Language technology combines alumna Tiina Lindh-Knuutila's interests

According to Tiina Lindh-Knuutila, language technology and machine learning offer enormous opportunities for many kinds of people
Tiina Lindh-Knuutila
'A degree from a good university and the networks built there carry really far. I established a lot of good friendships in my student days, and we are like a network of mentors to each other,' says Tiina Lindh-Knuutila. Image: Marko Knuutila.

Tiina Lindh-Knuutila works as a solutions architect at Lingsoft, a company providing language and translation services. She has had a grandstand view of how language technology and machine learning have taken major leaps forward in a short period of time. According to Tiina, the demand for solutions offered by language technology is growing constantly, and employers are practically begging experts in the field to work for them.

How did you become interested in language technology in the first place?

It all began from my wide-ranging interest in language and communication, but also in technical solutions that enable closer examination of languages and make people's lives easier. I have been relatively good in problem solving, mathematics and physics, and I come from a family with engineers.

I applied to study technology in a major with the highest number of optional courses on the offer. Later, language technology appeared as an option to major in, and I thought it would conveniently combine my interests. I also integrated some studies in general linguistics and phonetics at the University of Helsinki into my degree.

What was the best part of your studies?

I enjoyed everything! We had a wonderful group of students, and I liked being involved in guild activities. I served as a Master of Studies at the Guild of Electrical Engineering (Sähköinsinöörikilta) and served in its committees for several years. The guild activities gave me a chance to influence how the university and the students work together.

Teaching was good, and student life was really active. I also made an exchange visit to Telecom Paris University in Paris, which I would certainly not have been able to get into otherwise, but the Erasmus exchange programme made it possible. Aalto University has good cooperation networks, and being in exchange was an amazing experience!

What kind of working life tools did your studies give you?

When you have succeeded in your studies at this level, you learn self-direction and a mentality needed for working hard. In my studies and guild activities, I learned a lot about how things are done together and how matters work in meetings, for example. I also got a postdoctoral researcher position at Aalto University, as, after my doctoral thesis, I stayed at Aalto and worked in the Imaging Language research group at the Department of Neuroscience and Biomedical Engineering.

A degree from a good university and the networks built there carry really far. I established a lot of good friendships in my student days, and we are like a network of mentors to each other. When we see each other, we also talk about work and, without even noticing it, keep on learning more from each other.

I thought that my skills profile would be useful because I’m fluent in code and the languages spoken by salespersons and bosses.

Tiina Lindh-Knuutila

How did you end up in your current job?

I work as a solutions architect, or a general expert in language technologies, at Lingsoft. In my work, I try to make the customer's needs and the services we provide meet. For example, our customers may need speech recognition to make patient records dictated by doctors transformed into text faster or subtitling services, the demand for which has grown due to new accessibility requirements.

I ended up at Lingsoft simply by applying for a job – I know people in the field of language technology and thought that my skills profile would be useful because I’m fluent in code and the languages spoken by salespersons and bosses.

How does the future of the field look?

Fantastic! The opportunities are vast, and employers practically beg people in this field to work for them. Today, the level of technology is very high: the range of tools is enormous, and the computing capacity of computers has increased. The level of machine learning has improved in technical sense, but we still need a lot of experts who understand how things work. The sector is also beginning to understand ethical issues better. For example, in a situation where job applications are being classified into good and poor ones based on old applications, there is probably some bias error that we need to understand. The classification system may be, say, racist.

What kind of tips would you give to those starting their master's studies?

I don’t think everyone needs to understand algorithms at the level that they can modify them. However, it is important to understand probabilities and whether the algorithm can be trusted. It is also a good idea to deepen your theoretical understanding and not to take applied courses only. Nor should you be afraid of project courses that address actual problems.

I would recommend the sector to everyone interested in it. The field will not develop if there are only the same kind of people here. Technology needs different perspectives, because then we learn to take different kinds of matters into account. There is an enormous number of roles available: if you want to tune algorithms and spend less time talking to other people, you can do so. On the other hand, there are also roles in which you can interact with other people daily.

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