Advancements and Challenges in Generative AI: Architectures, Applications, and Ethical Implications

Architecture, classification, and major applications of Generative AI interfaces, specifically chatbots, are presented in this paper. Research paper details how the Generative AI interfaces work with various Generative AI approaches and show the architecture and their working. On the other hand, the generative model is built using advanced machine learning techniques to build dynamic, contextually relevant responses automatically. On the other hand, the retrieval-based model builds up with dependency on a predefined response library. The paper also discusses the use of Generative AI to populate Multimedia Knowledge Graphs (KGs), presenting technologies based on the semantic analysis of deep learning and NoSQL to more effectively integrate and retrieve data. The social and ethical challenges that come with the deployment of generative models are critically reviewed. These dialogues bring forward the balance that has to be maintained between progress and necessity in technological advancements, for which the call for ethical responsibility in developing AI is made. The paper presents a comprehensive review of state-of-the-art Generative AI with special focus on the promises and pitfalls in Generative AI research related to both natural language processing and knowledge management.