Representing meaning as a number, or more specifically a point or set in some vector space is fine. I'm not suggesting a more sophisticated concept of understanding than the status quo here, basically text in, a number/numbers out, which as I understand it is how most intent analysis works currently.

So meaning in this sense is very much quantifiable, yet how far along are we in parsing out even the most basic meanings? Can we build something which discriminates between "I'm moving in to <address>" and "I'm moving in on <date>", using the latest and greatest word embeddings? Not without some extra layers of external rules imposed on top. So the model does not 'understand', even in this limited scope of understanding.

Don't be fooled by the sentence recycling is all.