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Speaker "Tsvi Achler" Details Back

 

Topic

Explainable AI using brain-motivated neural networks

Abstract

Feedforward networks have been the basis of Artificial Neural Networks such as Deep, Convolution, and Recurrent Networks. However the internal decision processes of feedforward networks are difficult to explain: they are known to be a "black-box". 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 are now able to convert feedforward networks to our form and explain their internal workings. We will demonstrate some of these benefits.

Profile

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