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RBC Thought Leadership Podcast Page 18

Canadian Innovation in Crisis

Content Type: Podcast

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Mike is a serial entrepreneur, and founder of League, a Toronto startup focused on providing a digital alternative to traditional health insurance. Janet is the managing partner of Real Ventures, a Montreal-based VC firm. She’s also the first woman to head a major VC outfit in Canada. In today’s episode we discuss:

  1. Steps that every business needs to consider to overcome this pandemic
  2. The future of work in Canada, and how we can use technology to transform a lot of processes
  3. The impact this crisis will have on our ecosystem and how we can prepare for a very different future

The COVID-19 crisis has shattered the way we live our lives and experience the world around us. Economies have been frozen in time, causing reductions in greenhouse gas emissions and remarkable improvements in air quality around the globe. Once the health crisis resolves and economies re-start, will we continue the same habits of consumption and production?

Wednesday, April 22 marks Earth Day—an opportunity to reflect and set a path forward. As I reflect on the pandemic and the climate crisis, I am reminded of the conversation I had with iconic Canadian photographer Edward Burtynsky and gaming executive Vikas Gupta, who are pushing the boundaries of photography’s next frontier, using augmented reality, virtual reality and photogrammetry to create immersive, three-dimensional visual experiences. They joined us in January for RBC Disruptors, where we spoke about how technology is transforming the photographic experience—and the way we understand our impact on the planet.

Burtynsky is celebrated around the world for his unconventional landscapes, shot from the ground and the air, showcasing the enormous and enduring impact of human beings on the world around us. He has captured the urban sprawl of Los Angeles, an oil field left for dead in Azerbaijan, the spectacular imprint of China’s Three Gorges Dam, and a tire pile 40 million deep—which soon after went up in flames.

Highway #1, Los Angeles, California, USA, 2003

SOCAR Oil Fields #10, Baku, Azerbaijan, 2006

Then came the advent of digital, which allowed him to stitch together hundreds of photographs to create his renowned prints, massive in scale and detail. He began to collaborate with filmmakers and produced three major documentaries.

“This is what I call photography 2.0,” Burtynsky said. “I was able to do all the things I couldn’t do before—and get a better quality image.”

Marble Quarries #1, Carrara, Italy, 1993 (detail)

Oxford Tire Pile #1, Westley, California, USA, 1999

Wan Zhou #1, Three Gorges Dam Project, Yangtze River, China, 2002

Now, what he calls Photography 3.0 is opening up new doors for truth-telling.

At a chance meeting, Burtynsky connected with Gupta, a digital media and gaming veteran who’d worked with brands like Disney and Electronic Arts. They wondered: what would happen if you applied some of the principles of gaming to photography? If you actually transported people to some of the most extraordinary places and moments in time? Together, the two founded AVARA Media to find out.

One of their first AR experiences, part of the acclaimed Anthropocene project, made waves with a 3D augmented reality rendering of a historic event in Kenya: the massive burning of 100 tonnes of elephant tusks, designed to deter poachers in the country—and recreated at full scale from 3,000 photographs.

“To me, it was like an absolutely new form of photography that has again been born by digital,” Burtynsky said.

The leap could be as significant as the shift from black and white to colour photography.

“As an artist, it’s like a jump of that magnitude. Now you can use the same tool and bring out a three dimensional world in which you can walk around.”

Building Ivory Tusk Mound, April 25, Nairobi, Kenya, 2016

Inspired by the stickiness of gaming, AVARA Media is building an immersive augmented reality technology platform that puts people face-to-face with the biggest environmental and ecological issues our planet is facing—and shows how they can be part of the solution.

Now, instead of just looking at a rainforest landscape, you can immerse yourself in it—and even pick up a plastic water bottle floating down the river. Or travel to Indonesia, the native habitat for the endangered Sumatran tiger, and help create a digital ecosystem in which the animals do not go extinct.

The thinking is that by immersing you into the action, and gamifying the task, digital behaviour will transform into actual behaviour. Just maybe, people will think twice before buying that next plastic water bottle.

“We want to inform, impact and influence people so they ultimately become change agents towards a healthier planet,” Gupta said. “The idea around this is to inject enough game theory and gamification that there’s mass, mass appeal.”

Last year, the Royal Canadian Geographical Society and Canadian Geographic Education, with support from the RBC Foundation, partnered with The Anthropocene Project to develop an education program to expand its reach among youth and get into classrooms all across Canada. Through virtual and augmented reality, video and immersive lesson plans students can experience the history and science of different environments, and see with their own eyes the impacts of human activity, in places such as landfills. The new education program finally launched in Canada last month as part of Canadian Geographic Education’s new Online Classroom.

Technology may have made the world of photography a much more crowded space, but for Burtynsky, it’s opened up doors and worlds – and a whole new photographic art form.

