The Ouroboros Model, Proposal for Self-Organizing General Cognition Substantiated

Abstract

The Ouroboros Model has been proposed as a biologically-inspired comprehensive cognitive architecture for general intelligence, comprising natural and artificial manifestations. The approach addresses very diverse fundamental desiderata of research in natural cognition and also artificial intelligence, AI. Here, it is described how the postulated structures have met with supportive evidence over recent years. The associated hypothesized processes could remedy pressing problems plaguing many, and even the most powerful current implementations of AI, including in particular deep neural networks. Some selected recent findings from very different fields are summoned, which illustrate the status and substantiate the proposal.

A Challenge in A(G)I, Cybernetics Revived in the Ouroboros Model as One Algorithm for All Thinking

“…This is more openly visible in the image-interpretation branch than in the production apps. Embedding and firsthand symbol grounding on a most basic bodily level are absent in the probed AI [1,2,3,5]. This is no new finding; it is just exemplified with the drawing in Figure 1.…”

“…The Ouroboros Model has been proposed as a general blueprint for cognition [5,10]. It features only two basic ingredients: a memory structed in a (non-strict) hierarchy and a process called consumption analysis.…”

“…At the same time, present-day AI approaches do not seem to be flexible or creative enough to find two options if asked for an interpretation of the drawing in Figure 1. This goes hand in hand with the wide absence of versatile building blocks, organized in some sort of hierarchy of concepts and abstractions, and, claimed as a direct consequence, lack of "common sense" reasoning [5].…”

AI and We in the Future in the Light of the Ouroboros Model: A Plea for Plurality

“…Some remedies for bringing light into the black boxes of AI, especially deep neural networks (DNNs), have been proposed. Relations to known models and concepts have been found to be decisive for making their internal operation somewhat understandable, and, at the same time, the basis laid down by explainable concepts (schemata) benefits the generation of suitable and efficient DNNs [14,23,24].…”

“…Known AI systems have expertise in rather limited domains, are brittle, and lack flexibility, e.g., in the sense of learning quickly [12]. It has been suggested that the current limitations of AI might be overcome by following the design principles that are exhibited by natural agents, such as those outlined by the biologically inspired cognitive architecture of the Ouroboros Model [13,14]. While progress has certainly been made, and principled arguments appear to be outdated, there still remains some way to go [15].…”