Welcome to the week after Ars limits! This article is the first in a short series of pieces that will summarize each of today’s lectures for the benefit of those who were unable to travel to DC for our first conference. We will be running one of these every few days for the next few weeks, and each of them will include an embedded video of the lecture (along with a transcript).
For today’s summary, we review our talk with Amazon Web Services technology evangelist Dr. Nashlie Sephus. Our discussion was entitled “Breaking Barriers to Machine Learning.”
What barriers?
Dr. Sephus came to AWS via a roundabout where he grew up in Mississippi before eventually joining a technical startup called Partpic. Partpic was a company with artificial intelligence and machine learning (AI / ML) with a nice premise: Users could take pictures of tools and parts, and the Partpic app would algorithmically analyze the pictures, identify the part and provide information about what the part was and where to can buy more of it. Partpic was acquired by Amazon in 2016, and Dr. Sephus took his machine learning skills to AWS.
When asked, she identified herself access as the biggest barrier to the greater use of AI / ML – in many ways it’s yet another wrinkle in the old problem of the digital divide. A core component in being able to use the most common AI / ML tools is to have reliable and fast Internet access, and based on experience from her background, Dr. Sephus, that lack of access to technology in primary schools in poorer areas of the country. country puts children on a path away from being able to use the kind of tools we are talking about.
Moreover, lack of early access to resistance to technology leads later in life. “You’re talking about a concept that a lot of people think is pretty scary,” she explained. “A lot of people are scared. They feel threatened by technology.”
U-division of things
One way to tackle the gap here, in addition to simply increasing access, is to change the way technologists communicate about complex topics like AI / ML to ordinary people. “I understand that we as technologists many times just like to build cool stuff, right?” said Dr. Sephus. “We are not thinking about the long-term effect, but that is why it is so important to have that diversity of thoughts at the table and the different perspectives.”
Dr. Sephus said AWS has hired sociologists and psychologists to join its tech teams to figure out ways to tackle the digital divide by meeting people where they are instead of forcing them to get to the technology.
Simply reformulating complex AI / ML topics in the form of everyday actions can remove barriers. Dr. Sephus explained that one way to do this is to point out that almost everyone has a cell phone and when you talk to your phone or use face recognition to unlock it or when you get recommendations for a movie or for the next song to listen to – these things are all examples of interaction with machine learning. Not everyone knows, especially tech laymen, and showing people that these things are powered by AI / ML can be revealing.
“Meeting them where they are, showing them how these technologies affect them in their daily lives, and having programming out there in a way that is very accessible – I think that’s something we should focus on,” she said.