I’ve been pondering this question and wanted to get some of your thoughts on it.

Kernel functions finds distances between two inputs relative to each other in some transformed space. Neural networks on the other hand finds the exact location of of the input in its transformed space. Are there benefit and downsides between the two transformations? Why are kernel functions used instead of specifying the direct transformation from input to transformed space