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.
Stephen J. Green Department of Computer Science, University of Toronto Toronto, Ontario CANADA M5S 3G4 sj green cs, utoronto, ca Abstract We discuss an automatic method for the construction of hypertext links within and between newspaper articles. The method comprises three steps: determining the lexical chains in a text, building links between the paragraphs of articles, and building links between articles. Lexical chains capture the semantic relations between words that occur throughout a text. Each chain is a set of related words that captures a portion of the cohesive structure of a text. By considering the distribution of chains within an article, we can build links between the paragraphs. By comparing the chains contained in two different articles, we can decide whether or not to place a link between them. We also present the results of an experiment designed to measure inter-linker consistency in the manual construction of hypertext links between the paragraphs of newspaper articles. The results show that inter-linker consistency is low, but better than that obtained in a previous experiment. Introduction The popularity of graphical interfaces to the World Wide Web (WWW) has shown that a hypertext interface can make what was once a daunting task, accessing information across the Internet, considerably easier for the novice user.
Siri is about to get a lot smarter thank to Carnegie Mellon researcher Russ Salakhutdinov, who announced today that he is joining Apple to lead the company's artificial intelligence efforts. Excited about joining Apple as a director of AI research in addition to my work at CMU. Apply to work with my teamhttps://t.co/U2hQl2GdhA Although he's not a household name, Russ Salakhutdinov is one of the biggest deep learning figures in academia. His hiring by Apple comes after the company has been criticized for Siri's weak performance compared to rival digital assistants from Google, Amazon and Microsoft. Before working at CMU, Salakhutdinov worked at Toronto University and MIT.
This year, we have seen an acceleration of Silicon Valley tech giants opening AI research labs around the world as they seek to gain traction among researchers and fulfill their global ambitions. In the past six months or so, Google brought labs to China and France, Facebook opened labs in Pittsburgh and Seattle, and Microsoft announced plans to open labs near universities in Berkeley, California and Melbourne, Australia. This trend shows no signs of slowing down. Last month, Samsung announced labs in Cambridge, Moscow, and Toronto. This week, Nvidia announced plans to open a new lab in Toronto, while Google shared plans to open a lab in Accra, Ghana, Google's first in Africa and perhaps the first of any tech giant in Africa.
It all started at a small academic get-together in Whistler, British Columbia. The topic was speech recognition, and whether a new and unproven approach to machine intelligence--something called deep learning--could help computers more effectively identify the spoken word. Microsoft funded the mini-conference, held just before Christmas 2009, and two of its researchers invited the world's preeminent deep learning expert, the University of Toronto's Geoff Hinton, to give a speech. Hinton's idea was that machine learning models could work a lot like neurons in the human brain. He wanted to build "neural networks" that could gradually assemble an understanding of spoken words as more and more of them arrived.