Summary of Position Paper: Agent Ai Towards a Holistic Intelligence, by Qiuyuan Huang et al.
Position Paper: Agent AI Towards a Holistic Intelligence
by Qiuyuan Huang, Naoki Wake, Bidipta Sarkar, Zane Durante, Ran Gong, Rohan Taori, Yusuke Noda, Demetri Terzopoulos, Noboru Kuno, Ade Famoti, Ashley Llorens, John Langford, Hoi Vo, Li Fei-Fei, Katsu Ikeuchi, Jianfeng Gao
First submitted to arxiv on: 28 Feb 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 The proposed paper explores the development of Agent AI, an embodied system that integrates large foundation models into agent actions. The authors emphasize the need to shift focus from reductionism to systems that function as cohesive wholes, drawing from advancements in robotics, gaming, and healthcare. A novel large action model is presented to achieve intelligent behavior, the Agent Foundation Model. This work demonstrates remarkable capabilities across domains and tasks, challenging our understanding of learning and cognition. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Agent AI is a new way for computers to understand and interact with the world. It’s like having a robot that can learn and make decisions on its own. The authors are proposing a new type of model called Agent Foundation Model, which helps agents make better decisions. This paper shows how this new approach works in different areas, such as robotics, gaming, and healthcare. It also talks about the potential for AI to understand itself, like humans do. |
