Based on a wealth of neuroscience evidence, we have created HTM (Hierarchical Temporal Memory), a technology that is not just biologically inspired. It’s biologically constrained. When applied to computers, HTM algorithms are well suited for prediction, anomaly detection and ultimately sensorimotor applications. We believe this technology will be the foundation for the next wave of computing.
At the core of HTM are learning algorithms that can store, learn, infer and recall high-order sequences. Unlike most other machine learning methods, HTM algorithms learn time-based patterns in unlabeled data on a continuous basis. They are robust to noise, and high capacity, meaning they can learn multiple patterns simultaneously.
HTM algorithms work best with data that meets the following characteristics:
Our technology has been tested and implemented in software, all of which is developed with best practices and suitable for deploying in commercial applications.
The need for anomaly detection has grown, as the Internet of Things has produced a world that’s overflowing with streaming data. As these data sources continue to grow, so does the need for anomaly detection.
Uncovering anomalies allows you to:
We created the Numenta Anomaly Benchmark (NAB) in order to be able to measure and compare results from algorithms designed to find anomalies in streaming data. NAB is an open source framework consisting of:
Learn more about the Numenta Anomaly Benchmark.
HTM Studio is a free, desktop tool that lets you find real-time anomalies in your streaming data without having to program, code or set parameters.
Download HTM Studio and try it for yourself.
While Numenta does not build commercial applications, we have created example HTM applications in several fields such as monitoring stock performance, detecting unusual human behavior, and finding patterns in geospatial data. We are confident that many additional applications will be created in the future.
View and download our example applications.