 

In 1837, Thornton Blackburn, an escaped slave, launched Toronto’s first taxi company, and turned The City into a thriving enterprise that generated a small fortune for Blackburn and his wife Lucie — and yet somehow has been forgotten to history.

More than 180 years later, most Canadians would be pressed to cite the Blackburns or name a single black entrepreneur. Even though 1.2 million Canadians identify as black, representing 3.5% of the population, a Black in Canada survey found only 2,000 black-owned businesses of significant scale. (It’s not just a Canadian problem. In 2018, a U.S. study found just 1% of venture-backed founders were black.)

For Black History Month, RBC Disruptors talked to two entrepreneurs who are trying to do something about it. In 2018, Isaac Olowalafe Jr., a Toronto real estate investor, and Abdullah Snobar, executive director of Ryerson University’s DMZ start-up zone, launched the $1-million Black Innovation Fellowship to provide entrepreneurs with access to networks, seed capital and business partners like Shopify. Snobar stressed one word: “access.” According to the Black in Canada survey, black entrepreneurs say their biggest challenges are marketing (51%), networking and learning opportunities (51%) and finance (48%). Olowalafe says the tech sector is perhaps the best opportunity, given its rapid growth and concentration in cities like Toronto. “Ryerson is known for tech and diversity,” he told us. “How do we bring the two together?”

The opportunity for the country is enormous. While close to a quarter of Canadians identify as visible minorities, only one in eight small and medium-sized businesses is owned by one. Across every sector, role models are key. As Lola Adeyemi, a Nigerian-born, Toronto-based food entrepreneur, found, “One of the struggles when I started the business was having somebody who looked like me, has been through the same struggles as an immigrant like me.”

 

Entire industries and careers are being created or overturned, and the disruptors tend to be entrepreneurs and innovative organizations whose greatest asset is mindset.

We launched RBC Disruptors in 2015 to explore how this new tribe is using technology to change everything around us. Since then, we’ve profiled 50 remarkable organizations – Shopify, Slack, Ritual, Apple and Amazon, among them – and gained some remarkable insights.

If they had a playbook, they’d call it B.L.A.S.T.

  • Build … a culture that balances speed and resilience.
  • Learn … in order to grow.
  • Adapt … by changing constantly to user feedback.
  • Scale … by isolating pain points for users and scaling solutions.
  • Trust … your partners to take you to the next level.

Here are 21 lessons our Disruptors learned from blast off:

Build

Incumbents plan then build; disruptors build then plan. But neither gets far without a cultural rudder.

#1 Slack: Your first building block is culture

Stewart Butterfield’s world-beating communications tool, Slack, started as something else and kept changing. Something that didn’t change was his culture: it’s rooted in empathy, for the user. Butterfield only hires people who are obsessed with users, which means they have to be good listeners, asking questions and seeking answers. “If someone asks a question in a customer support ticket, it is completely unacceptable to say ‘they’re an idiot because they couldn’t figure this out,’” Butterfield says. “It’s ‘what are we doing wrong that they couldn’t figure this out?’”

#2 Apple: Make your customers feel loved, or at least liked

Everybody wants to go hang out in places where they feel welcome, or like they belong, or that people really like them or love them.

Being connection-obsessed helps many disruptors leap ahead of bigger competitors. It’s why so many cite Apple as an inspiration, even though it’s now the incumbent. It still obsesses over experience. “You have to figure out what your unique experience is,” says Angela Ahrendts, the retail guru who oversaw the Apple Store’s global expansion. Her driving belief: “Everybody wants to go hang out in places where they feel welcome, or like they belong, or that people really like them or love them.”

#3 PagerDuty: Build through diversity

At PagerDuty, a Canadian-founded startup based in San Francisco, nearly two-thirds of its leadership team are immigrants. Moreover, half its engineering leadership team are women, as is half the executive team. To get there, the company often deferred hiring decisions – at the risk of short-term growth – to get the right mix of people. “We think about diversity and difference as upside,” PagerDuty CEO Jennifer Tejada told us. “All the data points to better business outcomes – better respect, better results, better shareholder returns, better customer offerings.”

#4 1-800-GOT-JUNK: Paint a picture

Only after Brian Scudamore joined the Young Entrepreneurs’ Organization did he see how to get his business, 1-800-GOT-JUNK, to the next level. “I didn’t have the clarity of vision to get where I wanted to go. I asked myself, ‘what could pure possibility look like if nothing was in my way?’” He painted a word picture and put it on the wall:

  • We’ll be in the top 30 metros in North America.
  • We’re going to be on The Oprah Winfrey Show.
  • We’ll be the FedEx of junk removal.

That clarity of purpose allowed him to hire the people he needed to fill in the picture, and in seven years the company went from sales of under $2 million to more than $100 million.

