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Krishna, Guntamukkala Gopi. "Generative AI." International Journal of Advanced Engineering and Nano Technology 10, no. 8 (August 30, 2023): 1–3. http://dx.doi.org/10.35940/ijaent.g0474.0810823.
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Recent advancements in generative artificial intelligence (AI) have made it possible for machines to independently produce a variety of creative content. In the context of producing creative content, this essay examines the developments, difficulties, and ethical issues relating to generative AI. It looks into how generative models, such Generative Adversarial Networks (GANs) and Variational Auto encoders (VAEs), can produce realistic artwork like music, literature, and visuals. However, it is frequently discovered that GAN training is extremely unstable and frequently experiences non-convergence, mode collapse, and hyperparameter sensitivity [1]. The technical details of developing and optimizing generative models to produce desired results are covered in detail in this work. It also looks at the difficulties in guaranteeing the variety, creativity, and coherence of generated content. Additionally, the use of generative AI in the creation of original material raises ethical questions. Included in this are concerns about intellectual property, plagiarism, and possible effects on the creative industries. In specifically, the article explores the consequences of employing generative AI for content production in terms of authorship, human creativity, and the possible disruption of traditional creative practices. It also covers issues with fairness, bias, and appropriate application of generative models.
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Euchner, Jim. "Generative AI." Research-Technology Management 66, no. 3 (April 20, 2023): 71–74. http://dx.doi.org/10.1080/08956308.2023.2188861.
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Swaroopa, Pethota. "StoryCraft AI: Exploring Generative Approaches to Story Narration through AI." International Scientific Journal of Engineering and Management 03, no. 04 (April 23, 2024): 1–9. http://dx.doi.org/10.55041/isjem01633.
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In this project, we demonstrate a Storytelling AI system, which can generate short stories and complementary illustrated images with minimal input from the user. The system makes use of a text generation model, a text-to-image synthesis network, and a neural style transfer model. The final project is deployed into a website where users can build their stories. The field of AI has made significant changes in various domains, including Natural Language Processing and Generative models. One captivating application of these advancements is in the realm of storytelling. This project introduces a novel approach to story narration using a Generative AI model, specifically leveraging the GPT architecture. Traditional storytelling involves human creativity, imagination, and the ability to craft engaging narratives. However, the integration of AI into storytelling brings about new opportunities and challenges. In this project, we delve into the methodologies and techniques used to train a GPT-based model for generating coherent and captivating stories. In the end, we found that using computers for storytelling can be exciting, but we need to work together to ensure the stories are great and meaningful. Key Words: Artificial Intelligence (AI), Generative Adversarial Network (GAN), Generative Pre-trained Transformer (GPT)
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Rathod, Rohan. "Renaissance on Generative AI." International Journal for Research in Applied Science and Engineering Technology 12, no. 6 (June 30, 2024): 1354–59. http://dx.doi.org/10.22214/ijraset.2024.63324.
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Abstract: Generative Artificial Intelligence (Generative AI) stands at the forefront of innovation, promising to revolutionize creative content generation across various domains. This paper delves into the multifaceted implications of Generative AI in reshaping the landscape of artistic expression. Through an extensive literature survey and analysis, we explore the applications, advancements, and challenges of Generative AI in text generation, visual arts, and music composition. From state-of-the-art models like OpenAI's GPT series to cutting-edge techniques such as Generative Adversarial Networks (GANs) and Transformer architectures, Generative AI enables the automated creation of diverse and high-quality content. However, ethical considerations regarding authenticity, bias, and ownership of AI-generated content remain paramount. By uncovering key findings and insights, this paper aims to guide future research, development, and responsible integration of Generative AI in fostering a renaissance of artistic innovation and collaboration
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Kars, Muhammet Emir. "Generative AI in Education." London Journal of Social Sciences, no. 6 (September 20, 2023): 144–51. http://dx.doi.org/10.31039/ljss.2023.6.114.
