Richard Zemel

One of Canada’s biggest banks has purchased a promising artificial intelligence, or AI, startup that was co-founded by two U of T alumni and a local lawyer and entrepreneur – further evidence of how big corporations are eager to scoop up the country's top AI talent.

TD Bank said this week that it has agreed to buy Layer 6 for an undisclosed sum, although the Globe and Mail cites sources who say the price tag was in excess of $100 million when retention payments to employees are included.

The acquisition comes as the bank seeks to cut costs by using AI to automate repetitive tasks and scan large datasets in search of patterns that can help it better understand its customers.

“We’ve talked a lot in the past about disruption and what would happen to banks, and ‘Are your business models broken?’” Bharat Masrani, the bank’s CEO, said at an industry conference in Toronto, according to the Globe.

“And I say, ‘This [acquisition] is a great example of how TD’s adapting to this new reality and new expectation, and how we are remaining on the leading edge with our customers.’

Where is AI headed in 2018? Your phone will know you better than your friends do, U of T researcher predicts

Christopher.Sorensen — Thu, 04 Jan 2018 15:05:18 +0000

Where is AI headed in 2018? Your phone will know you better than your friends do, U of T researcher predicts

Christopher.Sorensen Thu, 01/04/2018 - 10:05

Cutline

Richard Zemel is a U of T professor of computer science and the Vector Institute's research director (photo by Johnny Guatto)

Chris Sorensen

Topic

Our Community

Artificial Intelligence

Computer Science

Creative Destruction Lab

��Ƶ

Research & Innovation

Richard Zemel

Technology

Vector Institute

From self-driving cars to finding disease cures, artificial intelligence, or AI, has rapidly emerged as a potentially revolutionary technology – and the pace of innovation is only set to speed up.

To get a sense of where the field is headed in 2018, U of T News sat down with the ��Ƶ's Richard Zemel, a professor of computer science and the research director at the Vector Institute for artificial intelligence research.

He was just back from the annual Neural Information and Processing Systems (NIPS) conference in Long Beach, Calif. – a once staid academic gathering that’s more than tripled in size over the past five years, drawing dozens of giant corporations.

Zemel’s take? Get ready for a world where businesses enjoy unprecedented insight into their products and services, Toronto continues its ascent as a major AI research hub, and digital assistants like Apple's Siri and Amazon's Alexa become ultra-personalized.

“It will be like being friends with someone for many years,” predicts Zemel, who spoke on the sidelines of an event at the Creative Destruction Lab (CDL), one of U of T's numerous entrepreneurship hubs. “The computer or phone may know more about you, potentially, than anyone else.”

What can we expect to happen in AI over the next 12 months?

One of the things that will affect people in their daily lives will be personalization. People know about personalized assistants like Alexa and Siri, but those are just the first generation. They’re going to get a lot better in the next six to 12 months. They will be able to really understand what you’re asking and be able to formulate answers and get to know you better – not just look things up in your calendar or on the web.

Is that a function of improved speech recognition capabilities?

Speech will be part of it. But there’s all this other information about you that’s available. It’s your daily habits, what you do and where you go. So, if people allow it – if you give it access to your emails and photos, what you look at online, watch on TV and the books you read – it’s going to be a much bigger package. It will be like being friends with someone for many years. The computer or phone may know more about you, potentially, than anyone else. So it’s a question of combining all that information and getting a real profile of your tastes.

Beyond personal assistants, there’s a lot of other industries that are potentially going to be impacted by this technology, if they aren’t already. What other sectors do you think we may be hearing about?

Education is one example. There will be more systems that learn how you learn best. These could be online learning tools that are custom tailored to you. There will also be lots of manufacturing applications. Here at CDL, there’s a lot of companies who are using sensing technologies to find out what’s happening in the environment – ranging from smart cities down to a company I was just chatting with that’s putting sensors in cows’ milk to determine how healthy it is. All of these things that were typically very expensive to assess can now be done with a few sensors and a lot of training data.

Medicine is another area where you have a ton of data, although there’s a lot of privacy and data-sharing issues in that. The big companies like Microsoft and Google have talked for years about getting into it, but they’ve always stopped because of privacy issues. But I think there’s enough momentum now that there will be progress in health, ranging from health records to medical imaging diagnoses and robotics in surgery.

On the research side, which areas do you find exciting right now?

