Morphological Computation and Learning to Learn In Natural Intelligent Systems And AI

TL;DR

This paper explores how insights from natural systems, especially neuroscience and morphology, can inform and enhance machine learning and artificial intelligence development.

Contribution

It investigates the role of morphological computation and learning to learn in natural intelligent systems and AI, highlighting their potential to improve deep learning models.

Findings

Insights from neuroscience can inspire new AI models

Morphological computation enhances learning efficiency

Biological principles can guide AI architecture design

Abstract

At present, artificial intelligence in the form of machine learning is making impressive progress, especially the field of deep learning (DL) [1]. Deep learning algorithms have been inspired from the beginning by nature, specifically by the human brain, in spite of our incomplete knowledge about its brain function. Learning from nature is a two-way process as discussed in [2][3][4], computing is learning from neuroscience, while neuroscience is quickly adopting information processing models. The question is, what can the inspiration from computational nature at this stage of the development contribute to deep learning and how much models and experiments in machine learning can motivate, justify and lead research in neuroscience and cognitive science and to practical applications of artificial intelligence.