Networks may learn to perform tasks by “cheating” in unknown and unexpected ways which may be a liability for the developer. Feedforward networks are the basis of Artificial Neural Networks 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 feedback neural networks motivated by Neuroscience which allows the internal decision process easier to understand. Developers, regulators, and users can better understand their AI, reduce unexpected surprises and liability by having feedforward networks converted to our Illuminated form to explain the internal decision processes. We will demonstrate some of these benefits
Tsvi Achler has a unique background focusing on the neural mechanisms of recognition from a multidisciplinary perspective. He has done extensive work in theory and simulations, human cognitive experiments, animal neurophysiology experiments, and clinical training. He has an applied engineering background, has received bachelor degrees from UC Berkeley in Electrical Engineering, Computer Science and advanced degrees from University of Illinois at Urbana-Champaign in Neuroscience (PhD), Medicine (MD) and worked as a postdoc in Computer Science, and at Los Alamos National Labs, and IBM Research. He now heads his own startup Optimizing Mind whose goal is to provide the next generation of machine learning algorithms