Easier and More-Transparent Machine Learning
Short Technical Video
Introductory video explaining the difference between our approach and conventional methods
Explainable AI Demonstration
From this explanation it is clear even to someone unfamiliar with Machine Learning that recognition is not happening as intended. Although this network performs well, it is focusing on pixels outside of where digit information is typically found.
We take an existing Machine Learning - Neural Network (a type called SVM) trained on a Post-office hand written numerical image data set (MNIST) and with OM technology demonstrate what it is doing.
Longer video about the neuroscience motivation, brain-like phenomena, and more detailed examples.
Optimizing Mind in the News!
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 founded Optimizing Mind whose goal is to provide the next generation of machine learning algorithms.