Raquel Urtasun

Entrepreneurship Week

U of T Entrepreneurship

Centre for Engineering Innovation & Entrepreneurship

Desjardins Speaker Series: Driving Innovation with Raquel Urtasun, Founder of Waabi from U of T Entrepreneurship on Vimeo.

Startups

Raquel Urtasun may be a world-leading expert in machine learning and computer vision – but that doesn’t mean she likes to talk about her abilities.

In fact, the CEO of self-driving vehicle company Waabi says she had to overcome her humble nature when trying to convince big-name investors she had the winning formula to transform the self-driving industry.

“You need to say [to venture capitalists] – why you, why this team and why this technology in a way that’s very convincing,” says Urtasun.

The approach worked. Urtasun, a professor in the department of computer science in the University of Toronto’s Faculty of Arts & Science, secured $100 million in initial capital – one of the largest rounds of seed funding in Canadian history. She’s since worked on developing the next-generation of AI-powered technology for self-driving trucks.

She recently spoke about her entrepreneurial journey during a keynote speech at True Blue Impact Day, which marked the culmination of the seventh annual U of T Entrepreneurship Week.

Here are three takeaways from her talk:

Be bold and aim high

As someone who prefers to show others she can do something, Urtasun says she knew that asking for $100 million from a slew of big-name investors would not come naturally.

But she was confident in her detailed plan.

“In front of the venture capitalists, you cannot be humble,” she says. “They will never fund you.”

Urtasun says she made sure to draw a roadmap of how she intended to use the money.

“I didn’t want to worry about fundraising in six or nine months,” she adds. “I wanted the money so we could execute and build this transformational technology.”

Be strategic about your team

Urtasun is the sole founder of Waabi, but she says her team and the investors she’s worked with have been incredibly important in building the company. She adds that she knew from the beginning she wanted to work with investors from the technology and AI sector – including fellow U of T AI luminaries Geoffrey Hinton and Sanja Fidler – because they would better understand Waabi’s mission.

“I think it’s important to have people you can rely on and that have each other’s back,” she says.

The investments from Uber, Khosla Ventures, BDC Capital and others have allowed the company to develop Waabi World, an advanced simulator to test its autonomous vehicles, and Waabi Driver, its first generation of self-driving trucks.

Ignore the competition

“My philosophy is to write a strategy and focus on that,” Urtasun says, adding that it’s important to tune out naysayers.

Case in point: When she first founded the company two years ago, she says she heard people say that she was getting into the self-driving sector “too late.”

“What I heard a lot was ‘everybody’s in this path, what are you doing?’ What I hear today is ‘how did you perfectly time this?’” she says. “And nothing changed, right? I just focused on doing my thing.”

AI startup Cohere raises US$40-million for natural language software: Globe and Mail

rahul.kalvapalle — Wed, 08 Sep 2021 18:22:17 +0000

AI startup Cohere raises US$40-million for natural language software: Globe and Mail

rahul.kalvapalle Wed, 09/08/2021 - 14:22

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Aidan Gomez, pictured here in 2017, introduced the concept that underpins the Cohere's natural language processing technology in a research paper (photo by Nina Haikara)

Topic

Alumni

Entrepreneurship

Geoffrey Hinton

Startups

Cohere Inc., an AI startup co-founded by University of Toronto alumni and backed by some of the university’s most recognized AI luminaries, has raised US$40 million as it readies the launch of technology that could revolutionize how humans converse with machines, the Globe and Mail reported.

Cohere’s software is based on a technology called “transformers” – AI tools that that boost the performance of natural language processing (NLP) programs, the newspaper said. The result is a product that can improve the quality of conversations between humans and machines, as in the case of customer service chatbots.

CEO Aidan Gomez, who previously interned with U of T Emeritus Distinguished Professor Geoffrey Hinton at his Google lab, introduced the concept of transformers in a 2017 paper. He said the company, founded in 2019, is preparing to make its product more widely available in the coming months. “We really want to make it useful to everyone,” Gomez told the Globe.

