Summary of Bridging Generative Networks with the Common Model Of Cognition, by Robert L. West et al.
Bridging Generative Networks with the Common Model of Cognition
by Robert L. West, Spencer Eckler, Brendan Conway-Smith, Nico Turcas, Eilene Tomkins-Flanagan, Mary Alexandria Kelly
First submitted to arxiv on: 25 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE); Neurons and Cognition (q-bio.NC)
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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 This research paper presents a theoretical framework for integrating large generative network models with artificial intelligence’s Common Model of Cognition. The authors achieve this by reorganizing modules in the Common Model into shadow production systems, which provide peripheral processing for higher-level reasoning. This novel structure enables seamless connections between cognitive architectures and generative neural networks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large generative network models are now combined with the Common Model of Cognition to improve artificial intelligence. The old model is rearranged to create “shadow” parts that help the main part think better. This new way of combining things lets AI connect its thinking processes with big neural networks. |
