Relevant bibliographies by topics / Generative AI

Academic literature on the topic 'Generative AI'

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Published: 14 September 2024

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Journal articles on the topic "Generative AI"

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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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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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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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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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Idrisov, Baskhad, and Tim Schlippe. "Program Code Generation with Generative AIs." Algorithms 17, no. 2 (January 31, 2024): 62. http://dx.doi.org/10.3390/a17020062.

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Our paper compares the correctness, efficiency, and maintainability of human-generated and AI-generated program code. For that, we analyzed the computational resources of AI- and human-generated program code using metrics such as time and space complexity as well as runtime and memory usage. Additionally, we evaluated the maintainability using metrics such as lines of code, cyclomatic complexity, Halstead complexity and maintainability index. For our experiments, we had generative AIs produce program code in Java, Python, and C++ that solves problems defined on the competition coding website leetcode.com. We selected six LeetCode problems of varying difficulty, resulting in 18 program codes generated by each generative AI. GitHub Copilot, powered by Codex (GPT-3.0), performed best, solving 9 of the 18 problems (50.0%), whereas CodeWhisperer did not solve a single problem. BingAI Chat (GPT-4.0) generated correct program code for seven problems (38.9%), ChatGPT (GPT-3.5) and Code Llama (Llama 2) for four problems (22.2%) and StarCoder and InstructCodeT5+ for only one problem (5.6%). Surprisingly, although ChatGPT generated only four correct program codes, it was the only generative AI capable of providing a correct solution to a coding problem of difficulty level hard. In summary, 26 AI-generated codes (20.6%) solve the respective problem. For 11 AI-generated incorrect codes (8.7%), only minimal modifications to the program code are necessary to solve the problem, which results in time savings between 8.9% and even 71.3% in comparison to programming the program code from scratch.
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Yang, Yue. "The Study of Copyright Infringement Liability of Generative Artificial Intelligence." Lecture Notes in Education Psychology and Public Media 34, no. 1 (January 3, 2024): 88–95. http://dx.doi.org/10.54254/2753-7048/34/20231893.

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As AI continues to produce content at an unprecedented rate, it is essential to establish a robust legal framework to protect the rights of creators and appropriately assign liability in cases of copyright infringement. Generative AI technology has the characteristics of high data demand, strong human-computer interaction, weak interpretability, and weak stability. The main civil subjects in generative AI services are generative AI service providers and users. In the pre-training stage, if generative AI uses copyrighted works without authorization, it should be recognized as copyright infringement, and the relevant infringement liability should be borne by generative AI service providers. In the content generation stage, the high similarity between AI-generated content and prior works can be attributed to various factors, including flaws in the generative model, intentional design choices, and user input and guidance. Both the generative AI service provider and the user bear direct tort liability for the infringement. However, the question of whether generative AI service providers also bear indirect tort liability should be explored, considering their role and similarities to traditional ISPs. Generative AI service providers should fulfill their obligations, take appropriate actions to prevent infringement, and report any illegal activities to the relevant authorities.
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Alexander, Kuznetsov. "THE ANALYSIS OF THE EFFICIENCY OF GENERATIVE AI ALGORITHMS FOR CREATING A NATURAL DIALOGUE." American Journal of Interdisciplinary Innovations and Research 6, no. 6 (June 1, 2024): 26–34. http://dx.doi.org/10.37547/tajiir/volume06issue06-08.

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In the modern world, artificial intelligence (AI) plays an increasingly important role in various fields of human activity. One of the most promising areas of AI application is the generation of natural dialogue. The purpose of this work is to analyze the efficiency of generative AI algorithms for creating natural dialogue. The relevance of this topic is due to the growing interest in the use of AI to create dialogue systems capable of interacting with people in a natural way. The results of the study can be useful for developers of dialogue systems, researchers in the field of AI, as well as anyone interested in the application of AI in their everyday life. Natural language generation is a fundamental task in artificial intelligence, with applications ranging from chatbots to virtual assistants. This study provides a comprehensive analysis of the efficiency of various generative artificial intelligence algorithms for creating a natural dialogue. Their performance is assessed in generating consistent and contextually appropriate responses by evaluating modern models using quantitative metrics and human evaluation. Additionally, the study explores the impact of various training data sizes and techniques on the quality of a generated dialogue. The results provide insight into the strengths and weaknesses of current generative AI approaches in the generation of a dialogue.
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Dissertations / Theses on the topic "Generative AI"

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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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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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Santiago, Dionny. "A Model-Based AI-Driven Test Generation System." FIU Digital Commons, 2018. https://digitalcommons.fiu.edu/etd/3878.

