Jim Carnicelli's AI Blog

[ Audio Version ] I've been thinking more about perceptual-level thinking and how to implement it in software. In doing so, I've started formulating a model of how cortical neural networks might work, at least in part. I'm sure it's not an entirely new idea, but I haven't run across it in quite this form, so far. One of the key questions I ask myself is: how does human neural tissue learn? And, building on Jeff Hawkins' memory-prediction model , I came up with at least one plausible answer. First, however, let me say that I use the term "neuron" here loosely. The mechanisms I ascribe to individual neurons may turn out to be more a function of groups of them working in concert. Let me start with the notion of a group of neurons in a "neural bank". A bank is simply a group of neurons that are all looking at the same inputs, as illustrated in the following figure: Perhaps it's a region of the input coming from the auditory nerves. Or perhaps

[ Audio Version ] I've been getting too lost in the idiosyncrasies of machine vision of late and missing my more important focus on intelligence, per se. I'm changing direction, now. My recent experiences have shown me that one thing we haven't really done well is in the area of perceptual level intelligence. We have great sensors and cool algorithms for generating interesting but primitive information about the world. Edge detection, for example, can be used to generate a series of lines in a visual scene. But so what? Lines are just about as disconnected from intelligence as the raw pixel colors are. Where do primitive visual features become percepts? Naturally, we have plenty of systems designed to instantly translate visual (or other sensory) information into known percepts. Put little red dots around a room, for instance, and a visual system can easily cue in on them as being key markers for a controlled-environment system. This is the sort of thinking that is used in