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Journal articles on the topic 'Generative Artificial Intelligence'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

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

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The general objective of the research is to determine the advances related to Generative Artificial Intelligence. Methodology, in this research, 47 documents have been selected, carried out in the period 2014 - 2023; including: scientific articles, review articles and information from websites of recognized organizations. Results, Generative Artificial Intelligence is demonstrating its importance in various human activities, making it necessary to use it ethically and responsibly. Conclusions, the general objective of the research is to determine the advances related to Generative Artificial I

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2

Warankar, Mayuri. "Generative Artificial Intelligence." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem31146.

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Generative AI is a cool tech that helps machines create new stuff that looks a lot like what humans make. Think of it like a computer artist who can paint pictures, write stories, compose music, and even make videos. This paper is all about explaining generative AI: where it came from, what it's doing now, and how it's used. It works by using fancy computer programs and smart algorithms to understand patterns in data and then make new things that fit those patterns. It's super important because it's changing how we do things in fields like healthcare, entertainment, and education. But, it's no

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Barradas-Palmeros, Jesús-Arnulfo, and Efrén Mezura-Montes. "Exploring Generative Artificial Intelligence." REVISTA PARAGUAYA DE EDUCACIÓN A DISTANCIA (REPED) 5, no. 4 (2024): 21–28. http://dx.doi.org/10.56152/reped2024-dossieria2-art3.

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This work reviews the concept of Generative Artificial Intelligence (GAI), its main characteristics, and its classification inside the Artificial Intelligence (AI) field. In this way, the main GAI foundational algorithmic architectures for data generation are presented; each considers a previously gathered data set and generates novel and synthetic data. Subsequently, use cases for generative models reported in the literature are presented, ranging from applications in healthcare, education, and customer experiences. The deployment of GAI models still needs to overcome a series of challenges i

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Corchado, Juan M., Sebastian López F., Juan M. Núñez V., Raul Garcia S., and Pablo Chamoso. "Generative Artificial Intelligence: Fundamentals." ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal 12, no. 1 (2023): e31704. http://dx.doi.org/10.14201/adcaij.31704.

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Generative language models have witnessed substantial traction, notably with the introduction of refined models aimed at more coherent user-AI interactions—principally conversational models. The epitome of this public attention has arguably been the refinement of the GPT-3 model into ChatGPT and its subsequent integration with auxiliary capabilities such as search features in Microsoft Bing. Despite voluminous prior research devoted to its developmental trajectory, the model’s performance, and applicability to a myriad of quotidian tasks remained nebulous and task specific. In terms of technol

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Denning, Peter J. "Artificial Intelligence: Generative AI." Ubiquity 2025, July (2025): 1–19. https://doi.org/10.1145/3747356.

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Large language models (LLMs) are the first neural network machines capable of carrying on conversations with humans. They are trained on billions of words of text scraped from the internet. They generate text responses to text inputs. They have transformed the public awareness of artificial intelligence, bringing on reactions ranging from astonishment and awe to trepidation and horror. They have spurred massive investments in new tools for drafting texts, summarizing conversations, summarizing literature, generating images, coding simple programs, supporting education, and amusing humans. Expe

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Wang, Shuheng. "Research on Generative Artificial Intelligence Management." International Journal of Computer Science and Information Technology 2, no. 2 (2024): 350–56. https://doi.org/10.62051/ijcsit.v2n2.41.

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As a new technology, generative artificial intelligence technology not only promotes technological change, but also brings many governance risks because of its unique technical logic. For example, the introduction of massive data sets in the training process of generative artificial intelligence has the risk of data infringement, and the reinforcement learning model based on human feedback may exacerbate the generation and dissemination of false information, thus bringing a series of risks. Although the current law of our country has formed a preliminary framework for the regulation of generat

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Huang, David. "Using generative artificial intelligence in text generation." IET Conference Proceedings 2024, no. 24 (2025): 264–67. https://doi.org/10.1049/icp.2024.4493.

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8

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

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9

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

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Technological innovation has transformed educational settings, enabling artificial intelligence (AI)-driven teaching and learning processes. While AI is still in its embryonic stage in education, generative artificial intelligence has evolved rapidly, significantly shifting the teaching and learning context. With no clarity about the impacts of generative artificial intelligence on education, there is a need to synthesise research findings to demystify generative artificial intelligence and address concerns regarding its application in the teaching and learning process. This paper systematical

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Kaur Sidhu, Brahmaleen. "Generative Artificial Intelligence: Unveiling the Potential and Challenges." International Journal of Science and Research (IJSR) 13, no. 4 (2024): 1127–35. http://dx.doi.org/10.21275/sr24414234432.

