Illuminated AI using brain-motivated neural networks

Feedforward networks have been the basis of Artificial Neural Networks (AI) such as Deep, Convolution, Regression, Random Forest, and Recurrent Networks.  However the internal decision processes of feedforward networks are difficult to explain: they are known to be a "black-box". This is especially problematic in applications where consequences of an error can be severe such as" Medicine, Banking, or Self-Driving Cars. We have developed a new type of neural network motivated by neuroscience.  This allows the network to be more updatable, and the internal decision process easier to understand.  We convert feedforward networks to our Illuminated form and explain their internal workings, helping developers, regulators, and customers better understand feedforward networks and reduce risk.  We will demonstrate some of these benefits.