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Recently, the field of artificial intelligence (AI) has advanced significantly, with generativeAI rising to the top of the tech industry's most-discussed subjects list. Education like manyother fields could be transformed by generative AI like ChatGPT, Bard, DALL-E,Midjourney, and DeepMind, which all have the ability to revolutionize a number of industrieswith all the benefits and drawbacks they entail.The paper begins by providing an overview of generative AI, highlighting its capacity togenerate human-like text, images, and even interactive simulations. It delves into theunderlying principles and techniques that empower generative AI models, focusing onprominent models like ChatGPT and Midjourney.To assess the effectiveness of generative AI in educational contexts, the paper examinesstudies that have evaluated learning outcomes, student engagement, and teacher support.These findings provide insights into the efficiency of generative AI as a supplementaryeducational tool and its role in fostering innovative teaching practices. The paper also addressesconcerns around the technology being too integrated into education like over dependence andcheating.Finally, the paper discusses future possibilities and challenges for the integration of generativeAI in education. It proposes strategies for maximizing the benefits of this technology whileensuring ethical considerations are met. The paper concludes with a call for further researchand collaboration between AI experts, educators, policymakers, and other stakeholders toharness the full potential of generative AI in transforming the learning landscape.
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Luttrell, Regina, and Nicholas Bowman. "Generating Deep Discussion Around Generative AI." Newhouse Impact Journal 1, no. 1 (2024): 1–2. http://dx.doi.org/10.14305/jn.29960819.2024.1.1.10.
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Ali, Safinah, Prerna Ravi, Randi Williams, Daniella DiPaola, and Cynthia Breazeal. "Constructing Dreams Using Generative AI." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 21 (March 24, 2024): 23268–75. http://dx.doi.org/10.1609/aaai.v38i21.30374.
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Generative AI tools introduce new and accessible forms of media creation for youth. They also raise ethical concerns about the generation of fake media, data protection, privacy and ownership of AI-generated art. Since generative AI is already being used in products used by youth, it is critical that they understand how these tools work and how they can be used or misused. In this work, we facilitated students’ generative AI learning through expression of their imagined future identities. We designed a learning workshop - Dreaming with AI - where students learned about the inner workings of generative AI tools, used text-to-image generation algorithms to create their imaged future dreams, reflected on the potential benefits and harms of generative AI tools and voiced their opinions about policies for the use of these tools in classrooms. In this paper, we present the learning activities and experiences of 34 high school students who engaged in our workshops. Students reached creative learning objectives by using prompt engineering to create their future dreams, gained technical knowledge by learning the abilities, limitations, text-visual mappings and applications of generative AI, and identified most potential societal benefits and harms of generative AI.
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Idrisov, Baskhad, and Tim Schlippe. "Program Code Generation with Generative AIs." Algorithms 17, no. 2 (January 31, 2024): 62. http://dx.doi.org/10.3390/a17020062.
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Our paper compares the correctness, efficiency, and maintainability of human-generated and AI-generated program code. For that, we analyzed the computational resources of AI- and human-generated program code using metrics such as time and space complexity as well as runtime and memory usage. Additionally, we evaluated the maintainability using metrics such as lines of code, cyclomatic complexity, Halstead complexity and maintainability index. For our experiments, we had generative AIs produce program code in Java, Python, and C++ that solves problems defined on the competition coding website leetcode.com. We selected six LeetCode problems of varying difficulty, resulting in 18 program codes generated by each generative AI. GitHub Copilot, powered by Codex (GPT-3.0), performed best, solving 9 of the 18 problems (50.0%), whereas CodeWhisperer did not solve a single problem. BingAI Chat (GPT-4.0) generated correct program code for seven problems (38.9%), ChatGPT (GPT-3.5) and Code Llama (Llama 2) for four problems (22.2%) and StarCoder and InstructCodeT5+ for only one problem (5.6%). Surprisingly, although ChatGPT generated only four correct program codes, it was the only generative AI capable of providing a correct solution to a coding problem of difficulty level hard. In summary, 26 AI-generated codes (20.6%) solve the respective problem. For 11 AI-generated incorrect codes (8.7%), only minimal modifications to the program code are necessary to solve the problem, which results in time savings between 8.9% and even 71.3% in comparison to programming the program code from scratch.
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Yang, Yue. "The Study of Copyright Infringement Liability of Generative Artificial Intelligence." Lecture Notes in Education Psychology and Public Media 34, no. 1 (January 3, 2024): 88–95. http://dx.doi.org/10.54254/2753-7048/34/20231893.
