Jim Carnicelli's AI Blog

[ Audio Version ] On an exhilarating walk with my wife, we discussed the subject of how to build on the lessons I learned from my Pattern Sniffer project and its "neuron bank", documented in my previous blog entry . There are loads of things to do and it was not obvious how to squeeze more value out of what little I've done so far. But it finally became apparent. One thing that I was not happy about with Pattern Sniffer is that the world it perceives is "pure". There is just one pattern to perceive at a time. The world we perceive is rarely like this. As I walk along, I hear a bird singing, a car, and a lawn mower at the same time and am aware of each, separately. Clearly, there is lots of raw information overlap, yet I'm able to filter these things out and be aware of all three at once. Pattern Sniffer could see two things going on in its tiny 5 x 5 pixel visual field, but it would see them as a single pattern. This is the kind of sterile world so m

[ Audio Version ] Table of contents Introduction Unsupervised learning Finite resources Competing to be useful Confidence The simulation Learning in linear time All at once learning Learning while performing Noisy data Longevity Working memory Pattern invariance More to explore The nuts and bolts of the algorithm Introduction For over a year, I've been nursing what I believe is a somewhat novel concept in AI that superficially resembles a neural network and is inspired by my read of Jeff Hawkins' On Intelligence . Recently, I finally got around to writing code to explore it. I was deeply surprised by how well it already works that I thought it worthwhile to write a blog entry introducing the concept and make public my source code and test program for independent review. For lack of putting any real thought into it, I just named the project / program "Pattern Sniffer". My regular readers will recognize my frequent disdain for traditional artificial neural networks