Hosted by San Francisco Artificial Intelligence Meetup. On April 7th, we are
proudly having Yuwei Cui, Ph.D., and Research Engineer at Numenta who will
present us the heart of company’s technology – Hierarchical Temporal Memory.
HTM is a detailed computational theory of the neocortex. At the core of HTM are
time-based learning algorithms that store and recall spatial and temporal
patterns. HTM is well suited to a wide variety of problems; particularly those
involve streaming data and time-based patterns. The current HTM systems are able
to learn the structure of streaming data, make predictions and detect anomalies.
It is distinguished from other techniques in its ability to learn continuously
in a fully unsupervised manner.
HTM has been tested and implemented in software, all of which is developed with
best practices and is suitable for deploying in commercial applications. The
core learning algorithms are fully documented and available in an open source
project called NuPIC.