Very interesting - the human bias implicit to the structure of the data we collect might be critical, but I suspect there's probably a great number theory paper

Very interesting - the human bias implicit to the structure of the data we collect might be critical, but I suspect there's probably a great number theory paper somewhere in there that validates the Platonic Representation idea. How would you correct for something like "the subset of information humans perceive and find interesting" versus "the set of all information available about a thing that isn't noise" and determine what impact the selection of the subset has on the structure of things learned by AI architectures? You'd need to account for optimizers, architecture, training data, and so on, but the results from those papers are pretty compelling.