Neural networks can encode any computable function. KANs have no advantage in terms of computability. Why are they a promising pathway? Also, the splines in K

Neural networks can encode any computable function. KANs have no advantage in terms of computability. Why are they a promising pathway? Also, the splines in KANs are no more "explainable" than the matrix weights. Sure, we can assign importance to a node, but so what? It has no more meaning than anything else.