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Natural Morphological Computation as Foundation of Learning to Learn in Humans, Other Living Organisms, and Intelligent Machines

1
Department of Computer Science and Engineering, Chalmers University of Technology and the University of Gothenburg, 40482 Gothenburg, Sweden
2
School of Innovation, Design and Engineering, Mälardalen University, 721 23 Västerås, Sweden
Philosophies 2020, 5(3), 17; https://doi.org/10.3390/philosophies5030017
Received: 3 July 2020 / Revised: 10 August 2020 / Accepted: 25 August 2020 / Published: 1 September 2020
(This article belongs to the Special Issue Contemporary Natural Philosophy and Philosophies - Part 2)
The emerging contemporary natural philosophy provides a common ground for the integrative view of the natural, the artificial, and the human-social knowledge and practices. Learning process is central for acquiring, maintaining, and managing knowledge, both theoretical and practical. This paper explores the relationships between the present advances in understanding of learning in the sciences of the artificial (deep learning, robotics), natural sciences (neuroscience, cognitive science, biology), and philosophy (philosophy of computing, philosophy of mind, natural philosophy). The question is, what at this stage of the development the inspiration from nature, specifically its computational models such as info-computation through morphological computing, can contribute to machine learning and artificial intelligence, and how much on the other hand models and experiments in machine learning and robotics can motivate, justify, and inform research in computational cognitive science, neurosciences, and computing nature. We propose that one contribution can be understanding of the mechanisms of ‘learning to learn’, as a step towards deep learning with symbolic layer of computation/information processing in a framework linking connectionism with symbolism. As all natural systems possessing intelligence are cognitive systems, we describe the evolutionary arguments for the necessity of learning to learn for a system to reach human-level intelligence through evolution and development. The paper thus presents a contribution to the epistemology of the contemporary philosophy of nature. View Full-Text
Keywords: learning; learning to learn; deep learning; information processing; natural computing; morphological computing; info-computation; connectionism; symbolism; cognition; robotics; artificial intelligence learning; learning to learn; deep learning; information processing; natural computing; morphological computing; info-computation; connectionism; symbolism; cognition; robotics; artificial intelligence
MDPI and ACS Style

Dodig-Crnkovic, G. Natural Morphological Computation as Foundation of Learning to Learn in Humans, Other Living Organisms, and Intelligent Machines. Philosophies 2020, 5, 17. https://doi.org/10.3390/philosophies5030017

AMA Style

Dodig-Crnkovic G. Natural Morphological Computation as Foundation of Learning to Learn in Humans, Other Living Organisms, and Intelligent Machines. Philosophies. 2020; 5(3):17. https://doi.org/10.3390/philosophies5030017

Chicago/Turabian Style

Dodig-Crnkovic, Gordana. 2020. "Natural Morphological Computation as Foundation of Learning to Learn in Humans, Other Living Organisms, and Intelligent Machines" Philosophies 5, no. 3: 17. https://doi.org/10.3390/philosophies5030017

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