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Osadcha, Kateryna P., and Maryna V. Osadcha. "GENERATIVE ARTIFICIAL INTELLIGENCE VS HUMANS IN THE PROCESS OF CREATING CORPORATE IDENTITY ELEMENTS." Information Technologies and Learning Tools 98, no. 6 (2023): 212–30. http://dx.doi.org/10.33407/itlt.v98i6.5494.

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The emergence of new tools, the appearance of new technologies and improvements to existing ones have resulted in expansion of generative artificial intelligence. The technologies of generative artificial intelligence have already been used by people to perform not only intellectual tasks, but also creative ones, in particular in the field of design. Therefore, their capabilities in graphic design need to be studied. One of the routine tasks of a designer is the development of corporate identity elements (a logo, font, and colour). Designers can spend a lot of time on this, choosing different

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Timmerman, Jean Xia. "Educational Exposure to Generative Artificial Intelligence." FEDS Notes, no. 2025-02-26 (February 2025): None. https://doi.org/10.17016/2380-7172.3703.

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1.1 Generative AI Occupational Exposure (AIOE) MeasuresGenerative AI is distinct from prior forms of AI because of its ability to produce new content, and language modeling and image generation capture two key aspects of this. In this note, I use measures of generative AI occupational exposure (AIOE) to image generation and language modeling from Felten et al. (2023). Felten et al. (2021) define language modeling as the "ability to model, predict, or mimic human language" and image generation as the "the creation of complex images." Felten et al. (2021, 2023) constructed the AIOE measures usin

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Praloy Biswas. "Evaluating Generative Artificial Intelligence on Multilingual Sentiment Analysis." Journal of Electrical Systems 20, no. 3 (2024): 3502–13. http://dx.doi.org/10.52783/jes.4986.

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This study shows that natural language processing (NLP), large language models (LLM), and generative Artificial Intelligence are becoming the most important. LLM is considered essential for many NLP tasks, including question-answering, classification, interpretation, and writing. New LLMs must be able to analyze and create documents in different languages as they know how to train training materials in different languages such as ChatGPT, BLOOMZ, and others. Considering how frequently LLMs are used, it is critical to assess the effectiveness in multi-lingual environments. In a zero-shot contex

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Vinay Kumar Deeti. "Generative Artificial Intelligence in finance." International Journal of Science and Research Archive 12, no. 1 (2024): 3206–15. https://doi.org/10.30574/ijsra.2024.12.1.1091.

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Generative Artificial Intelligence drives substantial financial industry transformations because it helps develop and automate high-quality risk analysis models from both data-based selection and detailed automated operations. Through generative AI platforms financial institutions receive protection against fraud and achieve both automated trading solutions and custom financial systems using synthetic data platforms. Generative AI creates three core problems for financial services when used in their operations including model biases alongside privacy risks for safeguarding data along with conf

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Dimitrieska, Savica. "Generative Artificial Intelligence and Advertising." Trends in Economics, Finance and Management Journal 6, no. 1 (2024): 23–34. http://dx.doi.org/10.69648/eyzi2281.

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Generative artificial intelligence, the new buzzword in technology, is the next step in the evolution of traditional artificial intelligence. Unlike traditional AI that excels in data analyzing and automating processes, generative AI (GenAI) is a pioneer in creating new and original content. GenAI is very close to human intelligence, capable of logical thinking, imitating human behavior and armed with decision making capabilities. Generative AI creates new texts, images, music, 3D designs and codes, thus strongly influencing the activities, strategies, and consumer interactions of various indu

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Park, Pyungjong. "Artificial intelligence and Generative art." Journal of Contemporary Art Studies 28, no. 1 (2024): 39–64. http://dx.doi.org/10.29330/jcas.2024.06.28.1.39.

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17

Shabsigh, Ghiath. "Generative Artificial Intelligence in Finance." Fintech Notes 2023, no. 006 (2023): 1. http://dx.doi.org/10.5089/9798400251092.063.

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Lai, Paul B. S. "Generative artificial intelligence and surgeons." Surgical Practice 27, no. 3 (2023): 128–30. http://dx.doi.org/10.1111/1744-1633.12648.