#5 Lightspeed: Culture is about buildings, too

The physical environment matters. Google transformed Silicon Valley culture not with free food or flex hours but a campus-like culture that inspired curiosity. When he was building Lightspeed in Montreal, Dax DaSilva developed office spaces that were inclusive beyond the physical environment. His use of artwork speaks to Lightspeed’s culture of diversity and inclusive thinking. “The company has built out of culture just as much as code,” he says. “That’s always been our credo.”

Learn

Fail fast has become one of the most misappropriated lines of this current age of disruption. Innovation is not about failure. It’s about learning, at times through failure.

#6 Hubdoc: Hire for curiosity

As he was building Hubdoc, an app that helps businesses automate their bookkeeping, Jamie Shulman looked at his most successful employees and noticed a common denominator: “curiosity, being interesting and interested.” The company needed more curiosity, so he redesigned its hiring process. Today, the first recruiting round for sales staff is a simple sales call: the candidate has to call in and sell the company on their own product. The second round has nothing to do with sales: candidates have to come in and give a presentation about their passion, whether it’s fly fishing or the Dave Matthews Band.

#7 Ritual: If it doesn’t make you cringe a little, you’re not learning

Ray Reddy works with a simple assumption: Version 1 never works. His company Ritual started as a food-ordering app for individuals but then noticed it was used more by groups. Why? Coffee and lunch are very social things. The result was Piggyback, Ritual’s super successful feature that allows users to jump on co-workers’ orders for free delivery. That was the easy part. The first three versions of Piggyback failed because the Ritual team didn’t appreciate how office groups worked. His team cracked the feature based on those learnings and today more than 150,000 teams around the world use Piggyback. It’s a reminder of why so many disruptors talk about MVP, or minimal viable product. “Just do it as simply as you can to get a learning or a data point and then we’ll go from there,” is how Reddy sees it. How do you know when you don’t have a learning culture? When you release products that don’t make you cringe a little. “That’s when we’ve probably spent too much time on it,” he says.

https://youtube.com/watch?v=gae0_1S-92U%3Frel%3D0

#8 Clearbanc: Always be iterating

Michele Romanow is now famous because of Dragon’s Den, but her real success lies in a string of start-ups she’s built going back to university. A caviar farm. A coupon app. And now a platform for entrepreneurs to raise money. In none of those cases did she have a eureka idea. In innovation, there’s no eureka moment – just an endless cycle of iterations of building, breaking and improving. “I totally reject this notion of a Big Idea, and there’s lots of research to back this up. It’s where I see a lot of companies getting hung up. I promise you, when you have good ideas and great companies, they all start incredibly small.”

#9 Spin Master: Be open to ideas from anywhere

As a start-up from Toronto, Spin Master competes with the world’s biggest toymakers for the next great idea. The company can’t do that on its own. “It’s all about being open to ideas, from wherever they come from,” says Ronnen Harary, Spin Master’s co-founder and co-CEO. That requires personal relationships with hundreds of inventors worldwide, as well as an ability to scour the market for old favourites, such as the century-old Meccano or the 60-year-old Etch-A-Sketch, which in the right creative environment can be turned into something young again.

https://youtube.com/watch?v=gArdOoJfx8U%3Frel%3D0

#10 Singularity University: Crazy ideas can be the best ideas

Singularity University co-founder and CEO, Rob Nail, told us that exponential thinking means looking to the future of your industry 10, 20, 30 years down the road. It’s about growing 10X, not 10 per cent. Nail left us all feeling inspired to think bigger about how we work and how we innovate. He also spoke about embracing crazy ideas. “If those crazy ideas just so happen to be the future of this business 10 years from now, you need to set up a structure, a team or a process that will allow those to come in, or at least experiment with them in some way,” he said. “If you look at the horizon, all the systems set up 100 years ago are being tested – we are going to see fundamental change.”

Adapt

#11 Shopify: Always be listening

Perhaps Canada’s greatest disruptor is Shopify, whose digital retail platform supports more than 1,000,000 businesses worldwide, from artisans to giant consumer goods companies. A decade ago, it barely existed. Today it’s worth $36 billion. COO Harley Finkelstein says Shopify was able to make some crucial changes by using digital tools to connect with users. Such use of social media and paid search have empowered startups to contend against established players on a more equal footing. “It’s no longer about who has the most capital,” Finkelstein says. “It’s now who has the most creativity.”

https://youtube.com/watch?v=8WvtQ9BIwp0%3Frel%3D0

#12 Cloudflare: Whose problem are you solving?

One of the top tech leaders to come out of Canada is Michelle Zatlyn, co-founder and COO of website protection service Cloudflare. The company became a unicorn – worth more than $1 billion – by helping small organizations protect themselves against cybercrimes. As she puts it: “You go back to the fundamentals. Are you solving a meaningful problem that somebody will pay for? If you are solving a meaningful problem, how big is that problem?”

