But about ten years ago, in Hinton’s lab at the University of Toronto, he and some other researchers made a breakthrough that suddenly made neural nets the hottest thing in AI. Not only Google but other companies such as Facebook, Microsoft and IBM began frantically pursuing the relatively minuscule number of computer scientists versed in the black art of organizing several layers of artificial neurons so that the entire system could be trained, or even train itself, to divine coherence from random inputs, much in a way that a newborn learns to organize the data pouring into his or her virgin senses. With this newly effective process, dubbed Deep Learning, some of the long-standing logjams of computation (like being able to see, hear, and be unbeatable at Breakout) would finally be untangled. The age of intelligent computers systems — long awaited and long feared — would suddenly be breathing down our necks. And Google search would work a whole lot better.

This breakthrough will be crucial in Google Search’s next big step: understanding the real world to make a huge leap in accurately giving users the answers to their questions as well as spontaneously surfacing information to satisfy their needs. To keep search vital, Google must get even smarter.

This is very much in character for the Internet giant. From its earliest days, the company’s founders have been explicit that Google is an artificial intelligence company. It uses its AI not just in search — though its search engine is positively drenched with artificial intelligence techniques — but in its advertising systems, its self-driving cars, and its plans to put nanoparticles in the human bloodstream for early disease detection. As Larry Page told me in 2002:

We don’t always produce what people want. That’s what we work on really hard. It’s really difficult. To do that you have to be smart, you have to understand everything in the world, you have to understand the query. What we’re trying to do is artificial intelligence…the ultimate search engine would be smart. And so we work to get closer and closer to that.

Google was already well along that path when Geoff Hinton made his breakthrough. Over the years, the company has been a leader in using a more traditional form of what is called machine learning to make its search engine smarter. Only a few years into the company’s history, it hired a group of AI-savvy engineers and scientists who jiggered the search engine to learn things like synonyms. When millions of users used a certain word interchangeably with another (dog or puppy, for instance), Google would quickly utilize that knowledge to understand queries better. And when Google took on the task of translating web sites to deliver results from sites in different languages, its scientists made use of a process that fed massive amounts of translated documents and their sources into the system. That way, Google’s search engine “learned” how one language mapped to another. Using that AI procedure, Google could translate web sites into languages not spoken by any of its engineers.

Deep learning is now viewed as a step beyond that more straightforward variety of machine learning. Since it is based on the architecture of the human brain, its adherents argue that, in theory, deep learning is the launch pad for computer-based feats of intelligence not possible — at least not easily—with previous approaches. That’s why Hinton’s breakthrough is so important to Google, as well as every other company dealing in search and related problems. Google has worked hard in the past few years to reshape its search engine to generate a conversational experience. But to truly attain the skills of even a very young human being, the frontiers of AI must be expanded, and Deep Learning is the tool du jour for accomplishing this.

Explaining the circumstances by which neural nets earned the sobriquet Deep Learning isn’t easy. But Hinton is game to try, though I felt I detected a hopeless sigh when he learned he was addressing an English major.

Neural nets are modeled on the way biological brains learn. When you attempt a new task, a certain set of neurons will fire. You observe the results, and in subsequent trials your brain uses feedback to adjust which neurons get activated. Over time, the connections between some pairs of neurons grow stronger and other links weaken, laying the foundation of a memory.

A neural net essentially replicates this process in code. But instead of duplicating the dazzlingly complex tangle of neurons in a human brain, a neural net, which is much smaller, has its neurons organized neatly into layers. In the first layer (or first few layers) are feature detectors, a computational version of the human senses. When a computer feeds input into a neural net—say, a database of images, sounds or text files—the system learns what those files are by detecting the presence or absence of what it determines as key features in them. For example, if the task was to characterize emails as either spam or legitimate messages, neural net researchers might feed the system many messages, along with the label of either SPAM or NOT_SPAM. The network would automatically intuit complex features of words (“Nigerian prince,” “Viagra”), patterns of words, and information in the message header that would be useful in determining whether a message should be labeled spam or not.

In early neural net experiments, computers were unable to design features by themselves, so features had to be designed by hand. Hinton’s original contribution was helping establish a technique called “back propagation,” a form of feedback that allowed the system to more efficiently learn from its mistakes and assign its own features.

“Back in 1986, when we first developed back propagation, we were excited by the fact you could learn multiple layers of feature detectors, and we thought we solved the problem,” says Hinton. “And it was very disappointing that we didn’t make huge breakthroughs in practical problems. We were completely wrong in our guess about how much computation was needed and how many labeled examples were needed.”

But even though many researchers had lost faith in neural nets over the years, Hinton felt strongly that they would eventually be practical. In 1995, he and his students tried losing the labels, at least in the earlier parts of the learning process. This technique was called “unsupervised pre-training.” meaning that the system figures out how to organize input on its own. But Hinton says that the real key to making this work was a mathematical trick, an approximation that saved computation time as the information moved through the layers of neurons — this allowed for many more iterations to refine the network. As often happens, speed becomes transformative, in this case making it possible to perform learning that previous neural nets couldn’t attempt. It was as if a person could suddenly cram in, say, the equivalent of five hours of skiing practice in ten minutes.

With unsupervised learning, only in the latter stages would the system’s human masters intervene, by labeling the more desirable outputs and rewarding successful outcomes. “Think about little kids, when they learn to recognize cows,” says Hinton. “It’s not like they had a million different images and their mothers are labeling the cows. They just learn what cows are by looking around, and eventually they say, ‘What’s that?’ and their mother says, ‘That’s a cow’ and then they’ve got it. This works much more like that.” (Later, researchers would master an effective alternative to unsupervised learning that relied on better initializing techniques and the use of larger datasets.)

When Hinton’s group tested this model, it had the benefit of something unavailable at the time neural nets were first conceived — super fast GPUs (Graphic Processing Units). Though those chips were designed to churn out the formulae for advanced graphics, they were also ideal for the calculations required in neural nets. Hinton bought a bunch of GPUs for his lab and got two students to operate the system. They ran a test to see if they could get the neural network to recognize phonemes in speech. This, of course, was a task that many technology companies — certainly including Google — had been trying to master. Since speech was going to be the input in the coming age of mobile, computers simply had to learn to listen better