Relevant bibliographies by topics / Generative AI

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

Academic literature on the topic 'Generative AI'

Author: Grafiati
Published: 14 September 2024
Last updated: 29 March 2025

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Journal articles on the topic "Generative AI"

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Koda, Masahide. "Generative AI." Okayama Igakkai Zasshi (Journal of Okayama Medical Association) 136, no. 3 (December 2, 2024): 139–40. https://doi.org/10.4044/joma.136.139.

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Krishna, Guntamukkala Gopi. "Generative AI." International Journal of Advanced Engineering and Nano Technology 10, no. 8 (August 30, 2023): 1–3. http://dx.doi.org/10.35940/ijaent.g0474.0810823.

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Recent advancements in generative artificial intelligence (AI) have made it possible for machines to independently produce a variety of creative content. In the context of producing creative content, this essay examines the developments, difficulties, and ethical issues relating to generative AI. It looks into how generative models, such Generative Adversarial Networks (GANs) and Variational Auto encoders (VAEs), can produce realistic artwork like music, literature, and visuals. However, it is frequently discovered that GAN training is extremely unstable and frequently experiences non-convergence, mode collapse, and hyperparameter sensitivity [1]. The technical details of developing and optimizing generative models to produce desired results are covered in detail in this work. It also looks at the difficulties in guaranteeing the variety, creativity, and coherence of generated content. Additionally, the use of generative AI in the creation of original material raises ethical questions. Included in this are concerns about intellectual property, plagiarism, and possible effects on the creative industries. In specifically, the article explores the consequences of employing generative AI for content production in terms of authorship, human creativity, and the possible disruption of traditional creative practices. It also covers issues with fairness, bias, and appropriate application of generative models.
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Euchner, Jim. "Generative AI." Research-Technology Management 66, no. 3 (April 20, 2023): 71–74. http://dx.doi.org/10.1080/08956308.2023.2188861.

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Swaroopa, Pethota. "StoryCraft AI: Exploring Generative Approaches to Story Narration through AI." International Scientific Journal of Engineering and Management 03, no. 04 (April 23, 2024): 1–9. http://dx.doi.org/10.55041/isjem01633.

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In this project, we demonstrate a Storytelling AI system, which can generate short stories and complementary illustrated images with minimal input from the user. The system makes use of a text generation model, a text-to-image synthesis network, and a neural style transfer model. The final project is deployed into a website where users can build their stories. The field of AI has made significant changes in various domains, including Natural Language Processing and Generative models. One captivating application of these advancements is in the realm of storytelling. This project introduces a novel approach to story narration using a Generative AI model, specifically leveraging the GPT architecture. Traditional storytelling involves human creativity, imagination, and the ability to craft engaging narratives. However, the integration of AI into storytelling brings about new opportunities and challenges. In this project, we delve into the methodologies and techniques used to train a GPT-based model for generating coherent and captivating stories. In the end, we found that using computers for storytelling can be exciting, but we need to work together to ensure the stories are great and meaningful. Key Words: Artificial Intelligence (AI), Generative Adversarial Network (GAN), Generative Pre-trained Transformer (GPT)
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Rathod, Rohan. "Renaissance on Generative AI." International Journal for Research in Applied Science and Engineering Technology 12, no. 6 (June 30, 2024): 1354–59. http://dx.doi.org/10.22214/ijraset.2024.63324.

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Abstract: Generative Artificial Intelligence (Generative AI) stands at the forefront of innovation, promising to revolutionize creative content generation across various domains. This paper delves into the multifaceted implications of Generative AI in reshaping the landscape of artistic expression. Through an extensive literature survey and analysis, we explore the applications, advancements, and challenges of Generative AI in text generation, visual arts, and music composition. From state-of-the-art models like OpenAI's GPT series to cutting-edge techniques such as Generative Adversarial Networks (GANs) and Transformer architectures, Generative AI enables the automated creation of diverse and high-quality content. However, ethical considerations regarding authenticity, bias, and ownership of AI-generated content remain paramount. By uncovering key findings and insights, this paper aims to guide future research, development, and responsible integration of Generative AI in fostering a renaissance of artistic innovation and collaboration
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Luttrell, Regina, and Nicholas Bowman. "Generating Deep Discussion Around Generative AI." Newhouse Impact Journal 1, no. 1 (2024): 1–2. http://dx.doi.org/10.14305/jn.29960819.2024.1.1.10.

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Kars, Muhammet Emir. "Generative AI in Education." London Journal of Social Sciences, no. 6 (September 20, 2023): 144–51. http://dx.doi.org/10.31039/ljss.2023.6.114.

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Recently, the field of artificial intelligence (AI) has advanced significantly, with generativeAI rising to the top of the tech industry's most-discussed subjects list. Education like manyother fields could be transformed by generative AI like ChatGPT, Bard, DALL-E,Midjourney, and DeepMind, which all have the ability to revolutionize a number of industrieswith all the benefits and drawbacks they entail.The paper begins by providing an overview of generative AI, highlighting its capacity togenerate human-like text, images, and even interactive simulations. It delves into theunderlying principles and techniques that empower generative AI models, focusing onprominent models like ChatGPT and Midjourney.To assess the effectiveness of generative AI in educational contexts, the paper examinesstudies that have evaluated learning outcomes, student engagement, and teacher support.These findings provide insights into the efficiency of generative AI as a supplementaryeducational tool and its role in fostering innovative teaching practices. The paper also addressesconcerns around the technology being too integrated into education like over dependence andcheating.Finally, the paper discusses future possibilities and challenges for the integration of generativeAI in education. It proposes strategies for maximizing the benefits of this technology whileensuring ethical considerations are met. The paper concludes with a call for further researchand collaboration between AI experts, educators, policymakers, and other stakeholders toharness the full potential of generative AI in transforming the learning landscape.
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Johnson, Russell. "Guiding generative AI." Nature Chemical Biology 21, no. 3 (February 25, 2025): 311. https://doi.org/10.1038/s41589-025-01854-y.

