This is really significant. At this moment in history, the growth of computer power has made a bunch of important signal-processing and statistical tasks just feasible, so we are seeing things like self-driving cars, superhuman image recognition, and so on. But it's been very difficult to take advantage of the available computational power, because it's in the form of GPUs and clusters. TensorFlow is a library designed to make it easy to do exactly these things, and to scale them with your available computing power, along with libraries of the latest tricks in neural networks, machine learning (which is pretty close to "statistics").

As a bonus, it has built-in automatic differentiation so that you can run gradient descent on any algorithm — which means that you can just write a program to evaluate the goodness of a solution and efficiently iterate it to a local maximum. If you do this enough times, hopefully you'll find a global maximum. There are a variety of other numerical optimization algorithms, but gradient descent is very simple and broadly applicable.

And it runs in IPython (now Jupyter), which is a really amazingly powerful way to do exploratory software development. If you haven't tried Jupyter/IPython, google up a screencast and take a look.

I'm just repeating the stuff that's on the home page in a different form, but this is a really big deal. Most of the most significant software of the next five or ten years is going to be built in TensorFlow or something like it. (Maybe Dan Amelang's Nile; see Bret Victor's amazing video on the dataflow visualization he and Dan built for Nile recently at https://youtu.be/oUaOucZRlmE?t=24m53s, or try the live demo at http://tinlizzie.org/dbjr/high_contrast.html, or download and build the Nile system from https://github.com/damelang/nile.)

Apparently the Googlers think that too, because among the 39 authors of the white paper are several shining stars of systems research, including some who may be up for a Turing Award in the next decade or two.