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AIC Seminar Series

Towards explainable AI and easier updating using brain-motivated optimization

Tsvi AchlerOptimizing Mind

Notice:  Hosted by Rodrigo de Salvo Braz

Date:  Thursday, November 17th 2016 at 4:00pm

Location:  EK255 (SRI E building)  (Directions)

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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". For decades Neuroscientists have argued that brain networks are not feedforward. We have developed a new type of neural network which is not feedforward. It performs optimization during recognition - not to learn weights, but to determine neuron activation. This allows learning to be simpler, the network to be more updatable, and the internal decision process easier to understand. Moreover we are now able to convert feedforward networks to our form and explain their internal workings. We will demonstrate some of these benefits.

   Bio for Tsvi Achler

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.

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