The company’s two other co-founders are Nick Frosst, who also worked with Hinton at Google, and Ivan Zhang, a former U of T computer science student.

The Series A financing was provided by venture capital firms Index Ventures, Section 32 and Radical Ventures, as well as leading AI experts that included Hinton and Raquel Urtasun, a professor in U of T’s department of computer science in the Faculty of Arts & Science and founder of autonomous driving startup Waabi.

Read the story in the Globe and Mail

The road ahead: Raquel Urtasun's startup to ‘unleash full power of AI’ on self-driving cars

rahul.kalvapalle — Thu, 10 Jun 2021 19:31:01 +0000

The road ahead: Raquel Urtasun's startup to ‘unleash full power of AI’ on self-driving cars

rahul.kalvapalle Thu, 06/10/2021 - 15:31

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Raquel Urtasun, a U of T professor of computer science and world-leading expert in machine learning and computer vision, has launched her own Toronto-based, self-driving vehicle company with more than $100 million in funding (photo by Natalia Dolan)

Rahul Kalvapalle

Topic

In The Spotlight

Vector Institute

More than $100-million in funding. Two decades of artificial intelligence expertise. Ten years of experience in self-driving technology. A 40-strong team of scientists and engineers.

The list of resources at Raquel Urtasun’s fingertips as she takes the wheel of Waabi, an autonomous vehicle startup, is impressive to say the least. The goal? Use AI to finally resolve the technical and financial challenges that have hindered the full commercialization of self-driving technology.

It’s the first foray into entrepreneurship for Urtasun, a professor of computer science at the University of Toronto and one of the world’s leading experts in machine learning and computer vision. She says she was inspired to start her own company after four years as chief scientist and head of Uber ATG’s self-driving car lab in Toronto, where she realized need for a new generation of self-driving technologies that leverage AI’s full potential.

“The thought of what would be the best way to do this grew and grew in my head until it became clear that, if you really want to change technology, the best way to do it is to start a new company,” Urtasun says.

Urtasun’s new venture emerged from stealth mode earlier this week to announce one of the largest rounds of initial financing ever secured by a Canadian startup, raising more than $100 million from investors including Silicon Valley-based Khosla Ventures and Uber. Other investors include fellow U of T AI luminaries Geoffrey Hinton, a University Professor Emeritus, and Sanja Fidler, an associate professor of computer science, as well as Stanford University’s Fei-Fei Li and Pieter Abbeel of the University of California, Berkeley.

Urtasun says the self-driving industry’s current players aren’t taking full advantage of the power of AI.

“There is a little bit of AI there, but it doesn’t have a prominent role. Instead, it’s solving very specific sub-problems within the massive software stack – or brain of the self-driving car,” she says. “This causes difficulty in that it requires really complex, time-consuming manual tuning.

“As a consequence of this, scaling the technology is costly and technically very challenging.”

Waabi addresses this by utilizing “deep learning, probabilistic inference and complex optimization” to create a new class of algorithms, the likes of which Urtasun says have never been seen before in industry or academia.

Key to Waabi’s approach is its novel autonomous system – essentially, the software brain of the self-driving vehicle – that is “end-to-end trainable,” meaning the entire software stack can automatically learn from data, removing the need for constant manual tuning and tweaking.

The system is also “interpretable and explainable,” meaning it’s possible to deduce why it opts for certain manoeuvers over others – crucial for safety verification.

It’s also capable of complex reasoning, which Urtasun says is vital for eventual real-world applications.

“If you think about when you’re driving and arrive at an intersection, there’s a lot of things happening in your brain – you do very complex inference about what everybody’s doing at the intersection, how it will affect you, etc.” Urtasun says. “That’s what our new generation of algorithms provides – this ability to do really complex reasoning within the AI system.”

Waabi also has a revolutionary simulator system that can test the algorithms and software with “an unprecedented level of fidelity,” Urtasun says.

