Are classical machine learning techniques that don't involve neural networks / DL not considered "learning-based systems" anymore? I would argue that even something as simple as linear regression can and should be considered a learning-based system, even ignoring more sophisticated algorithms such as SVMs or boosted tree regression models. And these were in use for quite a while before the ImageNet moment, albeit not with the same level of visibility.
I've just accepted that these broad terms are really products of their time, and use of them just means people want to communicate at a higher level above the details. (Whether that's because they don't know the details, or because the details aren't important for the context.) It's a bit better than "magic" but not that different; the only real concern I have is vague terms making it into laws or regulations. I agree on linear regression, and I remember being excited by things like random forests in the ML sphere that no one seems to talk about anymore. I think under current standards of vagueness, even basic things like a color bucket-fill operation in a paint program count as "AI techniques". In the vein of slap a gear on it and call it steampunk, slap a search step on it and call it AI, or slap a past-data storage step on it and call it learning.