One of the most exciting areas – and it's reflected in the research I’m doing – is called transfer learning. That’s the idea of performing a new task without a lot of training data. This has a lot of applications in business. Let’s say a robot has to climb hills and take out the garbage, and it has a lot of data it’s trained on to do that. But now you give it a new task – moving a bin from one place to another, and it’s never done that before. So now it has to transfer its knowledge to this new task. The novel thing here is you’re training it with a huge amount of data, but you’re testing it on something else.

I’m guessing that’s more difficult to accomplish than it sounds.

Exactly. That’s the interesting piece in all of this. The things that seem easy to us, because people do them naturally, are typically the biggest challenge for these systems. That’s true for perception and speech. I mean, we speak pretty easily, but it’s taken computers a long time to figure out speech and produce speech. All these things we take for granted are big challenges.

Any other areas of research interest?

Another one goes back to what we were saying about personalization. If it takes off, you will need to allow the [AI] system to see all your personal details. So it becomes a question of whether you’re going to be hesitant to release your personal details because of privacy and fairness issues. So there’s a lot of research now – a really growing field – called fairness in machine learning. I do lot of research in this. In past years, there were just two or three papers at NIPS. This year there were 20.

Is this a technical issue or an ethical one?

It’s both. It’s an ethical and societal issue to define what it means to be fair, but the technical issue is how do you build a machine learning system that embodies those principles? It’s a very interesting area. The challenge is defining it in a good way, and then you take this definition and formalize it into a mathematical statement we can use to train up the machine learning.

There’s also a field called FAT ML, which is fairness, accountability and transparency in machine learning. That’s going beyond fairness and privacy to ask whether you can get explanations from the system, so it can be useful for doctors and lawyers. In those high-risk situations, you need systems that are more interpretable. That’s an increasingly important direction, too.

[U of T University Professor Emeritus] ��Ƶ made a statement last year about how the current paradigm for deep learning needs to be thrown out the window so we can start over. What do you make of his comments and what should the rest of us make of them?

What he’s talking about actually follows a trend in machine learning. And that’s to build more structure into the system. Now, a lot of people don’t like that point of view. They think of it as a plain vanilla system and you should allow it to learn everything. But he’s saying you actually need to build in some structure – capsule networks – where you learn about parts of objects and some parameters associated with them and how they’re related. There’s a lot of work in that area. There’s always been this debate between a sort of tabula rasa view of learning to something where you start with some structure and learn on top of it. So throwing out current deep learning might mean, in my view, that you want to incorporate some sort of structure and the key question is: What’s the right structure?

AI has been a big story for U of T and Toronto, particularly with the creation of the Vector Institute last year. Can you give me an update on what will be happening at Vector in 2018?

It’s exciting. We finally have the space to move into with desks, chairs and everything. People were worried we wouldn’t have enough critical mass to get going. But, actually, when we put it all together, there will be 90 people moving in, including students, post-docs and faculty. And that’s just the full-time people. There will be a lot affiliates coming in part-time from other parts of Toronto and the province, including the Universities of Guelph, Waterloo and McMaster. It’s going to be a real hub of AI activity. We’ll do some growing as well, hiring some additional research scientists and bringing in a new batch of grad students in September. We’re hiring some post-docs, as well as software and research engineers.

How will the relationship between Vector and U of T work?

The idea is people will be working at U of T in their faculties, but are also cross-appointed to Vector, so they will move back and forth. Many will also have students whose main desks will be at Vector, but they will also be teaching and holding talks on campus. The way I think about it is as an additional facility with a lot of good researchers who will facilitate collaboration.

In a broader sense, how do you see the concept of an AI hub developing in Toronto?

There’s a lot of AI around. A lot of the hospitals are doing AI and health is an application that we’re very interested in at Vector. We’re going to try and co-ordinate things to get the hospitals working and talking with each other and sharing data. We could also play an important role working with businesses when it comes to finding talent. One of our main aims is graduating master’s and PhD students. We’re not going to co-ordinate all AI, but we can be an important resource and hub for research.

What do you think are the misconceptions rattling around out there about AI?

One thing people don’t realize is that machine learning systems, the way they are right now, require a lot of data and a lot of labelled data – thousands of examples with annotations. If you don’t have that then you’re in the research field, not the applications field. People need to know that. You need a lot of data.

Is it difficult to get access to sufficient data in a less populous country like Canada?

Not really. There’s data everywhere. It’s just a question of harnessing it and figuring out how to get labels. We’re big players in this. People are trying to emulate what’s going on here. I had meetings with top people all over Europe, asking “How did you do Vector? We want to copy you.” I think we’re sitting on a model for the rest of the world.

