1. "Either everything is magic or nothing is", and magical statistical tools are currently some of the coolest magic we have harnessed.
2. Making realistic pictures of "Corgi's with sushi" is cool.
3. Papers describing architectures which can make ever more realistic or interesting pictures of corgis and sushi are deserving of academic recognition, even if they can't precisely describe how, just as renderers which can do so would be.
I've found a lot of papers which primarily study the theory of ML coming out of the UK, A yet very few of them end up being of much value to the advancement of the field in terms of applications.
Conversely, the people focused on making real applications ("tools") seem to also be innovating on the science.
Tesla and Edison contributed massively to the progress of the study and application of electricity and both spent nearly no time in academia and instead focused on practical applications in industry. Edison's methods of investigation were said by Tesla (somewhat admiringly) to be entirely empirical. I wonder if the UK's strict academic approach to ML may be holding them back from making bigger contributions to the field. I'm glad some are trying to be strictly scientific about it, but I'm also glad it's not everyone, because it doesn't seem to be what's delivering the "magic" we all want.