Numenta

Numenta is a research organization focused on developing a scientific understanding of the neocortex, the part of the brain responsible for higher-level cognitive functions. Founded in 2005 by Jeff Hawkins, Donna Dubinsky, and Dileep George, Numenta's primary goal is to reverse-engineer the principles of cortical computation and apply them to artificial intelligence.

The organization's core research revolves around Hierarchical Temporal Memory (HTM) theory. HTM is a biologically constrained machine learning framework that models the structure and function of the neocortex. It proposes that the neocortex learns by continuously forming and updating predictions about incoming sensory data. HTM algorithms aim to replicate this predictive learning mechanism, enabling systems to learn and adapt in real-time from streaming data.

Numenta's research has explored various applications of HTM, including anomaly detection, pattern recognition, and natural language processing. They have open-sourced much of their research and code, fostering a community of developers and researchers interested in biologically inspired AI. Their work is a significant contribution to the field of neuroscience and artificial intelligence, aiming to bridge the gap between biological intelligence and machine learning. The organization continues to publish research papers and develop open-source tools to advance its mission.