#13 Hootsuite: Don’t act small

The connection-driven economy allows entrepreneurs to use digital channels that make it easier and cheaper for buyers and sellers to find each other and do business. Any player can act any size in this space. “I want to build the best product we can, talk to our customers relentlessly and figure out what their needs are,” says Ryan Holmes, the founder and former CEO of social media dashboard Hootsuite. “If we do that, we get rewarded for it. And if we fail to do that, then we get punished and our competitors get rewarded. It’s as simple as that.”

https://youtube.com/watch?v=rJ9POgUmE28%3Frel%3D0

#14 WattPad: Data is the thing

Wattpad started as a story-sharing service, and quickly became a platform for 45 million users. CEO and co-founder Allen Lau expanded the social platform to include Wattpad Studios, a venue for Wattpad’s global community to develop movie and TV scripts, and Wattpad Brand Solutions, for writers to connect with businesses wanting copy. Lau’s secret weapon is a user community that is fiercely loyal. He may not have Facebook’s mass, but he’s able to track engagement in ways few others can. Take the typical novel that’s developed on Wattpad: with two billion data points collected daily, the company tracks what’s popular, gives personalized recommendations and can even tell what pages or chapters cause readers to tune out. “We get lots of signals about why a story is fascinating,” Lau said. “If you look at the entertainment industry, this is what they’re looking for.”

Scale

#15 iNovia: Think in the billions

Patrick Pichette is a Montreal-born finance expert who helped run Bell Canada and then moved to Silicon Valley to serve as CFO of Google during its hyper-growth years. One of his responsibilities was to vet all those side projects Googlers are encouraged to do with 20 per cent of their time. He was told by executive chairman Eric Schmidt to use a simple filter: ignore any proposal that didn’t aim for at least a billion users. Pichette retired from Google in 2015, and has since brought that growth mindset to the Quebec investment fund iNovia.

#16 Andreessen Horowitz: Growth is a mindset

One of the world’s most successful venture capital firms, Andreessen Horowitz, looks for entrepreneurs with growth mindsets. Even though that term has become a cliché, especially for consultants, it differentiates the real disruptors from many other very good entrepreneurs. Angela Strange is a Canadian who works as a General Partner at a16z, as it’s known, and sees that mindset much more in the Valley than in Canada. “It’s not even ambition,” she says. “It’s just the belief that you could start something that could become a global leader.”

#17 YouTube: Use platforms for scale

The American filmmaker Casey Neistat has 7.2 million subscribers on his YouTube channel, and his videos—most of which feature him as the narrator and star—have been viewed more than 1.6 billion times. Neistat is one of a new generation of digital celebrities that have found a home on YouTube, which now has more than 1 billion users. His success, according to YouTube’s managing director of global brand solutions Debbie Weinstein, is a great example of the democratizing power of the platform, which transformed advertising, the entertainment business, and mobile communications—and could transform the way businesses interact with their customers. “It’s a platform for anyone who has a story to tell to come and tell it,” she said. “And you can find huge audiences on YouTube.”

#18 Amazon: Divide and scale

At Amazon, leaders use another means of retaining speed and focus through rapid growth: subdivide a fast-growing business. “The way we’ve managed to grow so fast is to act like a federated group of startups,” says Al Lindsay, former Vice President, Alexa Engine Software at Amazon.com. “So Alexa began as a startup and then as Alexa grew and became really big, we kind of refactored ourselves back into a bunch of small startups again and we repeat this pattern fractally throughout the organization.”

Trust

#19 Helpful.com: Seek out competition

The iPhone, Uber and Airbnb were all born in that hothouse of disruption – Silicon Valley – and gained from its so-called ecosystem. There are big pools of talent and capital, of course, but also plenty of frenemies who help innovators thrive. Good disruptors are always looking for ways to harness their ecosystem – to find others to work with and learn from. And critically, to pay it forward. As the serial entrepreneur Dan Debow put it: “Innovation is not this magical elixir that you drink. It’s a result of a competitive environment.” Debow, tapped into the competitive ecosystem in Toronto when he co-founded Helpful.com, a video messenger for professionals, and then sold it to Shopify, which liked the team he had built.

Innovation is not this magical elixir that you drink. It’s a result of a competitive environment.

#20 Verified.Me: Always Be Building Your Bench

The best organizations have accountable leaders, and the best startups identify and empower those people early on. “There is a very clear level of accountability, a clear level of communication and a very real sense of urgency against the opportunity,” says Darrell MacMullin, a veteran fintech executive and Chief Commercial Officer at Verified.Me, an initiative to transform the way we manage digital IDs. He tries to imitate a shared characteristic of all the best companies in any field: they not only seek out the best recruits, but they develop what they already have. “Part of their culture is investing in their people, investing in their processes,” he says. “They’re always working on how they run meetings, how they get the right questions, how they get the right leaders to mentor the people on their teams.”

