There's no such thing as truly unsupervised learning - any machine learning algorithm is defined by some guidelines you've provided (or built-in) for what output it has to generate.

For example, you might think of data clustering as an unsupervised problem, but in reality you're still providing supervision in terms of what similarity measure to use and some control over how many clusters it should generate; and, at the end of the day, the output is always going to be a set of clusters, not, say, a stock tip or wry comment on the nature of your data!

I took it more to mean that, e.g., when solving for 1+1 it won't be allowed to answer "tomatoes".