<commit_before>---
title: Technology Overview
description: Based on a wealth of neuroscience evidence, we have created HTM (Hierarchical Temporal Memory), a theoretical framework for both biological and machine intelligence.
---

[apps]:   /applications/
[neuro]:  /neuroscience-research/

Based on a wealth of [neuroscience evidence][neuro], we have created HTM
(Hierarchical Temporal Memory), a theoretical framework for both biological and
machine intelligence. Our HTM technology is not just biologically inspired. It’s
biologically constrained. When applied to computers, HTM is well suited for
prediction, anomaly detection, classification 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
learns time-based patterns in unlabeled data on a continuous basis. HTM is
robust to noise, and high capacity, meaning that it can learn multiple patterns
simultaneously.<commit_msg>Machine Intelligence technology metatags<commit_after>---
title: Machine Intelligence Technology
keywords: machine intelligence
description: We have created a theoretical framework for biological and machine intelligence called HTM (Hierarchical Temporal Memory). When applied to computers, HTM is well suited for a variety of machine intelligence problems, including prediction and anomaly detection.
---

[apps]:   /applications/
[neuro]:  /neuroscience-research/

Based on a wealth of [neuroscience evidence][neuro], we have created HTM
(Hierarchical Temporal Memory), a theoretical framework for both biological and
machine intelligence. Our HTM technology is not just biologically inspired. It’s
biologically constrained. When applied to computers, HTM is well suited for
prediction, anomaly detection, classification 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
learns time-based patterns in unlabeled data on a continuous basis. HTM is
robust to noise, and high capacity, meaning that it can learn multiple patterns
simultaneously.
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