As a Data Scientist, it is Nela Reichsoellner's job to ensure that machines and vehicles can make intelligent decisions by themselves in the future. In tandem with her colleagues at Daimler TSS, she develops applications for machine learning and predictive maintenance, which are being used across the entire Group – from production right up to the vehicle itself. In her interview, the artificial intelligence specialist explains why intelligent machines are important for our future society, how data can make mobility safer and what role a hackathon played in her joining Daimler.
Ms Reichsoellner, at Daimler TSS you work on concepts to make machines more intelligent. What exactly does that involve?
Daimler TSS is our internal IT consultancy: We create holistic IT solutions for Daimler. This means that the different specialist units at Daimler come to us with their questions and data sources – for example in relation to a project on automated driving. Our various teams then support the specialist units, develop models, and make the data usable. My team looks at the topic of artificial intelligence (AI) and how it can be used for what we call ‘predictive maintenance’.
What exactly is predictive maintenance?
With predictive maintenance, we use artificial intelligence to predict precisely the technical condition of the vehicle. For example, when a component will soon be at its wear limit, the driver receives a warning in good time, before something breaks. This makes vehicles safer.
And the topic of ‘computer vision’?
This is all about artificial neuronal networks that learn from images. With the help of image data from a camera in the car, the systems register whether a pedestrian is standing or walking and what direction he is moving in. The result could then feed into the decision on how a self-driving vehicle behaves in traffic. We are currently working on a project with an application that, using images, recognizes our vehicle models and can precisely deliver the technical details to match.
Breakdowns will soon be a thing of the past thanks to artificial intelligence and predictive maintenance. In this way we make vehicles safer.
Where does your enthusiasm for data streams and artificial intelligence stem from?
I am just an AI person (laughing). We often work with very complex algorithms or have huge amounts of data. I find these kinds of challenges great. In my work, I develop applications that will support people in their daily life in future and that ensure greater safety. That is something really special. With artificial intelligence, in future we can have the machines recognize the correlations in data which are currently just too complex for us. This way, we will be able to achieve solutions that seem unattainable today. This offers us and our society huge opportunities.
What do you enjoy most about your work?
My work is very diverse. I spend a lot of my time programing. I enjoy developing applications and algorithms myself and to further the area of AI. I also like being invited to universities to lead workshops on occasion. It’s very refreshing to work with students.
How do you work together in your team?
There are seven colleagues here in Stuttgart, and a further eight in Ulm. We exchange our experiences and ideas on our projects and help each other find the best solution. Our team works on an interpersonal level – and that applies beyond the work sphere as well. Since we cannot meet in person on account of the Corona situation we even organized a few digital meet-ups after work.
How did you end up at Daimler?
I was studying media informatics at the Hochschule der Medien in Stuttgart and took part in the Daimler TSS Artificial Intelligence Garage Hackathon my team took third place in the programming competition and the contact people at Daimler TSS asked us to collaborate on an AI project. That was exciting and the creative collaboration with the colleagues was fun right from the beginning.
During my studies, I took part in the Daimler TSS Artificial Intelligence Garage Hackathon. Afterwards, the team asked me to work on a project with them.
And then you joined Daimler?
I first wrote my bachelor thesis with Daimler as a partner. My thesis was about a project in Computer Vision and the question on how vehicles are identified in satellite images – for example to recognize tailbacks. When I finished my studies, I then joined my current team in 2019.
What characterizes Daimler as an employer for you?
Daimler gives me the opportunity for personal development. I am starting my MA in Artificial Intelligence at the University of Bath. I consciously chose a university in Great Britain as I wanted to gain a different perspective on the topic. Daimler is flexible in this respect and what's more, Daimler cooperates with many universities. When it comes to studying and further training, there are very many opportunities here.
One last personal question: If you could travel in time, where would you go?
I have always been interested in history. I even studied history and archeology. I find ancient times especially fascinating. Back then, people had a very cosmopolitan culture and were ahead of us in many respects. The Etruscans were well advanced when it came to equality between men and women, for example. A journey to this time in history would surely be exciting! (laughing).
Nela Reichsoellner's enthusiasm for algorithms and data streams is in the family – after all, three of her four brothers are graduate computer scientists. Romanian by birth, she initially studied history with English as a second subject because she wanted to understand the history of the country she was living in better. She later studied archeology in Innsbruck. After moving to Stuttgart and inspired by her brothers, Nela Reichsoellner started studying media informatics at the “Hochschule der Medien” in Stuttgart. An internship offered her the opportunity to test an automated vehicle – and she was immediately fascinated by the technology behind it. Now, she uses her analytic abilities to not just make vehicles and machines at Daimler more intelligent, but she also uses her skills in her private life. In a current project, she combines her passion for AI and archeology, developing a concept for an app that uses images to precisely date historical objects and match them to a culture.