Vector / en Թϱ's self-driving vehicle superstar to lead Uber's new research lab in Toronto /news/u-t-s-self-driving-vehicle-superstar-lead-uber-s-new-research-lab-toronto <span class="field field--name-title field--type-string field--label-hidden">Թϱ's self-driving vehicle superstar to lead Uber's new research lab in Toronto</span> <div class="field field--name-field-featured-picture field--type-image field--label-hidden field__item"> <img loading="eager" srcset="/sites/default/files/styles/news_banner_370/public/2017-05-08--raquel-urtasun.jpg?h=afdc3185&amp;itok=4JawOkgH 370w, /sites/default/files/styles/news_banner_740/public/2017-05-08--raquel-urtasun.jpg?h=afdc3185&amp;itok=D6_SU4cL 740w, /sites/default/files/styles/news_banner_1110/public/2017-05-08--raquel-urtasun.jpg?h=afdc3185&amp;itok=BzetnEBF 1110w" sizes="(min-width:1200px) 1110px, (max-width: 1199px) 80vw, (max-width: 767px) 90vw, (max-width: 575px) 95vw" width="740" height="494" src="/sites/default/files/styles/news_banner_370/public/2017-05-08--raquel-urtasun.jpg?h=afdc3185&amp;itok=4JawOkgH" alt> </div> <span class="field field--name-uid field--type-entity-reference field--label-hidden"><span>ullahnor</span></span> <span class="field field--name-created field--type-created field--label-hidden"><time datetime="2017-05-08T09:51:54-04:00" title="Monday, May 8, 2017 - 09:51" class="datetime">Mon, 05/08/2017 - 09:51</time> </span> <div class="clearfix text-formatted field field--name-field-cutline-long field--type-text-long field--label-above"> <div class="field__label">Cutline</div> <div class="field__item">Թϱ Associate Professor Raquel Urtasun is joining Uber's new branch for Advanced Technologies Group (photo by Johnny Guatto) </div> </div> <div class="field field--name-field-author-reporters field--type-entity-reference field--label-hidden field__items"> <div class="field__item"><a href="/news/authors-reporters/jennifer-robinson" hreflang="en">Jennifer Robinson</a></div> </div> <div class="field field--name-field-author-legacy field--type-string field--label-above"> <div class="field__label">Author legacy</div> <div class="field__item">Jennifer Robinson</div> </div> <div class="field field--name-field-topic field--type-entity-reference field--label-above"> <div class="field__label">Topic</div> <div class="field__item"><a href="/news/topics/global-lens" hreflang="en">Global Lens</a></div> </div> <div class="field field--name-field-story-tags field--type-entity-reference field--label-hidden field__items"> <div class="field__item"><a href="/news/tags/raquel-urtasun" hreflang="en">Raquel Urtasun</a></div> <div class="field__item"><a href="/news/tags/vector" hreflang="en">Vector</a></div> <div class="field__item"><a href="/news/tags/computer-science" hreflang="en">Computer Science</a></div> <div class="field__item"><a href="/news/tags/self-driving-cars" hreflang="en">Self-Driving Cars</a></div> <div class="field__item"><a href="/news/tags/uber" hreflang="en">Uber</a></div> <div class="field__item"><a href="/news/tags/machine-learning" hreflang="en">machine learning</a></div> <div class="field__item"><a href="/news/tags/artificial-intelligence" hreflang="en">Artificial Intelligence</a></div> </div> <div class="clearfix text-formatted field field--name-body field--type-text-with-summary field--label-hidden field__item"><p>After years of conducting world-leading research on self-driving vehicles, Թϱ Associate Professor <strong>Raquel Urtasun</strong> is joining Uber –&nbsp;and bringing the American company’s first international research lab to Toronto.</p> <p>“I’m proud to welcome Raquel Urtasun to Uber,” said Uber CEO Travis Kalanick in <a href="https://newsroom.uber.com/uk-ua/atg-toronto/">a blog posting announcing the deal today</a>.</p> <p>“Raquel will remain in Toronto to lead a new branch of our Advanced Technologies Group –&nbsp;our first outside the U.S.,” he continued, adding the company hopes “to draw from the region’s impressive talent pool as we grow.”</p> <p>Kalanick added that Urtasun and her team “will further strengthen our self-driving engineering efforts in San Francisco and Pittsburgh. And, their work will complement the research underway at Uber AI Labs, led by <strong>Zoubin Ghahramani</strong> –&nbsp;a proud University of Toronto alum himself.”