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19

DeCotis, Paul A. "The Generative Artificial Intelligence Utility." Climate and Energy 40, no. 5 (2023): 21–26. http://dx.doi.org/10.1002/gas.22378.

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Generative Artificial Intelligence (Gen AI) is still in its infancy, yet it has started to transform the way people live, work, and play. The cutting‐edge technological innovation of Gen AI has unbounded potential and its contributions to the economy and society are still mostly hypothetical. In the short run we might overestimate its transformative impact and in the long run we might underestimate its impact. In either case, unprecedented change is upon us. Think of the changes to the ways in which we live, work, and play as technology evolved from wall‐mounted landline telephones to car phon

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Kshetri, Nir. "Generative Artificial Intelligence in Marketing." IT Professional 25, no. 5 (2023): 71–75. http://dx.doi.org/10.1109/mitp.2023.3314325.

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Wen, Chao Lung. "Medicine and generative artificial intelligence." Revista Brasileira de Medicina do Trabalho 22, no. 01 (2024): 01–03. http://dx.doi.org/10.47626/1679-4435-2024-221.

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Lee, In. "Generative Artificial Intelligence: Project Valuation." IT Professional 27, no. 2 (2025): 42–47. https://doi.org/10.1109/mitp.2025.3534817.

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Hurlburt, George F. "Prudence and Generative Artificial Intelligence." IT Professional 27, no. 2 (2025): 4–8. https://doi.org/10.1109/mitp.2025.3552883.

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Hasan, Sayyida S., Joshua J. Woo, Mark P. Cote, and Prem N. Ramkumar. "Generative Versus Nongenerative Artificial Intelligence." Arthroscopy: The Journal of Arthroscopic & Related Surgery 41, no. 3 (2025): 545–46. https://doi.org/10.1016/j.arthro.2024.12.001.

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25

Hasan, A. K. M. Kamrul. "Governance of Generative Artificial Intelligence." International Journal of Knowledge Management 21, no. 1 (2025): 1–21. https://doi.org/10.4018/ijkm.383061.

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The field of knowledge management and knowledge management systems is evolving and dynamic. In the era of developed information technology systems, the dynamics of knowledge creation and dissemination have also changed. Generative artificial intelligence (GenAI)—an embedded entity in the knowledge management system—has become a prominent area of research nowadays, while accountability, transparency, and ethics are common research agendas in institutional economics related to GenAI. The research in this paper has investigated the convictions behind GenAI adoption and how to develop a GenAI gove

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Furey, Pippa. "Generative artificial intelligence 6: simulation." Journal of Paramedic Practice 17, no. 6 (2025): 1–7. https://doi.org/10.12968/jpar.2025.17.6.cpd1.

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Simulation-based education plays a critical role in paramedic education by enabling learners to develop both clinical and non-technical skills in a safe, realistic environment. This article concludes a continuing professional development series on generative artificial intelligence (Gen AI), focusing on how this technology can support and enhance simulation design. It explores how prompt engineering can reduce facilitator workload, improve scenario consistency and support reflective practice through augmented debriefing. Practical examples of Gen AI-generated simulation prompts are presented a

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Ryabinin, A. E. "Education Testing System by Artificial Intelligence." Philosophical Problems of IT & Cyberspace (PhilIT&C), no. 2 (December 18, 2023): 90–107. http://dx.doi.org/10.17726/philit.2023.2.7.

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The article describes the possibilities of using and modifying existing machine learning technologies in the field of natural language processing for the purpose of designing a system for automatically generating control and test tasks (CTT). The reason for such studies was the limitations in generating theminimumrequired amount ofCTtomaintain student engagement in game-based learning formats, such as quizzes, and others. These limitations are associated with the lack of time resources among training professionals for manual generation of tests. The article discusses the applied research of th

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Sakirin, Tam, and Siddartha Kusuma. "A Survey of Generative Artificial Intelligence Techniques." Babylonian Journal of Artificial Intelligence 2023 (March 10, 2023): 10–14. http://dx.doi.org/10.58496/bjai/2023/003.

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Generative artificial intelligence (AI) refers to algorithms capable of creating novel, realistic digital content autonomously. Recently, generative models have attained groundbreaking results in domains like image and audio synthesis, spurring vast interest in the field. This paper surveys the landscape of modern techniques powering the rise of creative AI systems. We structurally examine predominant algorithmic approaches including generative adversarial networks (GANs), variational autoencoders (VAEs), and autoregressive models. Architectural innovations and illustrations of generated outpu

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Liu, Yitong. "Implications of generative artificial intelligence for the development of the media industry." Advances in Engineering Innovation 1, no. 1 (2023): 29–36. http://dx.doi.org/10.54254/2977-3903/1/2023006.

