It may be that it's the deep learning tech which will never quite get there. GPT-3 has similar shortcomings in its mimicry. We're 95% there, I guess, but may never quite reach 100%.
Nah, the current issues are just because we're trying to do everything in one step. Because we've built tools that have so much of a stimulus-response approach, few efforts have been made toward interfaces that ask for clarification ('when you say X, do you mean XYZ or XXX?').
Image-to-image and tuning already addresses many of these issues; just as inpainting works really well, it won't be long before we have select-and-repair, where you add an additional prompt like 'improve this part - the ice cream is fine, just work on the dog's muzzle.'
The mistakes the AI makes are too numerous and hard-to-define for this to work I think. They could perhaps be addressed by having two different models trained differently, each fixing the errors of the other. When humans draw a realistic artwork, it's not 'single-pass'; they have to iterate on the details to get it right.
I get the same feeling as well. This approach may well be eternal demo-ware, and you'll actually need AGI (or manual direction by a real human) to get to 100%.
Image-to-image and tuning already addresses many of these issues; just as inpainting works really well, it won't be long before we have select-and-repair, where you add an additional prompt like 'improve this part - the ice cream is fine, just work on the dog's muzzle.'