</p> <h3><a href="https://www.thestar.com/news/canada/2017/05/08/uber-opening-toronto-research-hub-for-driverless-car-technology.html">Read more at the <em>Toronto Star</em></a></h3> <h3><a href="https://www.bloomberg.com/news/articles/2017-05-08/uber-builds-ai-team-in-toronto-as-it-fights-self-driving-car-lawsuit-at-home">Read more at Bloomberg</a></h3> <p>Urtasun, who is part of the machine learning group in Թϱ’s computer science department, will continue to teach part time at the university.</p> <p>“The University of Toronto has long been considered a global leader in artificial intelligence research,” said Թϱ President <strong>Meric Gertler</strong>. “That’s why we’re so pleased to see Professor Raquel Urtasun, one of the world’s leading researchers in the field of machine perception, take on this incredibly exciting role.</p> <p>“We are equally pleased that she will remain a professor at the university, continuing to promote Toronto as the primary destination in the world for the best researchers in this fast-growing and critical field,” he said.</p> <p>Urtasun is also one of the co-founders of the newly formed Vector Institute, along with several Թϱ colleagues.</p> <h3><a href="/news/toronto-s-vector-institute-officially-launched">Read more about the Vector Institute</a></h3> <p>“With support from the Ontario and federal governments, Toronto has emerged as an important hub of artificial intelligence research, which is critical to the future of transportation,” Kalanick said in his blog posting. Uber is also a supporter of Vector with a “significant multi-year financial commitment.”</p> <p>Since the global demand for self-driving vehicle research took off two years ago, Urtasun has been globally in demand for her perception algorithms for self-driving cars.&nbsp;</p> <p>“Basically what I do is develop perception algorithms for self-driving cars,” she told <em>Թϱ</em> shortly before the Vector Institute was announced. “What this means is that you can think of the car seeing the scene via the sensors&nbsp;whether it’s a lidar [spinning laser on top of the vehicle] or cameras, and we basically develop the brain of the car where it has to transform what it sees into an explanation of what it is seeing.”</p> <p>Urtasun&nbsp;hopes&nbsp;her work is making these vehicles not only safer but cheaper to design and produce.</p> <p>Using the technology she and her students at Թϱ have developed will enable their self-driving vehicles to take a shortcut using cameras.</p> <p>With their system, a vehicle will “perceive what is in the scene just by seeing the scene,” she said.</p> <h3><a href="https://www.wired.com/2017/05/uber-hires-ai-superstar-quest-rehab-future/">Read more about Urtasun's new role at Uber</a></h3> </div> <div class="field field--name-field-news-home-page-banner field--type-boolean field--label-above"> <div class="field__label">News home page banner</div> <div class="field__item">Off</div> </div> Mon, 08 May 2017 13:51:54 +0000 ullahnor 107450 at Թϱ's Deep Genomics applies AI to accelerate drug development for genetic conditions /news/u-t-s-deep-genomics-applies-ai-accelerate-drug-development-genetic-conditions <span class="field field--name-title field--type-string field--label-hidden">Թϱ's Deep Genomics applies AI to accelerate drug development for genetic conditions</span> <div class="field field--name-field-featured-picture field--type-image field--label-hidden field__item"> <img loading="eager" srcset="/sites/default/files/styles/news_banner_370/public/2017-05-02-brendan-frey.jpg?h=afdc3185&amp;itok=wxZvw5DC 370w, /sites/default/files/styles/news_banner_740/public/2017-05-02-brendan-frey.jpg?h=afdc3185&amp;itok=uk1xPfaY 740w, /sites/default/files/styles/news_banner_1110/public/2017-05-02-brendan-frey.jpg?h=afdc3185&amp;itok=TjNY6ZnI 1110w" sizes="(min-width:1200px) 1110px, (max-width: 1199px) 80vw, (max-width: 767px) 90vw, (max-width: 575px) 95vw" width="740" height="494" src="/sites/default/files/styles/news_banner_370/public/2017-05-02-brendan-frey.jpg?h=afdc3185&amp;itok=wxZvw5DC" alt="Brendan Frey"> </div> <span class="field field--name-uid field--type-entity-reference field--label-hidden"><span>ullahnor</span></span> <span class="field field--name-created field--type-created field--label-hidden"><time datetime="2017-05-03T17:10:58-04:00" title="Wednesday, May 