One of the major key ingredients of the experience is machine learning. Machine learning in our world is defining a world of probabilities. Machine learning, particularly our kind, which is probabilistic, is not really about what you know, it's about what you don't know.
It's about being able to look at the world and not see duality, zeroes and ones, but to see infinite shades of grey. To see what's probable. You should imagine that, in our machine learning piece of the brain, which is just one component of the brain, pixels go in and what you get out of it is a probability distribution of likelihood.
So a pixel may go in and what comes out of it may be - hey, this pixel? 80 per cent chance that this pixel belongs to a foot. Sixty per cent chance it belongs to a head, 20 per cent chance that it belongs to the chest. Now this is where we chop the human body into the 48 joints which we expose to our game designers. What you see is infinite levels of probability for every pixel and if it belongs to a different body part.
That operation is, as you can imagine, a highly, highly parallelisable operation. It's the equivalent of saying, pixel in, work through this fancy maths equation and imagine you get a positive number, a positive answer, you branch right, you get a negative answer you branch left. Imagine doing this over a forest of probabilities. This is stuff where you'll get a thousand times performance improvement if you put it on the GPU rather than the CPU.
GPUs are machines designed for these types of operations. The core of our machine learning algorithm, the thing that really understands meaning, and translates a world of noise to the world of probabilities of human parts, runs on the GPU.
