Natural morphological computation as foundation of learning to learn in humans, other living organisms, and intelligent machines

The emerging contemporary natural philosophy provides a common ground for the integrative view of natural, artificial, and human-social knowledge practices. Learning process is central acquiring, maintaining, managing knowledge, both theoretical practical. This paper explores relationships between present advances in understanding learning sciences artificial (deep learning, robotics), (neuroscience, cognitive science, biology), (philosophy computing, mind, philosophy). question is, what at this stage development inspiration from nature, specifically its computational models such as info-computation through morphological can contribute to machine intelligence, how much on other hand experiments robotics motivate, justify, inform research neurosciences, computing nature. We propose that one contribution be mechanisms ‘learning learn’, step towards deep with symbolic layer computation/information processing framework linking connectionism symbolism. As all systems possessing intelligence are systems, we describe evolutionary arguments necessity learn system reach humanlevel evolution development. thus presents epistemology