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Ali, Safinah, Prerna Ravi, Randi Williams, Daniella DiPaola, and Cynthia Breazeal. "Constructing Dreams Using Generative AI." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 21 (March 24, 2024): 23268–75. http://dx.doi.org/10.1609/aaai.v38i21.30374.

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Generative AI tools introduce new and accessible forms of media creation for youth. They also raise ethical concerns about the generation of fake media, data protection, privacy and ownership of AI-generated art. Since generative AI is already being used in products used by youth, it is critical that they understand how these tools work and how they can be used or misused. In this work, we facilitated students’ generative AI learning through expression of their imagined future identities. We designed a learning workshop - Dreaming with AI - where students learned about the inner workings of generative AI tools, used text-to-image generation algorithms to create their imaged future dreams, reflected on the potential benefits and harms of generative AI tools and voiced their opinions about policies for the use of these tools in classrooms. In this paper, we present the learning activities and experiences of 34 high school students who engaged in our workshops. Students reached creative learning objectives by using prompt engineering to create their future dreams, gained technical knowledge by learning the abilities, limitations, text-visual mappings and applications of generative AI, and identified most potential societal benefits and harms of generative AI.
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Agrawal, Vishakha. "Systems for Generative AI: Challenges and Opportunities." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (January 23, 2025): 1–7. https://doi.org/10.55041/ijsrem30153.

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Abstract—The emergence of large-scale generative AI models, such as those powering text, image, and code generation, has introduced unprecedented systems challenges distinct from tradi- tional AI applications. This paper examines the unique demands of generative AI systems, focusing on the specific computational patterns of generative models, their distinctive inference re- quirements, and the specialized infrastructure needed for token generation, prompt processing, and context management. We analyze the particular challenges and opportunities in building systems optimized for generative workloads. Keywords - Generative AI , Autoregressive, Batch Process- ing, Prompt Caching, Prompt Template Management
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Dissertations / Theses on the topic "Generative AI"

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Lacan, Alice. "Transcriptomics data generation with deep generative models." Electronic Thesis or Diss., université Paris-Saclay, 2025. http://www.theses.fr/2025UPASG010.

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Cette thèse explore l'utilisation de modèles génératifs profonds pour améliorer la génération de données transcriptomiques, répondant aux défis de rareté des données dans la classification de phénotypes de cancers. Nous évaluons la capacité des Autoencodeurs Variationnels (VAEs), des Réseaux Antagonistes Génératifs (GANs) et des modèles de diffusion (DDPM/DDIM) à équilibrer réalisme et diversité sur des données tabulaires de haute dimension. Nous avons d'abord adapté des métriques d'évaluation, supervisées et non supervisées. Nous avons ensuite intégré un moduled'auto-attention basé sur les connaissances du domaine dans notre GAN (AttGAN), améliorantle compromis fidélité-diversité. Une contribution notable est l'augmentation de la performance de classification avec un nombre minimal de vraies données augmenté de données générées. Nous proposons également une première adaptation des modèles de diffusion pour l'expression des gènes, ainsi qu'une méthodologie d'analyse d'interpolation offrant des perspectives sur la diversité des données et l'identification de biomarqueurs. Enfin, nous présentons GMDA (Modélisation Générativeavec Alignement de Densités), un modèle génératif alternatif aux GANs, permettant une exploration contrôlée de l'espace des données, une stabilité et une architecture frugale. Cette thèse offre ainsi des perspectives pour la génération de données transcriptomiques et tabulaires au sens large
This thesis explores deep generative models to improve synthetic transcriptomics data generation, addressing data scarcity in phenotypes classification tasks. We focus on Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and diffusion models (DDPM/DDIM), assessing their ability to balance realism and diversity in high-dimensional tabular datasets. First, we adapt quality metrics for gene expression and introduce a knowledge-based self-attention module within GANs (AttGAN) to improve the fidelity-diversity trade-off. A main contribution is boosting classification performance using minimal real samples augmented with synthetic data. Secondly, another contribution was the first adaptation of diffusion models to transcriptomic data, demonstrating competitiveness with VAEs and GANs. We also introduce an interpolation analysis bringing perspectives on data diversity and the identification of biomarkers. Finally, we present GMDA (Generative Modeling with Density Alignment), a resource efficient alternative to GANs that balances realism and diversity by aligning locally real and synthetic sample densities. This framework allows controlled exploration of instance space, stable training, and frugality across datasets. Ultimately, this thesis provides comprehensiveinsights and methodologies to advance synthetic transcriptomics data generation
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TOMA, ANDREA. "PHY-layer Security in Cognitive Radio Networks through Learning Deep Generative Models: an AI-based approach." Doctoral thesis, Università degli studi di Genova, 2020. http://hdl.handle.net/11567/1003576.

