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Trend-spotting Google exec Colin McKay says �山ǿ�� prepared him for dynamic career path

Christopher.Sorensen — Thu, 26 May 2022 13:19:51 +0000

Trend-spotting Google exec Colin McKay says �山ǿ�� prepared him for dynamic career path

Christopher.Sorensen Thu, 05/26/2022 - 09:19

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After studying history and international relations at �山ǿ��, Colin McKay went on to work for the Office of the Privacy Commissioner of Canada and Google Canada (photo courtesy of McKay)

Peter Boisseau

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Colin McKay built an impressive career in government and industry by analyzing data and spotting trends – and he says his education in the University of Toronto's Faculty of Arts & Science contributed to his success.

McKay obtained a B.A. with distinction in history and international relations at �山ǿ�� as a member of Trinity College, followed by a master's in history. He worked for the Office of the Privacy Commissioner of Canada before moving to Google Canada, where he currently heads government affairs and public policy.

“There is a clear trend line in my life because the work I was doing for my undergrad and graduate degrees was really about information collection, analysis and communications,” he says. “What I studied in terms of information and intelligence analysis translated into my early jobs, and that flowed into an opportunity with the privacy commissioner.”

He started at the Office of the Privacy Commissioner of Canada – which provides advice to individuals about protecting personal information and enforces federal privacy laws – in 2007, just a few years after the advent of Facebook, YouTube and Twitter. The rising popularity of social media prompted new questions and fuelled debate around privacy.

“I felt I had insights into that policy dialogue, as well as the specific work, based in part on the studies I had done at �山ǿ��,” McKay says, adding that it also helped to be an early adopter of social media.

“The work at the privacy commissioner helped me take a deep dive into the other side of information use and analysis, which added to the collection of skills that got me the job at Google.”

McKay was drawn to the field of international relations because his father was a diplomat whose job took the family all over the world.

“I also liked the multidisciplinary nature of being able to jump between economics, political science, history and specialist courses,” says McKay.

“And I took my time at �山ǿ��, because I was taking all the courses that appealed to me intellectually, but also because I was viewing the subject matter from a variety of perspectives and disciplines.”

One of McKay’s main roles at Google is understanding the evolving regulatory environment around privacy and data collection and how that affects the company and consumers. He’s also charged with navigating that landscape with policymakers, researchers and politicians drafting new policies and legislation.

His �山ǿ�� education is an important foundation for that work as well, he says.

“There's a lot of conversation at the moment about how exactly we create a regulatory framework designed for 2022,” says McKay.

“It all goes to the multidisciplinary nature of my international relations degree. Whether it’s political science, economics or history,” he says.

“You have to work hard to understand the environment, the problems to be solved and the specific communities you deal with, to evaluate and execute decisions. That makes my role very interesting.”

He says his focus on following his own personal interests and professional challenges throughout his school years and career also gives him insights into the changing expectations of younger Canadians in the workforce.

“I'm not as confused as some of my peers about people being willing to abandon jobs or to fundamentally rethink what they want to do with their skills and their time. Because that's how I was initially trained and how I've behaved since then,” says McKay.

Previously a board member and vice chair at Media Smarts, a non-profit that promotes digital and media literacy, McKay says it's important for the public to be given adequate tools and training to appreciate the benefits – and risks – associated with technology.

“I think the overlap with the media literacy work I’ve been doing is that there is a level of expectation and burden on the individual to understand the implications for them of the technology they're using, both in the moment and in the future.”

Making music with code, exploring solar-powered cars – �山ǿ�� hosts STEM event

ullahnor — Fri, 19 May 2017 19:35:37 +0000

Making music with code, exploring solar-powered cars – �山ǿ�� hosts STEM event

ullahnor Fri, 05/19/2017 - 15:35

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Students from 56 classes across the GTA came to the event, planned by �山ǿ��'s Faculty of Applied Science & Engineering, Google Canada and Actua (photo by Roberta Baker)

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�山ǿ��'s Faculty of Applied Science & Engineering hosted more than 1,200 Ontario students in Grades 4 to 8 on Friday for Go North, a day of hands-on activities, demonstrations and workshops designed to ignite curiosity about the vast potential of science, technology, engineering and math (STEM).