“When people in the industry say they test millions of miles of simulation, they’re really only testing the motion-planning component – which is one piece among this big software stack,” she says.

“Waabi has the ability to simulate how the world looks at scale, how sensors observe the scene and the behaviours of humans in a way that’s very realistic and in real time.”

That means significantly fewer hours of on-road drive testing.

“Typically, companies have hundreds of vehicles that they’re driving so that they can observe how the system works. And every time you change something, you change the behaviour, so you have to drive again and again and again,” Urtasun says. “[Waabi] can develop, test in simulation and reduce the need for driving in the real-world.”

It also means a system that’s safer because it can be trained to manage not only typical driving scenarios, but also ‘edge cases’ – situations that arise at extreme operating parameters.

“We can train the system to handle those edge cases in simulation,” Urtasun says. “So, you end up with a system that is much safer, that you can develop faster and that requires less capital to develop because you need very few people compared to the traditional approach – and less testing in the real world.

“[You] really unleash the power of AI.”

The company’s name reflects its approach. “Waabi” means “she has vision” in Ojibwe (“a new vision to help solve self-driving,” Urtasun says) and means “simple” in Japanese – an ode to the simplicity of the software stack.

“[It’s] a perfect definition of our technology and a perfect name for our company,” Urtasun says. “Plus, it sounds cool.”

The potential applications for Waabi’s technology are wide-ranging, Urtasun says, but the initial focus will be the long-haul trucking sector – a departure from her time at Uber, where she worked on passenger vehicles. She notes that truck-driving is recognized as one of the most dangerous occupations, and that the industry suffers from a shortage of drivers. “Automation can serve those industry needs,” she says.

Urtasun adds that long-haul trucking is also a prudent area to focus on because there’s less complexity involved with highway driving than is the case in cities.

“Highways are still very difficult – don’t get me wrong – but they’re less complex compared to a city like Toronto, with all the things that might happen and how people follow the rules – well, very few people follow the rules. So, you need to handle all that complexity.”

Toronto’s notoriously bad traffic aside, Urtasun says there’s nowhere else she’d rather set up an AI company.

“When people ask me, ‘Why here?’ I say, ‘Why not?’ I love Toronto, I love Canada. It’s an amazing place to do innovation – there’s incredible talent and support from the government,” she says, pointing to Toronto’s emergence as a world-leading AI hub thanks to initiatives such as the Vector Institute for Artificial Intelligence, which she co-founded.

“It’s been incredible to see the transformation that the city has gone through,” she says. “It was the case that people were leaving and going to California. Now, not only are we retaining talent but so much incredible talent is coming in – even from Silicon Valley,”

There’s also plenty of talent to be tapped at U of T, Urtasun adds.

“We have amazing U of T students who are doing great work within the company,” she says. “I really look forward to partnering closely with U of T to provide opportunities to the incredible talent that the university has. For me, it’s always been very important to [help develop] students – so that continues to be the case.”

As the CEO of an AI-powered autonomous vehicle startup, Urtasun says it’s important for her to set an example for women and girls interested in pursuing careers in technology.

“I think it’s very important that young girls, in particular, realize that this is not a man’s world. Technology is going to change the world and they definitely have a say,” she says.

She adds Waabi and other technology companies benefit immensely from diverse leadership and perspectives – and so do their customers.

“It’s important that in order to solve complex problems, we have diversity of opinions, approaches and backgrounds,” she says. “Waabi excels at all three types of diversity, which I think is the way to build incredible technology as well as showcase the diversity of the users who are going to use the technology at the end of the day.”

U of T’s Raquel Urtasun raises $100 million for self-driving startup Waabi: reports

rahul.kalvapalle — Tue, 08 Jun 2021 23:08:48 +0000

U of T’s Raquel Urtasun raises $100 million for self-driving startup Waabi: reports

rahul.kalvapalle Tue, 06/08/2021 - 19:08

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(Steve Russell/Toronto Star via Getty Images)

Topic

Global Lens

The University of Toronto’s Raquel Urtasun, a leading global expert in machine learning and computer vision, has raised more than CAN$100 million (US$83.5 million) for her new self-driving technology startup Waabi, according to Canadian and U.S. media reports.

