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The women’s research in the AI ​​field is advancing, advancing important ethical discussions, and can inspire the next pay generation of AI professionals. We created VentureBitton Women at the AI ​​Awards to emphasize the importance of their voices, work and experience and to shed light on some such leaders. In this series, published Friday, we are diving into the conversation with this year Winners, Which we recently honored Transformation 2021.

VentureBits Woman in AI Entrepreneur Award winner Brianna Brownell did not go out of her way to earn acclaim in the field. She started making AI that would work for her – or at least she likes to tell jokes.

Indeed, she set out to create a company that would combine its data analytics background with AI. In 2015, she launched Pure Strategy, which uses ANIE (Autotamed Neural Intelligence Engine) to help companies understand unstructured data. She and her team invented algorithms from the start to get it started, and the system has been used to interact with patients and with each other in cultural knowledge, for example. He also does moonlight as a science communicator, inspiring not only young children – especially girls but everyone around him.

“Whether you’re interested in recognizing unsolicited machine learning models or the complexities of algorithms, or from a high-level future perspective on humanity and AI, Brianna makes you feel comfortable with her talent,” said Roger Sanford, CEO of Hacker. Were. Award.

Brownle told VentureBet that he was “very excited to win this award.” “It’s a great honor for me,” he said. “It was definitely a surprise because I think the competition was very intense.” Indeed it was, but we are excited to recognize Brownell’s work as an AI entrepreneur, and excited to talk more about his work, the role of AI entrepreneurship in the broader field, and bringing more women to the table.

This interview has been edited for interview and clarification.

VentureBet: Tell us a little bit about your work and approach to AI. How did you come to start a pure strategy? And what drives you overall?

Brownell: I started Pure Strategy after spending 10 years as a data scientist. I was still doing a lot by hand, but new technologies came out that made working with some data much easier. You began to see an understanding of natural language, and the neural network infrastructure became available in open source packages. It’s all really just gained momentum. I joked that I essentially wanted to program myself into a computer so I could create an AI that would do my job for me. And that’s essentially what I’ve decided to do – try using those technical tools to make analytics data easier and faster.

VentureBet: And when you were making your product ANIE, how many of the challenges did you face? And how did you get rid of them?

Brownell: There were many challenges for sure. The first was that many of the algorithms we used were not really discovered yet. And so we have a whole suite of proprietary practices 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 so it would generalize a lot of different cases. The other one was able to find and analyze the data we needed. The size and scale of the datasets we use for training made it very difficult to program programs effectively. Let’s say I set up a neural network for training, and then I have to wait 20 or 30 minutes to complete the first step. And so it took a lot of time and was a real challenge.

VentureBet: How do you view AI entrepreneurship as opposed to academic AI research 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 AI academia to entrepreneurship is that the data is very good when it is very good, the algorithm fits the modeling assumptions, and everything is fine-tuned. . But in the real world, everything is imperfect and the data is dirty. You will not be able to find the data you want, or you will have to find a way. You have to merge data sources. All sorts of small issues come up when you are dealing with real data, and at the same time I think my experience is working in the industry with many types of data, and with a variety of problems with data. . Because when you’re building a platform you’re trying to use a company, it doesn’t matter if it’s an academically perfect algorithm; It matters whether it works or not and if it helps the company make the right decision. And so I think it’s increasingly difficult for people to be really strong in both business results and theoretical AI. And so we essentially need translators, who can work on those lines and understand what is possible with AI and what is relevant for the business. So that intersection is really, really important.

VentureBet: Do you have any pieces of advice for AI-focused entrepreneurs? What is often overlooked? Or is there something you already know?

Brownell: It’s easy to create a simple model that will do something, but it’s very difficult to customize that model to work in a specific model and do it on that basis. If you have all the A.I. Take a look at the company’s failures, and I don’t know if you have Element AI. But they were [$257 million] In funding and all these amazing talents, and they struggled with it. And I think we all underestimate how valuable customization really is. I think that’s a crucial, critical factor. Big companies really struggle to get their heads around AI because there is no guarantee it will work. They like to go to the door with these huge claims and then many of these projects fail because they have become promising. And so I see that there is a big threat to the industry. The cemetery is full of AI companies that have made big claims.

VentureBet: Your nominee said you should always be the only woman in the room, which is more widely common in AI and tech. These problems and risks are talked about for a long time when it comes to AI in particular. But do you think anything is changing? And how will it all play a role in these ongoing discussions surrounding the importance of an ethical and responsible AI?

Brownell: In my first job, which was in finance, I was the only woman who worked in the whole company, really. And on my next job, I actually worked with a lot of female technical staff for female CEOs. And so I thought that women in data science and analytics are a common position in the world. And then when I went to Tech I got a rude awakening. And I think it’s really embarrassing because there’s so much promise with how AI can change society and the world. And not just more women, but people from the groups presented at the table as a whole, can help us solve problems that can’t be solved when you think of the group. And so I hope that as more women begin to become pioneers in AI, the types of use cases begin to become more interesting and more women choose this career. Because there is a huge need for different perspectives and new ways of thinking about how technology affects our lives.

VentureBet: You’re also working on a children’s show that revolves around explaining complex science topics such as AI to pre-teens. How did you get into it, and why is communication important to you?

Brownell: It’s very important to me. I actually have a few other things in the field that I’m working on: I write about physics and astronomy for Discovery, develop K-12 AI content with charities to make it more fun and accessible, and TED on AI specifier videos I am also working with children. I think it’s very important to reach out to students when they’re younger, because you don’t really know what career is possible when you grow up, unless you see it in your inner circle. I worked with an engineering association called APEGS, which has a program to encourage more women to consider engineering. And one of the things they talk about is that many women who have decided to go into engineering have had a relative or close family friend in the field who can see their skills and encourage them. And so being able to reveal to people about the career that is available, I think, is really complicated.

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