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As AI continues to produce content at an unprecedented rate, it is essential to establish a robust legal framework to protect the rights of creators and appropriately assign liability in cases of copyright infringement. Generative AI technology has the characteristics of high data demand, strong human-computer interaction, weak interpretability, and weak stability. The main civil subjects in generative AI services are generative AI service providers and users. In the pre-training stage, if generative AI uses copyrighted works without authorization, it should be recognized as copyright infringement, and the relevant infringement liability should be borne by generative AI service providers. In the content generation stage, the high similarity between AI-generated content and prior works can be attributed to various factors, including flaws in the generative model, intentional design choices, and user input and guidance. Both the generative AI service provider and the user bear direct tort liability for the infringement. However, the question of whether generative AI service providers also bear indirect tort liability should be explored, considering their role and similarities to traditional ISPs. Generative AI service providers should fulfill their obligations, take appropriate actions to prevent infringement, and report any illegal activities to the relevant authorities.
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Alexander, Kuznetsov. "THE ANALYSIS OF THE EFFICIENCY OF GENERATIVE AI ALGORITHMS FOR CREATING A NATURAL DIALOGUE." American Journal of Interdisciplinary Innovations and Research 6, no. 6 (June 1, 2024): 26–34. http://dx.doi.org/10.37547/tajiir/volume06issue06-08.
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In the modern world, artificial intelligence (AI) plays an increasingly important role in various fields of human activity. One of the most promising areas of AI application is the generation of natural dialogue. The purpose of this work is to analyze the efficiency of generative AI algorithms for creating natural dialogue. The relevance of this topic is due to the growing interest in the use of AI to create dialogue systems capable of interacting with people in a natural way. The results of the study can be useful for developers of dialogue systems, researchers in the field of AI, as well as anyone interested in the application of AI in their everyday life. Natural language generation is a fundamental task in artificial intelligence, with applications ranging from chatbots to virtual assistants. This study provides a comprehensive analysis of the efficiency of various generative artificial intelligence algorithms for creating a natural dialogue. Their performance is assessed in generating consistent and contextually appropriate responses by evaluating modern models using quantitative metrics and human evaluation. Additionally, the study explores the impact of various training data sizes and techniques on the quality of a generated dialogue. The results provide insight into the strengths and weaknesses of current generative AI approaches in the generation of a dialogue.
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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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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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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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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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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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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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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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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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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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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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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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Chung, Dahye, Jee Hyun Lee, Euna Cho, Hyunjoo Ahn, and Jeongmin Kho. "The Transformation of the Design Thinking Process with AI Intervention: Focusing on Generative Artificial Intelligence and Large Language Models." Korea Institute of Design Research Society 9, no. 2 (June 30, 2024): 25–44. http://dx.doi.org/10.46248/kidrs.2024.2.25.
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The rapid evolution of Generative Artificial Intelligence with innovative technologies is making a significant impact within the Design Thinking Process. By using the ChatGPT-4 model from OpenAI, this study aims to evaluate the impact of Generative AI on design activities and establish a theoretical framework by reevaluating the Design Thinking Process. This paper conducts both quantitative and qualitative analyses. The quantitative analysis is performed using experimental surveys, word frequency, co-occurrence frequency, and CONCOR analysis, and the qualitative analysis is carried out through post-experiment interviews. The findings, derived from the comparisons of teams with and without prior Generative AI experience, highlight the positive impact of Generative AI on problem-solving, idea generation, and the convergence within UX design practices. Additionally, through the in-depth interviews with participants, it is possible to confirm the novelty and usability of Generative AI in the Design Thinking Process. The intervention of Generative AI in the Design Thinking Process promises substantial benefits for enhancing the roles of UX designers and a shift in traditional design thinking, by facilitating increased creativity and productivity in UX design tasks. There is a call for broader recognition and integration of these technologies to redefine the UX design process and its outcomes. For a more integrated approach within the UX design methodology, future research should aim to explore Generative AI's extensive applications and implications in design.
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Iorliam, Aamo, and Joseph Abunimye Ingio. "A Comparative Analysis of Generative Artificial Intelligence Tools for Natural Language Processing." Journal of Computing Theories and Applications 2, no. 1 (February 26, 2024): 91–105. http://dx.doi.org/10.62411/jcta.9447.
