Comment by noduerme

This is a fascinating take as far as I can follow it, which unfortunately is not that far. But does any of this formal logic help with deriving a function that describes a vector? Because it seems like the greatest inefficiencies and bottlenecks in big data analysis e.g. training networks still boils down to how to find functions that approximate output comparable to expected vectors, whether that's done by symbolic regression or layers of transformations. It would be "magic" if you could operate only on the vectors-as-functions without needing to distill or compress the relationships between their inputs and outputs somehow.