Designing Artificial Cognitive Architectures: Brain Inspired or Biologically Inspired?

TL;DR

This paper discusses the limitations of traditional artificial neural networks and explores biologically inspired approaches for designing more explainable and effective artificial cognitive architectures.

Contribution

It highlights the need to shift from purely brain-inspired models to biologically inspired ones that incorporate natural intelligence principles.

Findings

ANNs lack explainability and theoretical grounding

Biologically inspired models offer promising alternatives

Natural intelligence forms are more suitable for AI development

Abstract

Artificial Neural Networks (ANNs) were devised as a tool for Artificial Intelligence design implementations. However, it was soon became obvious that they are unable to fulfill their duties. The fully autonomous way of ANNs working, precluded from any human intervention or supervision, deprived of any theoretical underpinning, leads to a strange state of affairs, when ANN designers cannot explain why and how they achieve their amazing and remarkable results. Therefore, contemporary Artificial Intelligence R&D looks more like a Modern Alchemy enterprise rather than a respected scientific or technological undertaking. On the other hand, modern biological science posits that intelligence can be distinguished not only in human brains. Intelligence today is considered as a fundamental property of each and every living being. Therefore, lower simplified forms of natural intelligence are more suitable for investigation and further replication in artificial cognitive architectures.