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As an important branch of artificial intelligence research, Generative Artificial Intelligence (GAI) mainly refers to artificial intelligence technology in which algorithms produce results under the given input data conditions. Generative artificial intelligence is one of the most active research directions in the field of artificial intelligence. Generative artificial intelligence has brought new technology innovation and content production mode. This paper discusses the new opportunities and challenges that generative artificial intelligence brings to the media industry from the aspects of t

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Liu, Yitong. "Implications of generative artificial intelligence for the development of the media industry." Advances in Engineering Innovation 1, no. 1 (2023): 29–36. http://dx.doi.org/10.54254//1/2023006.

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As an important branch of artificial intelligence research, Generative Artificial Intelligence (GAI) mainly refers to artificial intelligence technology in which algorithms produce results under the given input data conditions. Generative artificial intelligence is one of the most active research directions in the field of artificial intelligence. Generative artificial intelligence has brought new technology innovation and content production mode. This paper discusses the new opportunities and challenges that generative artificial intelligence brings to the media industry from the aspects of t

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Zavrazhnyi, Kostiantyn, Anzhelika Kulyk, and Olesia Antunes de Abreu. "THE INNOVATIVE IMPACT OF GENERATIVE ARTIFICIAL INTELLIGENCE ON DIGITAL BUSINESS TRANSFORMATION." Економіка розвитку систем 6, no. 2 (2024): 63–70. http://dx.doi.org/10.32782/2707-8019/2024-2-9.

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In the context of intensifying competition and evolving market dynamics, the deployment of cutting-edge technologies has become not merely a discretionary choice, but an indispensable imperative for any enterprise aspiring to achieve successful growth. Generative artificial intelligence, with its substantial potential for automation, personalisation and optimisation of business processes, is emerging as a highly promising avenue of digital transformation. This study is dedicated to investigating approaches and delineating strategies for aligning generative artificial intelligence with the requ

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Azionya, Caroline. "GPTs and digital superintermediaries: dynamics, dilemmas, dangers of generative AI – a conceptual framework." Communicare: Journal for Communication Studies in Africa 44, no. 1 (2025): 92–106. https://doi.org/10.36615/wfx19633.

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The arms race in generative artificial intelligence and artificial intelligence has transformed digital markets, with artificial-intelligence-powered platforms projected to drive market growth to nearly $740 billion by 2030. However, scholarly understanding of how these technologies affect platform competition remains limited. This article explores how generative pre-trained transformers influence digital superintermediaries’ market power and examines whether generative artificial intelligence capabilities reinforce or challenge existing platform dominance. Using a conceptual literature analys

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Balytska, Mariia, Martina Rašticová, Nataliia Versal, Ihor Honchar, Nataliia Prykaziuk, and Nataliia Tkalenko. "What drives economics students to use generative artificial intelligence?" Knowledge and Performance Management 8, no. 2 (2024): 51–64. http://dx.doi.org/10.21511/kpm.08(2).2024.05.

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The increasing integration of Artificial Intelligence (AI) into education requires studying the motives for its use among students. This study aims to identify the key motivations for economics students to use AI and compare these motivations by grade level and gender. The study examines satisfaction with the use of AI and analyzes the number of AI tools used. An anonymous empirical study was conducted among 264 students from the Faculty of Economics at Taras Shevchenko National University of Kyiv, Ukraine. Data analysis included descriptive statistical methods, non-parametric statistical meth

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Yoşumaz, İsmail. "Generative Artificial Intelligence and Usage in Academia." Fırat Üniversitesi Sosyal Bilimler Dergisi 35, no. 1 (2025): 1–24. https://doi.org/10.18069/firatsbed.1423208.

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Artificial intelligence is not a new concept. However, it has reached an important point with technological development. Today, there are many software developed using artificial intelligence and various application areas where they are used. Generative artificial intelligence, one of these areas, is a technology in machine learning of artificial intelligence, aiming to generate new content by training on large data sets. Generative artificial intelligence is used in fields such as health, business, finance, e-commerce, academic studies, and R&D. This study evaluates the use of generative

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Wang, Xiaolong, Zhijian He, and Xiaojiang Peng. "Artificial-Intelligence-Generated Content with Diffusion Models: A Literature Review." Mathematics 12, no. 7 (2024): 977. http://dx.doi.org/10.3390/math12070977.

