Slater Victoroff’s hands were bleeding, several of his fingernails were chipped and broken, and there was dust in his eyes. He was exhausted.
If he stopped moving, he would likely die. As he climbed up the mountain in the eastern Himalayas, he reached a particularly treacherous section.
“I actually had the decision laid out in front of me: Is life something worth fighting for?” Victoroff recalls. “It would’ve been easy to not continue.”
But he gathered his resolve and kept moving, and he and his group eventually reached the top of a peak about 18,000 feet high, he says. That was nearly six years ago. He was only 18.
Looking back on it now, such an experience “makes you see things a little bit differently,” Victoroff says. (In the above photo, he is the person in black on the far left.)
At age 24, Victoroff has already viewed the world through more lenses than many people do in a lifetime. He is the chief executive of Indico, a Boston machine learning and data analytics startup he co-founded three years ago while studying at Olin College of Engineering. He’s a trained computer engineer who likes to meditate and has two black belts in karate. He writes poetry and is a “voracious” bibliophile who prefers to read international poems in their original languages—he has picked up some Mandarin, Nepali, French, and Spanish, among others.
During a gap year between high school and college, Victoroff spent time in Nepal and China. That’s when he climbed the mountain, tried his hand at blacksmithing, took a vow of silence for 10 days while living at a Buddhist temple, and trained at a Chinese martial arts school, he says. He also spent time that year doing research at UCLA and tutoring students with learning disabilities.
Victoroff and his 15-person company still have a lot to prove. But as the young CEO of a startup working in one of the more intriguing (and competitive) areas of software, he’s worth watching. His wide-ranging and unusual background makes him all the more interesting as a business leader.
Investors see promise in Victoroff’s company, which is building software tools that help analyze text and images, and has a bigger goal of putting easy-to-use machine learning algorithms in the hands of more developers. To date, Indico has raised $3.2 million, Victoroff says, from General Catalyst Partners, Boston Seed Capital, .406 Ventures, Two Sigma Ventures, Rough Draft Ventures, Techstars Boston, and others.
Indico’s products are used by customers in marketing, retail, media, finance, and other industries. A marketing firm, for example, might use Indico’s software to track positive and negative mentions of a brand on social media or to analyze facial expressions to predict consumer attitudes toward products. A financial analyst, meanwhile, might use the software to automatically sift through hundreds of articles and data points, helping the person discover trends and develop investment theses, Victoroff says.
Indico is “like a co-pilot in some ways,” he explains. “We’re not automating away the person, but taking the grunt work out of the process, allowing them to do much more what they enjoy doing, which is critical thinking.”
Victoroff’s day job has more in common with his hobbies than one might think. He likes to push his own limits, try bold experiments, and stretch his creativity and problem-solving abilities—all crucial traits for entrepreneurs.
Victoroff, who grew up in the Los Angeles area, also points to several parallels between software development and martial arts training.
“When I initially approached machine learning, I think one of the biggest things that was very helpful is a lot of what you do in martial arts is about extremely conscious trial and error,” Victoroff says. “It’s about going through a motion that you might’ve done a hundred or a thousand times before, and recognizing every minute step along that path.” He adds, “So, it was extremely helpful as a programmer.”
Programming and martial arts both require “self-refinement,” “taking agency,” and “trying to be a motivator of my own future,” he adds.
Victoroff says he had never owned a computer before he set foot on Olin’s campus, and his major was originally chemical engineering. But after attending a career fair, he learned the job opportunities for chemical engineering were mostly in the oil and gas industry, which didn’t intrigue him, he says. Soon after picking up a computer programming textbook, he was hooked.
“It was love at first sight,” he says. After that, he was 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.”