Deep Learning for Patient-Specific Kidney Graft Survival Analysis Machine Learning

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.

Why Artificial Intelligence Should Be More Canadian


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.

Element AI global talent report finds Canada has third-largest concentration of AI researchers


Montreal-based Element AI has compiled a report and analysis on the global supply of AI researchers in an effort to get a better understanding of an industry in high demand. Overall, the report found that there are 22,064 PhD-educated researchers globally that are capable of working in AI research and applications, with only 3,074 candidates currently looking for work. The US had the highest concentration of researchers with 9,010 researchers, followed by the UK with 1,861 researchers. Canada fell in third place with 1,154 researchers. To conduct the broader survey, Element AI used results from LinkedIn searches that showed the total number of profiles according to specialized parameters.

Google goes north to Montreal's artificial intelligence scene


Notman House is a local ICT mecca in Montreal. Google's $3.4 million investment in the Montreal Institute for Learning Algorithms (MILA) and new lab opening in The City of Saints not only highlights the company's banking on artificial intelligence, but its faith in Canada's ICT industries to help it in that quest. Montreal is running neck and neck with Toronto and Vancouver to attract talent and incubate businesses. Though Ontario holds a lead over it, Montreal remains, "the second most popular location for most types of ICT jobs" after Toronto according to the Canadian Information and Communications Technology Council, and over 222,000 people are employed across various industries, from gaming to AI R&D. The lab will be led by University of Montreal and Twitter alumnus Hugo Larochelle, whose like-minded associates at MILA are already heavily invested in deep learning applications.

Google's next DeepMind AI research lab opens in Canada


Google's DeepMind artificial intelligence team has been based in the UK ever since it was acquired in 2014. However, it's finally ready to branch out -- just not to the US. DeepMind has announced that its first international research lab is coming to the Canadian prairie city of Edmonton, Alberta later in July. A trio of University of Alberta computer science professors (Richard Sutton, Michael Bowling and Patrick Pilarski) will lead the group, which includes seven more AI veterans. As Recode observes, you can chalk it up to a combination of familiarity and political considerations.