"There's more math and less magic behind AI than you might think."
Nora Engleitner holds a PhD in technical sciences. After her studies of mathematics at the JKU Linz and her doctorate at the Institute of Applied Geometry, she now works at Newsadoo in the Data Science department. What many people are probably asking themselves right now - why mathematics? The answers can be found here:
Project week "Applied Mathematics" at the age of 16. You finished your doctorate at 27. Your talents lie in areas in which others struggle with exams. What do you like about this field?
I am fascinated by the beauty of mathematics. It allows us to describe so many processes in our daily lives and it plays an important role in many areas. It's also a great feeling to prove a theorem after a lot of tinkering. I also appreciate this precision in mathematics. A statement is either true or false - there is little room for interpretation.
Where does this interest - and passion - come from?
I originally attended a high school with a focus on foreign languages and for a long time I wanted to study something along these lines. Until my physics teacher at the time told me that I should consider studying mathematics after. After two project weeks "Applied Mathematics" I was convinced. There I got an insight into the mathematics studies at JKU and saw how this subject area can be outside of school. After graduating from high school, I applied for a bachelor's degree in technical mathematics and then studied in a master's degree in industrial mathematics. During this time I also spent one semester in Texas. Afterwards I completed my doctorate at the Institute of Applied Geometry, although I never actually intended to do so. I received my doctorate in May 2019 and since November 2019 I am part of the Newsadoo team.
Mathematicians are often imagined with smoking heads in front of a blackboard with many complicated formulas, as professors at universities or at schools. And less in the media industry. Why did you choose a job at Newsadoo and how can you use your skills there?
Mathematics is very versatile. Just like us mathematicians, people often underestimate it. We can actually be employed anywhere, where logical thinking, a quick comprehension, structured problem solving and precise work is required. In our studies, we also learn how to program, for example. From banks to insurance companies, in consulting, in engineering companies, in research, in companies specialized in data science - you can find us everywhere. I find it exciting that Newsadoo, as a news aggregator, faces Google and Co. That's an important task. From a technical point of view, there is also a wide range of topics that interest me. Together with the flat hierarchies and the start-up spirit, a lot can be tried out and realized. Whether it's machine learning or data science, I learn a lot every day and discover new ways of doing things. There's more mathematics and less magic behind AI than you might think.
What do you enjoy most about your work at Newsadoo? What has been the biggest challenge so far?
I enjoy the fact that I'm constantly learning new things and that I'm always faced with problems that challenge me mentally. When I started at Newsadoo, for example, I knew very little about machine learning and natural language processing in particular. In the last year I got to know an incredible number of exciting methods and concepts. I can decide how I solve tasks and how I bring in my ideas. Being responsible for the entire AI area is quite challenging. But I can develop things according to my own ideas. There are a lot of hurdles - it's hard to say what the biggest one is. At the moment it is probably the management of our FFG project, in which we are developing our recommender system together with SCCH and RISC Software GmbH. It's not so easy to coordinate all project teams. Definitely a different challenge compared to developing algorithms.
How do you think Newsadoo's future looks like from a technical point of view? If you could see in the next weeks, months: What topics will keep you and the team busy then?
Definitely: Recommender Systems. We want to offer our users personalized news content, but we don't want to forget about diversity. People should be able to inform themselves comprehensively about news and benefit from the diversity of the media landscape. This balance is a hot topic, just like transparency in recommendation algorithms. It must be easy to understand who is suggested which news, why, and how. Filter bubbles and echo chambers can also be blown up in this way.
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