Summary of The Evolution and Future Perspectives Of Artificial Intelligence Generated Content, by Chengzhang Zhu et al.
The Evolution and Future Perspectives of Artificial Intelligence Generated Content
by Chengzhang Zhu, Luobin Cui, Ying Tang, Jiacun Wang
First submitted to arxiv on: 2 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This review examines the evolution of artificial intelligence generated content (AIGC) from rule-based systems to modern transfer learning models, highlighting how each milestone contributes uniquely to content generation. The paper uses a common example throughout to illustrate capabilities and limitations of AIGC methods in each phase, providing a consistent evaluation of methodologies and their development. Additionally, the study addresses critical challenges associated with AIGC and proposes actionable strategies to mitigate them. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial intelligence is changing how we create content like text, images, audio, and video. This review looks at how AI-generated content has improved over time. It shows how different approaches have helped or hurt content creation. The review also talks about the problems with this technology and offers ways to fix them. |
Keywords
» Artificial intelligence » Transfer learning
