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1
Koda, Masahide. "Generative AI." Okayama Igakkai Zasshi (Journal of Okayama Medical Association) 136, no. 3 (December 2, 2024): 139–40. https://doi.org/10.4044/joma.136.139.
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Krishna, Guntamukkala Gopi. "Generative AI." International Journal of Advanced Engineering and Nano Technology 10, no. 8 (August 30, 2023): 1–3. http://dx.doi.org/10.35940/ijaent.g0474.0810823.
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Recent advancements in generative artificial intelligence (AI) have made it possible for machines to independently produce a variety of creative content. In the context of producing creative content, this essay examines the developments, difficulties, and ethical issues relating to generative AI. It looks into how generative models, such Generative Adversarial Networks (GANs) and Variational Auto encoders (VAEs), can produce realistic artwork like music, literature, and visuals. However, it is frequently discovered that GAN training is extremely unstable and frequently experiences non-convergence, mode collapse, and hyperparameter sensitivity [1]. The technical details of developing and optimizing generative models to produce desired results are covered in detail in this work. It also looks at the difficulties in guaranteeing the variety, creativity, and coherence of generated content. Additionally, the use of generative AI in the creation of original material raises ethical questions. Included in this are concerns about intellectual property, plagiarism, and possible effects on the creative industries. In specifically, the article explores the consequences of employing generative AI for content production in terms of authorship, human creativity, and the possible disruption of traditional creative practices. It also covers issues with fairness, bias, and appropriate application of generative models.
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Euchner, Jim. "Generative AI." Research-Technology Management 66, no. 3 (April 20, 2023): 71–74. http://dx.doi.org/10.1080/08956308.2023.2188861.
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Swaroopa, Pethota. "StoryCraft AI: Exploring Generative Approaches to Story Narration through AI." International Scientific Journal of Engineering and Management 03, no. 04 (April 23, 2024): 1–9. http://dx.doi.org/10.55041/isjem01633.
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In this project, we demonstrate a Storytelling AI system, which can generate short stories and complementary illustrated images with minimal input from the user. The system makes use of a text generation model, a text-to-image synthesis network, and a neural style transfer model. The final project is deployed into a website where users can build their stories. The field of AI has made significant changes in various domains, including Natural Language Processing and Generative models. One captivating application of these advancements is in the realm of storytelling. This project introduces a novel approach to story narration using a Generative AI model, specifically leveraging the GPT architecture. Traditional storytelling involves human creativity, imagination, and the ability to craft engaging narratives. However, the integration of AI into storytelling brings about new opportunities and challenges. In this project, we delve into the methodologies and techniques used to train a GPT-based model for generating coherent and captivating stories. In the end, we found that using computers for storytelling can be exciting, but we need to work together to ensure the stories are great and meaningful. Key Words: Artificial Intelligence (AI), Generative Adversarial Network (GAN), Generative Pre-trained Transformer (GPT)
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Rathod, Rohan. "Renaissance on Generative AI." International Journal for Research in Applied Science and Engineering Technology 12, no. 6 (June 30, 2024): 1354–59. http://dx.doi.org/10.22214/ijraset.2024.63324.
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Abstract: Generative Artificial Intelligence (Generative AI) stands at the forefront of innovation, promising to revolutionize creative content generation across various domains. This paper delves into the multifaceted implications of Generative AI in reshaping the landscape of artistic expression. Through an extensive literature survey and analysis, we explore the applications, advancements, and challenges of Generative AI in text generation, visual arts, and music composition. From state-of-the-art models like OpenAI's GPT series to cutting-edge techniques such as Generative Adversarial Networks (GANs) and Transformer architectures, Generative AI enables the automated creation of diverse and high-quality content. However, ethical considerations regarding authenticity, bias, and ownership of AI-generated content remain paramount. By uncovering key findings and insights, this paper aims to guide future research, development, and responsible integration of Generative AI in fostering a renaissance of artistic innovation and collaboration
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Luttrell, Regina, and Nicholas Bowman. "Generating Deep Discussion Around Generative AI." Newhouse Impact Journal 1, no. 1 (2024): 1–2. http://dx.doi.org/10.14305/jn.29960819.2024.1.1.10.
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Kars, Muhammet Emir. "Generative AI in Education." London Journal of Social Sciences, no. 6 (September 20, 2023): 144–51. http://dx.doi.org/10.31039/ljss.2023.6.114.
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Recently, the field of artificial intelligence (AI) has advanced significantly, with generativeAI rising to the top of the tech industry's most-discussed subjects list. Education like manyother fields could be transformed by generative AI like ChatGPT, Bard, DALL-E,Midjourney, and DeepMind, which all have the ability to revolutionize a number of industrieswith all the benefits and drawbacks they entail.The paper begins by providing an overview of generative AI, highlighting its capacity togenerate human-like text, images, and even interactive simulations. It delves into theunderlying principles and techniques that empower generative AI models, focusing onprominent models like ChatGPT and Midjourney.To assess the effectiveness of generative AI in educational contexts, the paper examinesstudies that have evaluated learning outcomes, student engagement, and teacher support.These findings provide insights into the efficiency of generative AI as a supplementaryeducational tool and its role in fostering innovative teaching practices. The paper also addressesconcerns around the technology being too integrated into education like over dependence andcheating.Finally, the paper discusses future possibilities and challenges for the integration of generativeAI in education. It proposes strategies for maximizing the benefits of this technology whileensuring ethical considerations are met. The paper concludes with a call for further researchand collaboration between AI experts, educators, policymakers, and other stakeholders toharness the full potential of generative AI in transforming the learning landscape.
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Johnson, Russell. "Guiding generative AI." Nature Chemical Biology 21, no. 3 (February 25, 2025): 311. https://doi.org/10.1038/s41589-025-01854-y.
