AI is moving fast. Faster than it has in a long time. In research circles, "supervised learning", or data classification problems, are considered solved. Consider the magnitude of that statement: If we have sufficient labeled data, we can predict those labels accurately on data we haven't been exposed to: fraud, faces, diseases, you name it.

AI is moving fast for three reasons, which Andrew Ng summarizes neatly: 1) We have more data than ever before (some of which has been organized in fabulous datasets like ImageNet), much of which is being generated online or by sensors. 2) We have more powerful hardware than ever before -- this is the combination of distributed computing with GPUs. You could say that AI advances at the speed of hardware, or at least is limited by hardware advances. Brute force AI like deep learning is the main beneficiary. 3) We have seen a steady stream of algorithmic advances for years. Specialists despair of keeping up with the literature, research in AI is so feverish.

Others have pointed out additional factors: open-source projects that lower the barrier to entry for the algorithms; cloud computing services that open up access to the hardware. The choke right now, and what's slowing wider adoption, is skills. Many companies don't have the teams to implement AI well.

Some of those companies are also the noisiest about selling AI. I have deep doubts about some vendors to deliver on the hype they're encouraging. And I believe that will lead, not to an AI winter, but to a poisoning of the well that the AI sector will have to address for years to come.

But overselling and hype are inevitable symptoms of real advances in tech, and in some ways, the tech is advancing faster than the hype can keep up with, precisely because the people hyping it, whether salesmen or journalists, don't understand the true extent of what's going on.

AI is just math and code. In a sense, you could say we've entered the age of Big Math, which is the next stage after Big Data. The math is necessary to process the data and determine its meaning. The math takes the form of massive matrix operations that can be processed on the parallel calculators known as GPUs. To call it statistics, as the reporter of the piece does, is only partially true. The math involved in AI comes from probability, calculus, linear algebra and signal processing. It's more than fancy linear regression. And it's definitely more than just making predictions about customer behavior, however attractive that is for industry.

Asking why now about AI software is like asking why now about cars after the Model A came out. Because it's there, it's faster than horses, and it makes you look cool. Like cars or any other powerful technology, AI is part of a race, and that race is taking place between nations and companies. You can decide not to adopt AI, the same way newspapers decided to ignore the Internet, or the way the French decided to fight German panzers with mounted cavalry in WWI. There really is not choice whether or not to adopt AI-driven software. It's a question of when, not if. And for many companies, the when is now, because later will be too late.

To give one example of how fast it's moving: Deep learning has been widely thought to be uninterpretable, or without explanatory power, but that is changing with cool projects like LIME: https://homes.cs.washington.edu/~marcotcr/blog/lime/ Which is to say, for some problem sets, we'll be able to combine the impressive accuracy of DNNs with the reasons why they reached a given decision about the data.

On the hardware front, NVIDIA and Intel are racing to build faster and faster chips, even as startups like Wave Computing or Cerberas come out with their own, possibly faster chips, and Google works on TPUs for inference.