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.

翻译：人工智能领域充斥着各种优化方法。本文转变研究重点，致力于开发建模方法，以期更接近通用人工智能。为此，我们提出了一种将现实解读为信息源的新范式，并将其转化为能够捕获与表征此类信息的计算框架。该框架能够从仅处理空间分布式表征的简单原语出发，逐步构建经典认知架构的组成要素，如长时记忆与工作记忆。更重要的是，它通过无缝可扩展的层次化方式实现了这种垂直整合。