It’s one of the largest rounds of initial financing ever raised by a Canadian tech startup, according to Forbes magazine, with investors that include Silicon Valley-based Khosla Ventures and Uber, where Urtasun previously served as chief scientist of the company’s self-driving division, Uber ATG, in Toronto. Waabi’s backers also include U of T AI luminaries Geoffrey Hinton, University Professor Emeritus, and Associate Professor Sanja Fidler.

Waabi utilizes a “new generation of AI algorithms” that will make it possible to “generalize and learn from a small amount of data,” Urtasun, a professor in the department of computer science in the Faculty of Arts & Science and co-founder of the Vector Institute for Artificial Intelligence, told Forbes. She said the company’s novel simulation system is “much less capital-intensive” and requires less on-road driving to develop and refine. The technology will first be applied to automation of long-haul trucks.

The Globe & Mail reports that Urtasun, who moved to Toronto from the U.S. in 2014 to join U of T, is keen to build Waabi as a Canadian company.

“I want to make Toronto and Canada a leader in self-driving technology,” Urtasun told the Globe.

Read the story in Forbes

Read the story in TechCrunch

Read the story in the Globe and Mail

U of T's Raquel Urtasun hails Toronto's AI scene in Global News profile

rahul.kalvapalle — Tue, 01 Oct 2019 15:37:50 +0000

U of T's Raquel Urtasun hails Toronto's AI scene in Global News profile

rahul.kalvapalle Tue, 10/01/2019 - 11:37

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Raquel Urtasun (photo by Johnny Guatto)

Vector Institute

She was drawn to Toronto by the University of Toronto’s reputation for artificial intelligence (AI) research, and Raquel Urtasun believes the best is yet to come.

Urtasun, an associate professor in the department of computer science, envisions the city becoming a world leader in AI thanks in large part to the research and talent at U of T.

That was one of many insights shared by Urtasun, who is also chief scientist of Uber’s Advanced Technologies Group and head of its self-driving research lab in Toronto, in an interview with Global News for the broadcaster’s #TechinTO series. She is a co-founder of the Vector Institute for Artificial Intelligence.

Urtasun said self-driving technology has the potential to improve road safety and tackle congestion. She added that she hopes to make Toronto one of the first cities to see self-driving cars on its roads.

Originally from Spain, Urtasun identified Toronto’s diversity and the impactful research at institutions like U of T as key factors in attracting and retaining the world’s top technology talents – including herself.

“We see a lot of foreign talents that do not necessarily have any ties to Canada that want to come here because this is the place to be," she said.

Watch Raquel Urtasun on Global News

Uber seeks top Toronto talent with $200-million investment

Christopher.Sorensen — Thu, 13 Sep 2018 19:20:39 +0000

Uber seeks top Toronto talent with $200-million investment

Christopher.Sorensen Thu, 09/13/2018 - 15:20

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Uber CEO Dara Khosrowshahi listens while Raquel Urtasun, an associate professor at U of T and head of Uber's self-driving car lab in Toronto, answers a reporter's question during a press event at the MaRS Discovery District (photo by Chris Sorensen)

Chris Sorensen

Topic

Global Lens

Technology

Uber wants to take you wherever you need to go – and it’s betting Toronto’s best minds can help.

The ride-sharing giant said today it will invest more than $200 million in Toronto over the next five years to expand an existing research lab focused on self-driving cars – led by University of Toronto artificial intelligence whiz Raquel Urtasun – and set up its first-ever engineering facility in Canada.

Dara Khosrowshahi, Uber’s CEO, said the investment will boost Uber’s Toronto workforce to 500 employees over the next few years, up from about 200 today. The additional employees will include a mix of new hires, both local and from overseas, and the relocation of existing Uber engineers, he said.

