> Intelligence is in the properties of the data supplied to the kNN. Not in its distance function.

If this were true, then all people who observe the same training samples would learn equally well and predict equally well. But we don't. Some of us invent a better loss function than others or reject irrelevant data better. The representation formed by the data is surely derived, not fixed in its raw form. Forming a better representation of the data is the art of learning better. I'm convinced learning and remembering what you learn is more than kNN; in humans it involves data compression and recontextualization as you improve your loss function or tie your old representation to newly learned data — old raw data and representations that heretofore you had not believed were relevant to that objective (or lesson).