“I’m a capitalist,” says Janssen. “I want to make a lot of money. I think unless you actually create jobs and wealth, it’s great that you have this research, but without commercialization it means nothing.
“We should be world leaders in health and A.I., in oil and gas and A.I. and so on … Hopefully what we have here is a cluster out of artificial intelligence and machine learning, and the next big boom will come out of that.
“All software in the next decade will have an element of A.I. and M.L. as a part of it. We want to be a part of building out those platforms, those applications. We’ll be kind of the smaller, grittier, blue-collar one (A.I. company) that picks our fight.”
Janssen is a master at creating interesting work for himself, having started out as an entrepreneur when he was still a U of A business student. In the late 1990s tech boom, when investors were flocking to put money into dot-com start-ups, Janssen and friend Cory Wagner rented a cramped downtown office and went to work on a number of ideas, one of which they built into Investopedia.com, a financial education website.
Janssen and Wagner planned to build a big investment site with everything on it, but lacked the resources to do so. Instead, they started to build out an investment dictionary, thinking it was timeless content that might attract traffic for years.
Investopedia quickly became one of the fastest growing businesses in the province and the largest financial website in Canada, all from a tiny Edmonton office. “That’s one of the great things about the internet, it’s an equalizer,” Janssen says. “The proof is in the pudding. We drove results. It didn’t matter we were a bunch of 22-year-old Canadians teaching Americans how to invest.”
In 2007, Wagner and Janssen sold Investopedia.com to Forbes.
Afterwards, Janssen built on the same model, starting businesses like Techopedia, Safeopedia and Corrosionpedia.
He also hooked up with the U of A’s machine learning crew when he tried to find a way to get computers to generate rough drafts of articles for his various sites. They had no success whatsoever in this attempt, Janssen says, partly because they had taken on an extremely complex and difficult problem.
But the challenge made Janssen think about the potential of A.I. He then realized the U of A computing science department had grown into one of the most elite institutions on earth for artificial intelligence and machine learning.
“I was amazed at the quality of M.L. engineers and data scientists there was at the university,” he says. “I felt like an idiot for not actually realizing that this (A.I. expertise) was here. And meanwhile all the profs at the U of A are like, ‘How come no one gives us any respect? How come no one realizes what we’re doing here?’”
Janssen’s new company AltaML has gone from zero to 40 people in 14 months
To engage in machine learning, Janssen says you need both data to mine and a problem to tackle. Large Alberta businesses have both data and efficiency issues.
When he first approaches companies, Janssen says they’re often skeptical and misinformed about what A.I and M.L. is all about. “They think that A.I. is robots doing backflips and Skynet and all this other stupid stuff. But it’s a lot more boring. It’s going in and doing a very specific task really, really well.”
One of his goals, he says, is to keep all the brilliant foreign scientists now being trained at the U of A here in Edmonton. Only three of his 20 leading AltaML data scientists are Canadian born, he says. “We’re proving that if you have interesting projects and the right team, they will stay here.”
Essentially, Janssen hopes to take a new Alberta strength and build on an old Alberta strength.
Sounds like a plan — and one that we could use.
Oil and gas won’t be here forever and neither will those computing science geniuses if we can’t employ them.