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Generative artificial intelligence tools have recently attracted a great deal of attention. This is because of their huge advantages, which include ease of usage, quick generation of answers to requests, and the human-like intelligence they possess. This paper presents a vivid comparative analysis of the top 9 generative artificial intelligence (AI) tools, namely ChatGPT, Perplexity AI, YouChat, ChatSonic, Google's Bard, Microsoft Bing Assistant, HuggingChat, Jasper AI, and Quora's Poe, paying attention to the Pros and Cons each of the AI tools presents. This comparative analysis shows that the generative AI tools have several Pros that outweigh the Cons. Further, we explore the transformative impact of generative AI in Natural Language Processing (NLP), focusing on its integration with search engines, privacy concerns, and ethical implications. A comparative analysis categorizes generative AI tools based on popularity and evaluates challenges in development, including data limitations and computational costs. The study highlights ethical considerations such as technology misuse and regulatory challenges. Additionally, we delved into AI Planning techniques in NLP, covering classical planning, probabilistic planning, hierarchical planning, temporal planning, knowledge-driven planning, and neural planning models. These planning approaches are vital in achieving specific goals in NLP tasks. In conclusion, we provide a concise overview of the current state of generative AI, including its challenges, ethical considerations, and potential applications, contributing to the academic discourse on human-computer interaction.
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G, Ananya. "RAG based Chatbot using LLMs." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 06 (June 9, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem35600.
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Historically, Artificial Intelligence (AI) was used to understand and recommend information. Now, Generative AI can also help us create new content. Generative AI builds on existing technologies, like Large Language Models (LLMs) which are trained on large amounts of text and learn to predict the next word in a sentence. Generative AI can not only create new text, but also images, videos, or audio. This project focuses on the implementation of a chatbot based the concepts of Generative AI and Large Language Models which can answer any query regarding the content provided in the PDFs. The primary technologies utilized include Python libraries like LangChain, PyTorch for model training, and Hugging Face’s Transformers library for accessing pre-trained models like Llama2, GPT- 3.5 (Generative Pre-trained Transformer) architectures. The re- sponses are generated using the Retrieval Augmented Generation (RAG) approach. The project aims to develop a chatbot which can generate the sensible responses from the data in the form of PDF files. The project demonstrates the capabilities and applications of advanced Natural Language Processing (NLP) techniques in creating conversational agents that can be deployed across various platforms in the corporation, to enhance user interaction and support automated tasks. Index Terms—Generative AI, Artificial Intelligence, Natural Language Processing, Large Language Model, Llama2, Tran- formers, Document Loaders, Retrieval Augmented Generation, Vector Database, Langchain, Chainlit
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Xu, Sihan. "Decision tree C4.5 algorithm for generative AI technology ethics--Based on the results of the questionnaire." Applied and Computational Engineering 87, no. 1 (July 31, 2024): 33–40. http://dx.doi.org/10.54254/2755-2721/87/20241543.
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With the development of AI technology, generative AI has gradually entered the life of the public, for example, the explosion of CHAT-GPT has allowed more people to see the huge potential and obvious advantages of generative AI. However, in the process of generative AI operation, events that violate social responsibility and ethics often occur, which makes the research on the scientific and technological ethics of generative AI more urgent. In the past literature and research, many industry experts have analysed the impact of generative AI on specific industries, but everyone is or will be a user of generative AI, so we should pay attention to the study of the people's scientific and technological ethical issues of generative AI after putting aside the industry background, so this paper collects primary data by means of questionnaire surveys to find out the public's awareness of generative AI and their perception of generative AI. and attitudes towards generative AI, and using the decision tree C4.5 algorithm with Python as the tool, it is used to respond to people's awareness of generative AI and the public's perception of the relationship between the various factors of the ethical issues of
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Md Sumon Gazi. "Implications of Generative Al and Machine Learning on Automotive Industry Development & Reduction of Carbon Footprint: An Analysis of the U.S. Economy Perspective." Journal of Business and Management Studies 6, no. 3 (June 5, 2024): 134–43. http://dx.doi.org/10.32996/jbms.2024.6.3.15.