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Achieving high software quality today involves manual analysis, test planning, documentation of testing strategy and test cases, and development of automated test scripts to support regression testing. This thesis is motivated by the opportunity to bridge the gap between current test automation and true test automation by investigating learning-based solutions to software testing. We present an approach that combines a trainable web component classifier, a test case description language, and a trainable test generation and execution system that can learn to generate new test cases. Training data was collected and hand-labeled across 7 systems, 95 web pages, and 17,360 elements. A total of 250 test flows were also manually hand-crafted for training purposes. Various machine learning algorithms were evaluated. Results showed that Random Forest classifiers performed well on several web component classification problems. In addition, Long Short-Term Memory neural networks were able to model and generate new valid test flows.
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Olsen, Linnéa. "Can Chatbot technologies answer work email needs? : A case study on work email needs in an accounting firm." Thesis, Karlstads universitet, Handelshögskolan (from 2013), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-85013.

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Work email is one of the organisations most critical tool today. It`s have become a standard way to communicate internally and externally. It can also affect our well-being. Email overload has become a well-known issue for many people. With interviews, follow up interviews, and a workshop, three persons from an accounting firm prioritise pre-define emails needs. And identified several other email needs that were added to the priority list. A thematic analysis and summarizing of a Likert scale was conducted to identify underlying work email needs and work email needs that are not apparent. Three work email needs were selected and using scenario-based methods and the elements of PACT to investigating how the characteristics of a chatbot can help solve the identified work email overload issue? The result shows that email overload is percept different from individual to individual. The choice of how email is handled and email activities indicate how email overload feeling is experienced. The result shows a need to get a sense of the email content quickly, fast collect financial information and information from Swedish authorities, and repetitive, time-consuming tasks. Suggestions on how this problem can be solved have been put forward for many years, and how to use machine learning to help reduce email overload. However, many of these proposed solutions have not yet been implemented on a full scale. One conclusion may be that since email overload is not experienced in the same way, individuals have different needs - One solution does not fit all. With the help of the character of a chatbot, many problems can be solved. And with a technological character of a chatbot that can learn individuals' email patterns, suggest email task to the user and performing tasks to reducing the email overload perception. Using keyword for email intents to get a sense of the email content faster and produce quick links where to find information about the identified subject. And to work preventive give the user remainder and perform repetitive tasks on specific dates.
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Goldstein, ép Lejuste Déborah. "La transformation numérique des TPE/PME traditionnelles comme catalyseur du développement économique territorial : enjeux et impacts socio- économiques." Electronic Thesis or Diss., Limoges, 2024. http://www.theses.fr/2024LIMO0023.

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La révolution induite par la transformation numérique des très petites entreprises (TPE) et des petites et moyennes entreprises (PME) est sans précédent, impulsée par l'émergence rapide des technologies numériques. Cette mutation dépasse largement la simple modernisation des outils ; elle engendre un changement profond dans la manière dont ces entreprises interagissent avec leur environnement économique et social. Cette thèse soutient que cette transformation constitue un processus stratégique intégrant de manière exhaustive et novatrice les technologies digitales dans tous les aspects d'une organisation. Elle explore en profondeur comment la transformation numérique des TPE/PME traditionnelles peut agir comme un catalyseur du développement économique territorial, en analysant ses enjeux et impacts socio-économiques. En combinant des approches qualitative et quantitative, elle aborde la problématique sous plusieurs angles, incluant les dimensions organisationnelles, managériales et territoriales. Structurée autour de quatre axes d'analyse distincts, cette thèse par articles examine l'aspect stratégique de la transformation numérique et son rôle dans la résilience territoriale, la gestion des externalités générées par cette transformation, l'évolution du rôle du dirigeant, et l'impact de l'utilisation de l'IA générative dans la gestion des données et la prise de décision. Au-delà des résultats individuels des articles, plusieurs conclusions transversales émergent de la recherche, mettant en lumière l'importance croissante du numérique pour les TPE/PME traditionnelles, tout en soulignant la nécessité d'une approche équilibrée entre outils numériques et interactions humaines. En intégrant une évolution numérique de la théorie de la construction stratégique par le chef d’entreprise développée par Henry Mintzberg, cette thèse met en avant des recommandations pour les dirigeants et les institutions. Ces recommandations visent à promouvoir la culture numérique, faciliter la collaboration et offrir un soutien personnalisé pour la mise en œuvre de la transformation numérique au sein des entreprises. Notamment, cette approche, en mettant en évidence une perception différente du numérique et de la transformation numérique dans l'entreprise, favorise le développement d'écosystèmes mis en perspective avec l’attractivité territoriale. Enfin, cette thèse aspire à aider les acteurs économiques et institutionnels à naviguer avec succès dans l'ère numérique, en intégrant les principes de sobriété numérique et de responsabilité environnementale dans leurs stratégies, tout en favorisant l'innovation et la compétitivité
The revolution induced by the digital transformation of very small enterprises (VSEs) and small and medium-sized enterprises (SMEs) is unprecedented, driven by the rapid emergence of digital technologies. This change goes far beyond mere tool modernization; it brings about a profound shift in how these businesses interact with their economic and social environment. This thesis argues that this transformation constitutes a strategic process that comprehensively and innovatively integrates digital technologies into all aspects of an organization. It delves into how the digital transformation of traditional VSEs/SMEs can act as a catalyst for territorial economic development, analyzing its socio-economic issues and impacts. By combining qualitative and quantitative approaches, it addresses the issue from various angles, including organizational, managerial, and territorial dimensions. Structured around four distinct axes of analysis, this thesis through articles examines the strategic aspect of digital transformation and its role in territorial resilience, the management of externalities generated by this transformation, the evolution of the role of leadership, and the impact of using generative AI in data management and decision-making. Beyond the individual findings of the articles, several cross-cutting conclusions emerge from the research, highlighting the growing importance of digitalization for traditional VSEs/SMEs while underscoring the need for a balanced approach between digital tools and human interactions. By integrating a digital evolution of the theory of strategic construction by the business leader developed by Henry Mintzberg, this thesis puts forward recommendations for leaders and institutions. These recommendations aim to promote digital culture, facilitate collaboration, and provide personalized support for the implementation of digital transformation within businesses. Importantly, this approach, by highlighting a different perception of digitalization and digital transformation within the company, fosters the development of ecosystems in perspective with territorial attractiveness. Finally, this thesis aims to assist economic and institutional actors in successfully navigating the digital era by integrating principles of digital sobriety and environmental responsibility into their strategies, while fostering innovation and competitiveness
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Ko, Kai-Chung. "Protocol test sequence generation and analysis using AI techniques." Thesis, University of British Columbia, 1990. http://hdl.handle.net/2429/29192.