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Diffusion models have swiftly taken the lead in generative modeling, establishing unprecedented standards for producing high-quality, varied outputs. Unlike Generative Adversarial Networks (GANs)—once considered the gold standard in this realm—diffusion models bring several unique benefits to the table. They are renowned for generating outputs that more accurately reflect the complexity of real-world data, showcase a wider array of diversity, and are based on a training approach that is comparatively more straightforward and stable. This survey aims to offer an exhaustive overview of both the

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Kim, Kang Han. "The effective guarantee of data subject rights in response to the generative artificial intelligence era : Focusing on the review of the Personal Information Protection Act." Korean Constitutional Law Association 30, no. 4 (2024): 301–42. https://doi.org/10.35901/kjcl.2024.30.4.301.

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Generative artificial intelligence is equipped with more advanced functions than existing artificial intelligence technology, raising new problems that were difficult to predict in the past. For example, generative artificial intelligence handles and handles data containing personal information in the stage of learning and processing vast amounts of data, and in this process, it is very difficult for individual data subjects to recognize and control that their information is processed for generative artificial intelligence learning. In addition, unlike existing general artificial intelligence,

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Vukmirović, Dragan, and Dejana Kresović. "The transformative potential of generative artificial intelligence." Napredak 5, no. 2 (2024): 29–42. http://dx.doi.org/10.5937/napredak5-52069.

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This paper analyses the transformative potential of generative artificial intelligence at macro, meso, and micro levels of social and economic structures. The aim is to determine the impact of these technologies on various aspects of society and economy, including business operations and the labour market. The potential of new technologies to increase productivity, transform business models, and create new professional roles has been examined through a comprehensive analysis of data and studies. It has been concluded that generative artificial intelligence can fundamentally change the labour m

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Liu, Yuexi, Zhaokai Liang, and Jiangang Zhang. "Generative AI Reshaping International Trade Pattern: How Do Foreign Trade Enterprises Seize Opportunities." Advances in Economics, Management and Political Sciences 79, no. 1 (2024): 226–31. http://dx.doi.org/10.54254/2754-1169/79/20241828.

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The year 2023 is the first year of the outbreak of generative artificial intelligence applications, and digital transformation has also become the development trend of international trade today. Digital transformation, driven by the rapid development and adoption of generative AI technologies, has also become the dominant trend of international trade in the contemporary world. Generative AI, exemplified by ChatGPT, a powerful natural language generation model, has attracted global attention and sparked heated discussions about its implications and possibilities in different fields. Generative

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Cicero, Francesco. "L’ITALIANO DELLE INTELLIGENZE ARTIFICIALI GENERATIVE." Italiano LinguaDue 15, no. 2 (2023): 733–61. http://dx.doi.org/10.54103/2037-3597/21990.

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 I chatbot basati su modelli linguistici di grandi dimensioni sono un tipo di intelligenza artificiale in grado di interagire con gli utenti rispondendo con un testo coerente e contestualmente rilevante a un ampio numero di richieste. Si tratta di una tecnologia in rapido sviluppo, che promette di cambiare il modo in cui alcuni tipi di informazioni sono confezionati e diffusi: sempre più spesso ci si troverà a leggere un testo composto da una macchina. Questo articolo evidenzia alcuni aspetti essenziali dell’italiano delle intelligenze artificiali generative, attraverso l’analisi dei tes

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Kalota, Faisal. "A Primer on Generative Artificial Intelligence." Education Sciences 14, no. 2 (2024): 172. http://dx.doi.org/10.3390/educsci14020172.

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Many educators and professionals in different industries may need to become more familiar with the basic concepts of artificial intelligence (AI) and generative artificial intelligence (Gen-AI). Therefore, this paper aims to introduce some of the basic concepts of AI and Gen-AI. The approach of this explanatory paper is first to introduce some of the underlying concepts, such as artificial intelligence, machine learning, deep learning, artificial neural networks, and large language models (LLMs), that would allow the reader to better understand generative AI. The paper also discusses some of t

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Tivari, Gaurav D., Dr Satvik Khara, Dr Jay A. Dave, and Vishwa Patel. "Enhancing Reality: Exploring the Potential of Generative Artificial Intelligence." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 07 (2024): 1–13. http://dx.doi.org/10.55041/ijsrem36378.

