As a software engineer at Vulcan, Gracie Ermi is always thinking about how she can use technology.
Whether she’s developing artificial intelligence applications (AI) to help give conservationists the tools they need to protect endangered animals in Africa or integrating the latest technology to count sharks, Ermi is always looking for ways she might be able to use her work for social good.
We sat down with her to talk with her about how she applying artificial intelligence (AI) into wildlife conservation, the challenges she’s encountered and what is giving her hope.
How are you using technology like machine learning in your work?
We’re applying AI to help process and filter huge amounts of data so that we can equip people with the relevant information they need to help them do their job even better than they already do.
By using machine learning, we’re finding that we’re able to take this information and use it to play a crucial role in protecting elephants, rhinos and other endangered species through multiple projects across Vulcan. For example, we’re using it in our work to modernize wildlife surveys and our partnership with Sealife Response, Rehabilitation, and Research (SR3) to automatically analyze the health of killer whales.
What has surprised you most about working with AI?
Since coming to Vulcan as an intern and now in my full-time job, what has surprised me most is how hard it is to build useful AI products. It’s one thing to train machine learning models, but then integrating them into a system that people can actually utilize in their daily workstream is a much harder challenge that requires a lot of input from users. That side of building AI isn’t as fashionable right now, but it is essential to creating AI solutions that are accessible and impactful, which is the whole point after all.
What’s the biggest challenge you’ve run up against with AI and wildlife conservation?
We have to take hard looks at how we’re incorporating AI into wildlife conservation. Identifying places where AI could have a real benefit is a challenge. AI is not a magical solution to every problem, so we need to think critically about what projects AI fits into rather than trying to add AI into every project just to say we did.
How were you inspired you to become a software engineer?
I actually didn't learn how to code until I got to college. I really liked math and all kinds of science in high school and thought I wanted to be a physicist or pursue a career using a math degree. But, then I discovered computer science and was intrigued by how many different ways it could be used for helping people and our planet.
What makes you optimistic about the work you are doing?
Talking to wildlife experts always gives me a lot of hope. The fact that they really do see the technology that I am developing as something that can be useful in their work really drives home the impact that we can possibly make. I also think that the next generation deciding to pursue fields that will make a difference in the way we’re fighting climate change is so encouraging – they are going to help us find a solution to what seems like an impossible challenge.
Where do you find motivation?
Working with younger girls is a huge motivation factor for me. As a college student, there were very few women in tech that I could model my own career after. So, being able to now be that for the next generation of girls is always pushing me to do more.