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Women in AI are making inroads in research, leading vital ethical discussions, and inspiring the next generation of AI professionals. We created the VentureBeat Women in AI Awards to highlight the importance of their voice, work and experience, and to shine a light on some of these leaders. In this series, published on Fridays, we dig deeper into conversations with winners, which we recently honored at Transform 2021.

Briana Brownell, winner of VentureBeat’s Women in AI Entrepreneur Award, did not enter this field to win accolades. She decided to create an AI that would do her job for her – or at least that’s the joke she likes to tell.

Really, she decided to start a company that would combine her experience in data analysis with AI. In 2015, she launched Pure strategy, which uses an Automated Neural Intelligence Engine (ANIE) to help businesses understand unstructured data. She and her team invented algorithms from scratch to make it happen, and the system has been used by doctors to communicate with patients and with each other through cultural knowledge, for example. She also works in the moonlight as a science communicator, inspiring not only young children – especially girls – but everyone around her.

“Whether you’re interested in the intricacies of algorithms to validate unsupervised machine learning models or a high-level future vision of humanity and AI, Briana puts you at ease with her genius,” a said Roger Sanford, CEO of HCare, who nominated her to award it.

Brownell told VentureBeat that she was “extremely thrilled to have won this award.” “It is a huge honor for me,” she said. “It was definitely a surprise because I think the competition was pretty fierce.” Indeed, it was, but we are excited to recognize Brownell’s work as an AI entrepreneur, and even more excited to discuss with her more about her work, the role of AI entrepreneurship in a world. broader domain and bring more women to the table.

This interview has been edited for brevity and clarity.

VentureBeat: Tell us a bit about your job and your approach to AI. How did you come to launch Pure Strategy? And what motivates you overall?

Briana Brownell: I started Pure Strategy after spending about 10 years as a data scientist. I was still doing a lot by hand, but new techniques emerged that made working with some of these datasets much easier. You started to understand natural language and neural network infrastructure became available in open source packages. It all really accelerated. I joked that I basically wanted to program myself into the computer so that I could create an AI that would do my job for me. And that’s basically what I decided to do: try to use these technological tools to make data analysis easier and faster.

VentureBeat: And when you created your ANIE product, what were the challenges you encountered? And how did you overcome them?

Brownell: There were certainly a lot of challenges. The first was that many of the algorithms we use have not yet been invented. And so we have a whole suite of proprietary methods that make our platform perform at the level it needs. And so it was really a challenge because it was a lot of trial and error and a lot of building the system to generalize to a lot of different cases. The second was to be able to find and analyze the data we needed. The size and scale of the datasets we use for training have made it extremely difficult to program things effectively. I would like, say, set up a neural network to train, and then I would have to wait 20 or 30 minutes for it to do the first step. And so it took a long time and it was a real challenge.

VentureBeat: How do you see AI entrepreneurship in relation to academic research in AI and other aspects of the field? What are their unique roles and how can they best come together?

Brownell: I think one of the challenges people face in moving from academia to entrepreneurship is that they’re very, very good when the data is all right, the algorithm matches the assumptions in the modeling, and everything is sort of beautifully placed to fit the case. . But in the real world, everything is incomplete and the data is dirty. You may not be able to find the data you need, or you may need to find a way to approximate it. You might need to merge data sources. All kinds of little issues arise when working with real data, and this is where I think my experience working in the industry, with many different types of data and many different types of issues with data, m was really helpful. . Because when you’re building a platform that you’re going to try to get a business to use, it doesn’t matter if it’s the academically perfect algorithm; it is important to know whether it is working or not and whether it is helping the company to make the right decision. And so I find it increasingly difficult for people to be really strong in both business results and the theoretical realm of AI. And so, basically what we need is translators who can work in all of these areas and understand what is possible with AI and what is relevant to the business. So this intersection is really, really important.

VentureBeat: Do you have any advice for AI entrepreneurs. What is often overlooked? Or what would you have liked to know earlier?

Brownell: It’s easy to create a general template that will do something, but it’s very difficult to customize that template to work in a specific case and do it on a large scale. If you look at all the major failures of AI companies, and I don’t know if you’ve been following Element AI, for example. But they had [$257 million] in funding and all this amazing talent, and they struggled with that. And I think we all underestimate how valuable this personalization is. I think this is a critical factor. Big companies really have a hard time understanding AI because there’s no guarantee it will work. They love make those huge claims to get in the door, and then so many of those projects fail because they’re too promising. And so I see this as a big threat to the industry. The graveyard is littered with AI companies who have made huge claims.

VentureBeat: Your nominator said that you are often the only woman in the room, which of course is common for women in AI and tech in general. This issue has been talked about for a long time and the risks with regards to AI in particular. But do you feel like something is changing? And how does it all play out in these ongoing discussions about the importance of ethical and responsible AI?

Brownell: In my first job, which was in finance, I was the only woman working in the entire company, actually. And at my next job, I actually worked for a female CEO with a lot of female tech staff. And so I thought that the women in data science and analytics was just the normal state of the world. And then I had a rude awakening when I got into tech. And I think that’s a real shame because there are a lot of promises about how AI can change societies and the world. And not just more women, but people from globally underrepresented groups at the table can help us solve problems that cannot be solved only when you have group reflection. And so I hope that as more and more women start to become important in AI, the types of use cases start to get more interesting and more women choose that career. Because there is a huge need for diverse perspectives and new ways of thinking about the impact of technology on our lives.

VentureBeat: You’re also working on a kids’ show for CBC that revolves around explaining complex science topics – like AI – to tweens. How did you get there and why is science communication important to you?

Brownell: It is extremely important to me. I actually have a few other things I’m working on in this area: I write about physics and astronomy for Discovery, I develop K-12 AI content with charities to make it more fun and accessible, and I’m working with TED on how-to videos on AI. for children too. I think it’s very important to reach out to students when they’re young because you don’t really know what careers are possible when you grow up unless you see it around you. I’ve worked with an engineering association called Apex, which has a program to encourage more women to consider engineering. And one of the things they talk about is that a lot of the women who decided to go into engineering, had a relative or close family friend in the field who could see their skills and encourage them. And so being able to expose people to the kinds of careers available, I think, is really essential.


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