The Evolution and Future Perspectives of Artificial Intelligence Generated Content

TL;DR

This paper reviews the evolution of artificial intelligence generated content (AIGC), highlighting key milestones, evaluating methodologies, addressing challenges, and proposing strategies to improve content creation across various media.

Contribution

It provides a comprehensive historical overview of AIGC development, introduces a unified evaluation framework, and offers actionable strategies for future improvements.

Findings

AIGC evolved from rule-based to transfer learning models.

Each milestone contributed uniquely to content generation capabilities.

Strategies proposed to address key challenges in AIGC adoption.

Abstract

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.