Notes:
Hierarchical temporal memory (HTM) is a machine intelligence technology developed by Numenta, a company that specializes in developing machine learning algorithms and systems based on the principles of the neocortex, the part of the brain that is responsible for learning, memory, and perception.
HTM is a biologically constrained machine learning technology, which means that it is designed to be based on the principles of how the brain processes and stores information. It uses a hierarchical network of “neurons” that are organized into layers, similar to the structure of the neocortex, to learn and make predictions based on patterns in data.
HTM is used to build machine learning systems that are capable of learning and adapting to changing patterns in data. It has been applied to a range of applications, including anomaly detection, predictive maintenance, and financial forecasting.
Wikipedia:
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