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Ali, Safinah, Prerna Ravi, Randi Williams, Daniella DiPaola, and Cynthia Breazeal. "Constructing Dreams Using Generative AI." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 21 (March 24, 2024): 23268–75. http://dx.doi.org/10.1609/aaai.v38i21.30374.
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Generative AI tools introduce new and accessible forms of media creation for youth. They also raise ethical concerns about the generation of fake media, data protection, privacy and ownership of AI-generated art. Since generative AI is already being used in products used by youth, it is critical that they understand how these tools work and how they can be used or misused. In this work, we facilitated students’ generative AI learning through expression of their imagined future identities. We designed a learning workshop - Dreaming with AI - where students learned about the inner workings of generative AI tools, used text-to-image generation algorithms to create their imaged future dreams, reflected on the potential benefits and harms of generative AI tools and voiced their opinions about policies for the use of these tools in classrooms. In this paper, we present the learning activities and experiences of 34 high school students who engaged in our workshops. Students reached creative learning objectives by using prompt engineering to create their future dreams, gained technical knowledge by learning the abilities, limitations, text-visual mappings and applications of generative AI, and identified most potential societal benefits and harms of generative AI.
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Agrawal, Vishakha. "Systems for Generative AI: Challenges and Opportunities." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (January 23, 2025): 1–7. https://doi.org/10.55041/ijsrem30153.
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Abstract—The emergence of large-scale generative AI models, such as those powering text, image, and code generation, has introduced unprecedented systems challenges distinct from tradi- tional AI applications. This paper examines the unique demands of generative AI systems, focusing on the specific computational patterns of generative models, their distinctive inference re- quirements, and the specialized infrastructure needed for token generation, prompt processing, and context management. We analyze the particular challenges and opportunities in building systems optimized for generative workloads. Keywords - Generative AI , Autoregressive, Batch Process- ing, Prompt Caching, Prompt Template Management
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Gabriel, Sonja. "Generative AI in Writing Workshops: A Path to AI Literacy." International Conference on AI Research 4, no. 1 (December 4, 2024): 126–32. https://doi.org/10.34190/icair.4.1.3022.
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The widespread use of generative AI tools which can support or even take over several part of the writing process has sparked many discussions about integrity, AI literacy and changes to academic writing processes. This paper explores the impact of generative artificial intelligence (AI) tools on the academic writing pro-cess, drawing on data from a writing workshop and interviews with university students of a university teacher college in Austria. Despite the widespread assumption that generative AI, such as ChatGPT, is widely used by students to support their academic tasks, initial findings suggest a notable gap in participants' experience and understanding of these technologies. This discrepancy highlights the critical need for AI literacy and underscores the importance of familiarity with the potential, challenges and risks associated with generative AI to ensure its ethical and effective use. Through reflective discussions and feedback from work-shop participants, this study shows a differentiated perspective on the role of generative AI in academic writing, illustrating its value in generating ideas and overcoming writer's block, as well as its limitations due to the indispensable nature of human involvement in critical writing tasks.
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KIM, YOUN-SUNG. "A Study on how to Secure Competitiveness through Case Analysis of Media and Contents using Generative AI." International Journal of Religion 5, no. 12 (December 9, 2024): 1795–803. https://doi.org/10.61707/9r4wdc33.
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Due to the ChatGPT craze, all industries around the world have a hot Generative AI of interest. In particular, since Open AI announced AI Sora, which makes text into video on February 15, 2024, the media and content industries, both at home and abroad, have expressed expectations and a sense of crisis. By learning a large amount of Hyper-scale Data with artificial intelligence technology that actively generates results according to the specific needs of live Generative AI users, it is looking beyond the realm of creation, which can be called the human domain[1][2][3]. Although, unlike image-generating AI, the video live Generative AI service still has the limit of generating only short-length videos because it has to maintain temporal coherence. However, as live Generative AI developers supplement these problems and continue to offer advanced services, they will become the mainstream of live Generative AI in the media and content industries. Therefore, as of 2024, this study intends to present 3I(Inquiry-Inspection-Idea) as a strategy for competitive advantage in the media and content industries to respond to changes in the media and content industries that will be triggered by generative Generative AI along with ways to secure competitiveness through case analysis of generative Generative AI in the media and content industries[2].
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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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Moon, Su-Ji. "Effects of Perception of Potential Risk in Generative AI on Attitudes and Intention to Use." International Journal on Advanced Science, Engineering and Information Technology 14, no. 5 (October 30, 2024): 1748–55. http://dx.doi.org/10.18517/ijaseit.14.5.20445.
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Generative artificial intelligence (AI) is rapidly advancing, offering numerous benefits to society while presenting unforeseen potential risks. This study aims to identify these potential risks through a comprehensive literature review and investigate how user’s perceptions of risk factors influence their attitudes and intentions to use generative AI technologies. Specifically, we examined the impact of four key risk factors: fake news generation, trust, bias, and privacy concerns. Our analysis of data collected from experienced generative AI users yielded several significant findings: First, users' perceptions of fake news generation by generative AI were found to have a significant negative impact on their attitudes towards these technologies. Second, user trust in generative AI positively influenced both attitudes toward and intentions to use these technologies. Third, users' awareness of potential biases in generative AI systems was shown to affect their attitudes towards these technologies negatively. Fourth, while users' privacy concerns regarding generative AI did not significantly impact their usage intentions directly, these concerns negatively influenced their overall attitudes toward the technology. Fifth, users' attitudes towards generative AI influenced their intentions to use these technologies positively. Based on the above results, to increase the intention to use generated artificial intelligence, legal, institutional, and technical countermeasures should be prepared for fake news generation, trust issues, bias, and privacy concerns while improving users' negative perceptions through literacy education on generated artificial intelligence, and education that can be used desirable and efficiently.