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This paper delves into the realm of recent advancements in artificial intelligence, with a particular focus on Generative AI. Generative AI, an emerging field within AI, leverages machine learning algorithms and neural networks to generate original content across various mediums such as images, music, speech, and text. Its potential to revolutionize industries like advertising, gaming, and healthcare through personalized content creation, task automation, and enhanced accuracy in complex endeavors like drug discovery and medical diagnosis is profound. We explore different models of Generative

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Li, Jing. "Managing Copyright Infringement Risks in Generative Artificial Intelligence Data Mining." Highlights in Business, Economics and Management 39 (August 8, 2024): 282–87. http://dx.doi.org/10.54097/3p0kpc23.

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In the era of artificial intelligence, the rapid development of generative artificial intelligence represented by ChatGPT brings great convenience to human creation, but also causes many potential copyright risks and brings a series of new challenges to the field of intellectual property. The creative process of generative artificial intelligence mainly includes four stages: training data input, data learning, input instruction and content generation. Among them, the legal use of copyright works in the data input stage needs to be solved instantly. In order to solve this problem, we should fir

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Jovanovic, Mladan, and Mark Campbell. "Generative Artificial Intelligence: Trends and Prospects." Computer 55, no. 10 (2022): 107–12. http://dx.doi.org/10.1109/mc.2022.3192720.

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Liakos, Konstantinos G., Georgios K. Georgakilas, Fotis C. Plessas, and Paris Kitsos. "GAINESIS: Generative Artificial Intelligence NEtlists SynthesIS." Electronics 11, no. 2 (2022): 245. http://dx.doi.org/10.3390/electronics11020245.

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A significant problem in the field of hardware security consists of hardware trojan (HT) viruses. The insertion of HTs into a circuit can be applied for each phase of the circuit chain of production. HTs degrade the infected circuit, destroy it or leak encrypted data. Nowadays, efforts are being made to address HTs through machine learning (ML) techniques, mainly for the gate-level netlist (GLN) phase, but there are some restrictions. Specifically, the number and variety of normal and infected circuits that exist through the free public libraries, such as Trust-HUB, are based on the few sample

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Nawar, Tamer. "Generative Artificial Intelligence and Authorship Gaps." American Philosophical Quarterly 61, no. 4 (2024): 355–67. http://dx.doi.org/10.5406/21521123.61.4.05.

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Abstract The ever increasing use of generative artificial intelligence raises significant questions about authorship and related issues such as credit and accountability. In this paper, I consider whether works produced by means of users inputting natural language prompts into Generative Adversarial Networks are works of authorship. I argue that they are not. This is not due to concerns about randomness or machine-assistance compromising human labor or intellectual vision, but instead due to the syntactical and compositional limitations of existing AI systems in handling natural language promp

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The Lancet. "Rethinking research and generative artificial intelligence." Lancet 404, no. 10447 (2024): 1. http://dx.doi.org/10.1016/s0140-6736(24)01394-1.

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Chauhan, Arun. "Generative artificial intelligence and misinformation warfare." International Journal of Multidisciplinary Research and Growth Evaluation 5, no. 4 (2024): 997–1002. http://dx.doi.org/10.54660/.ijmrge.2024.5.4.997-1002.

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Abstract:

This research paper explores the intersection of generative AI and misinformation warfare, focusing on the sophisticated capabilities of AI technologies like Generative Adversarial Networks (GANs) and their potential misuse in creating deceptive content. Utilizing a mixed-methods approach, the study combines an extensive literature review with empirical data from a survey of 125 respondents, revealing high levels of awareness and concern about AI-generated misinformation. The findings indicate that AI-generated misinformation significantly impacts public opinion and societal trust, highlightin

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Hegde, Shrikanth N. "Generative Artificial Intelligence and Scientific Publications." APIK Journal of Internal Medicine 12, no. 4 (2024): 197–98. http://dx.doi.org/10.4103/ajim.ajim_62_24.

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Oh, Jaeryung. "Generative Artificial Intelligence and Publication Ethics." Journal of Retina 9, no. 2 (2024): 99–103. https://doi.org/10.21561/jor.2024.9.2.99.

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Davidian, Ken. "New Space and Generative Artificial Intelligence." New Space 11, no. 3 (2023): 148–50. http://dx.doi.org/10.1089/space.2023.29050.editorial.

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