TensorFlow: smarter machine learning, for everyone
November 9, 2015
Just
a couple of years ago, you couldn’t talk to the Google app through the noise
of a city sidewalk, or read a sign in Russian using Google Translate, or
instantly find pictures of your Labradoodle in Google Photos. Our apps just
weren’t smart enough. But in a short amount of time they've gotten much, much
smarter. Now, thanks to machine learning, you can do all those things pretty
easily, and a lot more. But even with all the progress we've made with machine
learning, it could still work much better.
So
we’ve built an entirely new machine learning system, which we call “TensorFlow.”
TensorFlow is faster, smarter, and more flexible than our old system, so it can
be adapted much more easily to new products and research. It’s a highly
scalable machine learning system—it can run on a single smartphone or across
thousands of computers in datacenters. We use TensorFlow for everything from
speech recognition in the Google app, to Smart Reply in Inbox, to search in
Google Photos. It allows us to build and train neural nets up to five times
faster than our first-generation system, so we can use it to improve our
products much more quickly.
We've
seen firsthand what TensorFlow can do, and we think it could make an even
bigger impact outside Google. So today we’re also open-sourcing TensorFlow. We hope
this will let the machine learning community—everyone from academic
researchers, to engineers, to hobbyists—exchange ideas much more quickly,
through working code rather than just research papers. And that, in turn, will
accelerate research on machine learning, in the end making technology work
better for everyone. Bonus: TensorFlow is for more than just machine learning.
It may be useful wherever researchers are trying to make sense of very complex
data—everything from protein folding to crunching astronomy data. Machine
learning is still in its infancy—computers today still can’t do what a
4-year-old can do effortlessly, like knowing the name of a dinosaur after
seeing only a couple examples, or understanding that “I saw the Grand Canyon
flying to Chicago” doesn’t mean the canyon is hurtling over the city. We have a
lot of work ahead of us. But with TensorFlow we’ve got a good start, and we can
all be in it together.
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