A Survey on Generative Artificial Intelligence: Models, Applications, and Research Challenges
C. Lavanya Ramesh R
Abstract
Purpose Generative Artificial Intelligence (GenAI) has emerged as a transformative area of machine learning, enabling systems to generate human-like content across literature, music, animation, images, and computer programming. This paper aims to examine contemporary research trends in GenAI, with particular emphasis on its applications, technological developments, computational requirements, and associated limitations. Design/Methodology/Approach The study adopts a structured literature review approach to examine ten relevant research papers on Generative Artificial Intelligence. The selected studies are reviewed comparatively to identify major research themes, technological developments, application areas, and limitations. Particular attention is given to developments in transformer architectures and Large Language Models (LLMs), along with computational complexity and the evolving capabilities of generative systems. Findings The review identifies significant developments in GenAI driven by transformer architectures and LLMs, which have expanded the ability of machines to generate diverse forms of human-like content. The analysis highlights growing applications across creative, technological, and content-generation domains while also identifying concerns related to computational complexity, system limitations, reliability, and responsible use. The reviewed literature demonstrates the rapidly evolving nature of GenAI research. Practical Implications The study provides researchers, technology practitioners, and organizations with an overview of current GenAI developments and application possibilities. The findings can support decision-making regarding the adoption and implementation of generative AI systems while highlighting computational requirements and limitations that should be considered when deploying these technologies. Originality/Value The paper provides a focused synthesis of ten contemporary research studies to identify emerging trends, applications, technological developments, and limitations in Generative Artificial Intelligence. Its value lies in bringing together developments in transformer architectures and LLMs with broader considerations of computational complexity and application potential, thereby providing a concise overview of the evolving GenAI research landscape.