Welcome to the week distance Ars Frontiers! This article is the first in a short series of articles summarizing all of today’s conversations for those who were unable to travel to the capital for our first conference. We’ll be running one of these every few days for the next two weeks, and each one will include an embedded video of the conversation (along with text).
In today’s summary, we’re moving on to our conversation with Amazon Web Services tech evangelist Dr. Nashley Cephus. Our discussion was titled “Breaking Barriers to Machine Learning.”
What are the obstacles?
Dr. Sephus came to AWS via a circular path, and grew up in Mississippi before eventually joining a tech startup called partial. Partpic was an artificial intelligence and machine learning (AI/ML) company with a neat premise: Users could take pictures of tools and parts, and the Partpic app would algorithmically analyze the images, identify the part and provide information about what the part was and where more could be purchased. Amazon acquired Partpic in 2016, and Dr. Sephus took her machine learning skills to AWS.
When asked, she specified being able to As the biggest barrier to greater use of AI/Machine Learning – in many ways, it’s another wrinkle in the old problem digital partition. A key component of being able to use the most popular AI/machine learning tools is having reliable and fast internet access, and drawing on the experience gained from her background, Dr. Sefus noted that the lack of access to technology in primary schools in poor areas of the country puts children on a path Far from being able to use the kinds of tools we’re talking about.
Moreover, lack of early access leads to resistance to technology later in life. “You’re talking about a concept that a lot of people think is very scary,” she explained. “A lot of people are afraid. They feel threatened by technology.”
Not dividing things
One way to address the gap here, in addition to simply increasing access, is to change the way technologists communicate about complex topics like AI/machine learning to lay people. “I understand that as tech experts we often like to build cool things, right?” Dr. Sephus said. “We don’t think about the long-term impact, but that’s why it’s so important to have that diversity of thinking at the table and those different perspectives.”
Dr. Sephus said that AWS is hiring sociologists and psychologists to join its tech teams to discover ways to address the digital divide by meeting people where they are rather than forcing them to come to technology.
Simply reframing complex AI/machine learning topics in terms of day-to-day actions can remove barriers. Dr. Sevos explained that one way to do this is by pointing out that nearly everyone has a mobile phone, when you talk to your phone or use facial recognition to unlock it, or when you get recommendations for the next movie or song to listen to — these are all examples of interaction with Machine learning. Not everyone is critical of it, especially tech laymen, and showing people that this stuff is AI/machine learning driven can be pretty straightforward.
“Meeting them wherever they are, showing them how these technologies affect them in their daily lives, putting the software in an accessible way — I think that’s something we should focus on,” she said.
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