Women in AI: Catherine Breslin helps companies develop AI strategies | TechCrunch

Women in AI: Catherine Breslin helps companies develop AI strategies | TechCrunch

Giving women academics and others focused on AI their well-deserved — and overdue — time in the spotlight, TechCrunch is publishing A series of interviews Focused on notable women who have contributed to the AI ​​revolution. We're publishing these pieces throughout the year as the AI ​​boom continues, highlighting important work that often goes unrecognized. Read more profiles Here.

Catherine is the founder and director of Breslin. Kingfisher Labswhere she helps companies develop AI strategies. He has spent more than two decades as an AI scientist and has worked for Cambridge University, Toshiba Research, and even Amazon Alexa. She was previously an advisor at VC fund Deeptech Labs and was the solutions architect director at Cobalt Speech & Language.

He studied at Oxford University for undergrad before earning a Masters and PhD at Cambridge University.

Briefly, how did you get your start in AI? What drew you to the field?

I always liked maths and physics at school and chose to study engineering at university. This is where I first learned about AI, although it wasn't called AI at the time. I became intrigued by the idea of ​​using computers to do speech and language processing that we humans find easy. From there, I studied for a PhD in Voice Technology and worked as a researcher. We're at a point in time where there have been huge strides for AI recently, and I think there's a huge opportunity to create technology that improves people's lives.

What work in the AI ​​field are you most proud of?

In 2020, in the early days of the pandemic, I founded my own consulting company with the mission of bringing real-world AI expertise and leadership to organizations. I'm proud of the work I've done with my clients on varied and interesting projects and also that I've been able to do this in a really flexible way around my family.

How do you navigate the challenges of the male-dominated tech industry and, by extension, the male-dominated AI industry?

It's hard to measure precisely, but 20% of the AI ​​field is female. I also think the percentage goes down as you get more senior. For me, one of the best ways to navigate this is to build a support network. Of course, support can come from people of either gender. Sometimes, though, it's reassuring to talk to women who are going through similar situations or who have seen similar problems, and it's great not to feel alone.

The second thing for me is to be thoughtful about where I spend my energy. I believe we will see lasting change only when more women get into senior and leadership positions, and not if women spend all their energies on fixing the system instead of advancing their careers. There is a practical balance between driving change and focusing on your day-to-day work.

What advice would you give to women aspiring to enter the AI ​​field?

AI is a huge and exciting field with a lot going on. There's also a lot of noise that seems like a constant stream of papers, products and models being released. It is impossible to maintain everything. Moreover, not every paper or research result will be significant in the long run, no matter how glossy the press release. My advice is to find a niche you're really interested in growing in, learn all you can about that niche, and tackle problems you're motivated to solve. are doing This will give you the solid foundation you need.

What are the most pressing issues facing AI as it evolves?

Progress over the past 15 years has been rapid, and we've seen AI move out of the lab and into products without having to step back to accurately assess the situation and predict outcomes. An example that comes to mind is how much better our voice and language technology performs in English than in other languages. This does not mean that researchers have neglected other languages. Significant efforts have been made in non-English language technology. Yet, an unintended consequence of improving English language technology means that we are creating and introducing technology that does not serve everyone equally.

What issues should AI users be aware of?

I think people should be aware that AI is not a silver bullet that will solve all problems in the next few years. It may be quick to create an impressive demo, but it takes a lot of work to create an AI system that works consistently well. We should not ignore the fact that AI is designed and built for humans.

What is the best way to build AI responsibly?

Building AI responsibly means incorporating diverse feedback from the start, including from your users and anyone affected by your product. Thoroughly testing your systems is important to make sure you know how well they perform in different scenarios. Testing gets a reputation for being a boring chore compared to the excitement of dreaming up new algorithms. Still, it's important to know if your product really works. Then there's the need to be honest with yourself and your users about both the capabilities and limitations of what you're building so that your system isn't abused.

How can investors best push for responsible AI?

Startups are creating many new applications of AI, and investors have a responsibility to be thoughtful about what they choose to fund. I'd love to see more investors voice their vision of the future we're building and how AI is responsible.

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