Deep Learning doesn't necessarily require a huge amount of data. What DL does is allow you to fit more complex relationships between input and output than would

Deep Learning doesn't necessarily require a huge amount of data. What DL does is allow you to fit more complex relationships between input and output than would be the case with, say, a bog standard linear model. If a relationship is complex and also clearly defined in your data, then you don't necessarily need much. In general it's true that may not be the case, but that doesn't make "deep learning requires lots of training data" true, only that "the data used for deep learning models is typically noisy on top of representing a complex relationship". It's a semantic difference, but a very important one if we're to avoid going down the road of just mindlessly throwing compute at every problem. And if we do that, we'll just wind up with millions of Rube Goldberg machines instead of actually solving problems. The change in logic of the real world thing is absolutely spot on, though. Over enough time it becomes basically impossible to disentangle effects.