How we learned to break down barriers to machine learning - Ars Technica

A core component of being able to utilize most common AI/ML tools is having reliable and fast Internet access, and drawing on experience from her background, Dr.

Sephus pointed out that a lack of access to technology in primary schools in poorer areas of the country sets kids on a path away from being able to use the kinds of tools we're talking about.

"You're talking about a concept that a lot of people think is pretty intimidating," she explained.

One way of tackling the divide here, in addition to simply increasing access, is changing the way that technologists communicate about complex topics like AI/ML to regular folks.

Sephus said.

Sephus said that AWS has been hiring sociologists and psychologists to join its tech teams to figure out ways to tackle the digital divide by meeting people where they are rather than forcing them to come to the technology.

Simply reframing complex AI/ML topics in terms of everyday actions can remove barriers.

Sephus explained that one way of doing this is to point out that almost everyone has a cell phone, and when you're talking to your phone or using facial recognition to unlock it, or when you're getting recommendations for a movie or for the next song to listen to—these things are all examples of interacting with machine learning.

Not everyone groks that, especially technological laypersons, and showing people that these things are driven by AI/ML can be revelatory.