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This thesis addresses two major issues in protocol conformance testing: test sequence generation and test result analysis. For test sequence generation, a new approach based on the constraint satisfaction problem (CSP) techniques, which is widely used in the AI community, is presented. This method constructs a unique test sequence for a given FSM by using an initial test sequence, such as a transition tour or an UIO test sequence, and incrementally generating a set of test subsequences which together represent the constraints imposed on the overall structure of the FSM. The new method not only generates test sequence with fault coverage which is at least as good as the one provided by the existing methods, but also allows the implementation under test (IUT) to have a larger number of states than that in the specification. In addition, the new method also lends itself naturally to both test result analysis and fault coverage measurement. For test result analysis, the CSP method uses the observed sequence as the initial sequence, constructs all fault models which satisfy the initial sequence and introduces additional subsequences to pinpoint the IUT fault model. In addition, a second method for test result analysis is proposed, which is originated from a model of diagnostic reasoning from first principle, another well-known AI techniques which produces all minimal diagnoses by considering the overall consistency of the system together with the observation. Unlike the first method, the second method does not require the computation of all fault models explicitly, and hence is considered to be more suitable for large systems. To our knowledge, the proposed methods in this thesis represent the first attempt in applying AI techniques to the problem of protocol test sequence generation and analysis.
Science, Faculty of
Computer Science, Department of
Graduate
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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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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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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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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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Cronin, Irena. Understanding Generative AI Business Applications. Berkeley, CA: Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0282-9.

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Parra Pennefather, Patrick. Creative Prototyping with Generative AI. Berkeley, CA: Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9579-3.

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Nguyen-Duc, Anh, Pekka Abrahamsson, and Foutse Khomh, eds. Generative AI for Effective Software Development. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-55642-5.

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Beckingham, Sue, Jenny Lawrence, Stephen Powell, and Peter Hartley. Using Generative AI Effectively in Higher Education. London: Routledge, 2024. http://dx.doi.org/10.4324/9781003482918.

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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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Ahmed Benraouane, Sid. "Generative AI." In AI Management System Certification According to the ISO/IEC 42001 Standard, 9–18. New York: Productivity Press, 2024. http://dx.doi.org/10.4324/9781003463979-3.

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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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Taulli, Tom. "Data." In Generative AI, 21–45. Berkeley, CA: Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9367-6_2.

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Taulli, Tom. "Auto Code Generation." In Generative AI, 127–43. Berkeley, CA: Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9367-6_6.

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Taulli, Tom. "Large Language Models." In Generative AI, 93–125. Berkeley, CA: Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9367-6_5.

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Taulli, Tom. "The Transformation of Business." In Generative AI, 145–74. Berkeley, CA: Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9367-6_7.

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Conference papers on the topic "Generative AI"

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Dhar, Rudra, Karthik Vaidhyanathan, and Vasudeva Varma. "Leveraging Generative AI for Architecture Knowledge Management." In 2024 IEEE 21st International Conference on Software Architecture Companion (ICSA-C), 163–66. IEEE, 2024. http://dx.doi.org/10.1109/icsa-c63560.2024.00034.

