While the current class of deep learning techniques is helping fuel the AI wave, one of the frequently cited drawbacks is that they require a lot of data to work. But how much is enough data? "I would say pretty much any business that has tens or hundreds of thousands of customer interactions has enough scale to start thinking about using these sorts of things," Jeff Dean, a senior fellow at Google, said in an onstage interview at the VB Summit in Berkeley, California. "If you only have 10 examples of something, it's going to be hard to make deep learning work. If you have 100,000 things you care about, records or whatever, that's the kind of scale where you should really start thinking about these kinds of techniques."

Just five years ago, artificial intelligence-enabled computers could barely recognize images fed to them, much less analyze them anything like people can. But suddenly, they've turned the tables. "In 2011 their error rate was 26 percent," says Jeff Dean, chief of the Google Brain project, which along with other tech giants has helped lead a recent revolution in image recognition as well as speech recognition and self-driving cars. Now, he says, computers' ability to view and analyze images (pictured) exceeds what human eyes can do. "If you'd have told me that would be a possible just a few years ago, I would've never believed you," Dean said during an appearance at a research event in Heidelberg, Germany.