Gloriose
Meet Gloriose
Gloriose worked as a data science engineer before pursuing a Masters in Artificial Intelligence in 2019/20 at Queen Mary University in London.
Her aim has been to build on her knowledge so that she can contribute more to her team and understand how AI can help empower smaller communities. According to Gloriose, the support of her DeepMind scholarship has allowed her to focus on her studies without feeling financial pressure, and to gain precious insights into a career in research.
"Your voice is needed. Your experience is needed. Your perspective and understanding are needed. Work for the people and communities that are often forgotten, and don’t be afraid to get involved.”
In conversation with Gloriose
How did having a DeepMind mentor help your development?
My mentor helped me look at different ways to tackle my project, and shared his own experiences which really helped me have a realistic expectation of working in the field. He mentioned that his first year at DeepMind was like the first year of a PhD, which highlighted the importance of having a learning and teachable mindset if you want to go far as a researcher. I wished I’d had more conversations with my mentor; even the few conversations I had with him were very enlightening. He was also very relaxed and down to earth, which I really appreciated.
What’s next in your career? What’s one thing you want to accomplish moving forward?
I have always been passionate about the use of AI to help and empower smaller communities, because personally at the end of the day, my community is the place where I belong - it is home. I have been wondering about how we can use AI to provide personalised solutions to sustain and help micro-communities. I hope to help do this by working with my own community: for example, I’ve recently been volunteering to help build computer science skills among younger people with African heritage.
What is something you learned as a DeepMind scholar that you’ll always remember?
Researchers in the company come from very different backgrounds.
The field of AI is in need of diverse participation. That includes engineers and those who are experts in the field where AI solutions are needed most. For example, if AI solutions are needed to predict students' grades, getting educators, sociologists and ground-level experts involved could definitely help in building a more accurate predictor.
What advice would you give to someone from an under-represented background considering pursuing a degree in the field of AI and machine learning?
Your voice is needed. Your experience is needed. Your perspective and understanding are needed. Be involved in the design and don't be afraid of being underrepresented. Work for the people and communities who are often forgotten.