Students from 56 classes across the GTA came together to make music with code, develop video games, look under the hood of a solar-powered car and participate in workshops on robotics, structures and 3D-printing.

Created in partnership with Google Canada and Actua, a national STEM charity, Go North is designed to encourage students to become the inventors, innovators and makers of tomorrow by playing and experimenting with technologies today.

They also heard from inspiring speakers about the exciting career paths made possible by studies in STEM, and participated in a live science demonstration from Science Max host Phil McCordic.

Participants shared their experiences on social media throughout the day using the hashtag #GoNorthU. Here's what they had to say:

�山ǿ�� students have 'Googliness,' says Google exec

geoff.vendeville — Fri, 10 Mar 2017 20:30:44 +0000

�山ǿ�� students have 'Googliness,' says Google exec

geoff.vendeville Fri, 03/10/2017 - 15:30

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�山ǿ�� President Meric Gertler spoke with Don Harrison, a graduate of �山ǿ��'s Faculty of Law, at Google about his university experience (photo by Rebecca Wilkowski Photography)

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Don Harrison, a graduate of �山ǿ��'s law school, speaks to prospective students

"Googliness" is hard to put your finger on. It’s a quality used to describe employees and new hires at the tech giant – a mix of passion, drive and creative thinking.

The University of Toronto and Canadian culture bring these traits out in students, according to Don Harrison, Google’s vice president of corporate development.

Googliness isn’t just about being smart.

“You also have to operate in a way that’s respectful, kind and builds consensus,” he said. “�山ǿ�� does a great job of producing that.”

And he should know. He graduated from �山ǿ��’s Faculty of Law in 1997, then worked for a law firm that helped take Google public. He later joined the tech company and now leads its corporate development branch, managing global mergers and acquisitions, and working on the company’s largest transactions including Motorola and Deepmind, which specializes in artificial intelligence.

Harrison hosted fellow alumni and American students interested in applying to �山ǿ�� at an event in Sunnyvale, Calif., on Thursday. In conversation with �山ǿ�� President Meric Gertler, he reflected on how his university experience set him up for a career at one of the world’s top-10 largest tech companies.

American students curious about studying at �山ǿ�� attended an information session in Sunnyvale, Calif., this week (photo by Rebecca Wilkowski Photography)

The university teaches its students to operate in diverse workplaces, and they would fit right in at Google, Harrison told �山ǿ�� News. One reason for that is the university’s location, at the heart of Canada’s largest city, where about half the population were born abroad.

Another draw for students is the quality of the university's researchers, many of whom are laying the groundwork for a high-tech future.

“�山ǿ�� has managed to be at the forefront of a bunch of trends,” he said. “Two that jump out at me, artificial intelligence and machine learning techniques, are more and more becoming the foundation of new products and industries.”

The university is home to a pioneer in the field, Geoffrey Hinton, who is often referred to as the “godfather of neural networks” and whose work paved the way for Google Translate. Hinton now splits his time between teaching at �山ǿ�� and working at Google as an engineering fellow.

Read about Hinton in the Toronto Star

With professors like Hinton, �山ǿ�� provides a world-class education in subjects like artifical intelligence, Harrison said.

“I feel like, for a budding software engineer or computer scientist, �山ǿ�� provides a good place to get training and background, and access to the best companies in the world,” he said.

Zoya Bylinskii can attest to that. A computer science graduate and panelist at a student recruitment event in Boston on Tuesday, she spent part of her undergraduate years developing an application allowing a computer to recognize objects by breaking it up into component parts.

“It's using principles of perceptual organization, how people perceive the world, how they take parts and group things into a whole,” she explained. “It really goes to the depths of human object recognition and applies it to computational algorithms to make them smarter in a natural way.”

After graduating from �山ǿ�� in 2012, she is continuing her studies at the Massachusetts Institute of Technology.

“�山ǿ��'s computer science program does open up a lot of opportunities,” she said.

Many American students seem intent to move in the opposite direction to study at the University of Toronto. Earlier this year, �山ǿ��’s Vice-President-International Ted Sargent said applications from American students were up 70 per cent.

The California event was the last in a series of student recruitment events held across the United States last week. The week kicked off with an event in New York City featuring alumnus and Saturday Night Live creator Lorne Michaels.

