Preprint Review Version 1 This version is not peer-reviewed

How Biological Concepts and Evolutionary Theories Are Inspiring Advances in Machine Intelligence

Version 1 : Received: 13 September 2021 / Approved: 14 September 2021 / Online: 14 September 2021 (10:40:01 CEST)

How to cite: Gutai, A.R.; Gorochowski, T.E. How Biological Concepts and Evolutionary Theories Are Inspiring Advances in Machine Intelligence. Preprints 2021, 2021090234 (doi: 10.20944/preprints202109.0234.v1). Gutai, A.R.; Gorochowski, T.E. How Biological Concepts and Evolutionary Theories Are Inspiring Advances in Machine Intelligence. Preprints 2021, 2021090234 (doi: 10.20944/preprints202109.0234.v1).

Abstract

Since its advent in the mid-twentieth century, the field of artificial intelligence (AI) has been heavily influenced by biology. From the structure of the brain to evolution by natural selection, core biological concepts underpin many of the fundamental breakthroughs in modern AI. Here, focusing specifically on artificial neural networks (ANNs) that have become commonplace in machine learning, we show the numerous connections between theories based on coevolution, multi-level selection, modularity and competition and related developments in ANNs. Our aim is to illuminate the valuable but often overlooked inspiration biologists have provided AI research and to spark future contributions at this intersection of biology and computer science. Although recent advances in AI have been swift, many significant challenges remain requiring innovative solutions. Thankfully, biology in all its forms still has a lot to teach us, especially when trying to create truly intelligent machines.

Keywords

artificial intelligence; artificial neural networks; genetic algorithms; evolution; neuroevolution; machine learning

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