The first genuinely impressive AI assistant may well have a Canadian accent. Facebook announced today that it is tapping into Canada's impressive supply of artificial-intelligence talent and expertise by creating a major AI research center in Montreal. Several big recent advances in AI can be traced back to Canadian research labs, and Facebook is hoping that the new lab may help it take advantage of whatever comes next. The new center will focus, in particular, on an area of AI known as reinforcement learning (see "10 Breakthrough Technologies 2017: Reinforcement Learning"). The center will seek to apply this and other novel approaches to language, with the aim of producing more coherent and useful virtual assistants, says Yann LeCun, director of AI research at Facebook.
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.
Bras, Ronan Le (Cornell University) | Dilkina, Bistra (Cornell University) | Xue, Yexiang (Cornell University) | Gomes, Carla (Cornell University) | McKelvey, Kevin (US Forest Service) | Schwartz, Michael (US Forest Service) | Montgomery, Claire (Oregon State University)
Our work is motivated by an important network design application in computational sustainability concerning wildlife conservation. In the face of human development and climate change, it is important that conservation plans for protecting landscape connectivity exhibit certain level of robustness. While previous work has focused on conservation strategies that result in a connected network of habitat reserves, the robustness of the proposed solutions has not been taken into account. In order to address this important aspect, we formalize the problem as a node-weighted bi-criteria network design problem with connectivity requirements on the number of disjoint paths between pairs of nodes. While in most previous work on survivable network design the objective is to minimize the cost of the selected network, our goal is to optimize the quality of the selected paths within a specified budget, while meeting the connectivity requirements. We characterize the complexity of the problem under different restrictions. We provide a mixed-integer programming encoding that allows for finding solutions with optimality guarantees, as well as a hybrid local search method with better scaling behavior but no guarantees. We evaluate the typical-case performance of our approaches using a synthetic benchmark, and apply them to a large-scale real-world network design problem concerning the conservation of wolverine and lynx populations in the U.S. Rocky Mountains (Montana).
Canada has been investing in machine learning and artificial intelligence (AI) for longer than most of the industrialized world. Dr. Geoff Hinton of Google helped ignite the field of graphics processing unit (GPU) deep learning at the University of Toronto. Then he became chief scientific advisor to the Vector Institute, which in collaboration with the University, aims to produce the largest number of deep learning AI graduates and innovators globally. It's the home of computer scientist Yoshua Bengio, who is another pioneer of AI technology. Hundreds of AI researchers and doctoral students are concentrated at McGill University and the University of Montreal.