Women in AI Dinner, Toronto: Highlights

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Deep learning in healthcare, AI for financial solutions, chatbots to help children with disabilities. These were just a few of the topics that this evening’s guests at the Women in AI Dinner are working on.

"There’s room for more women in AI, we’d all agree, and everyone in this room agrees. I’m here to learn from everyone in this room, and I hope we can all learn from each other."
Alison Paprica, Vector Institute

This evening saw leading minds in AI and deep learning come together for a champagne reception and three-course meal at the Aperture Room in Toronto. RE•WORK visited Montreal last October for the Deep Learning Summit, which received zealous support from both the Tourism Board and Canadian Government. Once again, Canada has demonstrated its prominence as a global leader in AI by encouraging us to showcase some of the world leaders in what is fast becoming one of the major players in AI.

Both men and women working at the forefront of AI discussed their research and applications over champagne, and we heard conversations between CIFAR, EY, TD Bank, Tinder, Borealis AI, IBM and many more.

"I’m an AI enthusiast, and I’m really here for exploration and to learn from people in the field - and of course to support women in the field."  
Ksenia Shkaruta, Bank of America

Conversations surrounded the importance of showcasing diversity in a vastly male-dominated industry, and guests were found brainstorming how we can encourage more women and other minorities into the industry. Discussions included having more female role models in leadership positions, as well as making tech and AI more relatable to younger women via influencers, social media platforms, and popular culture.


Alison Paprica, VP Health Strategy and Partnerships from Vector Institute was this evening’s compère and kicked off the dinner by welcoming our guests, thanking our fantastic sponsors, “from the perspective of the Vector Institute, we would not exist without the support of TD Bank”.  Alison is Vector’s corporate lead for health strategy, and she oversees health research collaborations, health data partnerships and health AI application projects. She also leads workshops and courses focused on the leadership and management of research at the University of Toronto where she is Assistant Professor.

"Vector Institute began as an initiative to stop our childhood generation from leaving Canada, and it’s grown to far more than this, with some of the leading startups coming out of Toronto, and it’s my true pleasure to be your host for this evening."

"When I went to do my PhD in Chemistry, I started working in an area that was far more representative of men than women, but when I went into industry, it was far more equal, interestingly it was in that environment that I learned about systematic equality. Very few drugs are tested on women and children, almost everything we’ve learned about drugs is based on men. This has had some really negative effects. When working on heart disease in women we faced several problems, all the studies had been done on men, so women were dying because we were unable to recognise the symptoms. In my role at the Ministry of Health and Long-term Care System, I made sure that both male and female study participants were tested to take into consideration all variations between genders."

Staying true to our focus of maximising networking throughout the evening, between both the starter and the main course, attendees were encouraged to move places and take a new seat next to another guest. Alison introduced our first speaker for the evening, Jennifer Gibbs, VP Head of the Office of the Chief Data Officer at TD Bank. Jennifer is working to develop and implement strategies, programs, and policies to improve and sustain the governance, management, protection, and value of TD's data assets. Jennifer provides guidance and oversight across TD's lines of business to support strategic data initiatives and regulatory and compliance expectations related to data while maintaining deep relationships with the Data Steward community, key stakeholders, and control partners.

"I’m thrilled to be here this evening to discuss topics we love, diversity and data. I’m a self-proclaimed data geek, and I’m currently working in a role focusing on governance and trust." Jennifer started with a story where she shared that she finally made time to join the school council at her kid’s school. She explained that the parents were very friendly and collaborative and, of course, passionate about their children. When the topic of social media chat came up, the feistiness of parents rose to the surface - they were concerned about bullying, mental health issues and other problems that stem from social media. “One mom went as far to say that she thought social media was like bringing the devil into the house". Here, Jennifer saw that parents didn’t necessarily have experience in social media and their experiences were biased. "It’s important to shed light on the positive lights of social media and the way it can provide positive outcomes - the conversation moved from the idea of a complete ban of cell phones to how we can bring parents in to understand social media. I think this brought a non-biased approach one step closer. As innovators, technologists, strategists, we must look at how AI can bring better outcomes for our fellow citizens. We must also look at potential risks - I’d like to assert a couple of things: all humans are biased, whether conscious or unconscious. Data is also inherently biased - data is given by humans. AI algorithms are created by biased humans I’m an optimist so I’ll get to that later!- but we shouldn’t be biased.” Jennifer explained how platforms make recommendations based on generalisations. Recently, on LinkedIn, high paying jobs were, in the majority, displayed to men, Amazon’s experiments hiring tool favoured male candidates for technical roles. Pokemon Go found that several users noticed there were fewer Pokemon in primarily black neighbourhoods, making kids in those areas alienated. Jennifer went on to look at this from the perspective of self-driving cars - regulations and policies are still being created, but how do we ensure we’re still creating technology that supports society as a whole? Do teams understand where the data comes from that drives your decisions? The more context teams have, the more effective they'll be at exploring how best to get to our goals whilst understanding the potential risks. How do we help open up the black box that is AI? We must open up a healthy discussion. FAIR listed 115 people working on AI, 15% of whom were women, whilst Google listed 200 people working on AI, 10% of whom were women - "at TD our mantra is 'bring your whole self to work' - this means don’t be afraid of who you are. Let’s create an environment where everyone can share their uniqueness."