#UofTGrad17: U of T AI grads choose Toronto

ullahnor — Thu, 08 Jun 2017 18:46:00 +0000

#UofTGrad17: U of T AI grads choose Toronto

ullahnor Thu, 06/08/2017 - 14:46

Cutline

Kevin Swersky graduates with a PhD in computer science today. He's joining Google Brain in Toronto (photo by Johnny Guatto)

Nina Haikara

Author legacy

Nina Haikara

Topic

City & Culture

Artificial Intelligence

With Toronto’s emerging prominence in AI, three U of T computer science graduates are deciding to stay put – a marked departure from earlier years when grads were often lured to Silicon Valley.

Kevin Swersky, who graduates with a PhD in computer science today, has worked with leading AI researchers like U of T’s ��Ƶ and Richard Zemel. He is joining Google Brain’s team in Toronto.

Rizwan Gilani, who came to U of T as an international student from Pakistan, graduates June 14 with a master's degree in applied computing. He was able to transfer a research internship in Seattle, working on Amazon’s smart speaker, into a full-time job. He now works for Amazon in Toronto.

And Eda Doko, who will also be receiving a master's degree next week, has been hired as a data scientist with Caseware, a company focused on software solutions for accountants and auditors.

“When the opportunity presented itself, it was really exciting,” says Swersky, who until recently was working with Google Brain's team in Cambridge, Mass. “A lot has changed, particularly with the announcement of the Vector Institute – something has changed in Toronto.”

Toronto has emerged as a hotspot for AI research in recent years – particularly in the field of deep learning, which was pioneered by Hinton. Further adding to the appeal: the recent launch of the Vector Institute for artificial intelligence research, a partnership between U of T, the province of Ontario and the federal government.

Last month, Uber announced it was tapping Raquel Urtasun, an associate professor of computer science at U of T, to lead its new R&D lab in Toronto. And, this week AI-powered legal research firm ROSS Intelligence, a U of T startup that relocated to Silicon Valley, held an official ribbon-cutting ceremony at U of T for its new R&D office in Toronto.

“Toronto is emerging as an AI research hub,” says applied computing graduand Gilani. “I already have family here – so everything just added up for me.”

Gilani discovered his interest in natural language processing (NLP), a subfield of AI and cognitive linguistics, the science of computers interacting with human language, while he was enrolled in a graduate course taught by Frank Rudzicz, a computer science assistant professor who also works as a scientist at the Toronto Rehabilitation Institute.

“While taking Dr. Rudzicz’s course, I was sure I wanted to work in NLP. I wanted to work on building scalable systems out of all the things we were studying in the class.”

Gilani undertook his applied research internship requirement at Amazon “Alexa” in Seattle, working on the voice service behind Amazon’s “smart speaker,” Echo. He'll now be working at Amazon Alexa's Toronto office.

“The office is downtown, close to U of T campus,” he says. “It’s the best of both worlds. I can stay close to the university and upcoming research, and can continue my development work.”

Doko, who lived for a time in her parent’s native country of Albania, returned to Toronto for university.

“U of T’s the best – and the applied master’s was the perfect opportunity. It gave me the perfect path to launch my career,” she said.

Swersky’s collaborations read as a who’s-who of AI.

His PhD supervisor is Zemel, who is the research director of Vector.

Former U of T postdoctoral researcher Hugo Larochelle, who now leads Google's Montreal group, Ryan Prescott Adams, also a former U of T postdoc, and U of T computer science graduate Jasper Snoek, both of Google Brain Cambridge, are his long-time colleagues, as is U of T computer science graduate Danny Tarlow of Google Brain Montreal.

He has worked with Hinton, a University Professor Emeritus and vice president, engineering fellow at Google, who incidentally helped recruit him to U of T’s grad program. Being at Google’s Toronto office will offer a special opportunity to collaborate with Hinton again, Swersky says.

He first joined Google as an intern in 2012, working on the speech recognition team.

“It was a year after a previous U of T computer science graduate, Navdeep Jaitly, had gone to Google and helped revolutionize their speech recognition system,” he says. “It was an exciting time. I got to work a bit on deep learning for speech recognition, in a real-world system.”

Being back in Toronto means Swersky can be closer to family and enjoy more of Toronto’s ramen food scene with his girlfriend.

“I grew up in Edmonton. Lived in Vancouver. Spent the summer in San Francisco. Lived in Boston and Toronto. You kind of get a feel for all these different places and an intuitive sense of your place in them,” he says.

“It’s hard to explain, but something that just feels innate – Toronto feels like coming home.”

