I was under the impression that modern word embedding techniques have emergent properties that start to approach conceptual understanding. The trick is figuring

I was under the impression that modern word embedding techniques have emergent properties that start to approach conceptual understanding. The trick is figuring out how to leverage that property properly. I believe Google Brain has a demo where they show translation between two languages without having any corpus specifically linking the two (i.e. the system learned English/Japanese translations and English/Greek translations and then translated Greek/Japanese) with a reasonable degree of accuracy. This seems to me to hint at a more conceptual understanding as opposed to a simple pattern matching activity. In either case, I 100% agree that the multi-modal model is the path forward. It feels like there's a lot of low hanging fruit in the area of model ensambles.