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Google Just Open Sourced TensorFlow, Its Artificial Intelligence Engine
Tech pundit Tim O’Reilly had just tried the new Google Photos app, and he was amazed by the depth of its artificial intelligence.
O’Reilly was standing a few feet from Google CEO and co-founder Larry
Page this past May, at a small cocktail reception for the press at the annual Google I/O conference—the centerpiece of the company’s year. Google had unveiled its personal photos app earlier in the day, and O’Reilly
marveled that if he typed something like “gravestone” into the search
box, the app could find a photo of his uncle’s grave, taken so long ago.
Google is open sourcing software that sits at the heart of its empire.
The app uses an increasingly powerful form of artificial intelligence called deep learning.
By analyzing thousands of photos of gravestones, this AI technology can
learn to identify a gravestone it has never seen before. The same goes
for cats and dogs, trees and clouds, flowers and food.
The Google Photos search engine isn’t perfect. But its accuracy is
enormously impressive—so impressive that O’Reilly couldn’t understand
why Google didn’t sell access to its AI engine via the Internet, cloud-computing style,
letting others drive their apps with the same machine learning. That
could be Google’s real money-maker, he said. After all, Google also uses
this AI engine to recognize spoken words, translate from one language to another, improve Internet search results, and more. The rest of the world could turn this tech towards so many other tasks, from ad targeting to computer security.
Well, this morning, Google took O’Reilly’s idea further than even he
expected. It’s not selling access to its deep learning engine. It’s open
sourcing that engine, freely sharing the underlying code with the world
at large. This software is called TensorFlow,
and in literally giving the technology away, Google believes it can
accelerate the evolution of AI. Through open source, outsiders can help
improve on Google’s technology and, yes, return these improvements back
to Google.
“What we’re hoping is that the community adopts this as a good way of
expressing machine learning algorithms of lots of different types, and
also contributes to building and improving [TensorFlow] in lots of
different and interesting ways,” says Jeff Dean, one of Google’s most important engineers and a key player in the rise of its deep learning tech.
‘If Google open sources its tools, this can make everybody else better at machine learning.’ Chris Nicholson
In recent years, other companies and researchers have also made huge strides in this area of AI, including Facebook, Microsoft, and Twitter. And some have already open sourced software that’s similar to TensorFlow. This includes Torch—a system originally built by researchers in Switzerland—as well as systems like Caffe and Theano.
But Google’s move is significant. That’s because Google’s AI engine is
regarded by some as the world’s most advanced—and because, well, it’s
Google.
“This is really interesting,” says Chris Nicholson, who runs a deep learning startup called Skymind.
“Google is five to seven years ahead of the rest of the world. If they
open source their tools, this can make everybody else better at machine
learning.”
To be sure, Google isn’t giving away all its secrets. At the moment,
the company is only open sourcing part of this AI engine. It’s sharing
only some of the algorithms that run atop the engine. And it’s not
sharing access to the remarkably advanced hardware infrastructure
that drives this engine (that would certainly come with a price tag).
But Google is giving away at least some of its most important data
center software, and that’s not something it has typically done in the
past.
Google became the Internet’s most dominant force in large part because of the uniquely powerful software and hardware it built inside its computer data centers—software
and hardware that could help run all its online services, that could
juggle traffic and data from an unprecedented number of people across
the globe. And typically, it didn’t share its designs with the rest of
the world until it had moved on to other designs. Even then, it merely
shared research papers describing its tech. The company didn’t open
source its code. That’s how it kept an advantage.
With TensorFlow, however, the company has changed tack, freely
sharing some of its newest—and, indeed, most important—software. Yes,
Google open sources parts of its Android mobile operating system and so
many other smaller software projects. But this is different. In
releasing TensorFlow, Google is open sourcing software that sits at the
heart of its empire. “It’s a pretty big shift,” says Dean, who helped
build so much of the company’s groundbreaking data center software,
including the Google File System, MapReduce, and BigTable.
Open Algorithms
Deep learning relies on neural networks—systems
that approximate the web of neurons in the human brain. Basically, you
feed these networks vast amounts of data, and they learn to perform a
task. Feed them myriad photos of breakfast, lunch, and dinner, and they
can learn to recognize a meal. Feed them spoken words, and they can learn to recognize what you say. Feed them some old movie dialogue, and they can learn to carry on a conversation—not a perfect conversation, but a pretty good conversation.
More Artificial Intelligence
Typically, Google trains these neural nets using a vast array of
machines equipped with GPU chips—computer processors that were
originally built to render graphics for games and other highly visual
applications, but have also proven quite adept at deep learning. GPUs are good at processing lots of little bits of data in parallel, and that’s what deep learning requires.
But after they’ve been trained—when it’s time to put them into
action—these neural nets run in different ways. They often run on
traditional computer processors inside the data center, and in some
cases, they can run on mobile phones. The Google Translate app
is one mobile example. It can run entirely on a phone—without
connecting to a data center across the ‘net—letting you translate
foreign text into your native language even when you don’t have a good
wireless signal. You can, say, point the app at a German street sign,
and it will instantly translate into English.
