Numenta
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| Privately held | |
| Industry | Analytics, Artificial Intelligence |
| Founded |
Redwood City, California (February 4, 2005) |
| Founder | Jeff Hawkins, Donna Dubinsky, Dileep George |
| Headquarters | Redwood City, California, U.S. |
|
Area served
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Worldwide |
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Key people
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Donna Dubinsky (CEO), Jeff Hawkins (Co-founder), |
| Products | Grok for IT Analytics |
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Number of employees
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11-50 |
| Website | numenta |
Numenta is a machine intelligence company that has developed a cohesive theory, core software, technology and applications based on the principles of the neocortex. The company was founded on February 4, 2005 by Palm founder Jeff Hawkins with his longtime business partner Donna Dubinsky and Stanford graduate student Dileep George. Numenta is headquartered in Redwood City, California and is privately funded.
Numenta has developed a number of example applications to demonstrate the applicability of its technology. Its first commercial product, Grok, offers anomaly detection for IT analytics, giving insight into IT systems to identify unusual behavior and reduce business downtime. Grok has since been licensed to their strategic partner, Avik Partners. Other applications include stock monitoring, geospatial tracking and rogue behavior.
In addition, Numenta has created NuPIC (Numenta Platform for Intelligent Computing) as an open source project.
The company name comes from the Latin (“pertaining to the mind”) genitive of (“mind”). The word Grok is a term that was coined by Robert A. Heinlein in his 1961 novel Stranger in a Strange Land.
Numenta's machine intelligence technology is called hierarchical temporal memory (HTM), and is a computational theory of the neocortex. This theory was first described in the book On Intelligence, written in 2004 by Jeff Hawkins and co-author Sandra Blakeslee. At the core of HTM are time-based learning algorithms that store and recall temporal patterns. The HTM algorithms are documented and available through its open source project, NuPIC. The HTM technology is suited to address a number of problems, particularly those with the following characteristics: streaming data, underlying patterns in data change over time, subtle patterns, time-based patterns.
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