Morphological computation and learning to learn in natural intelligent systems and AI
2021 (English)In: AISB Convention 2021: Communication and Conversations, The Society for the Study of Artificial Intelligence and Simulation of Behaviour , 2021Conference paper, Published paper (Refereed)
Abstract [en]
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.
Place, publisher, year, edition, pages
The Society for the Study of Artificial Intelligence and Simulation of Behaviour , 2021.
Keywords [en]
Computational neuroscience, Deep learning, Intelligent systems, Learning systems, Neurology, Brain functions, Cognitive science, Incomplete knowledge, Information processing models, Learning from natures, Learning to learn, Morphological computation, Natural intelligent systems, Learning algorithms
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:mdh:diva-55484Scopus ID: 2-s2.0-85109069070ISBN: 9781713829423 (print)OAI: oai:DiVA.org:mdh-55484DiVA, id: diva2:1580690
Conference
AISB Convention 2021: Communication and Conversations, 7 April 2021 through 9 April 2021
2021-07-152021-07-152021-07-15Bibliographically approved