Canada


Toronto's SickKids announces first-of-its-kind artificial intelligence position

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Dr. Anna Goldenberg, senior scientist in genetics and genome biology at SickKids, poses at the Peter Gilgan Centre for Research and Learning in Toronto. Inside the pediatric intensive care unit at Toronto's Hospital for Sick Children, an infant recovering from open-heart surgery is barely visible through the forest of whizzing and beeping machines that monitor his every vital sign. In the old days, those vital signs – a baby's heart rate, blood pressure, oxygen levels and other signals – would have flashed across a screen and then been lost to posterity. But in 2013, SickKids began collecting and storing the data that emanate from patients in their 42 intensive-care beds. The unit now has more than two trillion data points in its virtual vault, far more than a mere mortal could make sense of.


Brain imaging, machine learning show promising results in PTSD treatment: London, Ont. researchers - London

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Andrew Nicholson, PhD, lead author on the study and a post-doctoral fellow at Schulich Medicine & Dentistry and Dr. Ruth Lanius, Professor at Schulich Medicine & Dentistry and Lawson Scientist. Researchers out of Lawson Health Research Institute and Western University are shedding light on major advances in using brain imaging to classify mental illness. The new study used machine learning to classify, with 92 per cent accuracy, which subtype of post-traumatic stress disorder people had based on MRI scans. "We've known for a long time that people with post-traumatic stress can present quite differently, clinically," researcher, professor, and psychiatrist Dr. Ruth Lanius told 980 CFPL. "A group of them, about 70 per cent of them, have too much emotion, too much arousal. About 30 per cent of that group, actually, is very detached from their emotions, they're very shut down."


Can artificial intelligence ever keep up with the complicated world of human emotions?

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In the early 1990s, Lisa Feldman Barrett had a problem. She was running an experiment to investigate how emotions affect self-perception, but her results seemed to be consistently wrong. She was studying for a PhD in the psychology of the self at the University of Waterloo, Ontario, Canada. As part of her research, she tested some of the textbook assumptions that she had been taught, including the assumption that people feel anxiety or depression when, despite living up to their own expectations, they do not live up to the expectations of others. But after designing and running her experiment, she discovered that her test subjects weren't distinguishing between anxiety and depression.


Making AI accountable easier said than done, says U of A expert

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If you had to program a self-driving car, which option would you choose if only two were available: hit a pedestrian who suddenly appears in front of the vehicle or veer off into a baby carriage on the sidewalk? It's the kind of ethical conundrum that could shape artificial intelligence in years to come, and one of many the University of Alberta's Geoffrey Rockwell has been pondering lately. Earlier this month, the professor of philosophy and digital humanities joined a national brainstorming forum on the ethics of AI in Montreal, along with industry leaders, federal government officials and other academics, including philosophers. They gathered to grapple with an industry currently worth US$7.4 billion, according to figures circulated at the forum, and expected to reach almost US$16 trillion by 2065--amounting to a seismic shift in how we live and work. The forum followed the signing last June of the Canada-France Statement on Artificial Intelligence, meant to jump-start an international coalition charged with exploring the societal implications of a technology that promises to soon be as ubiquitous as the internet, only with the power to potentially make life-and-death decisions on our behalf.


What's New for Artificial Intelligence in 2019?

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POINT ROBERTS, Wash. and VANCOUVER, British Columbia, Jan. 15, 2019 (GLOBE NEWSWIRE) -- Investorideas.com, a global investor news source covering Artificial Intelligence issues a special edition of The AI Eye, looking at advancements in artificial intelligence in 2019 and beyond. Last year saw considerable development in artificial intelligence (AI), machine learning (ML) and natural language processing (NLP) in various sectors. This was seen in the proliferation of many startups using AI tech in various spaces, as well as the adoption of the technology by established industry leaders. As 2019 begins, there are already many indicators that AI growth will continue and accelerate. In a year-end review, Forbes B2B technology analyst and consultant David A. Teich highlighted the tendency of major tech companies to lead the way in the adoption of AI. "The technologies and techniques of AI and ML are still so new that the main adopters of the techniques are the large software companies able to hire and to invest in the necessary expertise," he said.


What's New for Artificial Intelligence in 2019?

