The world's tech powers are sending giant sums of money spinning into Canada, but while many see this as a sign of success, others are worried about researchers and intellectual property being swallowed wholesale. The country is in the midst of an artificial intelligence (AI) boom, with Google, Microsoft, Facebook, Huawei and other global heavyweights spending millions or even hundreds of millions of dollars on research hubs in Quebec, Ontario and Alberta. Canadian doors are open – some fear too open. Jim Hinton, an IP lawyer and founder of the Own Innovation consultancy, reckons that more than half of all AI patents in Canada end up being owned by foreign companies. What we need to be doing is getting money out of our ideas ourselves, instead of seeing foreign talent scoop it all up," said Hinton. "Otherwise we'll never have a Canadian champion." The country is home to hundreds of fledgling AI companies, including much-talked-about start-ups like Element AI and Deep Genomics, but they remain relatively small. "They don't have a strong market position yet," Hinton says. Deep learning pioneers such as Yoshua Bengio and Geoffrey Hinton (no relation to Jim) have nurtured top-notch talent in AI in Canada for years, back when AI was an emerging field. But despite Canadian inheriting this brilliant AI lead from the country's AI "godfathers", big foreign players have an unassailable advantage over homegrown efforts, Hinton said. "It's not an easy go for the average company to make a business out of AI.
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.
Artificial Intelligence these days has become a new key driver of economic growth. It is a significant field in technology right now. While several countries are racing towards AI supremacy, Canada is attracting the world's tech giants that are pouring mammoth amounts in the region. The country is currently in the midst of the AI boom as companies like Microsoft, Facebook, Google, Huawei, among others are spending huge capital on research hubs in Quebec, Ontario and Alberta. Canada is a world research leader and home to extraordinary AI-driven businesses, and has played a vital role in the advancement of AI.
Drone helped capture a stunning view of ice across the North Atlantic. The close-up drone video was taken by Andre Beyzaei, the climate hobbyist. "Usually you see a bit of sea-ice along the coasts and if you happen to fly the drone far enough, you may capture some icebergs much further away," says Beyzaei. "It's counterintuitive to most people, because it means you can have an increase in local ice hazards because of changing climate in high Arctic," said David Barber, Lead Author and University of Manitoba climate change scientist. The footage captured across the North Atlantic recorded the mesmerizing view of accumulating sea ice over Brighton, Newfoundland, but according to researchers, also serves as stark reminder of the impact of climate change.
Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations.