The Vector Institute

I've waxed enthusiastic before about Thought Vectors:

... the space of concepts (primitives) used in human language (or equivalently, in human thought) ... has only ~1000 dimensions, and has some qualities similar to an actual vector space. Indeed, one can speak of some primitives being closer or further from others, leading to a notion of distance, and one can also rescale a vector to increase or decrease the intensity of meaning.

... we now have an automated method to extract an abstract representation of human thought from samples of ordinary language. This abstract representation will allow machines to improve dramatically in their ability to process language, dealing appropriately with semantics (i.e., meaning), which is represented geometrically.

Apparently I am not the only one (MIT Technology Review):

... The Vector Institute, this monument to the ascent of ­Hinton’s ideas, is a research center where companies from around the U.S. and Canada—like Google, and Uber, and Nvidia—will sponsor efforts to commercialize AI technologies. Money has poured in faster than Jacobs could ask for it; two of his cofounders surveyed companies in the Toronto area, and the demand for AI experts ended up being 10 times what Canada produces every year. Vector is in a sense ground zero for the now-worldwide attempt to mobilize around deep learning: to cash in on the technique, to teach it, to refine and apply it. Data centers are being built, towers are being filled with startups, a whole generation of students is going into the field.

... words that have similar meanings start showing up near one another in the space. That is, “insane” and “unhinged” will have coordinates close to each other, as will “three” and “seven,” and so on. What’s more, so-called vector arithmetic makes it possible to, say, subtract the vector for “France” from the vector for “Paris,” add the vector for “Italy,” and end up in the neighborhood of “Rome.” It works without anyone telling the network explicitly that Rome is to Italy as Paris is to France.

... Neural nets can be thought of as trying to take things—images, words, recordings of someone talking, medical data—and put them into what mathematicians call a high-dimensional vector space, where the closeness or distance of the things reflects some important feature of the actual world. Hinton believes this is what the brain itself does. “If you want to know what a thought is,” he says, “I can express it for you in a string of words. I can say ‘John thought, “Whoops.”’ But if you ask, ‘What is the thought? What does it mean for John to have that thought?’ It’s not that inside his head there’s an opening quote, and a ‘Whoops,’ and a closing quote, or even a cleaned-up version of that. Inside his head there’s some big pattern of neural activity.” Big patterns of neural activity, if you’re a mathematician, can be captured in a vector space, with each neuron’s activity corresponding to a number, and each number to a coordinate of a really big vector. In Hinton’s view, that’s what thought is: a dance of vectors.

... It is no coincidence that Toronto’s flagship AI institution was named for this fact. Hinton was the one who came up with the name Vector Institute.

See also Geoff Hinton on Deep Learning (discusses thought vectors).