Feedforward networks are the basis of Artificial Neural Networks (AI) such as Deep, Convolution, Recurrent, Networks and even simpler Regression methods.  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.

Optimizing Mind has developed a new type of neural network motivated by neuroscience which allows the internal decision process easier to understand.

The simple workflow involves converting feedforward networks to our Illuminated form to explain their internal workings and reduce risk. This helps our customers: developers, regulators, and our customers' customers better understand their AI.  We will demonstrate some of these benefits.