The next time you pull out your smartphone and ask Siri or Google for advice, or chat with a bot online, take pride in knowing that some of the theoretical foundation for that technology was brought to life here in Canada. Indeed, as far back as the early 1980s, key organizations such as the Canadian Institute for Advanced Research embarked on groundbreaking work in neural networks and machine learning. Academic pioneers such as Geoffrey Hinton (now a professor emeritus at the University of Toronto and an advisor to Google, among others), the University of Montreal's Yoshua Bengio and the University of Alberta's Rich Sutton produced critical research that helped fuel Canada's rise to prominence as a global leader in artificial intelligence (AI). Stephen Piron, co-CEO of Dessa, praises the federal government's efforts at cutting immigration processing timelines for highly skilled foreign workers. Canada now houses three major AI clusters – in Toronto, Montreal and Edmonton – that form the backbone of the country's machine-learning ecosystem and support homegrown AI startups.
An accurate model of patient-specific kidney graft survival distributions can help to improve shared-decision making in the treatment and care of patients. In this paper, we propose a deep learning method that directly models the survival function instead of estimating the hazard function to predict survival times for graft patients based on the principle of multi-task learning. By learning to jointly predict the time of the event, and its rank in the cox partial log likelihood framework, our deep learning approach outperforms, in terms of survival time prediction quality and concordance index, other common methods for survival analysis, including the Cox Proportional Hazards model and a network trained on the cox partial log-likelihood.
Great White North is on the top of that list. Because of its powerful academic research labs, Toronto has supplied a lot of talent in the field but has been experiencing a brain drain. As an effort to retain talent and make Toronto a global supplier of AI capability, the University of Toronto gathered a team of globally renowned researchers and founded the Vector Institute. The independent, non-profit AI research institution has created a lot of buzz and attracted a great deal of funding to its ongoing projects. With a combination of research and commercial goals, according to The Toronto Star, It will be backed by more than $150 million in public and corporate funding.
As humans we feel nothing more viscerally -- in the most literal sense -- than our health. That makes this year's gathering of the Medical Image Computing and Computer Assisted Interventions Society -- MICCAI 2017 -- in Quebec City, Canada, one of the best ways to understand how deep learning is improving the lives of people all around us. The conference brings together leading biomedical scientists, engineers and clinicians to talk about new technologies in medical imaging and computer-assisted intervention, providing an early look at trends poised to sweep through the $6.5 trillion healthcare industry. This will be the group's biggest conference yet, with 1,300 attendees. And deep learning -- which pairs vast quantities of data with sophisticated neural networks to give computers amazing new capabilities -- deserves a lot of the credit, organizers say.
Canada has produced several big breakthroughs in artificial intelligence in recent years, and its government is keen to establish the country as a global epicenter of AI. The country's prime minister, Justin Trudeau, also hopes that the technology will learn Canadian values as it grows up. Speaking at a major AI event in Toronto today, Trudeau demonstrated an impressive enthusiasm for AI and machine learning, at one point even taking a stab at describing the concept of deep reinforcement learning, an approach that lets computers learn to do complex things that can't be programmed manually (see "10 Breakthrough Technologies 2017: Reinforcement Learning"). Both deep reinforcement learning and deep neural networks, which the method exploits, were pioneered by researchers working at Canadian universities. The country's government is now investing in big efforts to spur more AI research.