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Recently, Cognitive Radio (CR) has been intended as an intelligent radio endowed with cognition which can be developed by implementing Artificial Intelligence (AI) techniques. Specifically, data-driven Self-Awareness (SA) functionalities, such as detection of spectrum abnormalities, can be effectively implemented as shown by the proposed research. One important application is PHY-layer security since it is essential to establish secure wireless communications against external jamming attacks. In this framework, signals are non-stationary and features from such kind of dynamic spectrum, with multiple high sampling rate signals, are then extracted through the Stockwell Transform (ST) with dual-resolution which has been proposed and validated in this work as part of spectrum sensing techniques. Afterwards, analysis of the state-of-the-art about learning dynamic models from observed features describes theoretical aspects of Machine Learning (ML). In particular, following the recent advances of ML, learning deep generative models with several layers of non-linear processing has been selected as AI method for the proposed spectrum abnormality detection in CR for a brain-inspired, data-driven SA. In the proposed approach, the features extracted from the ST representation of the wideband spectrum are organized in a high-dimensional generalized state vector and, then, a generative model is learned and employed to detect any deviation from normal situations in the analysed spectrum (abnormal signals or behaviours). Specifically, conditional GAN (C-GAN), auxiliary classifier GAN (AC-GAN), and deep VAE have been considered as deep generative models. A dataset of a dynamic spectrum with multi-OFDM signals has been generated by using the National Instruments mm-Wave Transceiver which operates at 28 GHz (central carrier frequency) with 800 MHz frequency range. Training of the deep generative model is performed on the generalized state vector representing the mmWave spectrum with normality pattern without any malicious activity. Testing is based on new and independent data samples corresponding to abnormality pattern where the moving signal follows a different behaviour which has not been observed during training. An abnormality indicator is measured and used for the binary classification (normality hypothesis otherwise abnormality hypothesis), while the performance of the generative models is evaluated and compared through ROC curves and accuracy metrics.
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Misino, Eleonora. "Deep Generative Models with Probabilistic Logic Priors." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24058/.

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Many different extensions of the VAE framework have been introduced in the past. How­ ever, the vast majority of them focused on pure sub­-symbolic approaches that are not sufficient for solving generative tasks that require a form of reasoning. In this thesis, we propose the probabilistic logic VAE (PLVAE), a neuro-­symbolic deep generative model that combines the representational power of VAEs with the reasoning ability of probabilistic ­logic programming. The strength of PLVAE resides in its probabilistic ­logic prior, which provides an interpretable structure to the latent space that can be easily changed in order to apply the model to different scenarios. We provide empirical results of our approach by training PLVAE on a base task and then using the same model to generalize to novel tasks that involve reasoning with the same set of symbols.
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Mennborg, Alexander. "AI-Driven Image Manipulation : Image Outpainting Applied on Fashion Images." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-85148.

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The e-commerce industry frequently has to deal with displaying product images in a website where the images are provided by the selling partners. The images in question can have drastically different aspect ratios and resolutions which makes it harder to present them while maintaining a coherent user experience. Manipulating images by cropping can sometimes result in parts of the foreground (i.e. product or person within the image) to be cut off. Image outpainting is a technique that allows images to be extended past its boundaries and can be used to alter the aspect ratio of images. Together with object detection for locating the foreground makes it possible to manipulate images without sacrificing parts of the foreground. For image outpainting a deep learning model was trained on product images that can extend images by at least 25%. The model achieves 8.29 FID score, 44.29 PSNR score and 39.95 BRISQUE score. For testing this solution in practice a simple image manipulation pipeline was created which uses image outpainting when needed and it shows promising results. Images can be manipulated in under a second running on ZOTAC GeForce RTX 3060 (12GB) GPU and a few seconds running on a Intel Core i7-8700K (16GB) CPU. There is also a special case of images where the background has been digitally replaced with a solid color and they can be outpainted even faster without deep learning.
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Alabdallah, Abdallah. "Human Understandable Interpretation of Deep Neural Networks Decisions Using Generative Models." Thesis, Högskolan i Halmstad, Halmstad Embedded and Intelligent Systems Research (EIS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-41035.

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Deep Neural Networks have long been considered black box systems, where their interpretability is a concern when applied in safety critical systems. In this work, a novel approach of interpreting the decisions of DNNs is proposed. The approach depends on exploiting generative models and the interpretability of their latent space. Three methods for ranking features are explored, two of which depend on sensitivity analysis, and the third one depends on Random Forest model. The Random Forest model was the most successful to rank the features, given its accuracy and inherent interpretability.
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PANFILO, DANIELE. "Generating Privacy-Compliant, Utility-Preserving Synthetic Tabular and Relational Datasets Through Deep Learning." Doctoral thesis, Università degli Studi di Trieste, 2022. http://hdl.handle.net/11368/3030920.

