hierarchical temporal memory

Grok is based on the "Hierarchical temporal memory" model, developed by Numenta company.

Hierarchical Temporal Memory, a biomimetic machine learning model by Numenta.

Hierarchical temporal memory is an approach that models some of the structural and algorithmic properties of the neocortex.

Hierarchical temporal memory, a technology by Hawkins's startup Numenta Inc. to replicate the properties of the neocortex.

One such model is hierarchical temporal memory, which is a machine learning framework that organizes visual perception problem into a hierarchy of interacting nodes (neurons).

This theory is the basis for Numenta's technology, called Hierarchical Temporal Memory (HTM).

Hierarchical Temporal Memory (Microsoft PowerPoint presentation)

So far, they have come up with two major algorithmic frameworks: Hierarchical Temporal Memory and Fixed-sparsity Distributed Representations.

Hierarchical Temporal Memory (HTM), a model, a related development platform and source code by Numenta, Inc. (2008).

He also says PRTM even more strongly resembles Hierarchical Temporal Memory promoted by Jeff Hawkins in recent years.

He also says his approach is similar to Jeff Hawkins' hierarchical temporal memory, although he feels the hierarchical hidden Markov models have an advantage in pattern detection.

George and Hawkins published a paper that establishes a model of cortical information processing called hierarchical temporal memory that is based on Bayesian network of Markov chains.

The recently proposed Hierarchical temporal memory model may help resolving this dispute, at least to some degree, given that it explains how the neocortex extracts high-level (symbolic) information from low-level sensory input.

Donna Dubinsky, together with Jeff Hawkins and Dileep George recently founded Numenta, Inc. to further develop the pattern recognition software termed Hierarchical Temporal Memory.

In 2005, George pioneered Hierarchical temporal memory and cofounded the AI research startup Numenta, Inc. with Jeff Hawkins and Donna Dubinsky.

Hierarchical temporal memory (HTM) is a machine learning model developed by Jeff Hawkins and Dileep George of Numenta, Inc. that models some of the structural and algorithmic properties of the neocortex.

The Numenta Platform for Intelligent Computing (NuPIC) is a set of tools and a runtime engine, including embedded learning algorithms, that enables self-training and pattern recognition based on the theories of Hierarchical Temporal Memory (HTM).