“We think it’s going to be one of the biggest and most productive engineering hubs that we have in the world,” Khosrowshahi told a packed auditorium this morning at the MaRS Discovery District.

“We recognize Canada’s commitment to having the best talent and diversity and we’re excited to be a growing part of this community.”

Drawn by the region’s expertise in fast-growing fields like machine learning, Uber’s Toronto expansion comes on the heels of similar investments in Canada’s largest city by other giants of the tech industry. Just this week, computer chip-maker Intel said it planned to set up an engineering lab north of the city that focuses on graphics processing units, or GPUs (increasingly used in machine learning applications), while Microsoft said it planned to open a new office in downtown Toronto and hire 500 employees by 2022, as well as an equivalent number of co-op students and interns.

Other big multinationals who have recently opened or expanded their operations in Toronto – all with a connection to U of T or its researchers – include chip-maker NVIDIA and consumer electronics giants LG and Samsung.

As for Uber, Khosrowshahi said the company’s Toronto research lab, launched by the firm’s Advanced Technologies Group (ATG) last year, has already shown “incredible progress” with regards to incorporating AI and advanced computer vision technologies into Uber’s fleet of self-driving cars.

“The work of the advanced technologies group is incredibly important to Uber and our vision to make self-driving cars on the Uber network a reality,” Khosrowshahi said. “The central facet to making these self-driving cars reliable and safe is the AI software that they run – and that’s the AI software that Raquel and her team are developing.”

Dara Khosrowshahi, Uber’s CEO, said its Toronto research lab for self-driving cars, launched last year, has already shown “incredible progress” (photo by Chris Sorensen)

An expert in machine learning and computer vision, Urtasun splits her time between Uber’s Toronto lab and U of T, where she is an associate professor in the computer science department. She told attendees at the MaRS event that working with Uber “has taken my research to the next level.”

While Uber is mostly known as a ride-hailing service, the San Francisco-based company increasingly operates a broad range of mobility services, ranging from food delivery service Uber Eats to its carpooling service UberPool. This week Uber revealed a new twist to the latter offering in Toronto: Uber Express Pool, which would see users walk a short distance to catch their shared rides – a tweak the company says will allow it to devise more efficient routes and reduce travel times.

Khosrowshahi said Uber’s Toronto expansion was driven both by a desire to acquire the best talent and the need to be “locally relevant” in the cities where it operates.

“One of our philosophies we have as a company is: For us to build globally, we have to live globally,” he said. “We have to understand cities other than San Francisco, where our headquarters are located, more than most companies out there.”

Yung Wu, the CEO of MaRS, said Uber represents what has, until recently, been a missing piece of the puzzle for Toronto’s burgeoning tech industry: big, global firms whose commitment to the city gives local talent a reason to stay in Canada when looking for work.

“I’m personally excited and encouraged by Uber’s commitment to be here as an active, contributing member of our community,” Wu said.

“Today’s announcement is yet another sign of how our tech sector can collaborate with the best in order to compete and change the world together.”

U of T computer engineer leads network to optimize hardware for AI

noreen.rasbach — Fri, 20 Jul 2018 19:13:05 +0000

U of T computer engineer leads network to optimize hardware for AI

noreen.rasbach Fri, 07/20/2018 - 15:13

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Andreas Moshovos leads the NSERC Strategic Partnership Network in Computer Hardware for Emerging Sensory Applications (COHESA), which is dedicated to making high-performance processors for machine learning and artificial intelligence

Tyler Irving

Topic

Faculty of Applied Science & Engineering

Geoffrey Hinton

Vector Institute

Subheadline

As machine learning algorithms – such as those that enable Siri and Alexa to recognize voice commands – grow more sophisticated, so must the hardware required to run them. Andreas Moshovos, a professor of computer engineering in the Faculty of Applied Science & Engineering, is leading a national research network that aims to create the next generation of computing engines optimized for artificial intelligence.

