Something related I haven’t heard get much attention is the concept of hierarchical clustering, groups of groups of groups of nodes, in the context of language/concept space.
I think that, and the idea of “what is the remaining error from prediction on level x? Can I solve some that error by predicting on a more abstract level x+1?” are two of the main organizing patterns going on in the cortex.
https://en.m.wikipedia.org/wiki/Hierarchical_clustering
Specifically, I think there is promise in looking at how concepts cluster in hierarchies under different randomized starting conditions, different bootstraps of data, different clustering algorithms. My prediction is that clusters which are robust to such permutations are more likely to represent clean cleavings of reality at the joints, and thus more likely to accurately represent natural abstractions, and be found in a variety of general AI models as well as in a variety of human cultures.
Something related I haven’t heard get much attention is the concept of hierarchical clustering, groups of groups of groups of nodes, in the context of language/concept space. I think that, and the idea of “what is the remaining error from prediction on level x? Can I solve some that error by predicting on a more abstract level x+1?” are two of the main organizing patterns going on in the cortex. https://en.m.wikipedia.org/wiki/Hierarchical_clustering Specifically, I think there is promise in looking at how concepts cluster in hierarchies under different randomized starting conditions, different bootstraps of data, different clustering algorithms. My prediction is that clusters which are robust to such permutations are more likely to represent clean cleavings of reality at the joints, and thus more likely to accurately represent natural abstractions, and be found in a variety of general AI models as well as in a variety of human cultures.