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And then there's learning from noisy labels. Lots of work on that as well.
I don't know if that's a great example of the benefits of domain experts for ML. She specifically cherrypicked images which are themselves extremely unusual for old photographs (and you've probably seen them shared on social media before precisely because they are such colorful pioneering color photographs, with a level of quality & detail that wouldn't become common in color photography for a long time afterwards). And the point about minimizing average error tending to make for conservative image choices rather than sampling from the distribution is an old one which is familiar to anyone with the most passing interest in NN colorization - if you look up the (now 3-4-year-old) library used, Antic's DeOldify, his discussion is pretty much all about how to fight the averaging using GAN techniques which encourage the colorization model to at least pick a mode and get nice bright colors that way while minimizing GAN use as much as possible (because GANs are miserable to train). You don't need a Twitter 🧵 lecturing you on this deep insight, everyone is already well-aware of that the first time they train a decolorizer and go "why is it so brown".