Also depends on privacy constraints, sometimes you can't persist the data.

Doesn't it also matter whether what you're hitting is an isotropic homogeneous solid and how much of that is aggregate v fill when we talk about celestial body surface composition?

I'll be using this content to illustrate, thanks!

That is fascinating work.

I'd like to read the paper and will, given the time; are the results promising?

It seems reasonable that a graph with a small branching factor could reasonably replicate logarithmic search complexity of the input space to at least some extent; I'm very interested in exploring this space.

Chat gpt (large language models, in general) is a great generalist and would be likely very useful in predicting 'root node' locations in a knowledge graph which would allow finding the correct content from a minimal subset.

Chat gpt sucks with details, yes, but for use in a recommendation algorithm which depends on the graph, I think that issue could be minimized.

Education first.

ChatGPT, coupled with a dynamic, searchable (log(n) query) knowledge graph, and an algorithm to optimize that graph to maximize educational growth.