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The U.S. automotive industry is instrumental to the economy and contributes significantly to the American GDP, employment, and global trade. The principal objective of this research paper is to examine the implications of Generative AI and ML in advancing the automotive industry from the U.S. economic perspective. Generative AI is the latest frontier in artificial intelligence software development, where algorithmic generation can be achieved across various types of content: text, images, audio, and video. The generative AI in the Automotive market at the global level had witnessed boisterous growth and commanded a value of approximately $389.47 million by 2023. The analysis exposed that North American regions are dominating the market, which was attributed to high technological infrastructure, along with partnerships among automotive companies with research institutes and universities to foster AI innovations. Application analysis exposed that Advanced Driver Assistance Systems (ADAS) had the biggest market share, indicating a strong focus on developing and implementing Generative AI technologies to enhance driver safety and vehicle autonomy. Followed by, Connected Car Technologies, representing growing efforts towards implementing generative AI solutions that will improve vehicle connectivity, infotainment, and user experience. The impact of Generative AI and Machine Learning can be witnessed in terms of virtual prototyping, generative automotive designs, consolidation with the CAD system, supply chain optimization, Sensor Fusion and Perception Enhancement as well as automotive manufacturing process optimization.
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Shastry, K. Aditya, B. A. Manjunatha, T. G. Mohan Kumar, and D. U. Karthik. "Generative Adversarial Networks Based Scene Generation on Indian Driving Dataset." Journal of ICT Research and Applications 17, no. 2 (August 31, 2023): 181–200. http://dx.doi.org/10.5614/itbj.ict.res.appl.2023.17.2.4.
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The rate of advancement in the field of artificial intelligence (AI) has drastically increased over the past twenty years or so. From AI models that can classify every object in an image to realistic chatbots, the signs of progress can be found in all fields. This work focused on tackling a relatively new problem in the current scenario-generative capabilities of AI. While the classification and prediction models have matured and entered the mass market across the globe, generation through AI is still in its initial stages. Generative tasks consist of an AI model learning the features of a given input and using these learned values to generate completely new output values that were not originally part of the input dataset. The most common input type given to generative models are images. The most popular architectures for generative models are autoencoders and generative adversarial networks (GANs). Our study aimed to use GANs to generate realistic images from a purely semantic representation of a scene. While our model can be used on any kind of scene, we used the Indian Driving Dataset to train our model. Through this work, we could arrive at answers to the following questions: (1) the scope of GANs in interpreting and understanding textures and variables in complex scenes; (2) the application of such a model in the field of gaming and virtual reality; (3) the possible impact of generating realistic deep fakes on society.
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Rios-Campos, Carlos, Jessica Del Consuelo Luzuriaga Viteri, Elixer Alexandra Palma Batalla, Juan Francisco Castro Castro, Jorge Bautista Núñez, Edilbrando Vega Calderón, Francisco Javier Gómez Nicacio, and Melissa Yaneth Pretell Tello. "Generative Artificial Intelligence." South Florida Journal of Development 4, no. 6 (August 14, 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 Intelligence. Artificial intelligence has evolved from predictive to generative. Key Techniques: Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Autoregressive Models. Countries are establishing standards for the ethical use of AI, while respecting human rights. Currently, AI has many applications in human activity, but the ethical use of AI is necessary. Various countries are establishing regulations in this regard. Generative Artificial Intelligence is demonstrating its importance in various human activities, making it necessary to use it ethically and responsibly. The specific objectives of the research are to identify the applications and the software of Generative Artificial Intelligence. Applications: Generating realistic images, creating natural language text, composing music. Generative artificial intelligence (AI) tools, such as Bard, ChatGPT, and GitHub CoPilot.
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Mandvikar, Shreekant, and Alekhya Achanta. "Process Automation 2.0 with Generative AI Framework." International Journal of Science and Research (IJSR) 12, no. 10 (October 5, 2023): 1614–19. http://dx.doi.org/10.21275/sr231021200626.
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Yoo, Sunghee. "Ethical Issues Posed by ‘Generative-AI’ (G-AI) - Response strategies for ‘Good AI Society’." Journal of the Korean Bioethics Association 24, no. 1 (June 30, 2023): 1–29. http://dx.doi.org/10.37305/jkba.2023.06.24.1.1.
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Leung, Tiffany I., Taiane de Azevedo Cardoso, Amaryllis Mavragani, and Gunther Eysenbach. "Best Practices for Using AI Tools as an Author, Peer Reviewer, or Editor." Journal of Medical Internet Research 25 (August 31, 2023): e51584. http://dx.doi.org/10.2196/51584.
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The ethics of generative artificial intelligence (AI) use in scientific manuscript content creation has become a serious matter of concern in the scientific publishing community. Generative AI has computationally become capable of elaborating research questions; refining programming code; generating text in scientific language; and generating images, graphics, or figures. However, this technology should be used with caution. In this editorial, we outline the current state of editorial policies on generative AI or chatbot use in authorship, peer review, and editorial processing of scientific and scholarly manuscripts. Additionally, we provide JMIR Publications’ editorial policies on these issues. We further detail JMIR Publications’ approach to the applications of AI in the editorial process for manuscripts in review in a JMIR Publications journal.
