Large Language Models." In Generative Artificial Intelligence. Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0403-8_5.

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Solanki, Shivam R., and Drupad K. Khublani. "Text-to-Image Generation." In Generative Artificial Intelligence. Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0403-8_2.

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Solanki, Shivam R., and Drupad K. Khublani. "Advanced Techniques for Large Language Models." In Generative Artificial Intelligence. Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0403-8_7.

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Actes de conférences sur le sujet "Generative Artificial Intelligence"

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Heimburg, Vincent, Maximilian Schreieck, and Manuel Wiesche. "Generative Artificial Intelligence Platform Ecosystems." In 2024 IEEE International Conference on Engineering, Technology, and Innovation (ICE/ITMC). IEEE, 2024. https://doi.org/10.1109/ice/itmc61926.2024.10794416.

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Basha, Thippaluri Yahid, Barma Kalyani, and Yelisetti Sandeep. "Generative Artificial Intelligence in Legal Drafting." In 2024 International Conference on Computational Intelligence for Green and Sustainable Technologies (ICCIGST). IEEE, 2024. http://dx.doi.org/10.1109/iccigst60741.2024.10717541.

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R, Kaviyaraj. "Generative Artificial Intelligence: Transforming the Future." In 2024 International Conference on Emerging Technologies and Innovation for Sustainability (EmergIN). IEEE, 2024. https://doi.org/10.1109/emergin63207.2024.10961802.

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Wang, Chaoming, Veronica Sundstedt, and Valeria Garro. "Generative Artificial Intelligence for Immersive Analytics." In 16th International Conference on Information Visualization Theory and Applications. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013308400003912.

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Gomez-Jaramillo, Sebastian, Juan Fernando Rivera-Quevedo, and Juan David Hernandez-Lopez. "VOCATIONAL RECOMMENDATION SYSTEM USING GENERATIVE ARTIFICIAL INTELLIGENCE." In 17th annual International Conference of Education, Research and Innovation. IATED, 2024. https://doi.org/10.21125/iceri.2024.2250.

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Routray, Sudhir K., Mahesh K. Jha, K. P. Sharmila, Abhishek Javali, M. Pappa, and Monika Singh. "Generative Artificial Intelligence: Principles, Potentials and Challenges." In 2024 2nd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS). IEEE, 2024. https://doi.org/10.1109/icssas64001.2024.10760992.

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Tewari, Naveen, Mukesh Joshi, Sandeep Kumar Budhani, Vinay Kandpal, Arun Kumar Rai, and Bhawana Jeena. "Comparative Analysis of Generative Artificial Intelligence Tools." In 2024 1st International Conference on Advances in Computing, Communication and Networking (ICAC2N). IEEE, 2024. https://doi.org/10.1109/icac2n63387.2024.10895489.

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Mulongo, Ndala Yves. "Optimisation of Manufacturing Workflow Through Generative Artificial Intelligence." In 2024 International Symposium on Networks, Computers and Communications (ISNCC). IEEE, 2024. http://dx.doi.org/10.1109/isncc62547.2024.10758970.

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Ersöz, Betül, Şeref Sağıroğlu, and Halil İbrahim Bülbül. "Generative Artificial Intelligence Opportunities and Threats: Bibliometric Analysis." In 2024 9th International Conference on Computer Science and Engineering (UBMK). IEEE, 2024. https://doi.org/10.1109/ubmk63289.2024.10773519.

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Villaplana, Hannah Danielle Ladera, Jaeyoung Kwak, Michael H. Lees, Hongying Li, and Wentong Cai. "Application of Generative Artificial Intelligence for Epidemic Modeling." In 2024 Winter Simulation Conference (WSC). IEEE, 2024. https://doi.org/10.1109/wsc63780.2024.10838918.

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Rapports d'organisations sur le sujet "Generative Artificial Intelligence"

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Roberts, Kamie. Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile. National Institute of Standards and Technology, 2024. http://dx.doi.org/10.6028/nist.ai.600-1.

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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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Stark, Cameron. Generative Artificial Intelligence Tools for Red Teams. Office of Scientific and Technical Information (OSTI), 2024. https://doi.org/10.2172/2516859.

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Miller, Tess, and Prosper Habada. The Use of Generative Artificial Intelligence in Higher Education. ResearchHub Technologies, Inc., 2025. https://doi.org/10.55277/researchhub.c1cov3ww.1.

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Miller, Tess, and Prosper Habada. The Use of Generative Artificial Intelligence in Higher Education. ResearchHub Technologies, Inc., 2025. https://doi.org/10.55277/researchhub.wznaufbr.1.

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Miller, Tess, and Prosper Habada. The Use of Generative Artificial Intelligence in Higher Education. ResearchHub Technologies, Inc., 2025. https://doi.org/10.55277/rhj.j0pita1t.

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Miller, Tess, and Prosper Habada. The Use of Generative Artificial Intelligence in Higher Education. ResearchHub Technologies, Inc., 2025. https://doi.org/10.55277/rhj.ql8ttmax.

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Miller, Tess, and Prosper Habada. The Use of Generative Artificial Intelligence in Higher Education. ResearchHub Technologies, Inc., 2025. https://doi.org/10.55277/rhj.chgt2e44.

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Miller, Tess, and Prosper Habada. The Use of Generative Artificial Intelligence in Higher Education. ResearchHub Technologies, Inc., 2025. https://doi.org/10.55277/researchhub.r5qxu8nr.1.

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Miller, Tess, and Prosper Habada. The Use of Generative Artificial Intelligence in Higher Education. ResearchHub Technologies, Inc., 2025. https://doi.org/10.55277/rhj.bdw7vil7.

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