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glued to a screen for 12 hours a day for the first couple of years.
Victoroff went on to win prizes in programming and data visualization at HackMIT in 2013, and he honed his abilities both in classes at Olin and in a variety of internships and jobs, including stints at Pearson and EdX, according to his LinkedIn profile. He dropped out of school in 2014, about three classes short of earning his degree, he says. He’s not sure he’ll return. He says the skills taught in those remaining courses—entrepreneurship and math—are probably a moot point for him now.
“Olin’s belief, at a very fundamental level, is your goal is to gain the tools you need,” he says, “and to fundamentally master the art of learning by experience. It’s very project-based.” He’s an advocate of that method of training engineers. “Personally, I find lectures to be a waste of time,” he adds.
“My three years spent at Olin transformed the way I approach engineering,” he continues. “They believe engineers should not be divorced from the reason they’re building things. It’s not just the ‘how’ of what you build, but also the ‘why’ of what you build. Why does this matter?”
Victoroff thinks machine learning and automated technologies matter because they have the potential to transform and improve people’s lives. Self-driving cars are his favorite example of that, as he points to the number of deaths that might be avoided if artificial intelligence software took the wheel in a coordinated way. “The more comfortable we get with computers automating pieces of our lives,” Victoroff says, “the better off we’re going to be in the future.”
It’s still early days for machine learning technologies—we’re in the “dark ages,” as Victoroff puts it. Still, the biggest hurdle for the industry might not be a technological problem; it could be humans’ “discomfort in ceding control,” Victoroff says. “I think that’s one of the greatest existential crises we’re going to have to resolve in order to really advance this field.”