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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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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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Nahla, Febri, and Anis Masruri. "Analysis of the Impact of Artificial Intelligence on Information-Seeking Behavior." JPUA: Jurnal Perpustakaan Universitas Airlangga: Media Informasi dan Komunikasi Kepustakawanan 14, no. 2 (December 2, 2024): 69–75. https://doi.org/10.20473/jpua.v14i2.2024.69-75.
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Background of the study: The need for information in the era of globalization is rapidly growing, causing users to strategize to obtain information effectively. One of the strategies used in information retrieval is generative AI applications. Purpose: This study aims to discuss generative AI applications used in information retrieval and the impact of generative AI usage on information-seeking behavior. Methods: Library research is used to collect data from various relevant literature sources. Finding: The use of generative AI in information retrieval provides positive impacts, such as speeding up the search process and presenting concise and easily understood information. However, the use of generative AI also has negative effects, such as inaccurate data and potential dependence on technology. Conclusion: Although generative AI provides convenience in information retrieval, there are negative risks, such as data inaccuracy and overdependence on technology. Therefore, regulations and oversight are needed to prevent these negative impacts ABSTRAK Latar Belakang: Kebutuhan informasi di era globalisasi semakin berkembang pesat, yang menyebabkan para pengguna informasi harus mengatur strategi untuk mendapatkan informasi secara efektif. Salah satu strategi yang digunakan adalah dengan memanfaatkan AI generatif. Tujuan: Penelitian ini bertujuan untuk membahas aplikasi-aplikasi AI Generatif yang digunakan dalam pencarian informasi serta dampak dari penggunaan AI generatif terhadap perilaku informasi. Metode: Metode penelitian yang digunakan adalah library research dengan pengumpulan data dari berbagai sumber literatur yang relevan. Temuan: Penggunaan AI generatif dalam pencarian informasi memberi dampak positif, seperti mempercepat proses pencarian dan menyajikan informasi yang ringkas dan mudah dipahami. Meskipun demikian, penggunaan AI generatif juga menimbulkan dampak negatif, yaitu data yang kurang akurat dan potensi ketergantungan pada teknologi. Kesimpulan: Meskipun AI generatif memberikan kemudahan dalam pencarian informasi, terdapat resiko negatif seperti ketidakakuratan data dan ketergantungan berlebihan pada teknologi. Oleh karena itu, diperlukan aturan dan pengawasan untuk mencegah dampak negatif tersebut.
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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 (July 10, 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 AI, highlighting their strengths and limitations. Despite being in its early stages, Generative AI presents a promising avenue for research and development, offering numerous unexplored opportunities. Examples of prominent Generative AI models such as ChatGPT and DALL-E are provided, elucidating their applications across diverse domains. Looking forward, the potential applications of Generative AI are vast, including the development of virtual assistants for human interaction, bolstering cybersecurity, and designing intelligent robots for industrial tasks. As Generative AI continues to advance, it holds the promise of driving innovation and transformation across industries, paving the way for growth and progress in the future. Key Words: Generative AI, artificial intelligence, content generation, machine learning, neural networks, industry applications, innovation.
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Journal, IJSREM. "Generative AI for Healthcare: Applications, Challenges, and Ethical Considerations." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (December 10, 2024): 1–6. https://doi.org/10.55041/ijsrem39600.
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Generative Artificial Intelligence (AI) is rapidly transforming the healthcare sector, offering novel approaches to medical imaging, drug discovery, personalized medicine, and data privacy through the generation of synthetic datasets. This paper explores the applications, challenges, and ethical considerations surrounding the use of generative AI in healthcare. Key applications of this technology include enhancing diagnostic capabilities by generating high-quality medical images, accelerating the drug discovery process by simulating chemical compounds, and tailoring treatment plans through personalized medicine. Generative AI's ability to create synthetic patient data also provides a promising solution for safeguarding patient privacy while advancing medical research. However, the integration of generative AI into healthcare is met with several challenges. These include data quality issues, which can compromise the accuracy and reliability of AI-generated outputs, and the black-box nature of many AI models, making it difficult for healthcare professionals to fully understand or trust the systems. Moreover, the technical limitations, such as high computational costs and the difficulty of integrating AI with existing healthcare infrastructure, pose additional barriers to widespread adoption. The ethical considerations of generative AI in healthcare are equally significant. Concerns over patient privacy and data security remain central, particularly when synthetic data is generated and used for research purposes. Furthermore, the potential for algorithmic bias to influence healthcare outcomes raises questions about fairness and equity in AI-driven decisions. Establishing clear lines of accountability and ensuring that AI systems comply with existing regulatory frameworks are essential for building trust and safeguarding patient well-being. Looking forward, the paper highlights the importance of developing explainable AI systems that offer greater transparency and integration with human decision-making processes. Future advancements in personalized medicine and drug discovery will rely on cross-disciplinary collaboration between AI researchers, healthcare professionals, and policymakers. Ultimately, the paper emphasizes that while generative AI holds tremendous potential for revolutionizing healthcare, its success will depend on addressing both the technical and ethical challenges it presents. Keywords: Generative Artificial Intelligence, Medical Imaging, Healthcare, Personalized Medicine, Synthetic Datasets, Diagnostic Capabilities, Patient Privacy, Explainable AI, Algorithmic Bias
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Hu, Fangfei, Shiyuan Liu, Xinrui Cheng, Pengyou Guo, and Mengqi Yu. "Risks of Generative Artificial Intelligence and Multi-Tool Governance." Academic Journal of Management and Social Sciences 9, no. 2 (November 26, 2024): 88–93. http://dx.doi.org/10.54097/stvem930.
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While generative AI, represented by ChatGPT, brings a technological revolution and convenience to life, it may raise a series of social and legal risks, mainly including violation of personal privacy and data security issues, infringement of intellectual property rights, generation of misleading and false content, and exacerbation of discrimination and prejudice. However, the traditional AI governance paradigm oriented towards conventional AI may not be adequately adapted to generative AI with generalized potential and based on big models. In order to encourage the innovative development of generative AI technology and regulate the risks, this paper explores the construction of a generative AI governance paradigm that combines legal regulation, technological regulation, and ethical governance of science and technology, and promotes the healthy development of generative AI on the track of safety, order, fairness, and co-governance.