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Due tendenze hanno rapidamente ridefinito il panorama dell'intelligenza artificiale (IA) negli ultimi decenni. La prima è il rapido sviluppo tecnologico che rende possibile un'intelligenza artificiale sempre più sofisticata. Dal punto di vista dell'hardware, ciò include una maggiore potenza di calcolo ed una sempre crescente efficienza di archiviazione dei dati. Da un punto di vista concettuale e algoritmico, campi come l'apprendimento automatico hanno subito un'impennata e le sinergie tra l'IA e le altre discipline hanno portato a sviluppi considerevoli. La seconda tendenza è la crescente consapevolezza della società nei confronti dell'IA. Mentre le istituzioni sono sempre più consapevoli di dover adottare la tecnologia dell'IA per rimanere competitive, questioni come la privacy dei dati e la possibilità di spiegare il funzionamento dei modelli di apprendimento automatico sono diventate parte del dibattito pubblico. L'insieme di questi sviluppi genera però una sfida: l'IA può migliorare tutti gli aspetti della nostra vita, dall'assistenza sanitaria alla politica ambientale, fino alle opportunità commerciali, ma poterla sfruttare adeguatamente richiede l'uso di dati sensibili. Purtroppo, le tecniche di anonimizzazione tradizionali non forniscono una soluzione affidabile a suddetta sfida. Non solo non sono sufficienti a proteggere i dati personali, ma ne riducono anche il valore analitico a causa delle inevitabili distorsioni apportate ai dati. Tuttavia, lo studio emergente dei modelli generativi ad apprendimento profondo (MGAP) può costituire un'alternativa più raffinata all'anonimizzazione tradizionale. Originariamente concepiti per l'elaborazione delle immagini, questi modelli catturano le distribuzioni di probabilità sottostanti agli insiemi di dati. Tali distribuzioni possono essere successivamente campionate, fornendo nuovi campioni di dati, non presenti nel set di dati originale. Tuttavia, la distribuzione complessiva degli insiemi di dati sintetici, costituiti da dati campionati in questo modo, è equivalente a quella del set dei dati originali. In questa tesi, verrà analizzato l'uso dei MGAP come tecnologia abilitante per una più ampia adozione dell'IA. A tal scopo, verrà ripercorsa prima di tutto la legislazione sulla privacy dei dati, con particolare attenzione a quella relativa all'Unione Europea. Nel farlo, forniremo anche una panoramica delle tecnologie tradizionali di anonimizzazione dei dati. Successivamente, verrà fornita un'introduzione all'IA e al deep-learning. Per illustrare i meriti di questo campo, vengono discussi due casi di studio: uno relativo alla segmentazione delle immagini ed uno reltivo alla diagnosi del cancro. Si introducono poi i MGAP, con particolare attenzione agli autoencoder variazionali. L'applicazione di questi metodi ai dati tabellari e relazionali costituisce una utile innovazione in questo campo che comporta l’introduzione di tecniche innovative di pre-elaborazione. Infine, verrà valutata la metodologia sviluppata attraverso esperimenti riproducibili, considerando sia l'utilità analitica che il grado di protezione della privacy attraverso metriche statistiche.
Two trends have rapidly been redefining the artificial intelligence (AI) landscape over the past several decades. The first of these is the rapid technological developments that make increasingly sophisticated AI feasible. From a hardware point of view, this includes increased computational power and efficient data storage. From a conceptual and algorithmic viewpoint, fields such as machine learning have undergone a surge and synergies between AI and other disciplines have resulted in considerable developments. The second trend is the growing societal awareness around AI. While institutions are becoming increasingly aware that they have to adopt AI technology to stay competitive, issues such as data privacy and explainability have become part of public discourse. Combined, these developments result in a conundrum: AI can improve all aspects of our lives, from healthcare to environmental policy to business opportunities, but invoking it requires the use of sensitive data. Unfortunately, traditional anonymization techniques do not provide a reliable solution to this conundrum. They are insufficient in protecting personal data, but also reduce the analytic value of data through distortion. However, the emerging study of deep-learning generative models (DLGM) may form a more refined alternative to traditional anonymization. Originally conceived for image processing, these models capture probability distributions underlying datasets. Such distributions can subsequently be sampled, giving new data points not present in the original dataset. However, the overall distribution of synthetic datasets, consisting of data sampled in this manner, is equivalent to that of the original dataset. In our research activity, we study the use of DLGM as an enabling technology for wider AI adoption. To do so, we first study legislation around data privacy with an emphasis on the European Union. In doing so, we also provide an outline of traditional data anonymization technology. We then provide an introduction to AI and deep-learning. Two case studies are discussed to illustrate the field’s merits, namely image segmentation and cancer diagnosis. We then introduce DLGM, with an emphasis on variational autoencoders. The application of such methods to tabular and relational data is novel and involves innovative preprocessing techniques. Finally, we assess the developed methodology in reproducible experiments, evaluating both the analytic utility and the degree of privacy protection through statistical metrics.
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Gopinathan, Muraleekrishna. "Toward embodied navigation through vision and language." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2025. https://ro.ecu.edu.au/theses/2894.

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Embodied AI is a challenging but exciting field in which a robot learns to interact with human-living spaces to perform various tasks. This thesis studies the embodied navigation problem in which a robotic agent navigates in a previously unseen indoor environment based on a challenging task. In particular, the Vision-and-Language Navigation (VLN) task requires a robot to navigate based on a descriptive human-language instruction. This thesis aims to improve VLN agents on four key aspects - their understanding of the environment, training via additional data, correcting navigational errors, and predicting the layout of the environment for better planning. First, we address the planning aspect of VLN. We introduce our What Is Near (WIN) method to enhance navigation planning by predicting local neighbourhood maps using knowledge of living spaces. Next, we study the reverse problem of VLN, where instructions are generated from trajectory demonstrations. Our Spatially-Aware Speaker (SAS) model attends to panoramic visual context and action history to decode instructions. To enhance training, a Path Mixing dataset, derived from the existing expert annotated dataset, is used and adversarial training is applied to improve instruction variety. We observe that ambiguity in the instructions and environments leads to navigation errors and agents being lost. Our work, StratXplore, proposes the optimal navigation frontier by evaluating all available options stored in the agent’s memory based on novelty, recency, and instruction alignment. Finally, we aim to minimise the sim-to-real gap in VLN by focusing on environment mapping in a realistic indoor simulator. The research uses the Benchbot simulator, which features a photorealistic continuous action space and realistic sensors, to map objects in indoor environments under varying conditions and sensor noises. A 2D-3D fusion pipeline is developed to evaluate state-of-the-art 3D detection models in different simulated environments. Experimental results from each study show that our methods improve upon existing work. This thesis is an encouraging step towards realising intelligent social robots with applications in healthcare, education, and industry.
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Haidar, Ahmad. "Responsible Artificial Intelligence : Designing Frameworks for Ethical, Sustainable, and Risk-Aware Practices." Electronic Thesis or Diss., université Paris-Saclay, 2024. https://www.biblio.univ-evry.fr/theses/2024/interne/2024UPASI008.pdf.

