Summary of Analogical Reasoning Within a Conceptual Hyperspace, by Howard Goldowsky et al.
Analogical Reasoning Within a Conceptual Hyperspace
by Howard Goldowsky, Vasanth Sarathy
First submitted to arxiv on: 13 Nov 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 approach combines the computational power of complex-sampled hyperdimensional computing (HDC) with Conceptual Spaces Theory (CST), a theory of semantic meaning. The authors aim to operationalize CST’s abstract analogical inference approaches by developing an HDC-based architecture that can perform category-based and property-based analogical reasoning. Preliminary results in a toy domain demonstrate the feasibility of this approach, which could have implications for artificial intelligence and machine learning applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Analogical thinking is when we understand one thing because it’s similar to another thing. This paper combines two ideas: Complex-Sampled Hyperdimensional Computing (HDC) and Conceptual Spaces Theory (CST). CST helps us think about analogies, but doesn’t tell us how to do it. The authors propose a way to use HDC to make this happen. They created an architecture that can reason about categories and properties in the same way humans do. Early tests show this approach works in a small test area. |
Keywords
» Artificial intelligence » Inference » Machine learning