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Yang, Zhi, and Yuanhong Xu. "Study on the Path of Generative Artificial Intelligence Copyright Protection under the Strategy of Intellectual Property Power." Scientific Journal Of Humanities and Social Sciences 6, no. 7 (July 13, 2024): 172–79. http://dx.doi.org/10.54691/my07eq41.
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Generative AI copyright protection is in response to the inherent requirements of AI development and intellectual property protection. At present, generative AI copyright protection faces problems such as insufficient legal basis for prevention and control mechanisms, unsmooth preventive systems, and poor operation mechanisms. Therefore, to solve the dilemma of generative AI copyright protection, it is necessary to take the principle of risk prevention as the concept, clarify the legal basis for generative AI copyright prevention, reasonably define the scope of generative AI, establish a multi-principal protection system to protect generative AI copyright, introduce diversified ways to help generative AI copyright maintenance, and set up a composite responsibility system to solidify the generative AI copyright protection bottom line, and establish an administrative protection system to protect the copyright protection bottom line. The bottom line of copyright protection is an administrative protection path to help generative artificial intelligence copyright protection efforts to promote the healthy development of the two in the integration.
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Choi, Su-Hyung, and Jin-Kook Lee. "Generative AI for Automated Urban Housing Floor Plan Generation." Korean Journal of Computational Design and Engineering 28, no. 4 (December 31, 2023): 514–23. http://dx.doi.org/10.7315/cde.2023.514.
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Shin, Bomi, Kyoung Min Ryu, Kyeong Yong Kim, Sungmo Kang, and Jaeeun Joo. "A study on exploring strategies for developing digital literacy using generative AI: Focusing on the development of mathematics teaching and learning materials based on ChatGPT." Korean School Mathematics Society 27, no. 2 (June 30, 2024): 151–76. http://dx.doi.org/10.30807/ksms.2024.27.2.003.
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This study focused on the potential of generative AI in the development of digital literacy and aimed to develop ChatGPT based mathematics teaching and learning materials that can be used in middle and high school mathematics classes. To this end, we extracted “information processing and generation”, “digital problem solving”, and “digital concept formation” as components of digital literacy that can be developed in mathematics classes using generative AI, and we set the teaching and learning phases of “AI utilization,” “AI analysis,” “AI creation,” and “AI critical evaluation” in AI literacy conceptual system and then we specified the framework for developing mathematical teaching and learning materials using Generative AI for cultivating digital literacy. Based on this, we developed mathematics teaching and learning materials for “digital concept formation” and “digital problem solving” that can be used in mathematics classes dealing with trigonometric ratios for acute angles, dot products of vectors, stat- istical problem settings, and the truth and falsity of propositions. In this study, specified the framework for developing materials and teaching and learning materials can provide meaningful implications to researchers and teachers who are interested in using generative AI as a didactical instrument in mathematics classes.
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Andrei, Andreia Gabriela, Mara Mațcu-Zaharia, and Dragoș Florentin Mariciuc. "Ready to Grip AI's Potential? Insights from an Exploratory Study on Perceptions of Human-AI Collaboration." BRAIN. Broad Research in Artificial Intelligence and Neuroscience 15, no. 2 (July 5, 2024): 01–22. http://dx.doi.org/10.18662/brain/15.2/560.
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One of the emerging technologies arising with Industry 4.0 is generative artificial intelligence (AI). Despite its disruptive nature and controversies, the effective and ethical use of AI is increasingly preoccupying organizations of all sizes as well as their employees. Focusing on generative AI, this paper presents findings from a qualitative study that provides insights into how Generation Z, the newest workforce, perceives human-AI collaboration. Based on in-depth interviews and a micro-meso-macro approach, the study reveals a dual perspective. Participants recognized the advantages AI brings, such as increased efficiency, productivity, and information availability. However, they were concerned about various risks such as: technology addiction, job loss, data privacy and ethical issues. At the micro level, generative AI was seen as beneficial for providing information and inspiration, but over-reliance could limit people's skills and create dependency. At the meso, organizational level, it could increase efficiency and productivity, but potentially replace jobs. At the macro, societal level, generative AI could support innovation but risks dehumanizing communication and relationships. Data privacy and ethics concerns were expressed at all three levels, indicating that a combination of institutional safeguards and awareness of data privacy and ethics at all levels is required to achieve the full benefits of generative AI. This would help organisations to capitalise on technological advances and support the development of ethical use of AI tools.
