Google Sprints Ahead in AI Building Blocks, Leaving Rivals Wary
Google Sprints Ahead in AI Building Blocks, Leaving
Rivals Wary
By Jack Clark July 21, 2016 — 3:00 AM PDT
There’s a high-stakes race under way in Silicon Valley to
develop software that makes it easy to weave artificial intelligence technology
into almost everything, and Google has sprinted into the lead.
Google computer scientists including Jeff Dean and Greg
Corrado built software called TensorFlow, which simplifies the programming of
key systems that underpin artificial intelligence. That helps Google make its
products smarter and more responsive. It’s important for other companies too
because the software makes it dramatically easier to create computer programs
that learn and improve automatically. What’s more, Google gives it away.
But for some competitors, there’s a big downside to
adopting Google’s standard. Using TensorFlow will help Google recruit more AI
experts by training them on the same tool it uses internally, spotting their
code, and hiring the best contributors. It could also let the search-engine
provider exert outsize influence over the burgeoning AI ecosystem. If the
internet giant dominates in this field, it could gain an advantage in the
fast-growing cloud-computing business, turning the popularity of its software
into real revenue.
"It’s the next big area, and people are worried
Google’s going to own the show," said Ed Lazowska, a computer science
professor at the University of Washington who has served on the technical
advisory board of Microsoft Corp.’s research lab. "There is a network
effect, and it’s a really excellent system."
Google initially used TensorFlow internally for products
like its Inbox and Photos apps. The company made it available for free in
November. Technology companies like Microsoft Corp., Amazon.com Inc. and Samsung
Electronics Co. rushed to give away their own versions, hoping to get the most
outside developers using their standards.
The company that wins will benefit from the collective
efforts of thousands of developers using, but also updating and improving, its
system. That’s an advantage when it comes time to make money from the new
asset. Whoever has the most popular software will have the best chance of
creating commercial cloud services for AI because potential customers will
already know how to use it.
Amazon and Samsung declined to comment. Microsoft did not
respond to requests for comment on Wednesday.
Success in these types of open-source projects sometimes
yields big rewards. Google released Android for free in 2008, and it’s now the
most widely used mobile operating system with over 400,000 developers and more
than a billion users. Google generates billions of dollars a year from ads
shown on Android devices and the cut it gets from revenue app developers make
through the operating system.
‘Linux Level’
Since emerging, TensorFlow has become the most popular AI
programming project on software code sharing service GitHub, leapfrogging
well-regarded systems created by universities and corporate rivals, according
to data gathered by Bloomberg.
On launch day, TensorFlow had around 3,000
"stars" on GitHub, meaning that number of programmers had bookmarked
the code, indicating interest. As of July 13, it had 27,873. Two other popular
AI software projects, Theano and Torch, have less than a fifth of that
following. In 2014, Torch was the leader. A Microsoft tool called CNTK,
released for free in January, and Amazon’s free DSSTNE, which rolled out in
May, have so far failed to dent Google’s lead much.
Linux, an open-source operating system launched in 1991,
now helps run everything from supercomputers to phones to airplanes and helped
turn Red Hat Inc. into a $13 billion enterprise software company. Linux has
33,967 stars on GitHub. "It’s kind of crazy," said Dean, a top Google
engineer and one of the main developers of TensorFlow. "We’re almost to
Linux level."
‘Green Field’
Google will soon begin generating revenue from this lead.
It plans to offer a version of TensorFlow that runs on its Google Cloud
Platform service, letting people and businesses pay to run their AI software in
Google’s data centers.
Google made the software free so it could give the
community a useful tool "and everyone could standardize on that,"
said Corrado, a senior research scientist at Google. "In a giant green
field, trying to build a fence around the next blade of grass is really
absurdist. It’s really better to help everybody run into that field."
That openness, and continual Google updates, have lured
developers like computer-vision startup Matroid, which re-wrote its software to
work with TensorFlow, after building on another free AI tool called Caffe.
Kindred, a robotics startup, made a similar switch.
Looking Elsewhere
Not everyone is so keen. As TensorFlow’s usage grows,
some companies are realizing an increasingly important part of the technology
toolkit is controlled by Google, and they don’t want to exacerbate that trend.
They’re "skeptical about using a language backed by
another large company," said Soumith Chintala, a Facebook Inc. artificial
intelligence researcher and one of the people behind Torch.
The unease stems from the fact Google can tweak
TensorFlow to suit its own purposes, he said. If the company changes the
software too much, then other companies that have adopted it will need to make
a copy of the software and rewrite it to suit their own needs -- an expensive
and time-consuming process known as forking.
That’s led some to look elsewhere. Skymind, which makes
free AI software, has had more than five customers tell it they are wary of
using TensorFlow, said CEO Chris Nicholson. He declined to name any of the
companies, citing non-disclosure agreements.
Since TensorFlow launched, designers of other AI
programming projects have been inundated with queries from companies that don’t
want to rely on Google. Several reached out to the creators of Theano,
developed mainly at the University of Montreal, to see if they can donate
resources to the project, according to Yoshua Bengio, a professor who leads AI
research at the school.
The same happened with Torch, said Chintala. Facebook
does much of its AI research with Torch, and Chintala helps guide development
of the project in his spare time. He and other backers moved Torch into a
non-profit organization called SPI Inc. in May to make it easier for more
people to work on the language and donate to it.
‘Counterpoint’
"One of the reasons we want to stick with Torch is
to create a strong counterpoint" to TensorFlow, said Clement Farabet, who
helped develop Torch. He now works at Twitter Inc., which uses Torch to run AI
systems that analyze images and select tweets people may want to read. It’s
better for the community if there’s a choice of AI software, he said.
Google could solve some of these problems by donating
TensorFlow to a neutral third-party, said Bengio, who has discussed his ideas
with the company. This structure could "provide neutral software for
all," he said.
Google has no plans to do that, but it’s open to letting
outside people have a say in what code gets merged into the main software, said
Jason Freidenfelds, a spokesman for Google.
Google’s strategy may be dictated by past failings, said
Reza Zadeh, chief executive officer of Matroid, who worked at Google a decade
ago. Back then, Google developed the Google File System and MapReduce to store
and analyze lots of data. It published research papers on them, but no code.
Some employees at Yahoo! Inc. used the research to create Hadoop, technology
that underpins public company Hortonworks Inc. and larger private rival
Cloudera Inc.
"They’ve learned from that," said Zadeh.
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