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L'intelligence artificielle (IA) transforme rapidement le monde, redéfinissant les relations entre technologie et société. Cette thèse explore le besoin essentiel de développer, de gouverner et d'utiliser l'IA et l'IA générative (IAG) de manière responsable et durable. Elle traite des risques éthiques, des lacunes réglementaires et des défis associés aux systèmes d'IA, tout en proposant des cadres concrets pour promouvoir une Intelligence Artificielle Responsable (IAR) et une Innovation Numérique Responsable (INR).La thèse commence par une analyse approfondie de 27 déclarations éthiques mondiales sur l'IA pour identifier des principes dominants tels que la transparence, l'équité, la responsabilité et la durabilité. Bien que significatifs, ces principes manquent souvent d'outils pratiques pour leur mise en œuvre. Pour combler cette lacune, la deuxième étude de la recherche présente un cadre intégrateur pour l'IAR basé sur quatre dimensions : technique, IA pour la durabilité, juridique et gestion responsable de l'innovation.La troisième partie de la thèse porte sur l'INR à travers une étude qualitative basée sur 18 entretiens avec des gestionnaires de secteurs divers. Cinq dimensions clés sont identifiées : stratégie, défis spécifiques au numérique, indicateurs de performance organisationnels, impact sur les utilisateurs finaux et catalyseurs. Ces dimensions permettent aux entreprises d'adopter des pratiques d'innovation durable et responsable tout en surmontant les obstacles à leur mise en œuvre.La quatrième étude analyse les risques émergents liés à l'IAG, tels que la désinformation, les biais, les atteintes à la vie privée, les préoccupations environnementales et la suppression d'emplois. À partir d'un ensemble de 858 incidents, cette recherche utilise une régression logistique binaire pour examiner l'impact sociétal de ces risques. Les résultats soulignent l'urgence d'établir des cadres réglementaires renforcés, une responsabilité numérique des entreprises et une gouvernance éthique de l'IA.En conclusion, cette thèse apporte des contributions critiques aux domaines de l'INR et de l'IAR en évaluant les principes éthiques, en proposant des cadres intégratifs et en identifiant des risques émergents. Elle souligne l'importance d'aligner la gouvernance de l'IA sur les normes internationales afin de garantir que les technologies d'IA servent l'humanité de manière durable et équitable
Artificial Intelligence (AI) is rapidly transforming the world, redefining the relationship between technology and society. This thesis investigates the critical need for responsible and sustainable development, governance, and usage of AI and Generative AI (GAI). The study addresses the ethical risks, regulatory gaps, and challenges associated with AI systems while proposing actionable frameworks for fostering Responsible Artificial Intelligence (RAI) and Responsible Digital Innovation (RDI).The thesis begins with a comprehensive review of 27 global AI ethical declarations to identify dominant principles such as transparency, fairness, accountability, and sustainability. Despite their significance, these principles often lack the necessary tools for practical implementation. To address this gap, the second study in the research presents an integrative framework for RAI based on four dimensions: technical, AI for sustainability, legal, and responsible innovation management.The third part of the thesis focuses on RDI through a qualitative study of 18 interviews with managers from diverse sectors. Five key dimensions are identified: strategy, digital-specific challenges, organizational KPIs, end-user impact, and catalysts. These dimensions enable companies to adopt sustainable and responsible innovation practices while overcoming obstacles in implementation.The fourth study analyzes emerging risks from GAI, such as misinformation, disinformation, bias, privacy breaches, environmental concerns, and job displacement. Using a dataset of 858 incidents, this research employs binary logistic regression to examine the societal impact of these risks. The results highlight the urgent need for stronger regulatory frameworks, corporate digital responsibility, and ethical AI governance. Thus, this thesis provides critical contributions to the fields of RDI and RAI by evaluating ethical principles, proposing integrative frameworks, and identifying emerging risks. It emphasizes the importance of aligning AI governance with international standards to ensure that AI technologies serve humanity sustainably and equitably
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Hagström, Adrian, and Rustam Stanikzai. "Writer identification using semi-supervised GAN and LSR method on offline block characters." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-43316.

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Block characters are often used when filling out forms, for example when writing ones personal number. The question of whether or not there is recoverable, biometric (identity related) information within individual digits of hand written personal numbers is then relevant. This thesis investigates the question by using both handcrafted features and extracting features via Deep learning (DL) models, and successively limiting the amount of available training samples. Some recent works using DL have presented semi-supervised methods using Generative adveserial network (GAN) generated data together with a modified Label smoothing regularization (LSR) function. Using this training method might improve performance on a baseline fully supervised model when doing authentication. This work additionally proposes a novel modified LSR function named Bootstrap label smooting regularizer (BLSR) designed to mitigate some of the problems of previous methods, and is compared to the others. The DL feature extraction is done by training a ResNet50 model to recognize writers of a personal numbers and then extracting the feature vector from the second to last layer of the network.Results show a clear indication of recoverable identity related information within the hand written (personal number) digits in boxes. Our results indicate an authentication performance, expressed in Equal error rate (EER), of around 25% with handcrafted features. The same performance measured in EER was between 20-30% when using the features extracted from the DL model. The DL methods, while showing potential for greater performance than the handcrafted, seem to suffer from fluctuation (noisiness) of results, making conclusions on their use in practice hard to draw. Additionally when using 1-2 training samples the handcrafted features easily beat the DL methods.When using the LSR variant semi-supervised methods there is no noticeable performance boost and BLSR gets the second best results among the alternatives.
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Abdelghani, Rania. "Guider les esprits de demain : agents conversationnels pour entraîner la curiosité et la métacognition chez les jeunes apprenants." Electronic Thesis or Diss., Bordeaux, 2024. http://www.theses.fr/2024BORD0152.

