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How to frame a good research problem? Part 1

This time we talk about challenges related to the formulation of a research problem and about ideas for interesting research with Dr. Tomasz Michalak, leader of IDEAS NCBR’s work team.

25 July 2022

A good research problem – what does it mean exactly? What is it characterised by?

TT: A good research problem is crucial to any researcher’s work. It allows them to choose one issue they want to dedicate their time and energy to from hundreds of thousands of exciting issues. This requires them to define a strategy for how they will do science. A researcher has to decide whether he wants to choose a topic that is relevant, broadly understood but also very much under siege, or one that is more niche and original, although requiring a greater understanding of the research community? As a general rule, the work of formulating a research problem should start by asking a question, e.g. whether the performance of a neural network depends on the number of layers, or by posing a hypothesis, e.g. deep neural networks get better results than similarly constructed networks with less depth and then verifying it. A characteristic feature of a well-formulated research problem is that the answer to the question posed is not apparent and requires experimentation.

What are young researchers’ most common mistakes when looking for a research idea?

TT: I think that both young and old scientists, as a rule, make many mistakes. At the end of the day, that is what science is all about, to be wrong and then arrive at the truth based on those mistakes. When looking for an idea for a study, it is worth paying attention to several aspects: whether my idea is original, whether someone has not already come up with a similar method, and whether the problem actually posed is well motivated, e.g., real applications or limitations of existing methods, finally: is this topic fascinating to me? Often, as researchers, we try to follow the crowd and focus on popular issues or of interest to colleagues. It is worth following your own interests and taking advantage of your strengths – every one of us has a child’s curiosity, and it is worth getting carried away.

Where to look for inspiration?

TT: Life brings the best inspiration, especially the practical application of the methods described in the scientific articles. It often turns out that methods that are extruded on synthetic data sets and tested in laboratory conditions, in practice, are worthless. It is also helpful to periodically look at and participate in the most important events related to our research, especially scientific conferences on machine vision and machine learning methods. These are conferences such as CVPR, NeurIPS or ICML, where my co-authored papers have been published.

WE HAVE SET OUT TO INCREASE THE R&D POTENTIAL IN THE FIELD OF ARTIFICIAL INTELLIGENCE.

How has the process of formulating a research problem changed over the years? What did it look like at university, during your doctorate, and what does it look like now?

TT: The formulation of the research process takes a more general form over time. At the doctoral level, I needed to focus on one particular aspect of computer vision: building compact and efficient image descriptors using machine learning techniques. As time goes on, my research interests, as well as the scope of my work, generalize and allow me to work more holistically – I am still interested in the performance aspects of machine learning algorithms, but I am no longer limited to image representation – I consider both issues of computational conditioning, continuous learning or other aspects of performance.

Do researchers consider any external factors when thinking about a research problem? Do events that happen in the world or the facility of obtaining funding affect this process?

TT: Of course. People manage research funding agencies. Grant applications are also awarded by “mere mortals”. Like all of us, they are exposed to the general information noise, follow trends and have their views. You have to take these factors into account, but it is worth finding a niche for yourself that suits your research interests, makes you curious and gets you out of bed in the morning. Without such intrinsic motivation, it is not easy to conduct meaningful research projects. On the other hand, if the research ideas are completely disconnected from reality, the chance of implementing the results will be low, and yet we all, especially scientists, like to see the results of our work and feel that it is of value.

I do hereby certify the conformity of the above translation with the document in Polish provided to me electronically in the form of an electronic file.

mgr Maciej Jęczmiński, a duly sworn translator of English, entered in the Register of Sworn Translators and Interpreters maintained by the Minister of Justice under number TP/73/21.

Repertory No. 325/2022

Kielce, on 20/07/2022

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21 June 2022

The IDEAS NCBR’s working group that will deal with research in the field of computer vision will be headed by habilitated doctor engineer Tomasz Trzciński, professor at the Warsaw University of Technology and at the Jagiellonian University. The research agenda of the group will focus on issues related to the effectiveness of artificial intelligence models both in the context of the accuracy and pace of computations, as well as resources necessary for their operation.

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