Powering Proxi with a Brain-Based AI Platform
Numenta and Gallium Studios both share a vision that leverages neuroscience in unique and powerful ways. Their collaboration is driving forward new and exciting possibilities in gaming and AI.
Numenta and Gallium Studios both share a vision that leverages neuroscience in unique and powerful ways. Their collaboration is driving forward new and exciting possibilities in gaming and AI.
Numenta and Gallium Studios both share a vision that leverages neuroscience in unique and powerful ways. Their collaboration is driving forward new and exciting possibilities in gaming and AI.
If you’ve been to numenta.com before, you may notice that something looks a little – or perhaps more than a little – different. After months of behind the scenes remodeling, we’ve launched our newly designed website inspired by Wikipedia.
A prominent figure known as the Oprah of China, Yang Lan, interviewed our co-founder, Jeff Hawkins for an upcoming documentary series on AI. She travelled across the globe to talk to knowledgeable researchers, technologists and leaders about their findings. In this blog, you’ll get an insider’s look at their interview.
In this blog, our co-founder Jeff Hawkins revisits an essay he wrote while he was a graduate student at UC Berkeley. The paper is titled “An Investigation of Adaptive Behavior Towards a Theory of Neocortical Function”, and it gives a nice historical perspective on Numenta.
Eric Jonas and Konrad Kording just released a provocative paper, “Could a neuroscientist understand a microprocessor?”. In their paper, they ask whether current neuroscience techniques could discover the operations of a simple microprocessor.
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.