". . . [B]uilding a true AI is likely to require investing years of work into machines that won’t solve a single business problem. Search engines and image recognizers make a lot of money but don’t think like people. Any true AI is likely to spend years stumbling around and making mistakes before it does anything useful, just like we do. Remember, training a human intelligence (the best kind we know about) takes about twenty years per installation, and that’s with millions of years of evolutionary advantage baked in. This means that for the machine learning problems that really count, money is likely to stay tight.

 

"So, you might wonder, if all this is true, why get excited about the upcoming AI winter? Simple. This is when all that expertise we’ve built up during the current boom will start filtering out into other fields. The dirty secret of the current AI boom is that not everyone is trying to build a self-driving car. In fact, for most businesses, the problems they need to solve are far more pragmatic. Often, companies need to make sense of the data they’re already sitting on and those datasets are usually too small and too messy for deep learning to be much use. Or they need to figure out how to take dodgy intermittent data from new sources like IoT sensors and make it reliable."