NATEA

Tsvi Achler

Tsvi Achler

MD/PhD CEO Optimizing Mind

“Debug and Approve your Deep Networks by Overcoming the Black Box Problem”

Abstract:

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

Biography:

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