Ok, so you have a correspondence theory. Not terribly novel or specific, but definitely a right focus. That’s correspondence between model and accessible environment, and the only way to quantify it is comparison between the two. Both are supposed to expand with an indefinite input stream to learn / build a model from, incrementally.
The only thing we can define is a starting point. Which is pixels, the limit of resolution. Start from the first pixel: initial model to predict from, and compare it to adjacent ones: immediate environment. Center-to-rim comparison in kernels, to compute your correspondence. That’s what edge detection operators (Sobel) do, they just use perverse terminology to describe it. And then you have to cluster these initial inputs according to resulting correspondence, because that’s compression: the only way to manage search (comparisons) across your ever-expanding model. The clusters are patterns: groups of input elements that can achieve above-average compression, or internal correspondence. There are fundamentally two types of clustering: centroid-based or vertical and connectivity based or lateral. The former is summation-first, forming a centroid, then comparison of elements to that centroid to define weights: grey-scale inclusion / exclusion future inputs. That’s how all statistical learning operates, inluding neural nets, think Perceptron. So you are not bringing anything new to the table here. But that primary summation is horribly lossy, leading to ridiculous number of backprop cycles needed to get anything meaningful. We and evolution hit on that mindless destruction of data because it’s easy. Compared to immediate cross-comparison and connectivity clustering, from flood-fill to graphs. Which would be a conceptually consitent and ultimately efficient way to maximize your correspondence, but whole lot more complex to do right. Anyway that’s my spiel. If you haven’t had enough, lots of updates since you last looked at it: http://www.cognitivealgorithm.info. ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/T30e487c54062d930-Md862e3a2579c3e1405f17a63 Delivery options: https://agi.topicbox.com/groups/agi/subscription
