Designing Artificial Cognitive Architectures: Brain Inspired or Biologically Inspired?
This addresses the problem of opaque AI systems for researchers and practitioners, but it is incremental as it builds on existing critiques without presenting new empirical results.
The paper argues that current Artificial Neural Networks (ANNs) lack explainability and theoretical grounding, resembling alchemy, and proposes that simpler biological intelligence forms are better suited for designing artificial cognitive architectures.
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.