It's not a merely semantic argument. Google's system did not learn that the sound 'cat' is associated with that particular concept. You need some kind of supervised learner to make that association.
My point is that you can still identify that it's a separate concept, even if you don't know what to call it. Even simple unsupervised learners (clustering) can do this.
Merely identifying it as a separate concept is not especially useful. Tagging an image with the 'cat' tag is useful; tagging it with the 'concept 50765' tag, not so much.
Well... sure, not as useful, but I still think it's interesting. For instance, in english we have multiple words (goat, sheep) for what in chinese is a single word (yang2). If an unsupervised model split our mammals which have fur and bleet into two categories 'concept 19281' and 'concept 19282', we might think that it's done well to separate the goats and the sheep, but the chinese speaker might think that it's failed to group the same animal together.
Now imagine that reversed, that what we considered one thing could be considered 2 or more by the model, we had just never thought of them separate because we had no words to describe them.
There are many of these examples, where one language has one concept that's split among others in another language, and the speakers of the first language might never know the difference unless those words exist.
