The focus on vector space as mentioned in the article "words can be represented as mathematical vectors, allowing similarities between related words to be calculated. For example, “boat” and “water” are close in vector space even though they look very different. Researchers at the University of Montreal, led by Yoshua Bengio, and another group at Google, have used this insight to build networks in which each word in a sentence can be used to construct a more complex representation—something that Geoffrey Hinton, a professor at the University of Toronto and a prominent deep-learning researcher who works part-time at Google, calls a “thought vector.”"

Is to me, the most significant way in which we can mimic the way the human cognitive process develops associations between things. Auto-association is key here.

In addition, understanding how to calculate similarities between vectors is also important.