Summary of Learning World Models with Hierarchical Temporal Abstractions: a Probabilistic Perspective, by Vaisakh Shaj
Learning World Models With Hierarchical Temporal Abstractions: A Probabilistic Perspective
by Vaisakh Shaj
First submitted to arxiv on: 24 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: 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 proposed internal world models aim to replicate human intelligence with type 2 reasoning capabilities, enabling machines to reason at multiple levels of spatio-temporal abstractions and scales. The paper identifies limitations in state space models (SSMs) as internal world models and proposes two new probabilistic formalisms: Hidden-Parameter SSMs and Multi-Time Scale SSMs. These formalisms integrate uncertainty in world states, enabling the development of scalable, adaptive hierarchical world models that can represent nonstationary dynamics across multiple temporal abstractions and scales. The approach is demonstrated to be effective in making long-range future predictions, outperforming contemporary transformer variants in some cases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machines are getting smarter! Researchers want them to think like humans, using a kind of thinking called type 2 reasoning. This means they need to understand things at different levels and scales, like big picture and small details. The problem is that current models aren’t good enough for this. A new way of modeling the world, called internal world models, could help. Two new approaches are proposed: Hidden-Parameter SSMs and Multi-Time Scale SSMs. These ideas can make predictions about the future, even a long time from now. This is like how our brains work! |
Keywords
» Artificial intelligence » Transformer