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La curiosité épistémique (CE), i.e. le désir d’explorer une information pour le plaisir qu’elle procure, est un phénomène étudié dans divers domaines. Plusieurs chercheurs ont souligné son rôle fondamental dans le développement cognitif et la promotion d’un apprentissage continu. De plus, la CE est considérée comme clé pour cultiver un esprit capable de s’adapter aux incertitudes du monde. Ces recherches ont suscité un grand intérêt pour la CE en éducation, la considérant essentielle pour permettre aux individus d’être actifs et maîtres de leur apprentissage. Ce sont des propriétés cruciales pour relever certains des défis éducatifs: offrir aux élèves un soutien adapté à leurs compétences et motivations, et les aider à être des apprenants autonomes et indépendants dans des environnements dynamiques et incertains. Malgré son importance, l’implémentation de la CE dans les salles de classe reste limitée. Notamment, l’une des principales expressions de la CE—le questionnement— est presque absente dans la plupart des établissements: les élèves sont souvent amenés à répondre aux questions des enseignants plutôt qu’à poser les leurs. Et lorsqu’ils posent des questions, elles sont généralement de bas niveau et, contrairement aux questions curieuses, ne cherchent pas de nouvelles informations majorantes aux connaissances antérieures. Cette thèse propose donc de développer des technologies éducatives qui visent à favoriser l’apprentissage dirigé par la CE, en entraînant les comportements de questionnement curieux et les compétences qui lui sont liées. Pour cela, nous proposons des interventions pour entraîner trois dimensions:1) Les compétences linguistiques de questionnement: On implémente un agent conversationnel pour aider les élèves à générer des questions curieuses lors de tâches de lecture-compréhension. L’agent fournit des indices spécifiques pour faciliter l’utilisation des mots interrogatifs composés et des constructions interrogatives. Différentes structures d’indices (phrase vs. série de mots-clés) et leurs modes de génération (manuellement vs. par GPT-3) sont étudiées. 2) Les compétences métacognitives (MC) liées à la CE: On crée des vidéos animées pour donner des connaissances déclaratives sur les compétences MC liées à la CE: l’autoréflexion, faire des hypothèses, formuler des questions et évaluer les nouvelles informations. On propose également des sessions pour pratiquer ces compétences lors de tâches de lecture-compréhension, en utilisant des indices donnés par des agents conversationnels conçus pour entraîner la MC procédurale. 3) Les perceptions sociales: On crée des vidéos animées pour expliquer la CE et sa mise en pratique pour corriger les idées négatives qu’ont les apprenants sur ce concept. Plus de 150 élèves français âgés de 9 à 11 ans ont été recrutés pour tester l’entraînement de ces dimensions. Combinées, ces dernières ont amélioré la sensibilité MC des élèves et leur perception de la curiosité. Ces deux facteurs ont, à leur tout, facilité les comportements de questionnement divergent. Cela a également conduit à un progrès d’apprentissage plus fort et à des expériences d’apprentissage positives et soutenables. Mais malgré ces résultats, nos méthodes présentent certaines limites, en particulier leur courte durée. Cette thèse encourage donc le travail sur des solutions plus durables afin d’examiner les effets à long terme sur la CE. Enfin, cette thèse souligne la nécessité de continuer à explorer les recherches sur le questionnement et la MC à l’âge de l’intelligence artificielle générative (IAG). Bien que la IAG facilite l’accès à l’information, elle nécessite encore de bonnes capacités de questionnement et de MC, pour prévenir les mésuages et/ou faciliter leur détection. Nous proposons un Framework liant l’utilisation efficace de la IAG en éducation, les compétences de questionnement et de MC, et la littératie en IAG. Nous présentons également une étude comportementale pour tester ces relations
Epistemic curiosity—the desire to actively seek information for its inherent pleasure—is a complex phenomenon extensively studied across various domains. Several researchers in psychology, neuroscience, and computer science have repeatedly highlighted its foundational role in cognitive development and in fostering lifelong learning. Further, epistemic curiosity is considered key for cultivating a flexible mindset capable of adapting to the world’s uncertainties. These insights have spurred significant interest in the educational field, recognizing curiosity as essential for helping individuals be active and in control of their learning. These properties are crucial for addressing some of today’s major educational challenges, namely offering students individualized support to suit their competencies and motivations, and helping them become able to learn autonomously and independently in their dynamic and uncertain environments. Despite this well-documented importance of curiosity in education, its practical implementation and promotion in the classroom remains limited. Notably, one of the primary expressions of curiosity— question-asking (QA)—is nearly absent in most of today’s educational settings. Several reports show that students often spend a lot of time answering teachers’ questions rather than asking their own. And when they do ask questions, they are typically low-level and memory-based, as opposed to curious questions that seek novel information. In this context, this thesis aims to develop educational technologies that can foster children’s curiosity-driven learning by practicing curious QA behaviors, and their related metacognitive (MC) skills. Ultimately, we implemented interventions to train three dimensions: 1) Linguistic QA Skills: We implement a conversational agent to train the ability to formulate curious questions using compound questioning words and correct interrogative constructions. It helps children generate curious questions during reading-comprehension tasks, by providing specific cues. The effectiveness of different cue structures (a sentence vs. series of keywords) and implementations (hand-generated vs. GPT-3-generated content) is studied. 2) Curiosity-related metacognitive Skills: We create animated videos to give declarative knowledge about curiosity and its related MC skills: the ability to self reflect, make educated guesses, formulate efficient questions, and evaluate newly-acquired information. We also propose sessions to practice these skills during reading-comprehension tasks using specific cues given by conversational agents we designed to train procedural MC. 3) Social Perceptions and beliefs: We create animated videos to address the negative constructs learners tend to have about curiosity. They explain the importance of curiosity and how to control it during learning. Over 150 French students aged 9 to 11 were recruited to test these trainings of the three dimensions. Combined, these latter enhanced students’ MC sensitivity and perception of curiosity. At their turn, these factors facilitated students’ divergent QA behaviors which, at their turn, led to stronger learning progress and positive, affordable learning experiences. But despite the positive results, our methods had limitations, particularly their short duration. We suggest testing longer-lasting interventions to examine their long-term effects on curiosity. Finally, this thesis highlights the need to continue exploring QA and MC research in the age of Generative Artificial Intelligence (GAI). Indeed, while GAI facilitates access to information, it still requires good QA abilities and MC monitoring to prevent misinformation and facilitate its detection. We thus propose a framework to link efficient GAI use in education to QA and MC skills, and GAI literacy. We also present a behavioral study we intend to conduct to test this framework
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Books on the topic "Generative AI"

