Summary of Understanding World or Predicting Future? a Comprehensive Survey Of World Models, by Jingtao Ding et al.
Understanding World or Predicting Future? A Comprehensive Survey of World Models
by Jingtao Ding, Yunke Zhang, Yu Shang, Yuheng Zhang, Zefang Zong, Jie Feng, Yuan Yuan, Hongyuan Su, Nian Li, Nicholas Sukiennik, Fengli Xu, Yong Li
First submitted to arxiv on: 21 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 The concept of world models has gained attention due to advancements in multimodal large language models like GPT-4 and video generation models such as Sora, central to artificial general intelligence pursuits. This survey reviews the literature on world models, categorizing them into two primary functions: constructing internal representations for understanding the present state or predicting future dynamics. The review highlights current progress in these categories and explores applications in autonomous driving, robotics, and social simulacra, focusing on how each domain utilizes these aspects. Key challenges and potential research directions are also outlined. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary World models are like super-powerful AI tools that help us understand the world or predict what will happen next. This report looks at all the different types of world models and how they’re used in areas like self-driving cars, robots, and virtual societies. It also talks about the challenges and where future research might go. |
Keywords
» Artificial intelligence » Attention » Gpt
