How RAG Will Usher In the Next Generation of LLMs and Generative AI
Retrieval-augmented generation may provide a big step forward in addressing many of the issues that keep enterprises from adopting AI.
Retrieval-augmented generation may provide a big step forward in addressing many of the issues that keep enterprises from adopting AI.
Retrieval-augmented generation may provide a big step forward in addressing many of the issues that keep enterprises from adopting AI.
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.
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.
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.