Appropriate terminology aside, deep learning at one point at least clearly represented neutral networks as a "one size fits all" algorithmic data structure beca

Appropriate terminology aside, deep learning at one point at least clearly represented neutral networks as a "one size fits all" algorithmic data structure because it could (more) easily scale, be distributed, etc. and could produce results unsupervised in a way that other ML algos could not. Now with all the discussion on ethics and bias in AI, not sure if unsupervised black box architectures really have a place in the discussion ...