Inside the second of two operational data centers at Facebook Prineville; a third is under construction.

The real surprise: none of it is really kept under wraps. In fact, Facebook announced last year that Big Sur would be an open-source project before it had even placed the system in its Prineville data center and in a handful of other locations around the country. The company has since submitted the designs of Big Sur to the Open Compute Project. The data center community , started by Facebook in 2010, is designed to make hardware more energy-efficient and share what the company, and its competitors, learns from the ever-growing number of server farms around the country.

You could even build a rudimentary version of Big Sur yourself, using eight off-the-shelf — albeit very expensive — Nvidia GPUs and reference designs from manufacturer Quanta, just like Facebook does. But without rigging thousands of those GPU-based systems together, as the company has done in Prineville, you can’t achieve the kind of AI training capabilities it was designed for. Building a true Big Sur installation requires the kinds of resources that only a large company, such as Google or Microsoft, would be willing to invest. (Both of those companies are part of the Open Compute Project and can build a version of Big Sur if they so choose.)

"We're not in the business of having secret things."

"We’re not in the business of having secret things," says Kevin Lee, a technical program manager at Facebook who oversees Big Sur and other server designs at Prineville. "Our goal is to understand the world, to push AI." Of course, Google has its own open-source AI-training software, TensorFlow, so Facebook has a competitive reason to continue sharing its secrets as well.

Lee says AI is one of the three core pillars of Facebook’s future. When outlining the company’s 10-year road map at the F8 developer conference in April, CEO Mark Zuckerberg explained how Facebook.com was the company’s first step, and its many mobile apps have been the second. Ten years from now, Zuckerberg wants Facebook to be taking the lead on internet connectivity and drones, augmented and virtual reality, and AI.

AI is helping Facebook software see and understand the world, decipher human language, reason on its own, and plan its own courses of action. Some of it is already operational. Facebook’s new multilingual composer lets you compose text in one language and have it automatically translated into others, for example. Another new feature uses Facebook’s AI to analyze photos and describe them to blind and visually impaired users. And every time you upload a photo, a Big Sur-trained image recognition algorithm recognizes the faces and suggests which people to tag.

Central to every one of these features is machine learning, an AI training technique that’s nearly as old as the field of AI itself. But thanks to the massive data sets now available and recent leaps in computing power, machine learning has become an increasingly effective way to improve this type of software over time. Facebook, like many of its competitors, uses machine learning to train neural networks, which are algorithms inspired by the human brain that draw patterns and pluck probabilistic findings out of complex data sets.

"The first time we trained a single neural net, it took three months," says Ian Buck, Nvidia’s VP of accelerated computing, who works closely with Facebook’s AI and data center teams. After optimizing the training hardware with newer Nvidia GPUs, the time was cut down to one month. With Big Sur using the latest Nvidia hardware, he adds, it’s now less than a single day to train a neural net to perform a task that once required a human being.