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Pedersen, Isabel. "Generative AI Adoption in Postsecondary Education, AI Hype, and ChatGPT’s Launch." Open/Technology in Education, Society, and Scholarship Association Journal 4, no. 1 (July 22, 2024): 1–19. http://dx.doi.org/10.18357/otessaj.2024.4.1.59.
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The rapid integration of generative artificial intelligence (AI) into postsecondary education and many other sectors resulted in a global reckoning with this new technology. This paper contributes to the study of the multifaceted influence of generative AI, with a particular focus on OpenAI's ChatGPT within academic settings during the first six months after the release in three specific ways. First, it scrutinizes the rise of ChatGPT as a transformative event construed through a study of mainstream discourses exhibiting AI hype. Second, it discusses the perceived implications of generative AI for writing, teaching, and learning through the lens of critical discourse analysis and critical AI studies. Third, it encourages the necessity for best practices in the adoption of generative AI technologies in education.
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Kang, Hyeonho, Euitae Han, and Jaeseok Jeong. "An Examination of Copyrightability and Creativity in AI-generated Works: Focusing on the Instrumentality of Generative AI." Korea Copyright Commission 146 (June 30, 2024): 5–42. http://dx.doi.org/10.30582/kdps.2024.38.2.5.
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With burgeoning interest in generative artificial intelligence (AI), the legal status of outputs generated by such technologies, henceforth referred to as AI-generated works, has become a pivotal issue. This manuscript examines the characteristics of prevalent generative AI systems and the intricate process behind AI-generated works. It underscores the imperative for a copyright discourse surrounding AI-generated works, in light of the aims and perspectives of extant copyright laws, and the policy objectives that underpin industrial advancement. The discourse posits the necessity to view generative AI as an innovative instrument for human creativity, substantiating the ‘instrumentality’ of generative AI with a multitude of evidences. Additionally, considering the uniqueness of AI-generated works compared to traditional creative works, the manuscript proposes criteria for acknowledging the copyrightability of AI-generated works, focusing on ‘Predictability of Generative Works’ to demonstrate the instrumentality of generative AI and ‘Specificity of Prompts’ to assess the creativity of AI-generated works. By presenting these new perspectives on AI-generated works, the paper aims to promote the development of culture, related industries, and the generative AI industry.
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Warankar, Mayuri. "Generative Artificial Intelligence." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (April 20, 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 not all fun and games. Generative AI also brings up some serious questions about privacy, fairness, and making sure we use it in good ways. This paper dives into all that, showing the good and the tricky parts of generative AI and how it's shaping our world. Key Words: Generative AI, technology, machine creativity, data patterns, applications, healthcare, entertainment, education, privacy, fairness, ethical use, societal impact.
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Ni, Jiahao. "Intellectual Property Protection Dilemmas and Legal Response Strategies Under the Perspective of Generative Artificial Intelligence." Journal of Education, Humanities and Social Sciences 28 (April 1, 2024): 854–59. http://dx.doi.org/10.54097/8q8mfm80.
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As generative artificial intelligence (AI) technology advances rapidly, its potential in innovation output poses significant challenges to the traditional intellectual property (IP) protection legal framework. This study concentrates on the technological foundations and creative applications of generative AI, analyzing its impact on aspects of IP law such as the definition of authors' rights, the criteria for originality, and the attribution of liability for infringement. The paper provides an overview of the development of generative AI technology and its applications in fields such as the arts and text generation, and delves into the legal contradictions and challenges it has sparked. Finally, it proposes strategies for updating the legal framework, balancing innovation and protection, and forecasts future legal trends, aiming to offer a legal perspective and strategic guidance for addressing IP issues arising from generative AI.
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Shukla, Abhishek. "Innovative Ways of Utilizing Generative AI for Graphical Big Data Analysis." Journal of Artificial Intelligence & Cloud Computing 3, no. 1 (February 29, 2024): 1–3. http://dx.doi.org/10.47363/jaicc/2024(3)222.