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Taulli, Tom. Generative AI. Berkeley, CA: Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9367-6.

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Huang, Ken, Yang Wang, Ben Goertzel, Yale Li, Sean Wright, and Jyoti Ponnapalli, eds. Generative AI Security. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-54252-7.

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Singh, Akansha, and Krishna Kant Singh, eds. Multimodal Generative AI. Singapore: Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-2355-6.

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Lyu, Zhihan, ed. Applications of Generative AI. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-46238-2.

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Narciso, Paolo. Generative AI in Education. Berkeley, CA: Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0844-9.

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Pratschke, B. Mairéad. Generative AI and Education. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-67991-9.

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Okadome, Takeshi. Essentials of Generative AI. Singapore: Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-0029-8.

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Bouzid, Ahmed, Paolo Narciso, and Weiye Ma. Generative AI For Executives. Berkeley, CA: Apress, 2024. https://doi.org/10.1007/979-8-8688-0950-7.

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Kulkarni, Akshay, Adarsha Shivananda, Anoosh Kulkarni, and Dilip Gudivada. Applied Generative AI for Beginners. Berkeley, CA: Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9994-4.

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Kansal, Aarushi. Building Generative AI-Powered Apps. Berkeley, CA: Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0205-8.

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Book chapters on the topic "Generative AI"

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Taulli, Tom. "AI Fundamentals." In Generative AI, 47–76. Berkeley, CA: Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9367-6_3.

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Das, Ravindra. "Introduction to Generative AI, Natural Language Processing, and the Digital Person." In Generative AI, 66–109. Boca Raton: CRC Press, 2024. http://dx.doi.org/10.1201/9781003503781-3.

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Das, Ravindra. "Introduction to Phishing." In Generative AI, 1–37. Boca Raton: CRC Press, 2024. http://dx.doi.org/10.1201/9781003503781-1.

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Das, Ravindra. "Overview of Artificial Intelligence, Neural Networks, and Machine Learning." In Generative AI, 38–65. Boca Raton: CRC Press, 2024. http://dx.doi.org/10.1201/9781003503781-2.

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Das, Ravindra. "Review of Cybersecurity Metrics." In Generative AI, 110–42. Boca Raton: CRC Press, 2024. http://dx.doi.org/10.1201/9781003503781-4.

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Das, Ravindra. "Conclusions." In Generative AI, 143–55. Boca Raton: CRC Press, 2024. http://dx.doi.org/10.1201/9781003503781-5.

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Taulli, Tom. "The Impact on Major Industries." In Generative AI, 175–88. Berkeley, CA: Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9367-6_8.

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Taulli, Tom. "Introduction to Generative AI." In Generative AI, 1–20. Berkeley, CA: Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9367-6_1.

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Taulli, Tom. "Core Generative AI Technology." In Generative AI, 77–91. Berkeley, CA: Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9367-6_4.

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Taulli, Tom. "The Future." In Generative AI, 189–202. Berkeley, CA: Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9367-6_9.

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Conference papers on the topic "Generative AI"

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Vinothkumar, S., S. Varadhaganapathy, R. Shanthakumari, S. Dhanushya, S. Guhan, and P. Krisvanth. "Utilizing Generative AI for Text-to-Image Generation." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10725454.

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Thakkar, Karan, Kunaal Vadgama, Krish Ranawat, Richa Sharma, and Monika Mangla. "Generative AI based Interior Designing." In 2024 International Conference on Electrical Electronics and Computing Technologies (ICEECT), 1–7. IEEE, 2024. http://dx.doi.org/10.1109/iceect61758.2024.10739260.

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Metwally, Mariam Alaa, and Milad Ghantous. "Detecting Generative AI in Images." In 2024 International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), 214–20. IEEE, 2024. https://doi.org/10.1109/miucc62295.2024.10783626.

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Menon, Gokulraj, Amal P. Peter, Ebin Jomon, Joe Varghese, and M. Anly Antony. "Apparel Customization Using Generative AI." In 2024 10th International Conference on Advanced Computing and Communication Systems (ICACCS), 1486–90. IEEE, 2024. http://dx.doi.org/10.1109/icaccs60874.2024.10717142.

