HTEC Group continues to celebrate women in Tech. In the next following weeks, we will introduce you to some of our amazing colleagues and their experience as women working in the field of Tech.
This week, our guest is a wonderful woman who deserves much more than this short interview, and surely we will extend the talk with her in one of our future HTEC Features. Aniko Kovac Krnjajic is a fresh new force in HTEC’s Machine Learning team.
Can you tell us a bit about yourself, your schooling, and your first jobs? Did you always know that working in tech was what you wanted to do?
Looking back, they say hindsight is 20/20, so — while I did have an unusual path to tech – in retrospect, it all kind of makes sense. My academic background is in the field of Linguistics, but I have had an interest in programming ever since elementary school when I exhausted all of the popular introductory computer courses, and the only one that was left was a programming course in Delphi, so I ended up taking it.
Fast forward into university, while doing my undergrad in linguistics, I stumbled upon the field of computational linguistics, which combined two of my seemingly diverging interests — programming and linguistics. I ended up studying Language Science and Technology in Germany for my master’s, which gave me more hands-on coding experience and provided me with a computer science base. I had the opportunity to be involved in some amazing projects and work on language modeling and text-to-speech synthesis, which led me to a sister-field of Computational Linguistics, Natural Language Processing, which is what eventually led me into Deep Learning and AI.
I was incredibly lucky to experience this versatility on my path, and even more, so that I was able to give back to others on their growth path through my involvement in education. I worked with a major science institution in Serbia as an expert associate teaching and mentoring high-school students, and I also had the chance to shape the future of tech through working with an educational startup based in the US reimagining STEM education.
Today, at HTEC Group, I am bringing all this, and more into my role as a Machine Learning Engineer, while also continuing to grow by working on my Ph.D. thesis with a topic in Computational Linguistics.
What do you find the most attractive about Natural Language Processing and Machine Learning?
I’m obviously biased here, but the opportunity to blend language research with coding is just too tempting to resist. From a theoretical perspective, I view the goals of Natural Language Processing — to perform language generation and analysis in a way that is indistinguishable from or even better than that of humans (at least in some aspects) truly interesting. I still remember the excitement I had over Karpathy’s article titled “The Unreasonable Effectiveness of Recurrent Neural Networks” in which we were able to see Shakespeare-style poetry generated by a machine learning algorithm trained solely on the probability of the occurrence of the next character. At this time, the field is making major advancements closing the gap between the way deep learning models work and what is postulated by theoretical approaches to language. So, it is really interesting to follow along and see how theory and implementation help shape each other.
Admittedly, getting involved with Machine Learning and Deep Learning also broadened my interest beyond language, so I am trying my hand in computer vision as well these days, and it is interesting to see just how much more we can accomplish with the help of these technologies.
There are many concerns about the lack of control and legislation around these new technologies. What’s your attitude towards this subject? What do you find the most problematic about ML and AI?
Being a popular topic, there are a lot of misconceptions about what AI is and what it can bring to us in our daily lives. I think nowadays most of us are using AI-based technologies daily without even realizing it to assist or automate our everyday activities like taking a photo with our phone, finding the next show to watch, being able to quickly translate from a foreign language, searching the web more efficiently, or even to receive oddly well-targeted product recommendations.
We are generating astonishing amounts of data daily which is what drives AI advancement and constantly broadens its areas of use. One of the major concerns is that we see companies using this data at scale without considerations of the possible environmental impacts of training these large-scale models on one hand, and — perhaps even more importantly — the lack of transparency of these systems. Raw, unprocessed data is only as good as its source, and, unfortunately, oftentimes the source is riddled with inherent bias. Ethical AI is working to bring some of these problems to light, but, being a cutting-edge technology with fast-paced advancement, we are only scratching the surface of regulation that will make sure this happens always and consistently and is governed by unified principles.
Can you tell us a bit more about the people you work with? I hear that people who “train” machines how to “think” are, in greater numbers, coming from non-engineering backgrounds?
I work with an amazing cross-functional team and we bring with us a range of experiences and interesting backgrounds that I believe make the project better. In Machine Learning specifically, you tend to find a versatile mix of people, engineers, and non-engineers alike, which makes sense in a lot of respects as we are not only working on implementing algorithms, but also developing them, and on preparing the data that is being used – a process which can often benefit from a specific subject matter expertise. I’m excited to see astronomers, psychologists, linguists, mathematicians, and others using these technologies more proactively and being involved in the process of shaping the future of data, machine learning, and AI.
What would be your advice for the women who are considering this profession?
Do it, and feel free to reach out for a chat if you are unsure where to start.
Can you name one woman who has inspired you in your career, and what was the lesson you learned from her?
It feels like this should be the place to name-drop a celebrity, but in my case, I was lucky to be surrounded by wonderful, inspiring women across the board who are not only experts in their respective areas but care deeply about supporting other women in developing their careers as well. I feel my path in Computational Linguistics was paved by Tanja Samardzic who is currently a Group Lead at the Language and Space Lab of the University of Zurich. I also received tremendous support in my journey by the researchers at the Faculty of Philosophy at the University of Novi Sad who helped fuel my curiosity in an area that on occasion may have seemed conflicting from the point of view of theoretical research. And last, but not least, I feel grateful to have had the chance to work with mentees and students who helped me grow by learning while teaching, and asking some of the toughest questions I came across to date.
What do you think is the best part of being a woman in the tech industry?
Tech is a booming industry with many opportunities. Working in a company like HTEC Group specifically, a company that cares deeply about company culture, work-life balance, and the overall happiness of their team members, there is a positive environment that fosters both professional and personal growth regardless of your gender.
At the same time, we cannot ignore the many aspects of the gender gap that is present in the industry, and in a glass-half-full manner, being a woman in the industry gives me the privilege to help pave the way for not only future generations but for my peers and colleagues who are looking to find an environment in which they can thrive.