3, 2017 - 17:10" class="datetime">Wed, 05/03/2017 - 17:10</time> </span> <div class="clearfix text-formatted field field--name-field-cutline-long field--type-text-long field--label-above"> <div class="field__label">Cutline</div> <div class="field__item">Թϱ Engineering Professor Brendan Frey is the founder and CEO of Deep Genomics, a startup company applying deep-learning techniques to revolutionize genomic medicine (photo courtesy of Deep Genomics) </div> </div> <div class="field field--name-field-author-reporters field--type-entity-reference field--label-hidden field__items"> <div class="field__item"><a href="/news/authors-reporters/marit-mitchell" hreflang="en">Marit Mitchell</a></div> </div> <div class="field field--name-field-author-legacy field--type-string field--label-above"> <div class="field__label">Author legacy</div> <div class="field__item">Marit Mitchell</div> </div> <div class="field field--name-field-topic field--type-entity-reference field--label-above"> <div class="field__label">Topic</div> <div class="field__item"><a href="/news/topics/global-lens" hreflang="en">Global Lens</a></div> </div> <div class="field field--name-field-story-tags field--type-entity-reference field--label-hidden field__items"> <div class="field__item"><a href="/news/tags/artificial-intelligence" hreflang="en">Artificial Intelligence</a></div> <div class="field__item"><a href="/news/tags/vector" hreflang="en">Vector</a></div> <div class="field__item"><a href="/news/tags/brendan-frey" hreflang="en">Brendan Frey</a></div> <div class="field__item"><a href="/news/tags/entrepreneurship" hreflang="en">Entrepreneurship</a></div> <div class="field__item"><a href="/news/tags/faculty-applied-science-engineering" hreflang="en">Faculty of Applied Science &amp; Engineering</a></div> <div class="field__item"><a href="/news/tags/startup" hreflang="en">Startup</a></div> <div class="field__item"><a href="/news/tags/drugs" hreflang="en">Drugs</a></div> <div class="field__item"><a href="/news/tags/faculty-arts-science" hreflang="en">Faculty of Arts &amp; Science</a></div> <div class="field__item"><a href="/news/tags/donnelly" hreflang="en">Donnelly</a></div> <div class="field__item"><a href="/news/tags/faculty-medicine" hreflang="en">Faculty of Medicine</a></div> </div> <div class="field field--name-field-subheadline field--type-string-long field--label-above"> <div class="field__label">Subheadline</div> <div class="field__item">Թϱ spinoff company combines leading research in both machine learning and genomic science to accelerate development of highly tailored medical treatments</div> </div> <div class="clearfix text-formatted field field--name-body field--type-text-with-summary field--label-hidden field__item"><p>Genetic mutations are the cause of countless diseases and disorders, from cancer to autism to cystic fibrosis.</p> <p>Now, startup company <a href="https://www.deepgenomics.com/">Deep Genomics</a> is applying decades of research into machine learning and genomic science to develop genetic medicines –&nbsp;accelerating treatments that address the root causes of these conditions.</p> <p>“If you have smoke billowing out of the tailpipe of your car, you don’t just put a filter on the tailpipe –&nbsp;you have to look under the hood and address the original problem,” says <strong>Brendan Frey</strong>, the co-founder and CEO of Deep Genomics, and a Թϱ engineering professor with cross-appointments in the department of computer science and the Donnelly Centre for Cellular and Biomolecular Research. “That’s what we’re doing: applying our platform for the discovery-phase development of medicines that address genetic problems.”</p> <p>Developing new drugs is expensive, slow and inefficient –&nbsp;when researchers identify a protein involved in a disease, pharmaceutical companies often use a ‘guess-and-test’ approach to see whether any of the known drug molecules in their arsenal is&nbsp;a match to the protein’s unique shape. Often, thousands of molecules need to be screened in order to generate a match.</p> <p>Frey’s team at Deep Genomics is looking at the first biological step in the process: at the genes that contain the blueprints for proteins and instructions on how and when to produce them.