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Kshirsagar, Vanita G., Digvijay G. Bhosale, Shubhangi Suryawanshi, Anita Sachin Mahajan, Pramod Patil, Jyotsna Vilas Barpute, Prashant G. Ahire, and Rahul A. Patil. "Generative AI Powered Forensic Device." In 2024 8th International Conference on Computing, Communication, Control and Automation (ICCUBEA), 1–6. IEEE, 2024. https://doi.org/10.1109/iccubea61740.2024.10774908.

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Pushkala, Sriharsha Anand. "Generative AI in battling Fraud." In 2024 IEEE 4th International Conference on ICT in Business Industry & Government (ICTBIG), 1–5. IEEE, 2024. https://doi.org/10.1109/ictbig64922.2024.10911802.

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Correa, Nelson, Antonio Correa, and Wlodek Zadrozny. "Generative AI for Consumer Communications: Classification, Summarization, Response Generation." In 2024 IEEE ANDESCON, 1–6. IEEE, 2024. http://dx.doi.org/10.1109/andescon61840.2024.10755794.

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Kim, Hyejin S. "RoboManuGen: Robot Data Generation Using Generative AI for Manufacturing." In 2024 15th International Conference on Information and Communication Technology Convergence (ICTC), 2001–4. IEEE, 2024. https://doi.org/10.1109/ictc62082.2024.10827732.

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Thummala, Sruthi, Saketh Thammishetti, Sharanya Varkol, Amarthya Thirunahari, and VVS Lakshmi Kanthey. "Event Management System Using Generative AI." In 2024 7th International Conference on Circuit Power and Computing Technologies (ICCPCT), 624–28. IEEE, 2024. http://dx.doi.org/10.1109/iccpct61902.2024.10673057.

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Borrison, Reuben, Markus Aleksy, and Marcel Dix. "Building Metadata Normalization Using Generative AI." In 2024 IEEE 19th Conference on Industrial Electronics and Applications (ICIEA), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/iciea61579.2024.10665241.

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Reports on the topic "Generative AI"

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Cevallos, Adrian, Lucia Latorre, Gianfranco Alicandro, Z’leste Wanner, Ignacio Cerrato, Jose Daniel Zarate, Juana Alvarez, et al. Tech Report Generative AI. Inter-American Development Bank, September 2023. http://dx.doi.org/10.18235/0005105.

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Generative AI is an emerging sub-domain of AI that is revolutionizing the use of technology as we know it. Its ability to generate new and unique content has great potential as a knowledge assistant, although it is still in the exploration phase. Instead of simply classifying, analyzing, or processing existing data, Generative AI attempts to generate new data that resembles the original and is indistinguishable from that created by humans.
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Brynjolfsson, Erik, Danielle Li, and Lindsey Raymond. Generative AI at Work. Cambridge, MA: National Bureau of Economic Research, April 2023. http://dx.doi.org/10.3386/w31161.

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Eisfeldt, Andrea, Gregor Schubert, and Miao Ben Zhang. Generative AI and Firm Values. Cambridge, MA: National Bureau of Economic Research, May 2023. http://dx.doi.org/10.3386/w31222.

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Beck, Sedefka, and Donka Brodersen. Generative AI in Economics: Teaching Economics and AI Literacy. The Economics Network, July 2024. http://dx.doi.org/10.53593/n4121a.

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Bown, Oliver. The challenges ahead for generative AI. Edited by Lachlan Guselli. Monash University, December 2023. http://dx.doi.org/10.54377/aac6-c1f6.

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Silver, Christina. Using Generative AI for Qualitative Analysis. Instats Inc., 2023. http://dx.doi.org/10.61700/qs6nzkddi20lw469.

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This comprehensive workshop is aimed at equipping researchers with the skills to utilize AI appropriately in their qualitative analysis. Covering a range of topics from understanding AI software tools to ethical considerations and troubleshooting, this seminar provides a unique opportunity to enhance research capabilities and gain a competitive edge in the academic field.
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Gans, Joshua. How will Generative AI impact Communication? Cambridge, MA: National Bureau of Economic Research, July 2024. http://dx.doi.org/10.3386/w32690.

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Bick, Alexander, Adam Blandin, and David J. Deming. The Rapid Adoption of Generative AI. Federal Reserve Bank of St. Louis, 2024. http://dx.doi.org/10.20955/wp.2024.027.

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Bick, Alexander, Adam Blandin, and David Deming. The Rapid Adoption of Generative AI. Cambridge, MA: National Bureau of Economic Research, September 2024. http://dx.doi.org/10.3386/w32966.

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Nguyen, Aiha, and Alexandra Mateescu. Generative AI's Labor Impact. Data & Society Research Institute, December 2024. https://doi.org/10.69985/gksj7804.

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As AI companies perpetuate the rhetoric that generative AI enhances efficiency and automates away drudgery - and pursue ambitions to automate everything from customer service to medical diagnoses - it is critical that claims about the technology's capabilities do not go unchallenged. This primer integrates workers' current and material experiences into public discourse and questions promotional language about the magical, infallible, and seemingly inscrutable qualities of AI. It underscores that AI does not exist outside of industries' current economic conditions and their attendant inequalities and power dynamics. While media speculation has focused on whether and how AI can either "augment" work or drive mass unemployment, looking at the technology's impact on different industries reveals a more complicated story. Generative AI and Labor: Power, Hype, and Value at Work shows that understanding how AI will affect work requires examining how work is organized, how industries are structured, and whose and what work is valued.
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