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Yan, Tingting. "Research on Personal Information Protection in the Context of Generative Artificial Intelligence." Scientific Journal Of Humanities and Social Sciences 6, no. 7 (July 13, 2024): 116–24. http://dx.doi.org/10.54691/vhyxgj39.
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Personal information is the foundation of generative AI, and ChatGPT-like generative AI needs to process a large amount of personal information at various stages such as model training, model generation, and model optimization, which also has a certain impact on traditional personal information protection rules. During the information collection phase, generative AI may fugitive the informed consent rules and infringe on the privacy rights of information subjects. In the information utilization stage, generative AI may impact basic personal information processing rules such as the principle of purpose limitation and the principle of openness and transparency, increasing the risk of personal information leakage. At the information generation stage, generative AI can generate false and discriminatory information. Therefore, in the context of generative AI, personal information protection is faced with the problems of the notification and consent rules being hollowed out, the principle of minimum necessity being voided, and the frequent leakage of personal information. Based on this, it is necessary to promote the transformation of the "personal control center to the risk control center" of the notification and consent rules, promote the risk-based interpretation of the principle of least necessary, and improve the risk-based personal information protection compliance system to solve the problem of personal information protection in the context of generative AI.
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Baraheem, Samah S., and Tam V. Nguyen. "AI vs. AI: Can AI Detect AI-Generated Images?" Journal of Imaging 9, no. 10 (September 28, 2023): 199. http://dx.doi.org/10.3390/jimaging9100199.
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The proliferation of Artificial Intelligence (AI) models such as Generative Adversarial Networks (GANs) has shown impressive success in image synthesis. Artificial GAN-based synthesized images have been widely spread over the Internet with the advancement in generating naturalistic and photo-realistic images. This might have the ability to improve content and media; however, it also constitutes a threat with regard to legitimacy, authenticity, and security. Moreover, implementing an automated system that is able to detect and recognize GAN-generated images is significant for image synthesis models as an evaluation tool, regardless of the input modality. To this end, we propose a framework for reliably detecting AI-generated images from real ones through Convolutional Neural Networks (CNNs). First, GAN-generated images were collected based on different tasks and different architectures to help with the generalization. Then, transfer learning was applied. Finally, several Class Activation Maps (CAM) were integrated to determine the discriminative regions that guided the classification model in its decision. Our approach achieved 100% on our dataset, i.e., Real or Synthetic Images (RSI), and a superior performance on other datasets and configurations in terms of its accuracy. Hence, it can be used as an evaluation tool in image generation. Our best detector was a pre-trained EfficientNetB4 fine-tuned on our dataset with a batch size of 64 and an initial learning rate of 0.001 for 20 epochs. Adam was used as an optimizer, and learning rate reduction along with data augmentation were incorporated.
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Quintais, João Pedro. "Generative AI, copyright and the AI Act." Computer Law & Security Review 56 (April 2025): 106107. https://doi.org/10.1016/j.clsr.2025.106107.
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Panda, Meethun. "GENERATIVE AI FOR DATA-CENTRIC AI DEVELOPMENT." INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING AND TECHNOLOGY 16, no. 1 (January 30, 2025): 1521–34. https://doi.org/10.34218/ijcet_16_01_112.
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Vartiainen, Henriikka, Matti Tedre, and Ilkka Jormanainen. "Co-creating digital art with generative AI in K-9 education: Socio-material insights." International Journal of Education Through Art 19, no. 3 (September 1, 2023): 405–23. http://dx.doi.org/10.1386/eta_00143_1.
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The rise of image-generating artificial intelligence (AI) tools has triggered changes in digital art and graphic design, provoking debates in the creative industry. However, scant research exists about children’s and youths’ insights into and encounters with generative AI. Building on sociocultural and new materialist perspectives, this exploratory study proposed to address this gap by exploring middle schoolers’ (N = 10) creative interaction with generative AI, particularly with text-to-image generative models. Qualitative content analyses of emerging learning activities evidenced how generative AI-formed relations were externalized through novel digital artefacts and collaborative discussions. Ideas evolved through peer collaboration organized around creative making with AI. Teachers facilitated relations between people and technology using dialogic teaching, providing room for unpredictability and critical reflection on the impacts of generative AI, especially authorship and copyright. The study concludes with a discussion of the potential uses of generative AI in future art education research and practice.
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R, Prof Seema. "STABLE DIFFUSION TEXT TO IMAGE USING AI." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (May 8, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem33350.
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The Stable Diffusion Text-to-Image Generation Project is an innovative endeavor in the field of generative adversarial networks (GANs) and natural language processing (NLP). This project aims to bridge the semantic gap between textual descriptions and visual content by utilizing the Stable Diffusion training framework to generate highly realistic and coherent images from text prompts. The project leverages recent advancements in deep learning techniques to tackle the challenging task of text-to image synthesis. The project introduces an innovative approach at the crossroads of generative adversarial networks (GANs) and natural language processing (NLP). It aims to bridge the semantic gap between textual descriptions and visual content by utilizing the Stable Diffusion training framework. The key goal is to generate highly realistic and coherent images from text prompts, leveraging recent deep learning Stable Diffusion. The Stable Diffusion training framework plays a central role in this project. It’s a sophisticated training methodology for GANs, designed to stabilize the training process. GANs have exhibited great potential in generating images but often suffer from issues like mode collapse and training instability. Keywords: Stable Diffusion, Text-to-Image Generation, Image Synthesis, Natural Language Processing(NLP), Generative Adversarial Networks (GANs)
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Hari Kiran Vuyyuru. "How AI Chatbot works: Simplifying the Magic of Conversational AI." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 1 (January 13, 2025): 645–52. https://doi.org/10.32628/cseit25111273.
