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Статті в журналах з теми "Generative AI"
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.
Повний текст джерелаEuchner, Jim. "Generative AI." Research-Technology Management 66, no. 3 (April 20, 2023): 71–74. http://dx.doi.org/10.1080/08956308.2023.2188861.
Повний текст джерелаSwaroopa, 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.
Повний текст джерелаRathod, 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.
Повний текст джерелаKars, 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.
Повний текст джерелаLuttrell, 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.
Повний текст джерелаAli, 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.
Повний текст джерелаIdrisov, 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.
Повний текст джерелаYang, 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.
Повний текст джерелаAlexander, 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.
Повний текст джерелаДисертації з теми "Generative AI"
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.
Повний текст джерелаMisino, Eleonora. "Deep Generative Models with Probabilistic Logic Priors." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24058/.
Повний текст джерелаMennborg, 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.
Повний текст джерелаAlabdallah, 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.
Повний текст джерелаPANFILO, 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.
Повний текст джерелаTwo 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.
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.
Повний текст джерелаAbdelghani, 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.
Повний текст джерелаEpistemic 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
Santiago, Dionny. "A Model-Based AI-Driven Test Generation System." FIU Digital Commons, 2018. https://digitalcommons.fiu.edu/etd/3878.
Повний текст джерелаOlsen, 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.
Повний текст джерелаGoldstein, 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.
Повний текст джерелаThe 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
Книги з теми "Generative AI"
Taulli, Tom. Generative AI. Berkeley, CA: Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9367-6.
Повний текст джерелаHuang, 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.
Повний текст джерелаLyu, Zhihan, ed. Applications of Generative AI. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-46238-2.
Повний текст джерелаPratschke, B. Mairéad. Generative AI and Education. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-67991-9.
Повний текст джерелаKulkarni, 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.
Повний текст джерелаKansal, Aarushi. Building Generative AI-Powered Apps. Berkeley, CA: Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0205-8.
Повний текст джерелаCronin, Irena. Understanding Generative AI Business Applications. Berkeley, CA: Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0282-9.
Повний текст джерелаParra Pennefather, Patrick. Creative Prototyping with Generative AI. Berkeley, CA: Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9579-3.
Повний текст джерелаNguyen-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.
Повний текст джерелаBeckingham, 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.
Повний текст джерелаЧастини книг з теми "Generative AI"
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.
Повний текст джерелаAhmed 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.
Повний текст джерелаTaulli, 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.
Повний текст джерелаTaulli, 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.
Повний текст джерелаTaulli, 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.
Повний текст джерелаTaulli, Tom. "The Future." In Generative AI, 189–202. Berkeley, CA: Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9367-6_9.
Повний текст джерелаTaulli, Tom. "Data." In Generative AI, 21–45. Berkeley, CA: Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9367-6_2.
Повний текст джерелаTaulli, 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.
Повний текст джерелаTaulli, 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.
Повний текст джерелаTaulli, 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.
Повний текст джерелаТези доповідей конференцій з теми "Generative AI"
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.
Повний текст джерелаHelmy, 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.
Повний текст джерелаDing, 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.
Повний текст джерелаTang, 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.
Повний текст джерелаIkoma, 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.
Повний текст джерелаFischer, 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.
Повний текст джерелаYoung, 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.
Повний текст джерелаKodali, 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.
Повний текст джерелаSasaki, 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.
Повний текст джерелаVaz, 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.
Повний текст джерелаЗвіти організацій з теми "Generative AI"
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.
Повний текст джерелаBrynjolfsson, 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.
Повний текст джерелаEisfeldt, 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.
Повний текст джерелаBeck, 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.
Повний текст джерелаBown, Oliver. The challenges ahead for generative AI. Edited by Lachlan Guselli. Monash University, December 2023. http://dx.doi.org/10.54377/aac6-c1f6.
Повний текст джерелаSilver, Christina. Using Generative AI for Qualitative Analysis. Instats Inc., 2023. http://dx.doi.org/10.61700/qs6nzkddi20lw469.
Повний текст джерелаGans, Joshua. How will Generative AI impact Communication? Cambridge, MA: National Bureau of Economic Research, July 2024. http://dx.doi.org/10.3386/w32690.
Повний текст джерелаBaytas, 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.
Повний текст джерелаBengio, 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.
Повний текст джерелаSouppaya, 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.
Повний текст джерела