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The distinction between "supervised" and "unsupervised" learning is fundamentally superficial -- an artifact of the semantics of the last couple decades of ML. All learning is guided by an objective (including all forms of ML and all forms of human learning).
What matters to progress towards greater generality in AI is *how* you learn -- for instance, gradient descent with parametric functions vs discrete search over a discrete program space. The supervision objective is an implementation detail. There will always be one anyway.
Now, from the standpoint of doing applied ML, the ability to solve problems without human labeling effort is huge. So unsupervised learning is a practical concept, but one that is theoretically irrelevant. It affects how you use ML, while being orthogonal to ML algorithms.
The important questions, from the applied ML angle, are: 1. What problems can you solve using data reconstruction as your objective? 2. What automatically-generated or passively-collected alternative to human-provided labels can you use to solve task X?
These are broadly "data science" questions as opposed to algorithmic questions.
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