Sam Ransbotham As a professor of business analytics at Boston College, he teaches a class on machine learning, and what he sees in the classroom both excites and terrifies him.
Some students are using AI tools to create and accomplish amazing things, getting more out of learning and technology “Myself and AI” than they ever imagined. But in other situations, he sees a related trend: Students “phone things into the machine.”
The result is a new kind of digital divide – but it’s not what you’d expect.
Boston College provides key tools at no cost to students, ensuring that socioeconomics is not a differentiator in the classroom. But Ransbotham, who hosts “Me, Me and AI” Podcast from MIT Sloan Management Review, concerned with “a divide in technology interests.”
Me, Myself and AI
“The deeper one is able to understand the tools and the technology, the more they are able to get out of those tools,” he explains. “A cursory use of a tool will “Myself and AI” yield a cursory result, and a deeper use will yield a deeper result.”
problem? “It’s a race to mediocrity. If you’re shooting for mediocrity, it’s really fast to get to mediocrity.”
He explained, “Boston College’s motto is ‘Ever to Excel.’ It is not ‘ever to mediocre’. And students’ ability to excel may be hindered by their ease with mediocrity.”
That’s one of the topics on this special episode of the Geekwire podcast in collaboration with Me, Myself, and AI. Sam and I compare notes from our podcast and share our own observations about emerging trends and the long-term impact of AI. This is a two-part series across our podcast — you can find the rest of our conversation here Me, Myself and AI feed.
Continue reading for takeaways from this episode.
AI has a measurement problem: Sam, who researched Wikipedia extensively more than a decade ago, sees present-day parallels. Before Wikipedia, Encyclopaedia Britannica was a company where employees produced books, paid a printer, and created measurable economic value. Then came Wikipedia, and Encyclopaedia Britannica didn’t last.
It has lost its economic value. But as he says: “Would any rational person say that the world is a worse place because we now have Wikipedia versus Encyclopaedia Britannica?
In other words, traditional economic metrics do not fully capture the net gain in value that Wikipedia has created for society. He sees the same “Myself and AI” measurement problem with AI.
“The data gives better insight into what you’re doing, the documents you have, and you can make slightly better decisions,” he said. “How do you measure it?”
Summary of Content vs. Generation: Sam’s “must have it” AI feature isn’t about creating content — it’s about distilling information to fit more into his 24 hours.
“We talk a lot about generation and generational power, what these things can create,” he said. “I’m using it a lot for what it can summarize, what it can distill.”
Finding value in AI, even when it’s wrong: Despite his concerns about students using AI to achieve mediocrity, Sam is optimistic about what people can do with AI tools.
“Often I find the tool is completely wrong and ridiculous and just says absolute rubbish,” he said. “But that rubbish makes me think about something – the way it’s wrong makes me think: Why is it wrong? … and how can I push it?”
Searching for signal in noise: Sam describes the goal of the Me, Myself and AI podcast as cutting through polarizing narratives about artificial intelligence.
“There’s a lot of hype about artificial intelligence,” he said. “There’s a lot of nonsense about artificial intelligence. And somewhere in it there’s some “Myself and AI” signal and some truth.”
Listen to the full episode above, subscribe to GeekWire on Apple , Spotify or wherever you listen, and check out the rest of our conversation Me, Me and AI podcast feed.