The NSERC Strategic Partnership Network in Computer Hardware for Emerging Sensory Applications (COHESA) brings together researchers from academia and industry to develop hardware that can deliver faster speeds and better performance for machine learning applications, from image recognition to autonomous vehicles.

Since the 1970s, the number of components that can be crammed into integrated circuits has doubled roughly every two years, a phenomenon known as Moore’s Law. The associated increases in performance have helped drive the current explosion in artificial intelligence. But these components can be arranged in many ways, and computer chip architecture can have a big impact on processing speed.

“The processors that are in your laptop or smartphone are general-purpose devices,” says Moshovos. “They are designed to execute many different kinds of algorithms. They do this reasonably well, but they are not going to be the fastest or the best at any one of them.”

By contrast, some processors are optimized for particular tasks. Moshovos points to the graphics accelerator chips found in most computers and smartphones. These speed up the repetitive calculations involved in generating graphics by completing thousands of them in parallel. The result is smoother, more fluid videos and faster gaming.

“You can’t optimize your hardware for every application, because it’s going to be too expensive,” says Moshovos. “On the other hand, where it is applicable, it’s been demonstrated that specialization can offer speeds from 10 to 1,000 times faster than general-purpose processors.”

This increased speed could be especially advantageous for machine learning. Imagine a self-driving car with a chip optimized to process visual information from road signs – in this application, even a few extra milliseconds could result in better decisions and safer operation.

By analyzing the types of calculations carried out by machine learning algorithms, Moshovos and his collaborators are looking for ways to simplify the number of mathematical operations they require.

“For many of the multiplications done in these algorithms, one of the numbers is either zero, or low enough that for all practical purposes it can be treated as zero,” says Moshovos. “When you multiply by zero, you always get zero, so these operations do nothing useful. This behaviour is more pronounced if we look inside the numbers at the bit level where most of them are zero. We’re trying to see how we can take some of them out and do something else useful in their place.”

COHESA builds on a strong track record of machine learning and artificial intelligence expertise at the University of Toronto. Last year the university launched the Vector Institute, which includes researchers such as University Professor Emeritus Geoffrey Hinton, known for pioneering the artificial intelligence technique called deep learning. Professor Brendan Frey of the Faculty of Applied Science & Engineering, who studied with Hinton, is leading research spinoff Deep Genomics, a company that aims to revolutionize medicine by combining expertise in machine learning and genomic science.

The NSERC COHESA network brings together researchers from a number of Canadian universities, including Yoshua Bengio at Université de Montréal and Raquel Urtasun at U of T. It also includes several industrial partners, from chip manufacturers AMD and Intel to large technology firms such as AMD, Google, Huawei, Intel, Microsoft and Qualcomm.

NSERC COHESA held its first annual general meeting at the University of Toronto on July 5-6, where more than 150 researchers across academia and industry heard a keynote talk by Bengio and participated in sessions on the network’s three major themes: intelligent sensing, hardware and system software.

“We’re focusing mostly on fundamental techniques that will improve the hardware, but the hardware itself isn’t the final goal, it’s a means to an end,” says Moshovos. “The fact that we also have some of the world leaders in machine learning is very unique. It will be very exciting to see what kinds of applications they come up with.”

Autonomous vehicles: U of T researchers make advances with new algorithm

noreen.rasbach — Thu, 21 Jun 2018 04:00:00 +0000

Autonomous vehicles: U of T researchers make advances with new algorithm

noreen.rasbach Thu, 06/21/2018 - 00:00

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From left, U of T researchers Wenjie Luo, Associate Professor Raquel Urtasun, and Bin Yang at Uber’s Advanced Technologies Group (ATG) Toronto (photo by Ryan Perez)

Nina Haikara

Topic

Global Lens

Technology

A self-driving vehicle has to detect objects, track them over time, and predict where they will be in the future in order to plan a safe manoeuvre. These tasks are typically trained independently from one another, which could result in disasters should any one task fail.

