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Evolution of Brains and Computers: The Roads not Taken
Version 1
: Received: 7 May 2022 / Approved: 9 May 2022 / Online: 9 May 2022 (14:12:27 CEST)
A peer-reviewed article of this Preprint also exists.
Solé, R.; Seoane, L.F. Evolution of Brains and Computers: The Roads Not Taken. Entropy 2022, 24, 665, doi:10.3390/e24050665. Solé, R.; Seoane, L.F. Evolution of Brains and Computers: The Roads Not Taken. Entropy 2022, 24, 665, doi:10.3390/e24050665.
Abstract
When computers start to become a dominant part of technology around the 1950s, fundamental questions about reliable designs and robustness were of great relevance. Their development gave rise to the exploration of new questions such as what made brains reliable (since neurons can die) and how computers could get inspiration from neural systems. In parallel, the first Artificial Neural Networks came to life. Since then, the comparative view between brains and computers has been developed in new, sometimes unsuspected directions. With the rise of deep learning and the development of connectomics, an evolutionary look at how both hardware and neural complexity have evolved or designed is required. In this paper, we argue that important similarities have resulted both from convergent evolution (the inevitable outcome of architectural constraints) and inspiration of hardware and software principles guided by toy pictures of neurobiology. Moreover, dissimilarities and gaps originate from the lack of major innovations that have paved the way to biological computing (including brains) that are completely absent within the artificial domain. As it occurs within synthetic biocomputation, we can also ask whether alternative minds can emerge from A.I.\ designs. Here we take an evolutionary view of the problem and discuss the remarkable convergences between living and artificial designs and what are the pre-conditions to achieve artificial intelligence.
Keywords
Evolution; brains; deep learning; embodiment; neural networks; artificial intelligence; neurorobotics
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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