#21 C100: Tell Your Story (No One Else Will)

There are lots of great stories of Canadian entrepreneurs changing the world, but the public often doesn’t know them. The C100 – a group of Canadians in the Valley — is now capturing those stories, to inspire and guide future entrepreneurs. “We have a thriving startup community, but we’re not always good at amplifying our stories,” said the C100’s executive director Laura Buhler. “We are hoping to change that so many more Canadian entrepreneurs can benefit from the knowledge and experience of others.”

For more lessons and ideas on the rapidly changing world around us, visit the RBC Disruptors Hub.

  

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The challenge is deepest amongst front-line employees – think of retail servers or call-centre operators – and the majority of those workers are women. According to a global survey by Ipsos, 54% of front-line employees are going to need some form of significant reskilling by 2022.

Despite the challenges, there is also an opportunity. With the right kind of training and reskilling, millions of Canadians could move to new and better jobs as machines take on more mundane and repetitive tasks.

At our most recent RBC Disruptors, we sat down with Carol Leaman, CEO of Waterloo-based Axonify, a micro-learning company on a mission to revolutionize the way companies retrain their front-line workers, to talk about how the disruption of learning can turn the age of automation into a positive force, and how women are set up to thrive in an automated future:

By Drawing on Skills They Already Possess

In conversations about the future of work, two words are heard often: perseverance and resilience. These are qualities that Leaman learned on the job as a 26-year-old accountant, working for a difficult boss, who one day looked at her and said, “We need $40 million, go find it.” She was terrified but stepped up and did it. “He taught me that you can do anything, you just need to decide that you can do anything.”

While Leaman recognizes that women tend to be in positions that are more susceptible to automation, women have these and other attributes that position them well to move into jobs that are growing in demand. RBC research found that 54% of the jobs at greatest risk of automation are held by women, but that women are better equipped with the generalist, digital and social skills that will be in high demand for the jobs of tomorrow.

“Women tend to have the foundational skills that we need to move into new jobs and new sectors. We are under greater threat, but in a better position for future mobility,” says Leaman.

Case in point, women are creating businesses at an unprecedented pace. “Women are extremely resourceful. I think as the workplace shifts, you’re going to see organizations take more action to support women in different career streams.”

By Learning New Skills – and Learning Them in New Ways

The impact of automation will be greatest among front-line workers – such as servers, retailers and customer service representatives – the majority of whom are women. To survive the displacement that will come about as automation gains a foothold in the services sector, significant reskilling will be required.

Leaman thinks micro-learning is the future of workplace reskilling. Platforms like Axonify’s offer bite-sized learning moments to individuals in those front-line jobs that enables them to learn and acquire new skills, while still performing at their jobs.

Axonify has taken that to the next level by working with a neuroscientist to develop an adaptive algorithm based on brain science. “Because of the amount of data we now collect, which is about 50 million data points a month across the globe, we can apply machine learning to that data, and extract provable correlations,” she says, such as how certain training leads to growth and revenue outcomes.

By Changing up the Look of Leadership

So how do you ensure your workforce is equipped for change? Encouraging female leadership is a good place to start. Female leaders, who are living, breathing and understanding the skills needed to succeed in the workplace of the future, are well equipped to navigate the evolution.

Yet, in new research from Plan International, only 10% of Canadian youth aged 14 to 24 picture a woman when they think of a CEO.

“I think the reason is that there are not enough successful role models, who reach the upper echelons of the corporate world, who have profile,” says Leaman.

More women in leadership roles will attract other women, acting as role models for the younger generation entering the workforce.

As a veteran leader, disruptor and innovator in the tech world, Carol Leaman gets it. Selected as one of the Best Workplaces for Women in Canada, Axonify has a strongly female leadership and women make up 45% of its employees, including product leaders, sales professionals and software developers – male-dominated cohorts in most tech firms.

“I think women are attracted to working with large numbers of other women because they see the possibilities.”

For more research on how women are ready for work in an automated future, download our report.

To learn more about the future of work, and the ways micro learning and inclusion play a role, listen to our latest podcast episode, recorded live at RBC Disruptors.

Listen on Apple Podcasts, Spotify or Simplecast

One of the biggest opportunities out there for digital disruption is much closer to home: your local hospital.

Canada’s health care system has everything that would inspire a Silicon Valley entrepreneur: scale, data, money—and a big problem to solve. It’s breaking under its own weight.

This year, the number of people turning 83 starts to tick up—that specific age is critical because it’s the average age at which people start to enter long-term care homes. In about four years, this wave of 83-year-olds hits Canada like a tsunami.

By the end of the 2020s, we’ll be spending about $200 billion on health care—and half of that will go to senior care. Even then, we’ll be short nearly 200,000 long-term care beds, and won’t have the support workers we’ll need. The numbers aren’t working.

At our most recent RBC Disruptors, we sat down with two health care leaders to talk about whether digital technology is our cure.