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This comprehensive article explores the fundamental mechanisms and capabilities of Generative AI, examining how this advanced language model achieves human-like conversational abilities. The article delves into the system's core components, including its sophisticated tokenization processes, context management mechanisms, next-token prediction capabilities, and training methodologies enhanced through human feedback. Through a detailed analysis of recent research findings, the article demonstrates how Generative AI chatbot transcends simple pattern matching to achieve complex reasoning, creative generation, and adaptive communication abilities across various specialized domains. Special attention is given to the model's applications in healthcare, education, and enterprise settings, highlighting its remarkable achievements and inherent limitations compared to human cognition.
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Sinha, Himanshu. "The Identification of Network Intrusions with Generative Artificial Intelligence Approach for Cybersecurity." Journal of Web Applications and Cyber Security 2, no. 2 (October 9, 2024): 20–29. http://dx.doi.org/10.48001/jowacs.2024.2220-29.
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Generative Artificial Intelligence (Generative AI) offers a paradigm shift to the way robots perceive and interact with data. Generative AI approaches aim to produce new data samples that closely match the original dataset, in contrast to standard AI models that concentrate on tasks such as categorisation or prediction. This ability has broad implications for a variety of fields, including network security. New generation of network intrusion defense brought forth by Generative AI is revolution using the area of network security. In this work, investigates the revolutionary potential of Generative AI in network intrusion detection, with the goal of developing a proactive and adaptable cyber protection mechanism. In this work the use of Generative AI use for identifying network intrusions, using the NSL-KDD dataset. The work begins with meticulous data preprocessing using handling missing values, encoding, feature scaling, and feature extraction to enhance the dataset and then focuses on using Generative AI for intrusion detection. In this work many models have been used like SVM, AE, DAE, and GANs, and in this GANs has the best accuracy with 91.12% compared with other models.
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Bryndin, Evgeniy. "Formation of reflexive generative A.I. with ethical measures of use." Research on Intelligent Manufacturing and Assembly 3, no. 1 (November 1, 2024): 109–17. http://dx.doi.org/10.25082/rima.2024.01.003.
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The application of reflexive generative AI in the social sphere will improve the quality of life of individuals and society. Its commercial application will require compliance with ethical standard measures to ensure that its use does not cause harm. The development, implementation and use of an ethical standard for the use of reflexive generative AI will increase the safety of its use. The ethical use of generative AI by individuals should be automatically regulated by it. The reflection of generative AI is implemented by the AGI multilogic and ensures the validity of content generation.
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Balch, Thomas. "Copyright and Generative AI." Journal of IP in Practice 1, no. 1 (December 24, 2024): 41–44. https://doi.org/10.5750/jipp.v1i1.2198.
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This is an opinion piece looking at the UK legal copyright framework, human creativity, generative artificial intelligence, and if you need to get permission from the creators or owners of on-line content such as writing, photos, music and film to scrape their content to build AI systems. There is huge concern in the UK’s economically vital and “soft power” driving creative industries, especially when the AI output competes directly with the very content that is used to make the generative AI tool work in the first place. There are essentially two opposing views and this article presents one side.
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Barqawi, Laila, and Mohammad Abdallah. "Copyright and generative AI." Journal of Infrastructure, Policy and Development 8, no. 8 (August 6, 2024): 6253. http://dx.doi.org/10.24294/jipd.v8i8.6253.
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This research investigates the relationship between Generative Artificial Intelligence (GAI), media content, and copyright laws. As GAI technologies continue to evolve and permeate various aspects of the media landscape, questions regarding the creation and protection of intellectual property have become paramount. The study aims to highlight the impact of GAI generated content, and the challenge it poses to the traditional copyright framework. Furthermore, the research addresses the evolving role of copyright laws in adapting to the dynamic landscape shaped by artificial intelligence. It investigates whether existing legal frameworks are equipped to handle the complexities introduced by GAI, or if there is a need for legislative and policy reforms. Ultimately, this research contributes to the ongoing discourse on the intersection of GAI, media, and copyrights, providing insights that can guide policymakers, legal practitioners, and industry stakeholders in navigating the evolving landscape of intellectual property in the age of artificial intelligence.
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Kshetri, Nir. "Generative AI in Advertising." IT Professional 26, no. 5 (September 2024): 15–19. http://dx.doi.org/10.1109/mitp.2024.3457328.
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Samuelson, Pamela. "Generative AI meets copyright." Science 381, no. 6654 (July 14, 2023): 158–61. http://dx.doi.org/10.1126/science.adi0656.
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Zohny, Hazem, John McMillan, and Mike King. "Ethics of generative AI." Journal of Medical Ethics 49, no. 2 (January 24, 2023): 79–80. http://dx.doi.org/10.1136/jme-2023-108909.
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Gerner, Alexander. "Critical Generative AI Aesthetics." Semeiosis - transdisciplinary journal of semiotics 11, no. 1 (December 2023): 69–99. http://dx.doi.org/10.53987/2178-5368-2023-12-05.
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Viomesh Singh. "VidTextBot using Generative AI." Journal of Information Systems Engineering and Management 10, no. 18s (March 11, 2025): 128–32. https://doi.org/10.52783/jisem.v10i18s.2894.
