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December 06, 2018
From serving its citizens to building the city of tomorrow—How Toronto is taking the lead in AI adoption
By: Jodie Wallis

The AI Effect is a podcast series exploring Canada’s burgeoning artificial intelligence ecosystem. Accenture’s AI leader co-hosts with reporter Amber Mac to look at AI’s explosive growth and the change—challenges and rewards—it can bring for individuals, business and society.

What makes a “Smart City” smart? If you didn’t have the constraints of existing infrastructure, what would you build? What’s in it for each of us and what—if anything—are we giving up?

These are some of the fascinating questions we set out to explore in Episode 7: Smart Cities.

Quayside is a new neighbourhood on Toronto’s waterfront that sits on 12 acres of largely unused land in the east end of the city. It began as a Request for Proposals by Waterfront Toronto and is now being planned by Waterfront Toronto in partnership with Sidewalk Labs, a subsidiary of Google’s parent company Alphabet Inc., who has committed $50 million to the planning phase.

The project, which has drawn both curiosity and concern, has bold plans. Craig Nevill-Manning, Head of Engineering at Sidewalk Labs, told us that the goal for Quayside is a neighbourhood designed from the ground up to be affordable, easy to get around in and sustainable with a much smaller environmental footprint than traditional cities. AI will be instrumental in helping to achieve this goal.

You know the expression it takes a village? Well it takes a village to design a smart city too. Nevill-Manning explains that his team consists of people who have worked in government, transportation, sustainability and technology, all working toward designing a city that is more like software than hardware. Whereas traditional cities are very hardware-oriented, making it difficult to change the infrastructure, smart cities will be much more flexible, adaptable and even personal.

But what—if anything—do residents have to give up in achieving the end goal? Some critics argue that smart cities rely on sensors and connected devices that collect data about every aspect of its residents’ lives, putting their privacy at risk.

Nevill-Manning points out that this doesn’t necessarily have to be invasive. He highlights research advances at the intersection of cryptography and machine learning that will allow individuals to control when to share their individual machine learning models with other services.

The Quayside initiative is providing us with a captivating glimpse into what may be possible for cities in the future—including for the broader city of Toronto. Mayor John Tory is excited about the potential benefits across a variety of areas.

The use cases for Canada’s largest city are seemingly endless according to Tory, and span mobility, citizen services and engagement, public security and sustainability to name a few high potential areas, and the benefits are seemingly endless as well. For example, when we start to apply AI to services that are provided to residents, like the registration process for community centre activities, we end up with a much more intelligent government that is more flexible, more customer friendly and less expensive.

Early experiments with smart traffic lights and crime detection are beginning to bear fruit in Toronto. Tory provides a recipe for the road ahead: raise the level of education first, and then buy in—take risks, make investments, change procurement policies, get behind local start—ups and be leaders.


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