#artificialintelligence

POINT ROBERTS, Wash. and VANCOUVER, British Columbia, Jan. 15, 2019 (GLOBE NEWSWIRE) -- Investorideas.com, a global investor news source covering Artificial Intelligence issues a special edition of The AI Eye, looking at advancements in artificial intelligence in 2019 and beyond. Last year saw considerable development in artificial intelligence (AI), machine learning (ML) and natural language processing (NLP) in various sectors. This was seen in the proliferation of many startups using AI tech in various spaces, as well as the adoption of the technology by established industry leaders. As 2019 begins, there are already many indicators that AI growth will continue and accelerate. In a year-end review, Forbes B2B technology analyst and consultant David A. Teich highlighted the tendency of major tech companies to lead the way in the adoption of AI. "The technologies and techniques of AI and ML are still so new that the main adopters of the techniques are the large software companies able to hire and to invest in the necessary expertise," he said.


Tech talks: Artificial intelligence takes on humans in new improv show

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Robots can deliver room service, conduct funeral ceremonies and lead workouts for the elderly. But can they tell a joke? Edmonton audiences can find out, starting next weekend, when local improv artists go head-to-head with artificial intelligence to see who is funnier. Called Improbotics, the show kicks off at 7:30 p.m. at Rapid Fire Theatre (located within the Citadel Theatre at 9828 101A Ave.) and runs Saturday nights from Jan. 12 until Feb. 2. The event is the brainchild of local improv artist and University of Alberta computer science PhD student Kory Mathewson, who has been performing improv for 15 years but began to connect theatre and artificial intelligence about three years ago as part of an improv group known as HumanMachine. HumanMachine consists of Mathewson, a colleague named Piotr Mirowski (an artificial intelligence researcher in London, England, who will not be at the upcoming Edmonton shows) and a piece of equipment known as A.L.Ex, which stands for Artificial Language Experiment -- a computer system that can do speech recognition, improvised dialogue and voice synthesis.


Supervised autoencoders: Improving generalization performance with unsupervised regularizers

Neural Information Processing Systems

Generalization performance is a central goal in machine learning, particularly when learning representations with large neural networks. A common strategy to improve generalization has been through the use of regularizers, typically as a norm constraining the parameters. Regularizing hidden layers in a neural network architecture, however, is not straightforward. There have been a few effective layer-wise suggestions, but without theoretical guarantees for improved performance. In this work, we theoretically and empirically analyze one such model, called a supervised auto-encoder: a neural network that predicts both inputs (reconstruction error) and targets jointly. We provide a novel generalization result for linear auto-encoders, proving uniform stability based on the inclusion of the reconstruction error---particularly as an improvement on simplistic regularization such as norms or even on more advanced regularizations such as the use of auxiliary tasks. Empirically, we then demonstrate that, across an array of architectures with a different number of hidden units and activation functions, the supervised auto-encoder compared to the corresponding standard neural network never harms performance and can significantly improve generalization.


I, Edmonton

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Ever since computers were clunky, whirring machines that took up entire floors, humans have marvelled at their potential, envisioning all the ways they could help or even be like us. Tapping into our own dark nature, science fiction tends to reach what creepily feels like the natural conclusion of obscenely smart machines with human dispositions; our demise. There's no robot apocalypse on the horizon, but the revolution is well under way. It's been here, in some form, since the '60s, and it's poised to lead the city, and world, in to the future. On April 1, 1964, U of A built Canada's first Department of Computing Science around five academics, a small support staff and the LGP-30, an 800-pound, deep freeze-shaped digital computer.


Battling 'biopiracy', scientists catalog the Amazon's genetic wealth

The Japan Times

TORONTO - In a bid to stop "biopiracy," researchers are building a giant database to catalog genetic material from the world's largest rainforest. From the rubber in car tires to cosmetics and medicines, genetic material contained in the Amazon region has contributed to discoveries worth billions of dollars. Communities living there, however, have rarely benefited from the genetic wealth extracted from their land -- a form of theft that legal experts call "biopiracy." Instead, forest dwellers often remain impoverished, which can drive them to find other ways to make money, such as illegal logging, according to Dominic Waughray, who heads the Amazon Bank of Codes project for the World Economic Forum. "At the heart of the conservation debate is: How do you find a way for a person in the forest to get more cash in their hand right now from preserving that habitat rather than cutting it down?" said Waughray.