  • Mike Wessinger is the founder and CEO of PointClickCare, one of Canada’s top software companies that is transforming elder care in North America.
  • Michelle DiEmanuele is President and CEO of Trillium Health Partners, a leading hospital with three sites in Ontario that treated 1.7 million patients last year.

New technologies, like artificial intelligence and the Internet of Things, will benefit health care in two ways. Importantly, these innovations will solve some of the issues around patient care in terms of safety and quality.

Just as urgently, tech could relieve worker shortages, while also creating demand for more skilled positions. Ideally, the transition will help free health care workers spend more time on the “human” side of their jobs, and also attract and retain a new generation who expect to see and work with new technologies and innovative approaches.

“It’s a very positive thing, but it’s going to happen slower than we would like,” DiEmanuele said.

That’s because game-changing health care will require significant investment up front—and in long-term care, where most senior care takes place, the margins are so razor-thin the sector struggles to attract new capital. Most hospitals in Canada also don’t have shareholder capital to use for new tech, forcing them to squeeze other budget priorities.

Even where the money is available, technology is not a cure-all. Consider, for example, that seniors have been slow to embrace new devices that could help with their care. DiEmanuele said the “non-adoption” rate among seniors, when presented with new technologies for self-care or managed care, is upwards of 50%.

Another part of the puzzle is making the job more attractive, in a country where unemployment is low and personal support workers start out making near-minimum wage.

Wessinger tries to put himself in the shoes of a typical support worker arriving at work after a long commute – “and the first thing you do when you get there is change adult incontinence products. If somebody offered you 25 cents more an hour to go work at Walmart—what are you going to do?”

To learn more about the promise of new technologies, and the many challenges of implementing them—think regulatory, security and privacy issues—listen to our latest podcast episode, recorded live at RBC Disruptors.

Even as we live more and more of our lives online, we still crave in-person experiences. Think back to last spring, when Toronto Raptors viewing parties were everywhere, capped off by a parade that brought together more than one million fans. Then just last month, half a million movie buffs descended on the Toronto International Film Festival.

As technology evolves, one thing that disruption hasn’t upended is the genuine human need for connection. The experience economy is thriving, and every disruptor needs to think about the signal that sends: today’s consumers want to feel like they are part of something. In a global survey by Live Nation, 66% of people said they are “starving for experiences that put them back in touch with real people and raw emotions.”

To learn more about drawing crowds in the digital age, and the disruptive force they represent, we hosted an RBC Disruptors conversation with the co-heads of the Toronto International Film Festival:

  • Cameron Bailey, Artistic Director & Co-Head, TIFF
  • Joana Vicente, Executive Director & Co-Head, TIFF

 

The number of households with multiple streaming services is growing at a rapid pace, but Vicente thinks back to the advent of television and the introduction of the home video, and comes out bullish on the future of theatres.

“There’s always been these kinds of disruptions, and there’s always been an answer,” Vicente said.

In an era of constant distraction, the theatre is the only place where you’re told to turn off your phone. You are required to get sucked into the story, to laugh or cry alongside your fellow audience members. And let’s be honest, you can’t beat the projection.

“I think that no matter how good your home theatre is, it’s not as good as what we have,” Bailey said. “Sorry!”

But what stands out about the streaming era is that it’s not just about shifting viewing habits; it’s about the potential impact of data on the art form itself. Directors may find themselves struggling between their artistic vision, and what the data says will get them on Netflix’s list of recommendations.

In our latest RBC Disruptors podcast episode, Cameron Bailey and Joana Vicente explain how they’re tackling the industry’s challenges, and the power of live audience in an age of digital experience.

Listen on Apple Podcasts, Spotify or Simplecast

The hottest app in China right now, Zao, swaps your face with Leonardo DiCaprio’s to amusing effect. Suddenly, you’re Jack on the bow of the Titanic. It’s fun, silly—and a peak at a looming challenge to our democracy.

Right now, it’s still relatively easy to spot a deepfake with the naked eye. The voice isn’t quite right, or the blinking pattern is unnatural. But as the machine learning that powers these impersonations gets better and better, the joke will be on us. In just a few short years, we won’t be able to believe our eyes and ears anymore.

Deepfakes pose a big threat to public trust, misleading people and spreading false information. When you consider 34% of Americans say the Internet is their preferred format for news consumption, the potential impact of deepfakes on election campaigns is staggering.

Already, ahead of the 2020 U.S. election, a video of House Speaker Nancy Pelosi slurring her way through a speech was viewed millions of times online, tricking people into thinking she was drunk. It took several days to clarify what had happened: the viral video wasn’t real; it had been doctored.

