Researcher, SINTEF Digital
How did your journey in AI start - what brought you to AI?
I came into AI and machine learning from astrophysics, where statistical analysis of large and heterogenous data has many similarities with modern machine learning. After 10 years in basic research, I had a desire to work on more practical problems, and decided to put my skills into applied research.
What is the biggest change you have observed in AI compared to when you entered the field at the start of your career?
At the start of my career, I did statistical data analysis. Today, it’s called machine learning and everyone is it.
What excites you about working in AI, and what are you working on right now?
The world face severe global challenges. First and foremost climate changes and reduction of biodiversity but also wars and unfair distribution of resources. For me, AI and machine learning are tools that may help solving some of these problems like reducing emission or exploiting resources in a better way. Currently I work on how machine learning and optimization can be used together in industrial settings to e.g. reduce emission from road construction, detecting faults in windmills, or fine-tuning cardboard production machines to produce less waste.
How do you see the number of female researchers and professionals in AI grow? How do you involve yourself in initiatives to introduce more women to AI, and what initiatives can we make to inspire more women to pursue careers within AI?
On a daily basis, my focus is on developing AI for solving industrial problems with little concern for gender biases. I guess my main contribution to introduction and recruiting women for AI is via showcasing diverse problems need diverse problem-solvers.
Why is it important for more women to get involved in AI?
Real world problems are diverse, and hence the solutions also need diversity. Why shouldn’t we recruit problem-solvers from the entire population?
What advice would you like to give women who are pursuing their careers in AI?
I would give the same advice to anyone pursuing a career in AI. I see a field that’s under continuous and rapid development, so the most useful thing I have learned is to learn. You will continuously need to update your knowledge. At the same time AI and machine learning are complex systems, so you need the ability to sit down and go through difficult material until you understand it. In addition, I have had to learn to prioritize and focus on doing things that create value for me and for the world even if my contributions are small and highly specialised in the big picture.
What advice do you have for organizations who wish to recruit and retain female researchers and professionals in AI?
The AI researchers and professionals are, and should be, a diverse group. That means that they also need diverse opportunities and diverse working conditions etc. Organizations can aim at enabling people to work for their passion and actively remove stress factors by providing salary, stability, inclusive work environments, work-life balance etc.
Who is your biggest role model within your field, and why?
All the students and young researchers working with AI. They’re smarter than me, have had better training and most of them seem to have higher ethical standards than previous generations. They want to solve problems not create them!
What role can NORA take on to empower diversity and inclusion in AI in Norway?
I want to commend NORA on setting high standards for gender representation at all events and actively addressing the issue. That’s an important part of showcasing diversity. NORA can in addition contribute by sharing research and guidelines for best practices.
Signe Riemer-Sørensen, PhD, researcher at SINTEF Digital. Her expertise is focused on the development of hybrid machine learning algorithms combining data driven methods and domain knowledge, for use in industrial settings, in particular within the domains of energy, construction and logistics.