1909 (FKA Palm Beach Tech Space)
Sparse Predictive Hierarchies (SPH) is a novel technology developed by Ogma Corp. It offers an alternative paradigm to that of Deep Learning (DL). SPH addresses some of the fundamental weaknesses of DL: lack of online/incremental learning, slow/inefficient learning, and biological implausibility. As the SPH approach operates fundamentally differently from the typical dense backpropagation networks of DL, we will describe SPH by first introducing concepts such as sparse coding, bidirectional hierarchies, predictions as context for further predictions, exponential memory, and world model building.
Hosted by Dan Ryan From Palm Beach Data Science
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