The Evolution and Future Perspectives of Artificial Intelligence Generated Content
It provides a guide for researchers and practitioners to select and optimize AIGC models across domains like text, images, audio, and video, but is incremental as it reviews existing developments.
This review traces the evolution of artificial intelligence generated content (AIGC) through four milestones, from rule-based systems to modern transfer learning models, using a unified framework to evaluate capabilities and limitations, and proposes strategies to address challenges for improving content creation quality and efficiency.
Artificial intelligence generated content (AIGC), a rapidly advancing technology, is transforming content creation across domains, such as text, images, audio, and video. Its growing potential has attracted more and more researchers and investors to explore and expand its possibilities. This review traces AIGC's evolution through four developmental milestones-ranging from early rule-based systems to modern transfer learning models-within a unified framework that highlights how each milestone contributes uniquely to content generation. In particular, the paper employs a common example across all milestones to illustrate the capabilities and limitations of methods within each phase, providing a consistent evaluation of AIGC methodologies and their development. Furthermore, this paper addresses critical challenges associated with AIGC and proposes actionable strategies to mitigate them. This study aims to guide researchers and practitioners in selecting and optimizing AIGC models to enhance the quality and efficiency of content creation across diverse domains.