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This essay explores the integration of Generative Artificial Intelligence (AI) models, including Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), in graphical big data analysis. Generative AI offers the generation of synthetic graphical data, improves data visualizations, and aids in pattern recognition within complex datasets. It presents innovative solutions to the challenges posed by large and intricate graphical datasets, enhancing the depth and accuracy of data analysis.
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Wang, Ruixi. "Review of Generative Models." Applied and Computational Engineering 8, no. 1 (August 1, 2023): 542–47. http://dx.doi.org/10.54254/2755-2721/8/20230269.
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The advancement of generative AI models has been remarkable since 2022. Several visually appealing generative AI models have been introduced to the public, including those for text and image generation. Despite being generated by large-scale neural networks and deep learning algorithms through extensive training, generative models are capable of achieving average or above-average quality and creativity in many fields, such as painting and literature. This paper will examine some of the AI models currently available, delve into their underlying principles and histories, and provide insight into what the future may hold. With the advancement of technology, we can expect to see even more innovative and creative applications in the future.
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Yi, Jun-Kai, and Yi-Fan Yao. "Advancing Quality Assessment in Vertical Field: Scoring Calculation for Text Inputs to Large Language Models." Applied Sciences 14, no. 16 (August 8, 2024): 6955. http://dx.doi.org/10.3390/app14166955.
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With the advent of Transformer-based generative AI, there has been a surge in research focused on large-scale generative language models, especially in natural language processing applications. Moreover, these models have demonstrated immense potential across various vertical fields, ranging from education and history to mathematics, medicine, information processing, and cybersecurity. In research on AI applications in Chinese, it has been found that the quality of text generated by generative AI has become a central focus of attention. However, research on the quality of input text still remains an overlooked priority. Consequently, based on the vectorization comparison of vertical field lexicons and text structure analysis, proposes three input indicators D1, D2, and D3 that affect the quality of generation. Based on this, we studied a text quality evaluation algorithm called VFS (Vertical Field Score) and designed an output evaluation metric named V-L (Vertical-Length). Our experiments indicate that higher-scoring input texts enable generative AI to produce more effective outputs. This enhancement aids users, particularly in leveraging generative AI for question-answering in specific vertical fields, thereby improving response effectiveness and accuracy.
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McMillan, John. "Generative AI and Ethical Analysis." American Journal of Bioethics 23, no. 10 (October 3, 2023): 42–44. http://dx.doi.org/10.1080/15265161.2023.2249852.
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Ebert, Christof, and Panos Louridas. "Generative AI for Software Practitioners." IEEE Software 40, no. 4 (July 2023): 30–38. http://dx.doi.org/10.1109/ms.2023.3265877.
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Coleman, Kathryn. "Generative AI and education ecologies." Pacific Journal of Technology Enhanced Learning 5, no. 1 (August 11, 2023): 19–20. http://dx.doi.org/10.24135/pjtel.v5i1.175.
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What role can generative AI have an art and design education? Given that we are in a year of change as open-source Open AI systems shift how we teach, learn, and assess in times of question-answering chatbot and personal assistance tools. Applying a post-human approach (Blaikie, et al, 2020) to education might help us rethink pedagogy (Wessels, et al, 2022), knowledge creation and scholarly publication for knowledge sharing. In this SoTEL Symposium presentation/discussion with the ASCILITE MLSIG I propose a move away from a humanist world view that continues to shape our thoughts around the binary of teacher-learner within our walled disciplinary and consider how we might Incorporate generative AI tools in the curriculum to foster interdisciplinary collaborations with the more-than human. What if we shifted teaching and learning to facilitate new ways of being on the planet, so that we prioritised ourselves, one another as well as non-human and more-than-humans in our educational ecologies. Building the digital literacies and computational thinking capabilities (George-Reyes, et al, 2021) to learn with GAI will create opportunities to thinking about the world and all its space and places, as interconnected and entangled.
In this trendsetter webinar I pose a series of questions and prompts that I had in conversation with Chatty G (ChatGPT) to consider how we might imagine and understand the world in different ways so that we might integrate generative AI and into our education ecologies in higher education.
Presentation: https://doi.org/10.26188/22281685
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Thawillarp, Supharerk. "Generative AI, ChatGPT and Medicine." PSU Medical Journal 3, no. 2 (July 7, 2023): 63–67. http://dx.doi.org/10.31584/psumj.2023259047.
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