AI washing muddies the artificial intelligence products market – Valutrics

When a technology is labelled AI, the vendor must provide information that makes it clear how AI is used as a differentiator and what problems it solves that can’t be solved “You have to go in with the assumption that it isn’t AI, and the vendor has to prove otherwise,” Hare said. “It’s like the big data era — where all the vendors say they have big data — but on steroids.”

“What I’m seeing is that anything typically called machine learning is now being labelled AI, when in reality it is weak or narrow AI, and it solves a specific problem,” he said.

IT buyers must hold the vendor accountable for its claims Beyond that, a vendor must share with customers the AI techniques it uses or plans to use in the product and their strategy for keeping up with the quickly changing AI market, Hare said.

The second problem Gartner highlights is that machine learning can address many of the problems businesses need to solve. The buzz around more complicated types of AI, such as deep learning, gets so much hype that businesses overlook simpler approaches.

“Many companies say to me, ‘I need an AI strategy’ and [after hearing their business problem] I say, ‘No you don’t,'” Hare said.

Really, what you need to look for is a solution to a problem you have, and if machine learning does it, great,” Hare said. “If you need deep learning because the problem is too gnarly for classic ML, and you need neural networks — that’s what you look for.”

Weak AI vs. strong AI

Broadly, there are two types of AI:

Weak AI is pervasive today in the form of chatbots, which serve a specified purpose.

Strong AI tools go much further; these tools come up with solutions to problems on their own, through massive amounts of data and cognitive computing capabilities.