August 28, 2016 5:56 AM ET

Software

Company Overview of Numenta, Inc.

Company Overview

Numenta, Inc. develops enterprise-level pattern recognition and anomaly-detection software solutions using machine intelligence, machine learning, cognitive computing, and other technologies. The company offers Grok for IT Analytics that automatically learns changing patterns in individual servers; detects anomalies when servers and applications behave differently from the norm; and automatically discovers time-based patterns in data, as well as generalizes from experience. It also offers Rogue Behavior Detection that models normal behavior of individuals; and detects changes in behavior indicative of unauthorized file access or unauthorized trading. In addition, the company offers Geospatia...

791 Middlefield Road

Redwood City, CA 94063

United States

Founded in 2005

Phone:

650-369-8282

Fax:

650-369-8283

Key Executives for Numenta, Inc.

Co-Founder, Chief Executive Officer and Director
Age: 61
Co-Founder and Director
Age: 59
Vice President of Research
Vice President of Engineering
Age: 58
Compensation as of Fiscal Year 2016.

Numenta, Inc. Key Developments

Numenta, Inc. Releases HTM Studio

Numenta, Inc. announced the release of HTM Studio for anomaly detection, a desktop tool that makes it easy for businesses to experiment with advanced machine intelligence algorithms to uncover anomalies in their streaming data. Simply add numeric, time-series data files to HTM Studio and with the click of a button, discover and visualize within minutes what anomalies can be found. HTM Studio provides an easy way for businesses interested in using Numenta’s technology for anomaly detection to undertake a proof of concept securely using their own data. If HTM Studio finds interesting anomalies, then a user can do a full implementation or deployment of HTM. HTM Studio is based on Numenta’s HTM (Hierarchical Temporal Memory) biologically inspired machine intelligence technology. HTM learns time-based patterns in unlabeled streaming data, making it ideal for prediction, classification and anomaly detection. Because of its ability to perform continuous, unsupervised learning, HTM can find subtle, temporal anomalies that other techniques cannot find.

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