The truth is in danger. That’s why, with the Canadian federal election underway, we hosted an RBC Disruptors conversation about how technology is transforming the vote. You can watch the full session, which featured panelists:

  • Zeynep Tufekci, Techno-Sociologist & Associate Professor, University of North Carolina
  • Kevin Chan, Global Director and Head of Public Policy, Canada, Facebook
  • Shuman Ghosemajumder, Chief Technology Officer, Shape Security

 

Now that we are waking up to the real threat posed by deepfakes, who is responsible for fighting back? And how? We don’t know how many fake videos are already in circulation, but we do know it doesn’t take long to create one: Shape Security wrote a blog detailing how they turned me into Simon Cowell as a fun side project in just a few days.

https://youtube.com/watch?v=5HUs-t5MUoQ%3Frel%3D0

In our latest RBC Disruptors podcast episode, Shape Security’s Shuman Ghosemajumder explains the making and propagating of deepfakes, and what legislators, Big Tech and ordinary citizens can do to protect our democratic institutions.

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Now, can we lead on tackling its ethical and societal implications?

The news is flooded with examples of AI fails: algorithms that favour male job applicants over women, or image recognition software failing to correctly identify people of colour.

Dr. Foteini Agrafioti, the Head of Borealis AI and one of the country’s strongest voices on ensuring AI is ethical, was announced last week as the co-chair of Canada’s new Advisory Council on AI. She led the latest RBC Disruptors conversation about battling bias in AI with Dr. Elissa Strome, Executive Director, Pan-Canadian AI Strategy at CIFAR, and Dr. Layla El Asri, Research Manager, Microsoft Research Montréal.

Here are their thoughts on what the scientific community, governments and ordinary citizens can do to confront bias in artificial intelligence, and position Canada as a leader in ethical AI.

1. Use Technology to Expose Bias

Bias has long existed in our society – and so it exists in our data. El Asri sees this as an opportunity. Unlike our own unconscious bias, we can at least uncover bias in an algorithm. To do this, companies need to be auditing their AI for bias every step of the way, as the major labs are now doing. El Asri credited Canadian leaders, such as AI pioneer Yoshua Bengio, for developing a will in Canada’s tech community to develop AI in a responsible way.

2. Diversify the Industry

Right now, artificial intelligence is being developed by a very narrow subset of society: mainly highly-educated men who went to the same schools, and now live in the same cities. Only 18% of AI researchers are women, a fact that Strome called “terrible.” Organizations like CIFAR are working to bring more voices into the development of AI, with initiatives such as the AI for Good Summer Lab, a seven-week training program for undergraduate women in AI.

3. Diversify the Data

AI is only as good as the data it’s trained on. “If your data is not representative enough, your model is not going to work,” El Asri said. There needs to be more vigilance in ensuring data is representative — an area where Canada has a homegrown advantage. If you’re working with data collected in a multicultural country like ours, you’re likely working with data that represents different ethnic backgrounds. This kind of data will be essential to building technology that works for everyone, especially when it comes to something like health care.

4. Talk to the Social Scientists

Right now, it’s really just the tech community and policy-makers talking about issues that are going to transform our society. We need to broaden that perspective, building in consultation with social scientists as an integral part of the development process. A recent CIFAR initiative brought together computer scientists and social scientists for a day to discuss the social, legal and ethical implications of AI. “The computer scientists were so eager to get their advice and insights,” Strome said. Similarly, at Microsoft, El Asri noted that their AI and ethics committees are made up of people from different disciplines, including anthropologists and historians.

5. Educate the Public

“There’s a lot of fear and misunderstanding and myths about AI,” Strome said. Over the next few years, it’s going to be critical to bring the public into the AI conversation. People need to be aware of the positive implications, as well as the risks, that AI will have on their lives. The better the next generation understands AI and its societal and ethical implications, the better prepared they’ll be to ask tough questions of their leaders. Agrafioti suggested that Canadian culture is particularly attuned to ensuring fairness, casting a critical eye on technology before implementing it. Our balance of technical expertise and social values is exactly what’s needed to make sure the product that gets to market is ethical.

6. Strong Governance

AI has been advancing much faster than any government can regulate it — so it was big news this week when the OECD adopted a set of AI principles, which set values-based standards for developing AI. Our leaders have an incredibly important role to play in developing policy and regulations around the use of AI, both domestically and internationally. Strome noted that Canada’s solid international reputation could go a long way in urging the world to play catch-up. Last summer, Prime Minister Trudeau and President Macron announced a joint Canada-France initiative on an International Panel on AI to support and guide the responsible adoption of AI, grounded in human rights. The first symposium will be in Paris this fall.

Solving bias in machines will take a human touch — and there’s no country better positioned than Canada to take the reins.

Listen to our conversation on the RBC Disruptors podcast about the potential of artificial intelligence.

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Ahead of the next RBC Disruptors event on May 23, “Battling Bias in AI,” our Thought Leadership team is examining the societal and ethical implications of artificial intelligence. In this interview series, John Stackhouse asks Layla El Asri, a Research Manager at Microsoft Research Montréal, about the role of humans in solving AI bias.

John: When did you first become aware and concerned about bias in AI?

