The Evolution and Future Perspectives of Artificial Intelligence-Generated Content
这篇综述梳理了人工智能生成内容从早期规则系统到现代迁移学习模型的四个发展阶段，通过统一示例评估各阶段方法的能力与局限，并针对关键挑战提出应对策略，帮助研究者选择与优化模型。
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 (TL) models, within a unified framework that highlights how each milestone contributes uniquely to content generation. In particular, this article 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 article 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.