Google is teaching machines to play Atari games like Space Invaders, Video Pinball, and Breakout. At DeepMind, a Google subsidiary based in Cambridge, England, researchers have built artificial intelligence software that's so adept at these classic games, it can sometimes beat a human player--and a professional, at that. This may seem like a frivolous, if intriguing, pursuit. If a machine can learn to navigate the digital world of a video game, Google says, it eventually could learn to navigate the real world, too. Today, this AI can play Space Invaders.

In open sourcing its artificial intelligence engine--freely sharing one of its most important creations with the rest of the Internet--Google showed how the world of computer software is changing. These days, the big Internet giants frequently share the software sitting at the heart of their online operations. Open source accelerates the progress of technology. In open sourcing its TensorFlow AI engine, Google can feed all sorts of machine-learning research outside the company, and in many ways, this research will feed back into Google. But Google's AI engine also reflects how the world of computer hardware is changing.

When Google open sourced its artificial intelligence engine last week--freely sharing the code with the world at large--Lukas Biewald didn't see it as a triumph of the free software movement. He saw it as a triumph of data. That's how you'd expect him to see it. He's the CEO of the San Francisco startup CrowdFlower, which helps online companies like Twitter juggle massive amounts of data. But after spending time at the Stanford AI Lab, he knows artificial intelligence.

Dharmendra Modha walks me to the front of the room so I can see it up close. About the size of a bathroom medicine cabinet, it rests on a table against the wall, and thanks to the translucent plastic on the outside, I can see the computer chips and the circuit boards and the multi-colored lights on the inside. It looks like a prop from a '70s sci-fi movie, but Modha describes it differently. "You're looking at a small rodent," he says. He means the brain of a small rodent--or, at least, the digital equivalent. The chips on the inside are designed to behave like neurons--the basic building blocks of biological brains.

Yann LeCun is among those bringing a new level of artificial intelligence to popular internet services from the likes of Facebook, Google, and Microsoft. As the head of AI research at Facebook, LeCun oversees the creation of vast "neural networks" that can recognize photos and respond to everyday human language. And similar work is driving speech recognition on Google's Android phones, instant language translation on Microsoft's Skype service, and so many other online tools that can "learn" over time. Using vast networks of computer processors, these systems approximate the networks of neurons inside the human brain, and in some ways, they can outperform humans themselves. This week in the scientific journal Nature, LeCun--also a professor of computer science at New York University--details the current state of this "deep learning" technology in a paper penned alongside the two other academics most responsible for this movement: University of Toronto professor Geoff Hinton, who's now at Google, and the University of Montreal's Yoshua Bengio. The paper details the widespread progress of deep learning in recent years, showing the wider scientific community how this technology is reshaping our internet services--and how it will continue to reshape them in the years to come.

We've all been seeing hype and excitement around artificial intelligence, big data, machine learning and deep learning. There's also a lot of confusion about what they really mean and what's actually possible today. These terms are used arbitrarily and sometimes interchangeably, which further perpetuates confusion. So, let's break down these terms and offer some perspective. Artificial Intelligence is a branch of computer science that deals with algorithms inspired by various facets of natural intelligence.

When in 2012 a computer learned to recognize cats in YouTube videos and just last month another correctly captioned a photo of "a group of young people playing a game of Frisbee," artificial intelligence researchers hailed yet more triumphs in "deep learning," the wildly successful set of algorithms loosely modeled on the way brains grow sensitive to features of the real world simply through exposure. Using the latest deep-learning protocols, computer models consisting of networks of artificial neurons are becoming increasingly adept at image, speech and pattern recognition -- core technologies in robotic personal assistants, complex data analysis and self-driving cars. But for all their progress training computers to pick out salient features from other, irrelevant bits of data, researchers have never fully understood why the algorithms or biological learning work. Now, two physicists have shown that one form of deep learning works exactly like one of the most important and ubiquitous mathematical techniques in physics, a procedure for calculating the large-scale behavior of physical systems such as elementary particles, fluids and the cosmos. The new work, completed by Pankaj Mehta of Boston University and David Schwab of Northwestern University, demonstrates that a statistical technique called "renormalization," which allows physicists to accurately describe systems without knowing the exact state of all their component parts, also enables the artificial neural networks to categorize data as, say, "a cat" regardless of its color, size or posture in a given video.

When Google used 16,000 machines to build a simulated brain that could correctly identify cats in YouTube videos, it signaled a turning point in the art of artificial intelligence. Applying its massive cluster of computers to an emerging breed of AI algorithm known as "deep learning," the so-called Google brain was twice as accurate as any previous system in recognizing objects pictured in digital images, and it was hailed as another triumph for the mega data centers erected by the kings of the web. "The research is representative of a new generation of computer science that is exploiting the falling cost of computing and the availability of huge clusters of computers in giant data centers," The New York Times wrote in 2012, "leading to significant advances in areas as diverse as machine vision and perception, speech recognition, and language translation." Indeed, in the two years since, Microsoft released a Skype service that uses deep learning to instantly translate conversions from one language to another, Facebook hired one of the leading experts in the field to boost image recognition and other tools on its service, and everyone from Twitter to Yahoo snapped up their own deep learning startups. But in the middle of this revolution, a researcher named Alex Krizhevsky showed that you don't need a massive computer cluster to benefit from this technology's unique ability to "train itself" as it analyzes digital data.

Mark Zuckerberg recently handpicked the longtime NYU professor to run Facebook's new artificial intelligence lab. The IEEE Computational Intelligence Society just gave him its prestigious Neural Network Pioneer Award, in honor of his work on deep learning, a form of artificial intelligence meant to more closely mimic the human brain. And, perhaps most of all, deep learning has suddenly spread across the commercial tech world, from Google to Microsoft to Baidu to Twitter, just a few years after most AI researchers openly scoffed at it. All of these tech companies are now exploring a particular type of deep learning called convolutional neural networks, aiming to build web services that can do things like automatically understand natural language and recognize images. At China's Baidu, they drive a new visual search engine.

Drawing on the work of a clever cadre of academic researchers, the biggest names in tech--including Google, Facebook, Microsoft, and Apple--are embracing a more powerful form of AI known as "deep learning," using it to improve everything from speech recognition and language translation to computer vision, the ability to identify images without human help. In this new AI order, the general assumption is that Google is out in front. The company now employs the researcher at the heart of the deep-learning movement, the University of Toronto's Geoff Hinton. It has openly discussed the real-world progress of its new AI technologies, including the way deep learning has revamped voice search on Android smartphones. And these technologies hold several records for accuracy in speech recognition and computer vision.