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Introduction: This research paper presents the design and implementation of a VidTextBot , it is a cutting-edge system that is used to integrate the video-to-text conversion using generative AI for analyzing the video content. The system will allow the users to upload the video or the youtube link. This youtube link or the video is processed to extract the audio, transcribe it into text, and extract subtitles if available. These outputs are stored into the database for smooth future reference and efficient data retrieval. By utilizing advanced NLP models like ChatGPT, the chatBot will help the user to interact with the video content and it will also answer the real time queries. The system’s architecture ensures seamless integration of transcription, subtitle extraction and AI interaction, which contribute to make it a user-friendly platform. Objectives: VidTextBot provides a unique solution, compared to the ordinary transcription tools, which focuses on real-time capabilities and scalability. Moreover, the paper searches for potential system enhancements, such as multi-language transcription support, personalized user experiences through authentication, and optimization for mobile platforms. The future advancement can involve integrating sentiment analysis and predictive models for deeper insights into video content. VidTextBot displays the potential of video processing and Generative AI, which offers an efficient way to analyze and interpret the video data. It addresses the growing demand for tools capable of making video data more accessible, insightful, and actionable.. Methods: The VidTextBot system allows the users to upload the video or provide the youtube link for the processing. The system then extracts the audio, and then transcribes it into text. It can also extract the subtitles if any of the youtube videos have it. This information is then stored into the database for efficient retrieval and future preferences. Then the system further uses the AI generated ChatBot , so that the users can interact with the video content and get real-time answers to all of the queries. Results: The VidTextBot using the Generative-AI System is definitely a new, innovative product changing the face of interaction with video content. Combining video/audio transcription, subtitle extraction, and AI-driven chatbot capabilities, the system makes video content accessible and more user-friendly.This project is based on real-world challenges, like the long running process of analyzing videos manually and the fact that video content would be hard to derive any valuable insight. The system lets users upload any video or provide a link from YouTube, allowing its audio to be converted into text that can be queried in real-time. Integration of advanced AI guarantees users will get the correct and context-related response to their questions, thereby ensuring it becomes both practical and efficient. Conclusions: The project illustrates a huge leap in how people consume and interact with video content. It combines speech recognition and generative AI to create an efficient, interactive, and user-centric solution. A system that is indeed a huge leap forward for smarter video content analysis, making it accessible and leading the way for further advancements in the field.
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Khokhlov, Yury. "Advancing operational efficiency in software companies through generative AI." American Journal of Engineering and Technology 07, no. 01 (January 20, 2025): 11–18. https://doi.org/10.37547/tajet/volume07issue01-03.
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Generative AI is rapidly reshaping the landscape of software (SW) companies’ operations, offering unprecedented capabilities for creating new code, documentation, designs, and more. By harnessing advanced machine learning architectures such as large language models (LLMs), agent-based frameworks, retrieval-augmented generation (RAG), and multimodal systems, organizations can reduce development cycles, improve service quality, and unlock innovative business opportunities. Recent articles highlight how these AI-driven approaches not only address routine tasks—such as boilerplate code generation or automated testing—but also facilitate more complex undertakings, including self-healing infrastructure and intelligent orchestration of multi-step workflows. However, integrating generative AI into software operations requires strategic planning around data governance, infrastructure scalability, workforce reskilling, and ethical guardrails. This research article examines the current applications of generative AI in software organizations, details emerging approaches for operational efficiency, and discusses implementation challenges. In doing so, it presents a holistic framework for understanding and adopting generative AI techniques—ranging from code completion to multimodal content creation—while emphasizing the synergy between agent-based architectures and retrieval-augmented generation. The discussion concludes with recommendations on how software firms can realize long-term benefits by blending AI-driven automation with robust oversight mechanisms, ensuring that generative AI becomes a catalyst for sustainable and ethical operational improvements.
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Sáez-Velasco, Sara, Mario Alaguero-Rodríguez, Vanesa Delgado-Benito, and Sonia Rodríguez-Cano. "Analysing the Impact of Generative AI in Arts Education: A Cross-Disciplinary Perspective of Educators and Students in Higher Education." Informatics 11, no. 2 (June 3, 2024): 37. http://dx.doi.org/10.3390/informatics11020037.
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Generative AI refers specifically to a class of Artificial Intelligence models that use existing data to create new content that reflects the underlying patterns of real-world data. This contribution presents a study that aims to show what the current perception of arts educators and students of arts education is with regard to generative Artificial Intelligence. It is a qualitative research study using focus groups as a data collection technique in order to obtain an overview of the participating subjects. The research design consists of two phases: (1) generation of illustrations from prompts by students, professionals and a generative AI tool; and (2) focus groups with students (N = 5) and educators (N = 5) of artistic education. In general, the perception of educators and students coincides in the usefulness of generative AI as a tool to support the generation of illustrations. However, they agree that the human factor cannot be replaced by generative AI. The results obtained allow us to conclude that generative AI can be used as a motivating educational strategy for arts education.
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Uppin, Mr Rohit B. "Introduction to Generative AI and its application in Education." International Journal for Research in Applied Science and Engineering Technology 12, no. 1 (January 31, 2024): 861–66. http://dx.doi.org/10.22214/ijraset.2024.57563.
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Abstract: Generative AI has made significant progress in re- cent years, with a growing range of applications in a variety of fields. Generative AI applications have catalyzed a new erain the synthesis and manipulation of digital content. Genera- tive AIis very recent technology which changed the way tradi-tional search engines work. The search engines work on the principles of information retrieval. However, openGL came up with use of Artificial Intelligence (AI) for synthesis of digital content and launched well known asChatGPT. The GenerativeAI differsfrom traditional AL as it takes text ,audio ,video andusing knowledge it generates new content in any form namely the text, audio or video. The generative AI has many profound applications. Generative AI is a rapidly developing field with the potential to revolutionize many industries and aspects of our lives. As the technology continues to advance, we can ex- pect to see even more groundbreaking and transformative ap-plications emerge. In this paper the introduction to GenerativeAI is detailed along with how generative AI can be used in ed-ucation.
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Baskara, FX Risang. "Generative AI as an Enabler of Sustainable Education: Theoretical Perspectives and Future Directions." British Journal of Teacher Education and Pedagogy 3, no. 3 (November 13, 2024): 122–34. http://dx.doi.org/10.32996/bjtep.2024.3.3.9.
