Generative AI Unveiled: Core Technologies, Real-World Applications, and Ethical Issues

Abstract: This research paper presents a comprehensive review of generative artificial intelligence (AI), its underpinning architectures, real-world models, and ethical considerations. Generative AI illustrates the development from traditional classification and forecasting systems to generative systems that can generate novel and distinct outputs across various fields. This paper examined existing architectures, e.g., Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Transformers, Convolutional Neural Networks (CNNs), and Large Language Models (LLMs). The paper also surveyed realworld models, with special consideration of conversational agents and chatbots, e.g., ChatGPT. A number of ethical issues were discussed, including bias and fairness with generative AI applications, issues of privacy with use, transparency and accountability, misuse, including deep fakes and misinformation, and governance issues. Limitations and future directions were discussed in detail, with an emphasis on the need to harness generative artificial intelligence effectively and safely with human oversight and ethical responsibility, which will require interdisciplinary collaboration. 

Generative Artificial Intelligence, Neural Networks, Generative Adversarial Networks, Variational Autoencoders, Transformer Models, Large Language Models, ChatGPT, AI Ethics, Bias Mitigation, Privacy Concerns, AI Governance, Deep Learning, Natural Language Processing, Content Generation, Artificial

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