It wouldn’t be the first time a “hot” technology segment failed to prove as revolutionary as folks initially suspected. Remember 3D printing? The hype suggested that, in a few short years, everyone would be printing replacement parts and household goods; but only a handful of enthusiasts ended up embracing 3D printers as home essentials. There’s also the case of Google Glass, which failed to revolutionize wearable devices—despite Google pumping millions of dollars into the project.
But A.I. is supposed to be different. For starters, the potential upside is nothing less than a wholesale change in civilization: deep-learning algorithms could automate all sorts of processes, from online customer service to medical imaging. Some of this technology is already present, if only in beta: for example, Google’s A.I.-powered microscope for processing and analyzing biological samples for cancer.
Yet those advances haven’t stopped some pundits from suggesting an A.I. “winter” will hit at some point. In a recent blog posting, Filip Piekniewski, an A.I. researcher, counted off instances in which the artificial intelligence hype has greatly exceeded the reality, including a lack of progress in Google’s Deepmind, deep learning (“does not scale,” he concluded), and autonomous driving, which has suffered some high-profile accidents in the past few months.
“Predicting the A.I. winter is like predicting a stock market crash—impossible to tell precisely when it happens, but almost certain that it will at some point,” Piekniewski wrote. “In my opinion there are such signs visible already of a huge decline in deep learning (and probably in A.I. in general as this term has been abused ad nauseam by corporate propaganda), visible in plain sight, yet hidden from the majority by the increasingly intense narrative.”
Until that winter comes, though, A.I.-related jobs continue to pay quite a bit, thanks to the field’s high degree of specialization—there are only so many tech pros with the required knowledge. In an April report, analyst firm McKinsey & Company suggested that various A.I. disciplines could generate “between $3.5 trillion and $5.8 trillion in value annually across nine business functions in 19 industries.” However, it estimated a mere 10,000 people with the right skill sets to meet that rising demand.
So even if artificial intelligence doesn’t deliver on its biggest promises in the short term, chances are good that companies will still need tech pros skilled in deep learning and A.I., if only to figure out how to use this technology as part of long-term strategy.