Read what Michaels had to say to prospective students

�山ǿ�� computer scientist takes international prize for groundbreaking work in AI

lanthierj — Wed, 18 Jan 2017 14:48:25 +0000

�山ǿ�� computer scientist takes international prize for groundbreaking work in AI

lanthierj Wed, 01/18/2017 - 09:48

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(photo by Johnny Guatto)

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Geoffrey Hinton, a University Professor in the department of computer science at the University of Toronto and VP Engineering Fellow at Google, has received the BBVA Foundation Frontiers Award in information and communications technology for his “pioneering and highly influential work” to endow machines with the ability to learn.

It marks the second year in a row that a �山ǿ�� computer science researcher has won the award. Last year, University Professor Stephen Cook was honoured for his pioneering and influential work on computational complexity.

The BBVA Foundation Frontiers of Knowledge Awards were established in 2008 to recognize outstanding contributions in a range of scientific, technological and artistic areas, along with knowledge-based responses to the central challenges of our times.

The international award is the latest honour for Hinton, whose revolutionary contributions to artificial intelligence (AI) have enabled such developments as speech and image recognition systems, personal assistant apps like Siri, driverless cars, machine translation tools and language processing programs.

Read about Hinton in The New York Times

His work has also advanced the use of medical images to diagnose whether a tumor will metastasize and the search for useful molecules in new drug discovery. Virtually any field of research that requires identifying and extracting key information from massive data sets has benefitted from advances stemming from Hinton’s work.

Hinton’s approach draws on the way the human brain is thought to function

Known as deep learning, Hinton’s approach draws on the way the human brain is thought to function, with attention to two key characteristics: its ability to process information in a distributed fashion with multiple brain cells interconnected, and its ability to learn from examples.

The computational equivalent involves the construction of neural networks – a series of interconnected programs simulating the action of neurons – and, as Hinton describes it, “teaching them to learn.”

“The best learning machine we know is the human brain,” says Hinton. “And the way the brain works is that it has billions of neurons and learns by changing the strengths of connections between them.”

“So one way to make a computer learn is to get the computer to pretend to be a whole bunch of neurons, and try to find a rule for changing the connection strengths between neurons, so it will learn things in a way similar to the brain.”

The idea behind deep learning is to present the machine with lots of examples of inputs, as well as desired outputs. “Then you change the connection strengths in that artificial neural network so that when you show it an input it gets the answer right.” Hinton’s research has focused on discovering what the rules are for changing these connection strengths. He views this as the path leading to a new kind of artificial intelligence where the computer learns from its own experience.

Neural networks are far from a new thing

Although their most important applications have emerged only recently, neural networks are far from a new thing. When Hinton began working in artificial intelligence – spurred by a desire to understand the workings of the human brain that had initially taken him into experimental psychology – his colleagues were already moving away from the neural networks that he defended as the best way forward. The first results had not lived up to their promise, but Hinton chose to persevere against the advice of his professor and despite his failure to raise the necessary research funding in his home country, the United Kingdom.

His solution was to emigrate, first to the United States and subsequently Canada, where he joined �山ǿ�� and was at last able to form a team and advance his work on neural networks.

By the mid-2000s, results came in that would draw scientists back to the neural network strategy. Hinton had created an algorithm capable of strengthening the connections between artificial networks enabling a computer to “learn” from its mistakes. In the resulting programs, various layers of neural networks processed information step by step. To recognize a photo, for instance, the first layer of neurons would register only black and white, the second layer would recognize a few rough features and so on until arriving at a face.

In the case of artificial neural networks, what strengthens or weakens the connections is whether the information carried is correct or incorrect, as verified against the thousands of examples the machine has been provided. By contrast, conventional approaches were based on logic, with scientists creating symbolic representations that the program would process according to pre-established rules of logic.

“I have always been convinced that the only way to get artificial intelligence to work is to do the computation in a way similar to the human brain,” says Hinton. “That is the goal I have been pursuing. We are making progress, though we still have lots to learn about how the brain actually works.”