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Helmy, Mona, Omar Sobhy, and Farida ElHusseiny. "AI-Driven Testing: Unleashing Autonomous Systems for Superior Software Quality Using Generative AI." In 2024 International Telecommunications Conference (ITC-Egypt), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/itc-egypt61547.2024.10620598.

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Ding, Sherry, and Veda Raman. "Harness the Power of Generative AI in Healthcare with Amazon AI/ML Services." In 2024 IEEE 12th International Conference on Healthcare Informatics (ICHI), 490–92. IEEE, 2024. http://dx.doi.org/10.1109/ichi61247.2024.00070.

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Tang, Jie, Yuxiao Dong, and Michalis Vazirgiannis. "Generative AI Day." In KDD '24: The 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 6699–700. New York, NY, USA: ACM, 2024. http://dx.doi.org/10.1145/3637528.3673872.

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Ikoma, Daisuke, Eisuke Aoki, Tomoki Taniguchi, Shinya Suzuki, and Tomoko Ohkuma. "Automatic Design Summary Generation with Generative AI." In WWW '24: The ACM Web Conference 2024. New York, NY, USA: ACM, 2024. http://dx.doi.org/10.1145/3589335.3651901.

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Fischer, Joel E. "Generative AI Considered Harmful." In CUI '23: ACM conference on Conversational User Interfaces. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3571884.3603756.

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Young, Darrell L., Perry Boyette, James Moreland, and Jason Teske. "Generative AI agile assistant." In Disruptive Technologies in Information Sciences VIII, edited by Bryant T. Wysocki, Misty Blowers, and Ramesh Bharadwaj. SPIE, 2024. http://dx.doi.org/10.1117/12.3011173.

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Kodali, Ravi Kishore, Yatendra Prasad Upreti, and Lakshmi Boppana. "Generative AI in Education." In 2023 IEEE 15th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM). IEEE, 2023. http://dx.doi.org/10.1109/hnicem60674.2023.10589199.

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Sasaki, Ryoichi. "AI and Security - What Changes with Generative AI." In 2023 IEEE 23rd International Conference on Software Quality, Reliability, and Security Companion (QRS-C). IEEE, 2023. http://dx.doi.org/10.1109/qrs-c60940.2023.00043.

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Vaz, Diogo, David R. Matos, Miguel L. Pardal, and Miguel Correia. "Automatic Generation of Distributed Algorithms with Generative AI." In 2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks - Supplemental Volume (DSN-S). IEEE, 2023. http://dx.doi.org/10.1109/dsn-s58398.2023.00037.

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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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Baytas, Claire, and Dylan Ruediger. Generative AI in Higher Education: The Product Landscape. Ithaka S+R, March 2024. http://dx.doi.org/10.18665/sr.320394.

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Bengio, Yoshua, Caroline Lequesne, Hugo Loiseau, Jocelyn Maclure, Juliette Powell, Sonja Solomun, and Lyse Langlois. Interdisciplinary Dialogues: The Major Risks of Generative AI. Observatoire international sur les impacts sociétaux de l’intelligence artificielle et du numérique, March 2024. http://dx.doi.org/10.61737/xsgm9843.

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In an exciting series of Interdisciplinary Dialogues on the societal impacts of AI, we invite a guest speaker and panellists from the fields of science and engineering, health and humanities and social sciences to discuss the advances, challenges and opportunities raised by AI. The first dialogue in this series began with Yoshua Bengio, who, concerned about developments in generative AI and the major risks they pose for society, initiated the organization of a conference on the subject. The event took place on August 14, 2023 in Montreal, and was aimed at initiating collective, interdisciplinary reflection on the issues and risks posed by recent developments in AI. The conference took the form of a panel, moderated by Juliette Powell, to which seven specialists were invited who cover a variety of disciplines, including: computer science (Yoshua Bengio and Golnoosh Farnadi), law (Caroline Lequesne and Claire Boine), philosophy (Jocelyn Maclure), communication (Sonja Solomun) and political science (Hugo Loiseau). This document is the result of this first interdisciplinary dialogue on the societal impacts of AI. The speakers were invited to respond concisely, in the language of their choice, to questions raised during the event. Immerse yourself in reading these fascinating conversations, presented in a Q&A format that transcends disciplinary boundaries. The aim of these dialogues is to offer a critical and diverse perspective on the impact of AI on our everchanging world.
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Souppaya, Murugiah. Secure Development Practices for Generative AI and Dual-Use Foundation AI Models: An SSDF Community Profile. Gaithersburg, MD: National Institute of Standards and Technology, 2024. http://dx.doi.org/10.6028/nist.sp.800-218a.ipd.

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