From Manifestations to Cognitive Architectures: a Scalable Framework

TL;DR

This paper introduces a scalable framework that interprets reality as an information source to build cognitive architecture components, advancing towards Artificial General Intelligence.

Contribution

It proposes a novel hierarchical framework translating reality into cognitive architecture elements from primitive spatial representations.

Findings

Framework successfully models Long Term Memory and Working Memory

Scalable hierarchical structure from primitive spatial representations

Potential to bridge the gap towards Artificial General Intelligence

Abstract

The Artificial Intelligence field is flooded with optimisation methods. In this paper, we change the focus to developing modelling methods with the aim of getting us closer to Artificial General Intelligence. To do so, we propose a novel way to interpret reality as an information source, that is later translated into a computational framework able to capture and represent such information. This framework is able to build elements of classical cognitive architectures, like Long Term Memory and Working Memory, starting from a simple primitive that only processes Spatial Distributed Representations. Moreover, it achieves such level of verticality in a seamless scalable hierarchical way.