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This theoretical research paper explores Generative Artificial Intelligence (AI) as a transformative force in sustainable education within the digital era. Through a comprehensive literature review of peer-reviewed articles, conference proceedings, and policy documents in sustainable education, AI in education, and learning theories, we propose a novel conceptual framework: Generative AI-Enabled Sustainable Education (GAISE). This framework synthesises principles from sustainable education theories, AI in education, constructivism, connectivism, and transformative learning. The GAISE model elucidates how Generative AI's capabilities in content generation, personalisation, adaptive learning, and natural language processing can enhance sustainability literacy and promote transformative learning experiences. Our analysis reveals the framework's potential to integrate Generative AI into curriculum design, teaching methodologies, assessment strategies, and teacher professional development for sustainable education. Critical ethical considerations include data privacy, equity, and human-AI collaboration in educational contexts. The paper identifies key challenges in implementing Generative AI for sustainable education and proposes future empirical research directions and policy recommendations. This work contributes to the intersection of AI and sustainable education, offering theoretical insights and practical pathways for educators and policymakers to leverage Generative AI in promoting sustainability competencies in education.
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Oh, Sunkyung, Mijung Jang, and Jung-eun Park. "Undergraduates’ Awareness of the Ethics of Generative AI Utilization in College Writing." Korean Association for Literacy 14, no. 4 (August 31, 2023): 69–96. http://dx.doi.org/10.37736/kjlr.2023.08.14.4.03.
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This study examines the utilization of generative AI among undergraduate students and their awareness of ethics in college writing. It also identifies the demand for ethics education related to writing using generative AI. A survey was conducted on 895 undergraduate students taking college writing courses at A University.
The study found that first, undergraduate students who have had experience with university writing tasks employed generative AI used ChatGPT the most; they used it substantially to generate ideas and collect data when writing general reports. In addition, more than half of the experienced users were satisfied with generative AI. Undergraduate students comply with writing ethics when performing college writing and are well aware of the problems in using generative AI, but they have low awareness of citation methods in writing when using generative AI. There was a high educational demand for citation methods and writing ethics when performing writing using generative AI. Moreover, there were some differences in the level of awareness and educational needs depending on the undergraduate major and the experience of using generative AI.
Accordingly, this study suggested the following measures in college writing subjects: education on how to comply with writing ethics and citation when using generative AI for writing, education that reflects the differences in students’ awareness and educational needs by major category, and education on how to use ChatGPT in generating ideas and collecting data when teaching how to write a report.
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Twomey, Robert. "Communing with Creative AI." Proceedings of the ACM on Computer Graphics and Interactive Techniques 6, no. 2 (August 12, 2023): 1–7. http://dx.doi.org/10.1145/3597633.
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Drawing on historical and contemporary examples, this paper discusses relationships between humans and machines in creative co-production with generative AI. It distinguishes between generative systems as augmenting tools and autonomous collaborators, and adopts the term communion to describe the close degree of exchange that is possible when working with these systems. It draws on historical examples of surrealist and AI artworks, and more recent examples made with Large Language Models and GAN-based generation. It concludes with a discussion of the primacy of text as an entry point to the possibilities of creative AI.
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Baek, Daeun, WanSang Son, Ji Hoon Song, and MYUNGHYUN YOO. "Meta-analysis of learning effectiveness using generative AI." Korean Association for Educational Information and Media 30, no. 4 (August 30, 2024): 1261–85. http://dx.doi.org/10.15833/kafeiam.30.4.1261.
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Garg, Dr Sweety, and Mr Adeen Shaikh. "AI Image Generator Utilizing OpenAI and Replit." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 09 (September 23, 2024): 1–15. http://dx.doi.org/10.55041/ijsrem37566.
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In this research, we leverage OpenAI's DALL- E 3 API in the assistance of generating images from a web interface which has been built on Replit. DALL-E 3 is a very advanced generative AI model due to its ability of generating complex and creative images from textual inputs. We intend to make use of the capabilities of DALL-E 3 and have it available to users in the generation of high-quality, custom images. Replit-the popular cooperative programming platform-secures the setup of the development environment, API request and response management, and user interfaces which allow users to interact with these AI models. The following apparently consider these factors at the implementation of AI in graphic design: better creativity, effective usage of time, and chance of creating unique aesthetic contents. Key Words: DALL-E 3 API, Generative AI, Web interface, Replit, Custom images, Graphic design.
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Dwivedi, Yamini, and Angel Kurde. "AI-Driven Innovation in CRM: Exploring the Potential of Generative Models for Customer Experience Optimization." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 10 (October 10, 2024): 1–4. http://dx.doi.org/10.55041/ijsrem37873.
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Artificial Intelligence (AI) is reshaping Customer Relationship Management (CRM), with generative models driving a new wave of innovation. This study investigates the potential of generative AI models in optimizing customer experience and streamlining CRM processes. By enabling more tailored customer interactions, predictive analytics, and automating intricate tasks, generative AI technologies are creating opportunities to enhance CRM efficiency. The research explores how generative AI can transform CRM practices, focusing on customer segmentation, sentiment analysis, and dynamic response generation. Through case studies and data exploration, the paper illustrates how organizations can leverage AI-driven CRM tools to improve customer satisfaction, engagement, and retention. It also examines challenges such as data security, model accuracy, and the complexities of integrating AI systems with traditional CRM platforms. By offering an in-depth analysis of AI’s role in CRM transformation, this research outlines strategies for future customer-centric approaches and underscores the growing impact of generative AI on improving customer management.
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Bajaj, Yatin, and Manoj Kumar Samal. "Accelerating Software Quality: Unleashing the Power of Generative AI for Automated Test-Case Generation and Bug Identification." International Journal for Research in Applied Science and Engineering Technology 11, no. 7 (July 31, 2023): 345–50. http://dx.doi.org/10.22214/ijraset.2023.54628.
