The Search Giant Is Making Its AI Open Source So Anyone Can Use It
In November 2007, Google laid the groundwork to dominate the mobile market by releasing Android, an open source operating system for phones. Eight years later to the month, Android has an an 80 percent market share, and Google is using the same trick—this time with artificial intelligence.
Today Google is announcing TensorFlow, its open source platform for machine learning, giving anyone a computer and internet connection (and casual background in deep learning algorithms) access to one of the most powerful machine learning platforms ever created. More than 50 Google products have adopted TensorFlow to harness deep learning (machine learning using deep neural networks) as a tool, from identifying you and your friends in the Photos app to refining its core search engine. Google has become a machine learning company. Now they’re taking what makes their services special, and giving it to the world.
INTRODUCING TENSORFLOW, THE ANDROID OF AI
TensorFlow is a library of files that allows researchers and computer scientists to build systems that break down data, like photos or voice recordings, and have the computer make future decisions based on that information. This is the basis of machine learning: computers understanding data, and then using it to make decisions. When scaled to be very complex, machine learning is a stab at making computers smarter. That's the broader, and more ill-defined field of artificial intelligence. TensorFlow is extraordinary complex, because of its precision and speed in digesting and outputting data, and can unequivocally be placed in the realm of artificial intelligence tools.
Here are the nitty-gritty details: the TensorFlow system uses data flow graphs. In this system, data with multiple dimensions (values) are passed along from mathematical computation to mathematical computation. Those complex bits of data are called tensors. The math-y bits are called nodes, and the way the data changes from node to node tells the overall system relationships in the data. These tensors flow through the graph of nodes, and that's where the name TensorFlow comes from.
Open-sourcing TensorFlow allows researchers and even grad students the opportunity to work with professionally-built software, sure, but the real effect is the potential to inform every machine learning company’s research across the board. Now organizations of all sizes—from small startups to huge companies on par with Google—can take the TensorFlow system, adapt it to their own needs, and use it to compete directly against Google itself. More than anything, the release gives the world’s largest internet company authority in artificial intelligence.
Stanford computer science professor Christopher Manning was given TensorFlow a little more than three months ago, and his students had the opportunity to tinker with the system. After just a few weeks of using it himself, Manning decided that he’s going to implement it into his curriculum.
Besides Android, he also likens the platform to Gmail, Google’s ubiquitous email application. There are competitors, but Gmail is cleaner and makes more sense in most applications.
“It’s not that before this there weren’t any high level libraries available for deep learning,” Manning says. “But in general these other libraries are things by three academics and a grad student.”
While the others, most notably Torch and Theano, do have small groups updating them, it’s nothing like the full force of the developers working on Google’s machine learning infrastructure. Manning says that while TensorFlow is a huge gift to the community (one capable of reducing time spent optimizing the neural networks by 100 times), they might indirectly benefit from open-sourcing their tools.
"A very small amount of companies have been trying to hire up a very large percentage of the talented people in artificial intelligence in general, and deep learning in particular,” Manning says. “Google is not a charity, I’m sure it’s also occurred to them that by ceding this, we will have a lot of Ph.D students who will be in universities and already liking Google deep learning tools.”
Jeff Dean, one of Google’s top engineers and one of the two people who could be listed as an author for TensorFlow (the other is Rajat Monga), is cautious about estimating the adoption in the community. He says that while it’s something Google has found immensely useful in their own work, the real test is whether the community will find it as capable. The idea is to provide a tool so the whole community will be able to go from not just ideas, but actual implementations of things more rapidly...MORE.
