Comprehensive Investigation of Algorithmic Models and Prospects in Artificial Intelligence Generated Content

Abstract

Artificial Intelligence Generated Content (AIGC) has quickly evolved into a critical paradigm for automated content creation, supplementing traditional professionally and user-generated content. Empowered by deep learning, AIGC enables the generation of excellent text, pictures, audio, and video across diverse domains. This paper provides an exhaustive investigation of the core algorithmic models that underpin AIGC technologies, including Generative opposite Networks, Variational Automatic Encoders, Diffusion Models, and Large Language Models. This paper analyzes their principles, technical advancements, and representative applications in visual, textual, and speech generation. Furthermore, the current limitations related to controllability, computational overhead, domain generalization, and ethical considerations were discussed. Looking forward, this paper highlights emerging trends and research directions aimed at improving interpretability, efficiency, and trustworthiness in AIGC systems. By combining technical insight with application-oriented discussion, this paper aims to provide a comprehensive foundation for future research and guide the safe, effective and large-scale deployment of generative artificial intelligence across industries.