Layla: I’ve really been thinking about it just for the last three or four years. After I joined Microsoft, that was about the same time that news stories were breaking about different issues surrounding bias and AI. There was the famous example with Google software, where their algorithm misclassified an African American as a gorilla. That was just one example of bias in the product because the data was not representative enough. It was very striking for me because it was really terrible.

Then there was this example, also with Google software, that if you would type a name would be mostly used within the African American community, then you would get ads about searching for a criminal record. And that’s bias in the system, because of bias in the way humans were using the system.

So those examples were really striking and really showed that things could go wrong if we weren’t more careful with the data that we were using in the models that we were putting out there.

The model just tries to optimize its performance. It doesn't ask, am I being fair?

John: As a scientist, how do you think about bias?

Layla: Your model can only be as biased as your data.

The model just tries to optimize its performance. It doesn’t ask, am I being fair? There are ways to put this into the model, but it needs to be put into the model. From a scientific point of view, that’s a matter of changing the objective of the models so that it not only tries to maximize performance, but is incentivized to not amplify bias or to try to reduce bias if possible.

John: So that opportunity to de-bug bias, if I can put it that way, is it as straightforward as you’re laying out? Just a matter of recoding?

Layla: You know, it really depends. It is possible in certain cases to try to kind of re-engineer the models so that it becomes less biased or unbiased. But in certain cases, it is just impossible. If you have data, for instance, that comes from human decisions — you might not even know of the bias that is present in the human decisions in the first place. And it’s a matter of really testing the model to see if it’s biased.

Sometimes in order to make it unbiased, you just need more data, especially when you have a problem with under-representation like you haven’t got data for darker skin in computer vision. There’s nothing you can do except collect data for darker skin and then retrain your models so that it learns about that data.

John: How do you at Microsoft come to grips with these challenges?

Layla: The way it’s been tackled here is very different. There are research groups that are dedicated to researching these questions — fairness, accountability, transparency and ethics. So questions like, what does it mean for a machine learning model to be fair? Fundamental questions that are yet to be answered. That’s at the research level.

And then there is also a committee within Microsoft which is called the AETHER Committee (AI and Ethics in Engineering and Research). This committee serves as kind of a consulting branch for product teams and leadership within Microsoft.

These groups know the technical issues with machine learning models, they know what they can and cannot do. And it’s really important to advise production and leadership about this, so we can all make an educated decision about whether or not it is safe to release machine learning at this stage or not.

Those are the kind of things that have been put in place within Microsoft as safeguards against unsafe use of AI, and ethical use of AI. And auditing has been a really impactful thing to do, too.

John: What kind of people are on these committees?

Layla: It’s actually a good mix of technical people and also people who have more of a sociological background. We have historians, we have anthropologists, sociologists, all working on these questions to try to understand the potential sociology consequences of certain patient learning technology.

I think we have to find a way to have a good working relationship between machine learning models and human beings, so that we can leverage the amazing adaptation capabilities of human beings and the amazing computational and kind of number-crunching capabilities of machine learning models.

John: You were talking earlier about the unintended consequences of machine learning. We have lots of unintended consequences with human learning and human decision-making. Should we be more confident in the ability of science to minimize the unintended consequences on the machine side?

Layla: You know in the future, I want to be optimistic that the answer will be yes.

But one really important flaw that I see right now, and which makes me lean towards no, is that the models are trained on historical data, so they cannot change. Unless you change their learning objectives, or you change the data that they were trained on. And currently they need a lot of data to learn something new.

Human beings, on the other hand, adapt very quickly. So if there is a problem with bias within your organization, you can talk to people and educate them and they will be able to react very quickly. A machine learning model right now will not be able to react very quickly. It will need a lot of data so it can be trained on it.

So I think that you know for the time being, as long as machine learning model cannot really adapt quickly and learn new things quickly, I think they have to work with human beings. And I think we have to find a way to have a good working relationship between machine learning models and human beings, so that we can leverage the amazing adaptation capabilities of human beings and the amazing computational and kind of number-crunching capabilities of machine learning models.

John: Maybe I can wrap up with a question about your own research in dialogue systems. Are voice and text biased? What should we, as consumers or producers of voice and text information, be thinking about?

Layla: If your model understands only certain people and certain voices and doesn’t understand for instance, the elderly or different accents, then you have a biased system because it doesn’t work for everybody. The good thing with human beings is that we kind of work with everybody, we kind of understand all sorts of accidents. Machine learning models might not always.

And even in text, if you only understand really well-formed English and your model doesn’t understand certain idioms or slang that might be used by certain communities, then you have a product that doesn’t really work for everybody and then you have a problem with bias.

You need to be able to understand all the people that you want to serve, really — all the people that you want your product to work for.

John: That’s fantastic. Really great insights. Thank you.

Layla: Great. Thank you.

Listen to our conversation on the RBC Disruptors podcast about the potential of artificial intelligence.

Listen on Apple Podcasts, Spotify or Simplecast