[sharma-arjun]

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.

[Jach]

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.