</p> <p>“There are many ways a protein could be causing a problem, resulting from different changes to the genome. We can see those changes at the level of individual genes,” says Frey. “Instead of focusing on proteins, we’re focusing on the genetic mutations that are the source of the problem.”</p> <h3><a href="/news/tracking-proteins-using-ai-u-t-scientists-develop-deep-learning-algorithm">Read more about startup by Frey's student using AI to analyze protein data</a></h3> <p>Most new drugs fail in clinical trials, and <a href="https://www.scientificamerican.com/article/cost-to-develop-new-pharmaceutical-drug-now-exceeds-2-5b/">the cost of developing a new drug is over $2.5 billion</a>.</p> <p>Frey hopes that by harnessing the massive amount of genetic data that has become available since the human genome was sequenced in 2001, Deep Genomics can help pharmaceutical companies significantly cut down on the number of failures, and pinpoint the winners earlier. The company plans to collaborate with pharmaceutical companies to develop compounds.&nbsp;</p> <p><a href="/news/vector-institute-points-toronto-global-hot-spot-ai-research">Frey is also a co-founder of the recently formed&nbsp;</a><a href="/news/vector-institute-points-toronto-global-hot-spot-ai-research">Vector Institute</a>, an academia-industry-government centre that solidifies Toronto’s position as a global hub for artificial intelligence research and development. With over $200 million in funding, the institute builds on Թϱ’s long-standing strength in branches of AI such as deep learning, machine learning, neural networks, augmented reality, self-driving and autonomous vehicles and robotics.</p> <p>“I think in the next 10 to 20 years, almost all aspects of Canadian society will be impacted by artificial intelligence, from farming to medicine to education,” says Frey. “Artificial intelligence, and deep learning in particular, is the best way to interpret data and then make rational, good choices. As the amount of data grows in all areas of society, AI will play a crucial role in making that happen.”</p> <p>In medicine, Deep Genomics has identified the most promising ways to tackle rare Mendelian disorders, a class of genetic conditions caused by mutations in a single gene.&nbsp;Over 350 million people worldwide are affected by rare Mendelian disorders. Frey says the first three conditions they’ll explore will be disorders of the central nervous system, eye and liver.</p> <p>“So far, we’ve been focusing on our core technology: using machine learning to gain new insights into how mutations anywhere in the genome contribute to disease conditions,” says Frey. “Now it’s time to use that platform to help pharmaceutical companies develop genetic medicines for some of these conditions that affect millions of people.”</p> <h3><a href="http://entrepreneurs.utoronto.ca/">Learn more about entrepreneurship and startups at Թϱ</a></h3> </div> <div class="field field--name-field-news-home-page-banner field--type-boolean field--label-above"> <div class="field__label">News home page banner</div> <div class="field__item">Off</div> </div> Wed, 03 May 2017 21:10:58 +0000 ullahnor 107226 at Tracking proteins using AI: Թϱ scientists develop deep learning algorithm /news/tracking-proteins-using-ai-u-t-scientists-develop-deep-learning-algorithm <span class="field field--name-title field--type-string field--label-hidden">Tracking proteins using AI: Թϱ scientists develop deep learning algorithm</span> <div class="field field--name-field-featured-picture field--type-image field--label-hidden field__item"> <img loading="eager" srcset="/sites/default/files/styles/news_banner_370/public/2017-05-02-cells.jpg?h=afdc3185&amp;itok=f2mDbcWg 370w, /sites/default/files/styles/news_banner_740/public/2017-05-02-cells.jpg?h=afdc3185&amp;itok=gTrrYz0l 740w, /sites/default/files/styles/news_banner_1110/public/2017-05-02-cells.jpg?h=afdc3185&amp;itok=JO-rdYLD 1110w" sizes="(min-width:1200px) 1110px, (max-width: 1199px) 80vw, (max-width: 767px) 90vw, (max-width: 575px) 95vw" width="740" height="494" src="/sites/default/files/styles/news_banner_370/public/2017-05-02-cells.jpg?h=afdc3185&amp;itok=f2mDbcWg" alt="photo of protein"> </div> <span class="field field--name-uid field--type-entity-reference field--label-hidden"><span>ullahnor</span></span> <span class="field field--name-created field--type-created field--label-hidden"><time