TensorFlow is a way of building and running these neural
networks—both at the training stage and the execution stage. It’s a set
of software libraries—a bunch of code—that you can slip into any
application so that it too can learn tasks like image recognition,
speech recognition, and language translation.
Google built the underlying TensorFlow software with the C++ programming language. But in developing applications for this AI engine, coders can use either C++ or Python,
the most popular language among deep learning researchers. The hope,
however, is that outsiders will expand the tool to other languages,
including Google Go, Java, and perhaps even Javascript, so that coders have more ways of building apps.
Jeff Dean. ioulex
According to Dean, TensorFlow is well suited not only to deep learning, but to other forms of AI, including reinforcement learning and logistic regression.
This was not the case with Google’s previous system, DistBelief.
DistBelief was pretty good at deep learning—it helped win the
all-important Large Scale Visual Recognition Challenge in 2014—but Dean says that TensorFlow is twice as fast.
In open sourcing the tool, Google will also provide some sample
neural networking models and algorithms, including models for
recognizing photographs, identifying handwritten numbers, and analyzing
text. “We’ll give you all the algorithms you need to train those models
on public data sets,” Dean says.
The rub is that Google is not yet open sourcing a version of
TensorFlow that lets you train models across a vast array of machines.
The initial open source version only runs on a single computer. This
computer can include many GPUs, but it’s a single computer nonetheless.
“Google is still keeping an advantage,” Nicholson says. “To build true
enterprise applications, you need to analyze data at scale.” But at the
execution stage, the open source incarnation of TensorFlow will run on
phones as well as desktops and laptops, and Google indicates that the
company may eventually open source a version that runs across hundreds
of machines.
A Change in Philosophy
Why this apparent change in Google philosophy—this decision to open
source TensorFlow after spending so many years keeping important code to
itself? Part of it is that the machine learning community generally
operates in this way. Deep learning originated with academics who openly
shared their ideas, and many of them now work at Google—including
University of Toronto professor Geoff Hinton, the godfather of deep learning.
But Dean also says that TensorFlow was built at a very different time from tools like MapReduce and GFS and BigTable and Dremel and Spanner and Borg. The open source movement—where Internet companies share so many of their tools in order to accelerate the rate of development—has
picked up considerable speed over the past decade. Google now builds
software with an eye towards open source. Many of those earlier tools,
Dean explains, were too closely tied to the Google infrastructure. It
didn’t really make sense to open source them.
“They were not developed with open sourcing in mind. They had a lot
of tendrils into existing systems at Google and it would have been hard
to sever those tendrils,” Dean says. “With TensorFlow, when we started
to develop it, we kind of looked at ourselves and said: ‘Hey, maybe we
should open source this.'”
That said, TensorFlow is still tied, in some ways, to the internal
Google infrastructure, according to Google engineer Rajat Monga. This is
why Google hasn’t open sourced all of TensorFlow, he explains. As
Nicholson points out, you can also bet that Google is holding code back
because the company wants to maintain an advantage. But it’s telling—and
rather significant—that Google has open sourced as much as it has.
Feedback Loop
Google has not handed the open source project to an independent third
party, as many others have done in open sourcing major software. Google
itself will manage the project at the new Tensorflow.org website. But
it has shared the code under what’s called an Apache 2 license,
meaning anyone is free to use the code as they please. “Our licensing
terms should convince the community that this really is an open
product,” Dean says.
Certainly, the move will win Google some goodwill among the world’s
software developers. But more importantly, it will feed new projects.
According to Dean, you can think of TensorFlow as combining the best of
Torch and Caffe and Theano. Like Torch and Theano, he says, it’s good
for quickly spinning up research projects, and like Caffe, it’s good for
pushing those research projects into the real world.
Others may disagree. According to many in the community, DeepMind, a
notable deep learning startup now owned by Google, continues to use
Torch—even though it has long had access to TensorFlow and DistBelief.
But at the very least, an open source TensorFlow gives the community
more options. And that’s a good thing.
“A fair bit of the advancement in deep learning in the past three or
four years has been helped by these kinds of libraries, which help
researchers focus on their models. They don’t have to worry as much
about underlying software engineering,” says Jimmy Ba, a PhD student at
the University of Toronto who specializes in deep learning, studying
under Geoff Hinton.
Even with TensorFlow in hand, building a deep learning app still
requires some serious craft. But this too may change in the years to
come. As Dean points out, a Google deep-learning open source project and
a Google deep-learning cloud service aren’t mutually exclusive. Tim
O’Reilly’s big idea may still happen.
But in the short term, Google is merely interested sharing the code.
As Dean says, this will help the company improve this code. But at the
same time, says Monga, it will also help improve machine learning as a
whole, breeding all sorts of new ideas. And, well, these too will find
their way back into Google. “Any advances in machine learning,” he says,
“will be advances for us as well.” Correction: This story has been updated to correctly show the
Torch framework was originally developed by researchers in Switzerland.
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