When 20 teams gathered for NextAI’s first venture day this September, competing to become the next Shopify, Uber or Wealthsimple – but powered by the machine learning technology this incubator hopes to help commercialize – the big winner was a machine-learning powered garbage bin that sorts and diverts waste away from landfills, while extracting consumption and demographic insights. It may seem odd, but over the past few years AI has spread ever more widely into our daily lives. Anticipating its next application could not only change the way we live our lives, but our entire economy.
“When I talk to people about AI and machine learning, I tell them I am excited for this technology because it has the potential to impact every sector,” says Graham Taylor, a CIFAR Azrieli Global Scholar and the academic director of NextAI’s incubator program. In this role Taylor oversaw the application of machine learning to fields as varied as finance, health, natural resources, human resources and waste management.
“The analogy is software. It’s virtually impossible to think of an industry that hasn’t been transformed by software. Machine learning is, in essence, software writing software. And while it’s a bit scary to think of it that way, you realize that the opportunities are endless.”
NextAI is just one of the AI initiatives Taylor, a machine learning expert and professor at the University of Guelph’s School of Engineering, has a hand in. From CIFAR’s Learning in Machines & Brains program, to the newly created Vector Institute, to his connections to the Creative Destruction Lab and his role as scientific advisor to a handful of machine learning start-ups, Taylor’s is deeply involved in Canada’s AI leadership.
Of course, all this would not be possible had it not been for his original brush with the field as a fourth-year systems design engineering student at the University of Waterloo. His professor had the students build their own AIs to play the board game Abalone and ran the course like a showdown. The teams were instructed to incorporate some form of learning into their AIs rather than have them be completely rules based. Taylor’s team put a lot of work into the project and won. He was hooked.