datetime="2017-05-03T11:56:25-04:00" title="Wednesday, May 3, 2017 - 11:56" class="datetime">Wed, 05/03/2017 - 11:56</time> </span> <div class="clearfix text-formatted field field--name-field-cutline-long field--type-text-long field--label-above"> <div class="field__label">Cutline</div> <div class="field__item">Yeast cells (purple) with DNA-containing nuclei (pink) and protein (green) residing in a cell's waste compartment </div> </div> <div class="field field--name-field-author-reporters field--type-entity-reference field--label-hidden field__items"> <div class="field__item"><a href="/news/authors-reporters/jovana-drinjakovic" hreflang="en">Jovana Drinjakovic</a></div> </div> <div class="field field--name-field-author-legacy field--type-string field--label-above"> <div class="field__label">Author legacy</div> <div class="field__item">Jovana Drinjakovic</div> </div> <div class="field field--name-field-topic field--type-entity-reference field--label-above"> <div class="field__label">Topic</div> <div class="field__item"><a href="/news/topics/global-lens" hreflang="en">Global Lens</a></div> </div> <div class="field field--name-field-story-tags field--type-entity-reference field--label-hidden field__items"> <div class="field__item"><a href="/news/tags/deep-learning" hreflang="en">Deep Learning</a></div> <div class="field__item"><a href="/news/tags/artificial-intelligence" hreflang="en">Artificial Intelligence</a></div> <div class="field__item"><a href="/news/tags/vector" hreflang="en">Vector</a></div> <div class="field__item"><a href="/news/tags/donnelly-centre" hreflang="en">Donnelly Centre</a></div> <div class="field__item"><a href="/news/tags/brendan-frey" hreflang="en">Brendan Frey</a></div> <div class="field__item"><a href="/news/tags/molecular-genetics" hreflang="en">Molecular Genetics</a></div> <div class="field__item"><a href="/news/tags/faculty-applied-science-engineering" hreflang="en">Faculty of Applied Science &amp; Engineering</a></div> <div class="field__item"><a href="/news/tags/faculty-medicine" hreflang="en">Faculty of Medicine</a></div> <div class="field__item"><a href="/news/tags/machine-learning" hreflang="en">machine learning</a></div> </div> <div class="clearfix text-formatted field field--name-body field--type-text-with-summary field--label-hidden field__item"><p>Թϱ researchers have developed a deep learning algorithm that can track proteins to help&nbsp;reveal what makes cells healthy and what goes wrong in disease.</p> <p>From self-driving cars to computers that can diagnose cancer, artificial intelligence (AI) is shaping the world in ways that are hard to predict, but for cell biologists, the change could not have come soon enough. With new and fully automated microscopes, scientists collect reams of data faster than they can analyze it. &nbsp;</p> <p>The protein-tracking algorithm, Dubbed DeepLoc,&nbsp;can recognize patterns in the cell made by proteins better and much faster than the human eye or previous computer vision-based approaches.</p> <p>“We can learn so much by looking at images of cells. How does the protein look under normal conditions? Do they look different in cells that carry genetic mutations or when we expose cells to drugs or other chemical reagents?” says <strong>Benjamin Grys</strong>, a graduate student in molecular genetics who recently co-authored a&nbsp;paper on the research.&nbsp;“People have tried to manually assess what’s going on with their data, but that takes a lot of time.”</p> <p><a href="http://msb.embopress.org/content/13/4/924">In the cover story of the latest issue of <em>Molecular Systems Biology</em></a>, teams led by the Donnelly Centre's <strong>Brenda Andrews</strong> and <strong>Charles Boone</strong>, both professors of molecular genetics, also describe DeepLoc’s ability to process images from other labs, illustrating its potential for wider use.</p> <p>“Right now, it only takes days to weeks to acquire images of cells and months to years to analyze them. Deep learning will ultimately bring the timescale of this analysis down to the same timescale as the experiments,” says <strong>Oren Kraus</strong>, a lead co-author on the paper and a graduate student co-supervised by Andrews and the Donnelly Centre's <strong>Brendan Frey</strong>, professor of electrical and computer engineering.