Interesting that the top level layers of that network correlates to what is essentially gene expression. To think of it, seems feasible to train a network with whole genome as input, and 3D scanned morphology as output. Again, top level weights in the network would end up encoding salient gene variances that cause morphological differences.

High cost to generate the data, but the first thing I’d do if I was setting up a designer baby startup.