After more networking and a seat swap between courses, Alison welcomed Afsaneh Fazly, Director of Research at Samsung Toronto AI Research Center to share her most recent work with the room. Afsaneh strongly believes that solving many of today's real-world problems requires an interdisciplinary approach that can bridge the gap between machine intelligence and human cognition, which is something they’re working on at Samsung.

“I’m from academia - I see myself as a scientist. I’m here to have a conversation with you guys! I’ve been working in linguistics, even though my background is in computer science, language has always fascinated in language has a strong connection to human intelligence, and therefore Artificial Intelligence.” Afsaneh spoke about how, thousands of years ago, philosophers were pondering on human intelligence; in the early days of computers in the 50s, people thought about simulating intelligence. AI machines are powerful, they can do tasks we’re really bad at, really well and really quickly, for example, complex math examples. However, we then think about intelligence operations like talking, reasoning etc. that are easy for us, but they’re hard for machines. “We know how logic can be divided into small steps, but how do we do tasks like reasoning so effortlessly? We then started to wonder ‘what does it mean for a machine to think?". Is a machine capable of being natural if it has a conversation where a human doesn’t identify it’s a machine? Afsaneh took the example of the thinking machine from Alan Turing. She explained that fooling a human doesn’t necessarily mean that the machine is intelligent. We’re working to see how we can programme humans to act like us, the ‘ultimate intelligent being’. “My fascination with AI stemmed from both of the following: it helps us understand intelligence, we can understand how language works and how children can understand something so quickly 2) we can build systems that can help people to make lives better. Looking at virtual assistants - do you find them useful? It’s about expectations - what if humans were helping you, what would your expectations be of that assistant - higher or lower than the VA?”

The wine was flowing and conversations were turning to individual’s applications of AI and how they’d been inspired from the first two presentations:

Alex Spence‏ @alex0spence: There is nothing more incredible than seeing a room full of driven, smart, and passionate women in #tech. So glad to be here at #reworkAI’s Women in AI dinner. I am surrounded by female innovators and industry leaders and couldn’t be more humbled.

@amandahughes223: Source of data is so important and knowing this is key in deciding what you can do with it #jennifergibbs #reworkAI

@SueBrittonFGS@jgibbs333 @TD_Canada @reworkAI #reworkAI women play a critical role in advancing AI “bring your whole self to work” “use our influence to bring in diverse thinking” @LeanneNorthwood @Celero   thanks for inviting me!

@Resh_dhir: Fascinating ‘Women in AI’ event! Great to be with so many accomplished women in one room @reworkAI #reworkAI

Rounding off the evening, Jekaterina Novikova, Director of Machine Learning at WinterLight Labs discussed “Language and Speech Processing: From Human-Robot Interaction to Alzheimer’s Prediction”. The Toronto-based Canadian company is developing a novel AI-based diagnostic platform that can objectively assess and monitor cognitive health. She began by explaining how there have been tremendous progressions in NLP and speech processing in recent years, and these are thriving areas in AI that are becoming more and more important. “Almost everyone has been exposed in one way or another to the newest technology that employs NLP, whether through a virtual assistant such as Siri, or a simple automated phone answering system. The range of possible applications able to create value from natural language processing is much broader.What is NLP? Area of AI where human language is processed computationally where the computer is pendent on what we see in the language. There are many areas of application, but I’ll talk about detection of cognitive diseases - this is what we’re doing at the moment.We’re specialising in Alzheimer's which is one of the most frequent types of dementia - more than 40 million people are currently suffering, and it’s set to double. It’s so important to find a solution. It’s not curable now, but we can learn to postpone it. WinterLight Labs are creating AI applications to employ ML models to detect these diseases by looking at tests. We want to create something more robust system. We believe it’s possible to create a system that’s robust enough to identify cognitive disease through NLP.”

Many attendees will also be joining us at the Deep Learning Summit and AI for Government Summit later this week in Toronto. There are still a few spaces remaining, so if you’re keen to learn more from experts such as Geoffrey Hinton, University of Toronto, Brendan Frey, Deep Genomics, Sara Hooker, Google Brain, Mathili Mavinkurve, Sightline Innovation, William Brendel, Snap Research and may more, register now.

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