Rise of the machines: U of T emerges as "ground zero" for the next wave of transformative technologies

Christopher.Sorensen — Tue, 16 May 2017 12:19:23 +0000

Rise of the machines: U of T emerges as "ground zero" for the next wave of transformative technologies

Christopher.Sorensen Tue, 05/16/2017 - 08:19

Cutline

Department of Computer Science Innovation Lab’s (DCSIL) recent Funding Innovation conference featured speakers talking about new emerging technologies (photo illustration by Marcos Gasparutti via Flickr)

Chris Sorensen

Author legacy

Chris Sorensen

Topic

Global Lens

Entrepreneurship

Artificial Intelligence

Subheadline

Conference explores how artificial intelligence, machine learning and other computing technologies are poised to change everything

Some of the world’s biggest companies, from banks to carmakers, are poised to have their businesses transformed by new computing technologies – many of which are being developed at the ��Ƶ.

John Ruffolo, the CEO of OMERS Ventures, delivered the message to a packed room at the Department of Computer Science Innovation Lab’s (DCSIL) recent Funding Innovation conference.

“U of T, over the past few years, has really exploded,” Ruffolo said, referring to the research and startups emerging in areas like artificial intelligence, machine learning and blockchain technologies, among others. “It really is ground zero.”

Ruffolo then laid out OMERS Ventures’ plan to focus on five broad areas of innovation where U of T students and faculty are making a big impact: e-commerce, fintech, automation of work, autonomous vehicles and synthetic biology.

“The rate of change is unprecedented,” Ruffolo said. “Disruption is happening at a pace that’s hard to keep up to.”

With $800 million under management, OMERS Ventures is the venture capital arm of one of Canada’s largest pension funds. It has made investments in such high-profile startups as Shopify, Hootsuite and D2L, among others.

Mario Grech, a director and co-founder of DCSIL, one of 10 accelerators on campus, said the organization has been “inundated” with calls from the global entrepreneurship community, and is also getting recognition from Silicon Valley.

The same is true of U of T's department of computer science. Associate Professor Raquel Urtasun was recently tapped by Uber to lead its new research lab in Toronto, which will focus on self-driving vehicles. Similarly, ��Ƶ, a University Professor Emeritus, referred to as the “godfather” of deep learning, was hired by Google four years ago. Both are key figures at the recently launched Vector Institute in Toronto, a collaboration between Ottawa, the province of Ontario, U of T and industry partners. The institute aims to promote research and commercialization of AI technologies, a field where Canada is widely regarded as a leader.

The day-long conference consisted of a series of panel discussions that featured U of T researchers, industry leaders and startup founders.

Richard Zemel, a professor in U of T’s department of computer science and the Vector Institute’s head of research, was among those who weighed in some of the ethical questions AI faces, including how to keep tabs on computers that are increasingly capable of learning without human input.

“Good AI is one that be controlled by the outside,” he said.

Others spoke about the challenges facing those charged with keeping people’s information safe online. The topic turned out to be extremely timely as the world’s biggest cyberattack – dubbed “WannaCry” – began to unfurl across 150 countries shortly after the panel’s members including Kumar Murty, a professor of mathematics, took the stage.

The so-called “trough of disillusionment” surrounding augmented and virtual reality technologies was another hot topic. The term refers to the period of disappointment that follows the initial hype surrounding a new technology development. In VR’s case, excitement over the propsects for the technology soared following Facebook’s US$2 billion purchase of VR headset-maker Oculus Rift back in 2014.

“We haven’t seen a whole lot of capital go into these companies so far, but that’s going to change,” promised Prashant Matta, a senior associate at OMERS Ventures.

One example of what’s coming down the pipe: JanusVR, a company co-founded by Karan Singh, a U of T computer science professor, is reimagining the Internet as a virtual world where content is explored by walking through different rooms.

Why do its proponents believe VR is still a revolution-in-waiting?

Matt McPherson is a U of T alum who is now working for Toronto's Quantum Capture, which seeks to make more realistic human representations inside virtual reality experiences. He recalls one particularly perspective-altering moment during a VR experience: “This avatar turned to me and said, ‘Hey buddy.’ It was so spooky because I was sitting alone in my office, but I was no longer by myself.”

Richard Zemel

TD Bank embraces AI future with purchase of startup built by U of T alumni

Read more about TD's deal to purchase Layer 6 in the Globe and Mail

Read more about TD's deal to purchase Layer 6 at Betakit

Where is AI headed in 2018? Your phone will know you better than your friends do, U of T researcher predicts

#UofTGrad17: U of T AI grads choose Toronto

Rise of the machines: U of T emerges as "ground zero" for the next wave of transformative technologies

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