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Critiques of World Models

Created by
  • Haebom

Author

Eric Xing, Mingkai Deng, Jinyu Hou, Zhiting Hu

Outline

This paper presents a critical examination and a novel architecture for the World Model, an algorithmic surrogate for the real-world environment in which biological agents experience and act. Inspired by science fiction novel Dune and the concept of hypothetical thinking in the psychological literature, we critically analyze several perspectives on existing World Models and argue that the main goal of World Models is to simulate all real-world feasibility possibilities for purposeful reasoning and action. Based on this critique, we propose a novel architecture for a hierarchical, multilevel, and mixed continuous/discrete representation general-purpose World Model, and provide a generative and self-supervised learning framework and a perspective on Physical, Agentic, and Nested (PAN) AGI systems.

Takeaways, Limitations

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Takeaways:
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It clearly defines the main goals of world models and provides direction for world model research by emphasizing simulation of all feasible possibilities.
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We propose a novel world model architecture based on hierarchical, multilevel, continuous/discrete mixed representations.
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We present a world model learning method that leverages a generative and self-supervised learning framework.
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Emphasizes the role and importance of the world model for building the PAN AGI system.
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Limitations:
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There is a lack of concrete implementation and experimental verification of the proposed architecture.
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No specific design and implementation plans for the PAN AGI system have been presented.
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Further research is needed to explore the scalability and generalizability of the proposed architecture.
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