&nbsp;</p> <h3><a href="/news/u-t-s-deep-genomics-applies-ai-develop-drugs-genetic-conditions">Learn more about Frey's startup&nbsp;which uses&nbsp;AI to develop drugs for genetic conditions</a></h3> <p>Kraus is now working with&nbsp;<strong>Jimmy Ba</strong>, a graduate student of AI pioneer <strong>Geoffrey Hinton</strong>, a Թϱ&nbsp;<a href="http://www.provost.utoronto.ca/awards/uprofessors.htm">University Professor&nbsp;Emeritus</a>&nbsp;in computer science who is the chief scientific adviser of <a href="/news/vector-institute-points-toronto-global-hot-spot-ai-research">the newly established Vector Institute</a>, to&nbsp;commercialize the method through a new startup. The goal of the startup named, Phenomic AI, is to analyse cell image-based data for pharmaceutical companies.</p> <p>“In an image based drug screen, you can actually figure out how the drugs are affecting different cells based on how they look rather than some simplified parameters such as live/dead or cell size,” says Kraus. “This way you can extract a lot more information about cell state form these screens. We hope to make the early drug discovery process all the more accurate by finding more subtle effects of chemical compounds.”</p> <p>Similar to other types of AI, in which computers learn to recognize patterns in data, DeepLoc was trained to recognize diverse shapes made by glowing proteins – labelled a fluorescent tag that makes them visible – in cells. But unlike computer vision that requires detailed instructions, DeepLoc learns directly from image pixel data, making it more accurate and faster.</p> <p>Grys and Kraus trained DeepLoc on <a href="http://www.thedonnellycentre.utoronto.ca/news/new-map-uncovers-traffic-life-cell-0">previously published data</a> that shows an area in the cell occupied by more than 4,000 yeast proteins&nbsp;– three-quarters of all proteins in yeast. This dataset remains the most complete map showing exact position for a vast majority of proteins in any cell. When it was first released in 2015, the analysis was done with a complex computer vision and machine learning pipeline that took months to complete. DeepLoc crunched the data in a matter of hours.</p> <p>DeepLoc was able to spot subtle differences between similar images. The initial analysis identified 15 different classes of proteins, each representing distinct neighbourhoods in the cell. DeepLoc identified 22 classes. It was also able to sort cells whose shape changed due to a hormone treatment, a task that the previous pipeline couldn’t complete.</p> <p>Grys and Kraus were able to quickly retrain DeepLoc with images that differed from the original training set, showing that it can be used to process data from other labs. They hope that others in the field –&nbsp;where looking at images by eye is still the norm –&nbsp;will adopt their method.</p> <p>“Someone with some coding experience could implement our method,” says Grys. “All they would have to do is feed in the image-training set that we’ve provided and supplement this with their own data. It takes only an hour or less to retrain DeepLoc and then begin your analysis.”</p> <h3><a href="http://entrepreneurs.utoronto.ca/">Learn more about entrepreneurship and startups at Թϱ</a></h3> </div> <div class="field field--name-field-news-home-page-banner field--type-boolean field--label-above"> <div class="field__label">News home page banner</div> <div class="field__item">Off</div> </div> Wed, 03 May 2017 15:56:25 +0000 ullahnor 107223 at Toronto's Vector Institute officially launched /news/toronto-s-vector-institute-officially-launched <span class="field field--name-title field--type-string field--label-hidden">Toronto's Vector Institute officially launched </span> <div class="field field--name-field-featured-picture field--type-image field--label-hidden field__item"> <img loading="eager" srcset="/sites/default/files/styles/news_banner_370/public/2017-03-30-vector-event.jpg?h=afdc3185&amp;itok=mxMhz_No 370w, /sites/default/files/styles/news_banner_740/public/2017-03-30-vector-event.jpg?h=afdc3185&amp;itok=iTa9xUjX 740w, /sites/default/files/styles/news_banner_1110/public/2017-03-30-vector-event.jpg?h=afdc3185&amp;itok=GptusP6Q 1110w" sizes="(min-width:1200px) 1110px, (max-width: 1199px) 80vw, (max-width: 767px) 90vw, (max-width: 575px) 