Nonlinear Computing and Nonlinear Artificial Intelligence

  • Behnam Kia
  • William DittoEmail author
Conference paper
Part of the Understanding Complex Systems book series (UCS)


The importance and the necessity of nonlinearity in Artificial Intelligence, AI, and deep learning are very well understood. A multi-layer neural network with linear activation function is equivalent to a single layer of neurons. It is nonlinearity of activation functions that adds complexity to each layer, transforming the network to a universal computing machine that can approximate any continuous function. However, nonlinearity and the complexity that it creates have not been investigated enough in AI and modern deep learning systems. NC State University’s Nonlinear Artificial Intelligence Lab focuses on nonlinearity and the complexity that comes with it, and investigates how this can be an engine of artificial intelligence. We peruse our research at different levels with different goals. In this article we explain our approach, and present an overview of our results.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Nonlinear Artificial Intelligence LabNorth Carolina State UniversityRaleighUSA

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