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Abstract: This paper explores the benefits, challenges, and real-world applications of automated test-case generation and bug identification using generative artificial intelligence (AI). In today's software development and testing landscape, ensuring code quality and minimizing bugs are crucial. However, manual testing methods can be time-consuming and error-prone. Generative AI algorithms, such as generative models, can automatically generate test cases based on inputs, specifications, or system behavior. These algorithms employ machine learning techniques to analyze codebases, uncover test scenarios, and produce comprehensive test cases with broad coverage. The advantages of automated test-case generation include amplified test coverage, supercharged efficiency and time savings, and seamless scalability. Generative AI models also excel in bug identification by scrutinizing codebases, execution traces, and test results. They can detect coding mistakes and identify anomalous patterns indicating potential bugs, memory leaks, or security vulnerabilities. However, challenges such as data quality and bias, domain specificity, and the need for human expertise must be addressed. Real-world applications of automated test-case generation and bug identification using generative AI include software development and security testing. By leveraging generative AI, organizations can enhance test coverage, improve efficiency, and ensure the quality of software products. To successfully implement this approach, challenges related to data quality, domain specificity, and human expertise must be navigated. Generative AI has the potential to revolutionize software testing and contribute to the development of robust and reliable systems in the complex digital landscape.
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Al Adily, Ammar. "Automating Incident Response with AI: Investigating how generative AI can streamline and automate incident response processes." International Journal of Advances in Engineering and Management 06, no. 12 (December 2024): 569–75. https://doi.org/10.35629/5252-0612569575.
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more complex, we need better and faster ways to respond to them. In this paper, we explore the use of generative AI to automate and simplify incident response processes. With the use of potent machine learning algorithms, latest generation language processing techniques, generative AI can help in Threat Detection, automated analysis of data and speedy decision making. In this work, we explore how generative AI should be integrated into existing Security Operations Centers (SOCs) to enhance the incident management workflows. In particular, the current study further looks into how AI-driven systems can automatically produce incident reports, provide contextual intelligence, and endorse remediation steps. We also present case studies of generative AI in practice, enabling its usage in real-world incident response scenarios. Results show that generative AI not only speeds up responses, but that it also improves accuracy and effectiveness of incident management. Yet, there remain significant challenges, including data privacy, algorithmic bias and the requirement for human oversight. Finally, I highlight future directions for research and implementation, the accomplishments of generative AI in turning incident response from reactive to proactive and more productive.
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Ullmann, Thomas Daniel, Chris Edwards, Duygu Bektik, Christothea Herodotou, and Denise Whitelock. "Towards Generative AI for Course Content Production: Expert Reflections." European Journal of Open, Distance and E-Learning 26, s1 (August 1, 2024): 20–34. https://doi.org/10.2478/eurodl-2024-0013.
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Abstract The wide availability of generative artificial intelligence (AI) for content production has resulted in a growing interest in the area of education, particularly for course content production purposes. This research has mapped out a set of curriculum production tasks and illustrated how generative AI can support three important tasks: the development of course outlines and content, the drafting of assessment instructions and the mapping of learning outcomes to benchmark statements. We evaluated the outputs of the generative AI with five experts: a course production expert, an academic expert, two Learning Design experts and an AI expert. The results indicate that generative AI enabled the generation of plausible content skeletons for content and first drafts of relevant content to aid the course production team’s brainstorming but also highlighted the importance of reviewing generated content. Our research indicates that generative AI can result in shifts in the delivery of these tasks.
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Belcher, Diane. "The promising and problematic potential of generative AI as a leveler of the publishing playing field." Journal of English for Research Publication Purposes 5, no. 1-2 (December 31, 2024): 93–105. https://doi.org/10.1075/jerpp.00025.bel.
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Abstract This perspective paper considers the affordances and challenges of generative AI for linguaculturally diverse scholars in a still English-dominant academic publishing world. Chief among the questions examined is whether AI offers a viable path forward toward greater research publication equity but, at the same time, something much more fraught. In other words, the paper explores how promising the apparent equalizing potential of generative AI may be. Might, for example, generative AI offer a smoother path to visibility, but in so doing, actually make the uniqueness of diverse scholars’ contributions far less visible? The paradoxical potential of generative AI is discussed first in this paper by surveying, at this particular phase of AI development, some of the salient advantages of generative AI use for research prewriting, writing, and post-writing, that is, AI’s ability to assist with such essentials as idea generation, text development, and text refinement. The apparent democratizing advantages of AI for each of these major stages of research writing are then interrogated and problematized. The paper concludes with a brief speculative path forward.
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Sakhare, Akash, Pruthviraj Chavan, Zaid Nandaniwala, Akanksha Patne, and Mrunalinee Desai. "A Step Forward to AGI: Integrating Agentic AI and Generative AI for Human-Like Intelligence." International Journal for Research in Applied Science and Engineering Technology 13, no. 2 (February 28, 2025): 221–30. https://doi.org/10.22214/ijraset.2025.66831.
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Abstract: Artificial general intelligence (AGI) is the ultimate goal of artificial intelligence, which should be able to mimic human-like cognitive abilities in a variety of tasks. The developing junction of generative AI—creative, content-generating systems—and agentic AI—autonomous, goal-directed systems—is one of the possible routes to AGI. We discuss a comparative study of these two paradigms, focusing on their different approaches, underlying difficulties, and potential synergies. This research proposes a new angle in the light of AGI future, regarding how the autonomy and problem-solving abilities of Agentic AI are going to create the ingenuity and adaptability of Generative AI. Second, we have drawn attention toward other significant features that are supposed to characterize next-generation intelligent systems, including interdisciplinary research and its ethical implications. To ensure the responsible development of AGI technologies, the paper's conclusion provides a direction for advancing AGI, emphasizing the importance of cooperation between AI researchers, ethicists, and industry practitioners.