This new open source library is based concepts first described in
Jeff Hawkins’ book On Intelligence and subsequently developed by
Numenta Inc. NuPIC consists of a set of machine learning algorithms
that accurately model layers of neurons in the neocortex. NuPIC’s
algorithms continuously learn temporal patterns, make predictions,
and detect anomalous behavior within streaming data. These are the
same algorithms and code used in Numenta’s commercial product, Grok.
NuPIC represents a new approach to machine learning and machine
intelligence. Given the large interest we have had from people
wanting to study these algorithms and apply them in novel ways we
created the NuPIC open source library and the accompanying
In this hands-on session, we’ll introduce NuPIC’s Online Prediction
Framework (OPF) and demonstrate how one creates models using an OPF
client. We’ll set up some live streaming data to pass into the
client and watch as NuPIC makes online inferences, learning the
changing patterns in the streaming data set. NuPIC and the OPF have
been applied to many scenarios and form the foundation for Numenta’s
commercial product, Grok. To master NuPIC you will have to become
comfortable with concepts such as sparse distributed representations
and on-line learning. You can read about these concepts and the
algorithms in this white paper:
Scott works on the algorithms and infrastructure at Numenta and
leads the engineering for the open source project. He previously
worked on conversion tracked ads at Google and studied computer
science at Cornell University. Originally from the Pacific
Northwest, Scott now lives in San Francisco.
Matt is currently the Open Source Community Flag-Bearer for
Numenta.org, as well as the Manager of Web Services for Numenta’s
commercial product, Grok. He’s been a committer on several OS
projects like Grails (at SpringSource) and Mojito (at Yahoo!). He
lives with his wife and two children in Cupertino, CA.
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