Understanding Generative AI: From Basic Principles to Real-World Applications

Akbar Sharief Shaik

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2024, vol. 10, issue 6, 835-841

Abstract: This comprehensive article examines the foundational principles, technical implementations, and societal implications of generative artificial intelligence (GenAI), a transformative technology that has revolutionized the creation of synthetic content across multiple domains. The article explores the architectural frameworks underpinning GenAI, focusing on Generative Adversarial Networks (GANs) and Transformer-based models, while detailing the sophisticated training methodologies and evaluation metrics that enable their functionality. Through an in-depth analysis of real-world applications in healthcare, creative industries, and software development, this article illuminates the technology's potential to enhance human capabilities and drive innovation. The investigation extends to critical ethical considerations, addressing security concerns surrounding deepfakes, challenges in bias mitigation, and complex intellectual property issues. Furthermore, the article presents a forward-looking perspective on research opportunities, policy implications, and industry best practices, emphasizing the importance of responsible development and deployment of generative AI systems. This article contributes to the growing body of knowledge on GenAI by providing a holistic understanding of its current state, challenges, and future directions, while highlighting the crucial balance between technological advancement and ethical considerations in shaping its evolution.

Keywords: Generative Artificial Intelligence; Generative Adversarial Networks; Transformer-based Models; Ethical Considerations in AI; Synthetic Content Creation (search for similar items in EconPapers)
Date: 2024
Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT241061120
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Persistent link: https://EconPapers.repec.org/RePEc:jbh:ijsrcs:v10:y2024:i6:id:475

DOI: 10.32628/CSEIT241061120

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