95vw" width="740" height="494" src="/sites/default/files/styles/news_banner_370/public/2017-03-30-vector-event.jpg?h=afdc3185&amp;itok=mxMhz_No" alt="photo of researchers and president"> </div> <span class="field field--name-uid field--type-entity-reference field--label-hidden"><span>ullahnor</span></span> <span class="field field--name-created field--type-created field--label-hidden"><time datetime="2017-03-30T18:37:17-04:00" title="Thursday, March 30, 2017 - 18:37" class="datetime">Thu, 03/30/2017 - 18:37</time> </span> <div class="clearfix text-formatted field field--name-field-cutline-long field--type-text-long field--label-above"> <div class="field__label">Cutline</div> <div class="field__item">Թϱ Associate Professor Raquel Urtasun (second from right) with (from left) Minister of Finance Bill Morneau, Ontario Minister of Research, Innovation and Science Reza Moridi, Premier Kathleen Wynne, Mayor John Tory and Թϱ President Meric Gertler </div> </div> <div class="field field--name-field-author-reporters field--type-entity-reference field--label-hidden field__items"> <div class="field__item"><a href="/news/authors-reporters/jennifer-robinson" hreflang="en">Jennifer Robinson</a></div> </div> <div class="field field--name-field-author-legacy field--type-string field--label-above"> <div class="field__label">Author legacy</div> <div class="field__item">Jennifer Robinson</div> </div> <div class="field field--name-field-topic field--type-entity-reference field--label-above"> <div class="field__label">Topic</div> <div class="field__item"><a href="/news/topics/breaking-research" hreflang="en">Breaking Research</a></div> </div> <div class="field field--name-field-story-tags field--type-entity-reference field--label-hidden field__items"> <div class="field__item"><a href="/news/tags/vector" hreflang="en">Vector</a></div> <div class="field__item"><a href="/news/tags/artificial-intelligence" hreflang="en">Artificial Intelligence</a></div> <div class="field__item"><a href="/news/tags/research" hreflang="en">Research</a></div> <div class="field__item"><a href="/news/tags/innovation" hreflang="en">Innovation</a></div> <div class="field__item"><a href="/news/tags/entrepreneurship" hreflang="en">Entrepreneurship</a></div> <div class="field__item"><a href="/news/tags/computer-science" hreflang="en">Computer Science</a></div> <div class="field__item"><a href="/news/tags/self-driving-cars" hreflang="en">Self-Driving Cars</a></div> <div class="field__item"><a href="/news/tags/geoffrey-hinton" hreflang="en">Geoffrey Hinton</a></div> <div class="field__item"><a href="/news/tags/raquel-urtasun" hreflang="en">Raquel Urtasun</a></div> </div> <div class="field field--name-field-subheadline field--type-string-long field--label-above"> <div class="field__label">Subheadline</div> <div class="field__item">Aims to produce the world’s largest number of deep learning graduates</div> </div> <div class="clearfix text-formatted field field--name-body field--type-text-with-summary field--label-hidden field__item"><p>With the creation of the Toronto-based <a href="http://vectorinstitute.ai/">Vector Institute</a>, Ontario and Canada are choosing to lead in the booming field of artificial intelligence, said Ontario Premier <strong>Kathleen Wynne</strong> today.</p> <p>“We can own this space. This is who we are,” she told a crowded room filled with University of Toronto researchers, officials from all three levels of government and top Canadian companies who are supporting Vector.</p> <p>The independent institute aims to produce more deep learning grads than any other institution in the world –&nbsp;as part of an effort to produce, attract and retain top talent. It will also build on the existing expertise of the globally renowned deep learning team at U&nbsp;of T.</p> <p><iframe allowfullscreen frameborder="0" height="500" src="https://www.youtube.com/embed/-2QiezKfTH0" width="750"></iframe></p> <p>The applications and implications of this ground-breaking research could be seen in demonstrations on self-driving vehicle technology by <strong>Raquel Urtasun</strong>, a Թϱ computer science associate professor, as well as Թϱ startup Deep Genomics.</p> <p>Founded by Professor <strong>Brendan Frey</strong> of the Edward S. Rogers Sr. Department of Electrical &amp; Computer Engineering, Deep Genomics uses deep learning to predict the molecular effects of genetic variation.