TensorFlow
TensorFlow From Google Artificial Intelligence
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.
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.
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.”
“We’re hoping, basically, to accelerate machine learning research and deployment”
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.
“We’re hoping, basically, to accelerate machine learning research and deployment,” Dean says. And while this is a big gift the community, the ideal scenario is that the community gives back, and shares what they’ve made with other researchers (and Google). “The machine learning community has been really good at polishing ideas, and that’s a really good thing, but it’s not the same thing as polishing working code associated with research ideas,” Dean says.
He also mentions that TensorFlow will help Google interns when they return back to their schools, because they can now access the once-proprietary systems on projects they might not have finished during their time at the company.
The TensorFlow system is a pretty complete package for an individual researcher. The system is a complete, standalone library associated with tools and an Apache 2.0 license, so it can be used in commercial settings. It can be compiled on desktops or laptops, or deployed on mobile (Android first, naturally, and then iOS to come later). It also comes with tutorials and documentation on how to modify and play with the platform.
Manning suggests that the ability to run deep learning algorithms on mobile devices will be an important factor that separates TensorFlow from other open-source systems.
For those who want to use the system as-is, Google is providing a version that researchers can start using right now (as pre-built binaries). There’s also an application programming interface (API), for software developers to train and control their TensorFlow models. And this isn’t a knockoff—it’s the literal system used in the Google app, and more than 50 other products.
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