Numenta Collaborates with Intel on their Neuroscience-Based Solution to Accelerate AI Inference on CPUs.
Earlier this month, I attended the annual Computational and Systems Neuroscience meeting (Cosyne) in Salt Lake City. Cosyne is a peer reviewed scientific conference that brings experimental and theoretical neuroscientists together to exchange data and ideas.
Those of you subscribing to the nupic-theory mailing list are aware that a new research paper describing a mathematical model for the spatial pooler (SP) has emerged. Many of us have asked “What is the math behind the SP?” or “How can I use the SP for machine learning”. The goal of this paper is to address those very questions, bridging the gap between HTM and the machine learning community.
While Numenta may not be in the business of selling traditional products, we are in the business of making our technology pervasive. To that end, we build sample applications that demonstrate the value of HTM, and we make the code available in our open source project.
What is Machine Intelligence vs. Machine Learning vs. Deep Learning vs. Artificial Intelligence (AI)?
We are frequently asked how we distinguish our technology from others. In our view, there are three major approaches to building smart machines. Let’s call these approaches Classic AI, Simple Neural Networks, and Biological Neural Networks. The rest of this blog post will describe and differentiate these approaches.