</p> <h3><a href="/news/vector-institute-points-toronto-global-hot-spot-ai-research">Read about the Vector Institute</a></h3> <p><img alt class="media-image attr__typeof__foaf:Image img__fid__4069 img__view_mode__media_original attr__format__media_original" src="/sites/default/files/2017-03-30-vector-event-fidler.jpg" style="width: 750px; height: 500px; margin: 10px;" typeof="foaf:Image"><br> <em><strong>Sanja Fidler</strong> (right), an assistant professor of computer science at Թϱ, focuses on object recognition, 3D scene understanding, and combining vision and language. She explains her research at Thursday's event to (from left to right) Minister of Finance Bill Morneau, Premier Kathleen Wynne and Mayor John Tory (photo by Lisa Lightbourn)&nbsp;</em></p> <p>In addition to $50 million in provincial support, Vector will also receive $40 million to $50 million as part of the Government of Canada’s Pan-Canadian Artificial Intelligence Strategy, federal Finance Minister Bill Morneau said. The $125-million strategy is also supporting similar institutes in Montreal and Edmonton.</p> <p>“We are home to some of the top talent when it comes to artificial intelligence,” Canadian Prime Minister Justin Trudeau said at an event earlier in the day in Brampton. “We can't afford to lose that competitive advantage, and all the good jobs that come along with it.”</p> <p>More than 30 companies have also committed a combined total of more than $80 million over 10 years to support the Vector Institute, reflecting the transformational potential of deep learning and machine learning in fields as diverse as health care, finance, insurance, education, retail, advanced manufacturing, construction and transportation.</p> <p>“What an amazing moment,” said Թϱ President <strong>Meric Gertler</strong>. “The governments of Ontario and Canada and our industry partners deserve huge credit for the visionary leadership represented in today’s announcement. On behalf of the University of Toronto, and our sister universities: Thank you for enabling us to seize this incredibly exciting opportunity.”</p> <p><img alt class="media-image attr__typeof__foaf:Image img__fid__4068 img__view_mode__media_original attr__format__media_original" src="/sites/default/files/2017-03-30-vector-event-gertler-embed.jpg" style="width: 750px; height: 500px; margin: 10px;" typeof="foaf:Image"><br> <em>Թϱ President Meric Gertler talked about how the university has long been considered a global leader in artificial intelligence research, and that expertise can now act as an anchor to bring together researchers, government and private sector actors through the Vector Institute (photo by Lisa Lightbourn)</em></p> <p>Using his time at the podium, <strong>Geoffrey Hinton</strong>, a Թϱ <a href="http://www.provost.utoronto.ca/awards/uprofessors.htm">University Professor</a> Emeritus in computer science and Vector’s new chief scientific adviser, deviated from the usual prepared remarks to deliver a pithy TED Talk-like PowerPoint on how neural networks and deep learning work.</p> <p>Toronto Mayor <strong>John Tory</strong> said the city’s diversity and livability will help attract the talent Vector and the AI industry in Canada needs to grow and prosper. The Vector Institute is “one more thing putting us on the map,” he said. “I want us to be leaders. I want us to compete. I want us to be the home of disruptors!”</p> <p><img alt class="media-image attr__typeof__foaf:Image img__fid__4073 img__view_mode__media_original attr__format__media_original" src="/sites/default/files/2017-03-30-vector-event-hinton_0.jpg" style="width: 750px; height: 500px; margin: 10px;" typeof="foaf:Image"><br> <em>Թϱ University Professor Emeritus Geoffrey Hinton, who&nbsp;is vice-president&nbsp;engineering fellow at Google and will serve as the chief scientific adviser of the newly created&nbsp;Vector Institute, gave the audience a&nbsp;mini-lecture about artificial intelligence (photo by Lisa Lightbourn)&nbsp;</em></p> </div> <div class="field field--name-field-news-home-page-banner field--type-boolean field--label-above"> <div class="field__label">News home page banner</div> <div class="field__item">Off</div> </div> Thu, 30 Mar 2017 22:37:17 +0000 ullahnor 106298 at