By 2009, the programs Hinton developed with his students were beating every record in the AI area. His approach also benefitted from other advances in computation: the huge leap in calculating capacity as well as the avalanche of data that was becoming available in every domain. Indeed many are convinced that deep learning is the necessary counterpoint to the rise of big data. Today Hinton feels that time has proved him right: “Years ago I put my faith in a potential approach, and I feel fortunate because it has eventually been shown to work.”

Eventual triumph of personal assistants and driverless vehicles

Asked about the deep learning applications that have most impressed him, he talks about the latest machine translation tools, which are “much better” than those based on programs with predefined rules. He is also upbeat about the eventual triumph of personal assistants and driverless vehicles: “I think it's very clear now that we will have self-driving cars. In five to 10 years, when you go to buy a family car, it will be an autonomous model. That is my bet.”

As to the risks attached to artificial intelligence, particularly the scenario beloved of science fiction films where intelligent machines rebel against their creators, Hinton believes that “we are very far away” from this being a real threat.

What does concern him are the possible military uses of intelligent machines, like the deployment of “squadrons of killer drones” programmed to attack targets in conflict zones. “That is a present danger which we must take very seriously,” he says. “We need a Geneva Convention to regulate the use of such autonomous weapons.”

Condensed from BBVA release

Google DeepMind's AlphaGo: meet the �山ǿ�� computer scientists who helped it win

sgupta — Tue, 02 Feb 2016 13:20:25 +0000

Google DeepMind's AlphaGo: meet the �山ǿ�� computer scientists who helped it win sgupta Tue, 02/02/2016 - 08:20

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Neural networks “learn principles and tactics,” �山ǿ��'s Chris Maddison says. “They don’t just memorize – they comprehend.” (all photos by Nina Haikara)

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Grad student, alumni among contributors to latest breakthrough in artificial intelligence

Go is one of the world’s oldest games, originating some 2,500 years ago in China.

Now the latest in artificial intelligence – Google DeepMind’s AlphaGo – has set a milestone in the game once believed impossible for a computer to learn, beating European champion Fan Hui 5-0.

“The rules of Go are very simple,” says computer science PhD student and Massey College fellow, Chris Maddison. “But it’s in that simplicity that complexity arises.”

Maddison is third author of new research published in Nature and announced by Google Research on Jan. 27, detailing AlphaGo’s success at mastering the game and beating previous state-of-the-art Go-bots in 99.8 per cent of games with even stones.

Read The Globe and Mail story

Go is fundamentally a game of conquering territories. The game proceeds with two players, alternating the placement of either black or white stones on a 19 by 19 grid. The goal is to define territory using the stones, and whoever controls most of the board by the end of the game, wins.

“You might think the right thing to do is to make big blobs to cover the board,” says Maddison. “But you can capture territory by completely surrounding your opponent’s connected stones. So, professional play leads to beautiful stringy patterns that cut and connect across the board.”

Artificial intelligence research has a long history of using games as microcosmic testing-grounds. Games are very precisely defined and allow researchers to evaluate their success. Last year, Google DeepMind taught a machine to play and win at all 49 classic Atari computer games. Go has always been seen as the last classical game where humans consistently outperform algorithms.

Google Demo Day for women gives top prize to Bridgit startup

sgupta — Thu, 10 Dec 2015 13:45:50 +0000

Google Demo Day for women gives top prize to Bridgit startup sgupta Thu, 12/10/2015 - 08:45

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To develop their startup, Lauren Hasegawa and Mallorie Brodie sought help from Creative Destruction Lab and The Next 36 at the University of Toronto

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Learn more about startups and entrepreneurship at �山ǿ��

Google Demo Day is held each year but this was the first time Google for Entrepreneurs chose to create a second event dedicated to female founders, addressing the fact that the vast majority of funding is invested in male-led ventures.

“We know that women around the world are creating and building incredible tech companies,” said Mary Grove, director of Google for Entrepreneurs. “We aim to help connect them with mentors, access to capital and shine a spotlight on their efforts.”

While Google Demo Day doesn’t offer any immediate prizes, Bridgit will reap the benefits of getting the tech giant’s stamp of approval. This could include getting their foot in the door with investors, attracting the attention of potential large corporate partners and being able to hire more quickly.

Startups who have pitched at the previous two Demo Days have gone on to raise approximately $3.5 million dollars in funding from investors who attended the pitches, followed by an additional several million dollars after the event.

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