Summary of From Manifestations to Cognitive Architectures: a Scalable Framework, by Alfredo Ibias et al.
From Manifestations to Cognitive Architectures: a Scalable Framework
by Alfredo Ibias, Guillem Ramirez-Miranda, Enric Guinovart, Eduard Alarcon
First submitted to arxiv on: 14 Jun 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 A novel approach to developing modeling methods for achieving Artificial General Intelligence (AGI) is proposed, shifting the focus from optimization methods. The paper presents a framework capable of capturing and representing information sourced from reality, which is then translated into a computational representation. This framework enables the construction of classical cognitive architectures, such as Long Term Memory and Working Memory, starting from primitive processes that operate on Spatial Distributed Representations. The method achieves scalability in a hierarchical manner. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial General Intelligence (AGI) is like super-smart AI! Researchers usually focus on making AI better at certain tasks. This paper tries to take things a step further by creating new ways to model information from the world around us. They propose a way to turn this information into something computers can understand and use. This new approach helps build ideas about how our brains work, like memory, starting with simple, spatial representations. |
Keywords
» Artificial intelligence » Optimization
