http://www.technologyreview.com/news/544356/heres-what-developers-are-doing-with-googles-ai-brain/
An artificial intelligence engine that Google uses in many of its
products, and that it made freely available last month, is now being
used by others to perform some neat tricks, including translating
English into Chinese, reading handwritten text, and even generating
original artwork.
The AI software, called TensorFlow,
provides a straightforward way for users to train computers to perform
tasks by feeding them large amounts of data. The software incorporates
various methods for efficiently building and training simulated “deep
learning” neural networks across different computer hardware.
Deep learning is an extremely effective technique for training
computers to recognize patterns in images or audio, enabling machines to
perform with human-like competence useful tasks such as recognizing
faces or objects in images. Recently, deep learning also has shown
significant promise for parsing natural language, by enabling machines
to respond to spoken or written queries in meaningful ways.
Speaking at the Neural Information Processing Society (NIPS) conference in Montreal this week, Jeff Dean,
the computer scientist at Google who leads the TensorFlow effort, said
that the software is being used for a growing number of experimental
projects outside the company.
These include software that generates captions for images and code that translates the documentation for TensorFlow into Chinese. Another project
uses TensorFlow to generate artificial artwork. “It’s still pretty
early,” Dean said after the talk. “People are trying to understand what
it’s best at.”
TensorFlow grew out of a project at Google, called Google Brain,
aimed at applying various kinds of neural network machine learning to
products and services across the company. The reach of Google Brain has
grown dramatically in recent years. Dean said that the number of
projects at Google that involve Google Brain has grown from a handful in
early 2014 to more than 600 today.
Most recently, the Google Brain helped develop Smart Reply,
a system that automatically recommends a quick response to messages in
Gmail after it scans the text of an incoming message. The neural network
technique used to develop Smart Reply was presented by Google
researchers at the NIPS conference last year.
Dean expects deep learning and machine learning to have a similar
impact on many other companies. “There is a vast array of ways in which
machine learning is influencing lots of different products and
industries,” he said. For example, the technique is being tested in many
industries that try to make predictions from large amounts of data,
ranging from retail to insurance.
Google was able to give away the code for TensorFlow because the data
it owns is a far more valuable asset for building a powerful AI engine.
The company hopes that the open-source code will help it establish
itself as a leader in machine learning and foster relationships with
collaborators and future employees. TensorFlow “gives us a common
language to speak, in some sense,” Dean said. “We get benefits from
having people we hire who have been using TensorFlow. It’s not like it’s
completely altruistic.”
A neural network consists of layers of virtual neurons that fire in a
cascade in response to input. A network “learns” as the sensitivity of
these neurons is tuned to match particular input and output, and having
many layers makes it possible to recognize more abstract features, such
as a face in a photograph.
TensorFlow is now one of several open-source deep learning software
libraries, and its performance currently lags behind some other
libraries for certain tasks. However, it is designed to be easy to use,
and it can easily be ported between different hardware. And Dean says
his team is hard at work trying to improve its performance.
In the race to dominate machine learning and attract the best talent,
however, other companies may release competing AI engines of their own.
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