Every time there is a revolution, and I will say AI is a revolution, just like the industrial revolution and then the digital revolution, and then the information revolution with the internet, this is the next big revolution. Any time you have a big revolution, and rightly so, people stand back and say, "What are all the unintended consequences? Should I be that excited about this?" As you see with each of those waves, nobody's looking back and saying, "Oh we should have never launched that internet thing, that was crazy." But at the time, there was a lot of concern of everyone being connected, everyone having access to data, and you can talk yourself into a situation where you're concerned.

Intel's (NASDAQ:INTC) data-center chief, Diane Bryant, recently presented at an investor conference. During the conference, Bryant went into quite some detail on the company's strategy to participate in the small but fast-growing field of artificial intelligence, or AI for short. Let's take a closer look at what the Intel executive had to say. Analyst Blayne Curtis said that "people don't sort of think of Intel as a play on AI." After giving a brief history of AI, Bryant explained that the whole point of artificial intelligence is to be able to gather "large amounts of data" and then applying "ever-more sophisticated algorithms" to ultimately give computers the ability to "both learn and predict."

Silicon Valley is full of chatter about artificial intelligence, deep learning neural networks, and machine learning. And Intel, the world's biggest chip maker, is becoming a lot more conversant in that chatter today. Intel executive Diane Bryant announced today that the company is working on a next-generation version of its high-end server chip, the Xeon Phi, for A.I. applications. Baidu will use the upcoming Xeon Phi chips in the data centers it is building for its Deep Speech platform, where its networks will be able to parse natural language speech as quickly and accurately as possible. By 2020, there will be more servers handling data analytics than any other workload, Bryant said.