ZAK STONE: It's been just over a year
since we open-sourced TensorFlow,
and we've been thrilled to see the adoption by the community,
and the pace of development both here at Google and all
around the world.
JEFF DEAN: TensorFlow is really the primary tool
that we're using for a lot of our machine
learning work in all of our products.
Towards the end of last year, we actually
rolled out a completely new translation system
that was based on deep neural nets.
In Gmail, we were actually able to roll out a TensorFlow model
that, by understanding the context of the message you just
received, we can predict likely replies,
and this is a feature we call Smart Reply.
LILY PENG: Diabetic retinopathy is the fastest growing cause
of blindness.
It's a complication of diabetes.
We gathered a very large data set
and had doctors grade the images,
and then we, using TensorFlow, trained a neural net
that does a pretty good job of predicting whether or not
there is a diabetic retinopathy in the image.
DOUG ECK: Can we use something like TensorFlow to make music,
to make art, and to allow us to communicate better
with each other?
With TensorFlow, we're able to think abstractly,
almost at a level of, like, improvisation with machine
learning.
We're able to try new things, to chunk models
together in ways that were impossible before we
had that kind of expressivity.
AMANDA HODGSON: Dugongs are classed as vulnerable to extinction
globally.
So we do a lot of aerial surveys using drones.
Then once you've done a survey of a really large area,
you end up with tens, if not hundreds of thousands
of photos.
The goal was to find a way to automate that whole process.
And that's where we've been using TensorFlow.
ZAK STONE: One of the things that we've
been focusing on this year with TensorFlow is performance.
We've been especially excited to release support
for distributed training.
MEGAN KACHOLIA: We want to make it easier for people to use,
so they don't have to necessarily know
all of the underlying internals in order
to get the distributed performance the best it can be.
[INAUDIBLE] is something that can compile down TensorFlow.
Maybe you want to compile your graph ahead of time
and then get it down to something much more compact,
in terms of memory size.
So that that way, you can easily load it and execute it
on something that might not have as much storage space,
like a mobile phone or some other portable smaller device.
RICK MAULE: When we introduced the hexagon vector extensions,
what we had in mind was enhancing user experiences
with imaging features.
ERICH PLONDKE: So the TensorFlow team
said that you only needed low precision multiplies
to be able to execute these neural networks efficiently.
So we did some tests and on the same graph, Inception_v3,
we were eight times faster and four times lower power
than running on the CPUs.
RICK MAULE: TensorFlow is great to work
with, easy to work with, lots of capability.
So our engineering teams and their engineering teams
working together, we were able to do something very exciting.
This is just the beginning of what
will end up being a long evolution of some great things
we can do with machine learning and image processing.
JOSH GORDON: In addition to sharing TensorFlow,
Google has also shared a ecosystem
of tools, which contains everything
you need to go all the way from research to production.
One such tool is TensorFlow serving,
and this is a open-source, high-performance serving
solution.
Another great tool, which is actually quite beautiful,
is the embedding visualizer.
And you can use the embedding visualizer
to interactively explore high-dimensional data sets.
On the education side, General Assembly
has done great work teaching TensorFlow.
NEHEMIAH LOURY: For my final project,
I was really interested in doing lyrics generation.
And TensorFlow was a really great match for that
because it allowed me to build out and utilize the models
that I needed to be successful.
ZAK STONE: The TensorFlow community
is thriving around the world, and we're
excited about as many people as possible being part of it.
TensorFlow is an open-source project for everyone.
We're looking forward to building this
into something even better and more useful and more powerful,
in collaboration with the whole worldwide community.