Researchers at the University of Toronto’s department of computer science and Uber’s Advanced Technologies Group (ATG) in Toronto have developed an algorithm that jointly reasons about all these tasks – the first to bring them all together. Importantly, their solution takes as little as 30 milliseconds per frame.

“We try to optimize as a whole so we can correct mistakes between each of the tasks themselves,” says Wenjie Luo, a PhD student in computer science. “When done jointly, uncertainty can be propagated and computation shared.”

Luo and Bin Yang, a PhD student in computer science, along with their graduate supervisor, Raquel Urtasun, an associate professor of computer science and head of Uber ATG Toronto, will present their paper, Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Net, at this week's Computer Vision and Pattern Recognition (CVPR) conference in Salt Lake City, the premier annual computer vision event.

To start, Uber collected a large-scale dataset of several North American cities using roof-mounted Li-DAR scanners that emit laser beams to measure distances. The dataset includes more than a million frames, collected from 6,500 different scenes.

Urtasun says the output of the LiDAR is a point-cloud in three dimensional space that needs to be understood by an artificial intelligence (AI) system. This data is unstructured in nature, and is thus considerably different from structured data typically fed into AI systems, such as images.

“If the task is detecting objects, you can try to detect objects everywhere but there's too much free space, so a lot of computation is done for nothing. In bird's eye view, the objects we try to recognize sit on the ground and thus it's very efficient to reason about where things are,” says Urtasun.

To deal with large amounts of unstructured data, PhD student Shenlong Wang and researchers from Uber ATG developed a special AI tool.

“A picture is a 2-D grid. A 3-D model is a bunch of 3-D meshes. But here, what we capture [with Li-DAR] is just a bunch of points, and they are scattered in that space, which for traditional AI is very difficult to deal with,” says Wang (pictured left).

Urtasun explains there's a reason AI works really well on images. Images are rectangular objects, made up of tiny pixels, also rectangular, so the algorithms work well on analyzing grid-like structures. But the LiDAR data is without any regular structure, making it difficult for AI systems to learn.

Their results for processing scattered points directly is not limited to self-driving, but any domain where there is unstructured data, including chemistry and social networks.

Nine papers will be presented at CVPR from Urtasun’s lab. Mengye Ren, a PhD student in computer science, Andrei Pokrovsky, a staff software engineer at Uber ATG, Yang and Urtasun also sought faster computation and developed SBNet: Sparse Blocks Network for Fast Inference.

“We want the network to be as fast as possible so that it can detect and make decisions in real time, based on the current situation,” says Ren. “For example, humans look at certain regions we feel are important to perceive, so we apply this to self-driving.”

U of T PhD student of computer science Mengye Ren at Uber ATG Toronto (photo by Ryan Perez)

To increase the speed of the whole computation, says Ren, they’ve devised a sparse computation based on what regions are important. As a result, their algorithm proved up to 10 times faster when compared to existing methods.

“The car sees everything, but it focuses most of its computation on what’s important, saving computation,” says Urtasun.

“So when there's a lot of cars [on the road], the computation doesn't become too sparse, so we don't miss any vehicles. But when it's sparse, it will adaptively change the computation,” says Ren.

The researchers released the SBNet code as it is widely useful for improving processing for small devices, including smartphones.

Urtasun says the overall impact of her group’s research has increased significantly when they’ve seen their algorithms implemented in Uber’s self-driving fleet, rather than reside solely in academic papers.

“We’re trying to solve self-driving," says Urtasun, "which is one of the fundamental problems of this century.”

Urtasun named one of Toronto's top influencers by Adweek magazine

noreen.rasbach — Wed, 06 Jun 2018 18:36:48 +0000

Urtasun named one of Toronto's top influencers by Adweek magazine

noreen.rasbach Wed, 06/06/2018 - 14:36

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Adweek says of Raquel Urtasun: "To give back to the next generation – not just in the scientific community, but all Canadians – she cofounded Vector Institute" (photo by Jonathan Lung)

Nina Haikara

Topic

City & Culture

Alumni