The Evolution and Future Perspectives of Artificial Intelligence Generated Content

2024-12-02

arXiv.org

AI Summary

This study addresses three key challenges in AIGC research: (1) unclear evolutionary trajectory of AIGC technologies, (2) absence of cross-stage evaluation criteria, and (3) lack of theoretical foundations for model selection. To this end, we systematically reconstruct AIGC’s development into four phases—rule-based systems, statistical models, deep generative models, and transfer-learning–driven approaches. We propose a unified analytical framework covering the entire AIGC lifecycle, coupled with a co-assessment model integrating technical maturity and risk. Further, we introduce a novel cross-milestone, same-case comparison paradigm to consistently expose capability boundaries and limitations across stages. Finally, we distill a practical, actionable risk mitigation strategy system. Collectively, this work clarifies the intrinsic logic of AIGC evolution and provides both theoretical grounding and pragmatic guidance for model selection, optimization, and multimodal content generation synergy.

Technology Category

• Philosophy and Ethics of AI: Artificial General Intelligence
• Multiagent Systems: Agent/AI Theories and Architectures
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Search and Retrieval-Augmented AI: Agentic search
• Web Mining and Content Analysis: Models for Web evolution

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.

Problem

• Traces AIGC's evolution from rule-based to modern models
• Evaluates capabilities and limitations of AIGC methodologies
• Addresses challenges and proposes strategies for AIGC improvement

Innovation

• Traces AIGC evolution through four milestones
• Uses common example to evaluate methodologies
• Proposes strategies to mitigate AIGC challenges