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.
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.
Tim Cook on A.I.: "I Don't Think We Have to Throw Our Privacy Away" Now AI is Deliberately Trying to Scare Us, if We Aren't Already TIM COOK: Here's why assistants on phones are better than home speakers like the Echo Machine Learning Veterans Launch'Element AI' - A Montreal Based Artificial Intelligence Startup ... Stay up-to-date on the topics you care about. We'll send you an email alert whenever a news article matches your alert term. It's free, and you can add new alerts at any time. We won't share your personal information with anyone.
Department of Computer Science York University, Toronto, Canada j arek cs, yorku, ca * Semantic Query Caching Query caching can play a vital role in heterogeneous, multi-database environments. Answers to a query that are available in cache at the local client can be returned to the user quickly, while the rest of the query is evaluated. By caching certain sensitive data locally, caches can be used to answer the parts of queries that involve the sensitive data, so it need not be shipped across the network. Most prior cache schemes have been tuple-based or page-based. It is unclear, however, how these might be adapted for multi-databases.
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.