[kjqgqkejbfefn]

>So far, all of the work on AGI has been the result of brute forcing. We've tried to develop a structural understanding of how the human brain works, and we've failed. So we've fallen back to torturing circuits into reorienting themselves into compression algorithms for human knowledge. The mechanisms that these tortured circuits used for doing so, the structures they produced in N-dimensional space to embody that knowledge -- we have very little understanding of how these things actually work under the hood.

And this is the way. (Machine) learning theory is in some way a meta-science about how to do science from facts in order to construct theories that effectively explain these facts. What you are asking for will never amounts to a short set of equations. There is not elegant theory of how to perceive numbers and this is why symbolic artificial perception, rule engines, spam detection, RDF ontologies, etc never took off. You're idealizing knowledge as a set of representations without ever reifying how these representations come into existence. We're departing a world of representation toward a world driven by "incarnations": you can't make sense of a how a brain works without the help of another brain, and this is why there is so many things being researched at the intersection of deep learning and neuroscience. I'd even go as far as considering this is in fact how brains work: they can be composed and decomposed monoidically.

In short:

>a structural understanding

There is no such thing

>the structures they produced in N-dimensional space [...] this brute force approach

This is a contradiction. I'm not saying there won't be "structural insights along the way" nor that throwing categories into the machine learning mix won't be useful, but the learning-like aspect that you denote by "brute force" is more fundamental, and in some way above the very processus of science.

[notpachet]

That's all very well and good from a theoretical, scientific perspective. But we're hooking these things up to real-world applications that often call for deterministic, structural understanding of their inner workings for safety reasons.