As every year, deepsense.ai invited talented and ambitious students interested in internships in the field of AI to apply for the Summer Internship Program. From over 2000 applications, the 10 most talented students were chosen to join our team.

deepsense.ai’s annual Summer Internship Program has been very popular among young AI specialists for many years now. From the very beginning, our primary intention has been to establish long-term cooperation with the most talented AI enthusiasts.

“The 2022 edition of the Summer Internship Program was unique in many respects. First of all, we registered a record number of applications – we had over 2,000 applications for 10 vacancies. What’s more, 8 out of 10 of our interns have continued cooperation with deepsense.ai as full-fledged team members”
said Renata Maszczykowska, HR Manager at deepsense.ai.

The success of the SIP would not be possible without the substantive and promotional support of the Polish Business Roundtable as part of the Career Program. For over 19 years PBR has been offering internships at the most reputable Polish companies, connecting ambitious students with the creators of Polish business. deepsense.ai joined the prestigious group of companies offering internships in 2018. The program, built on many years of experience and the support of experts from the Polish Business Roundtable, not only provides the highest-quality practical knowledge for interns, but also provides invaluable substantive and promotional support for the companies offering the internships. Every year, PBR conducts detailed intern satisfaction surveys, which provide additional insights for the participating companies. This year, deepsense.ai received an overall rating of 5.5 out of 6 in the satisfaction survey.

During the three-month program, interns expand their knowledge in the field of artificial intelligence and machine learning, undertaking real solutions as part of our commercial and R&D projects.

“Transformational – that’s the best way to describe my experience at deepsense.ai. From the very first day, I felt that I had to learn and adapt quickly in order to truly support the team. I was placed in four live projects with real clients, deadlines and expectations. Throughout those three months I had the opportunity to present deepsense.ai’s solutions and products to our client’s board members, I conducted competition research and was also allowed to develop my own project. Even better, after my internship was over I was offered the opportunity to work for deepsense.ai remotely from the US, where I’m currently participating in a student exchange at the University of California!”
said Jan Rosek, a participant in the Summer Internship Program 2022, who is now working as a Junior Business Analyst at deepsense.ai.

“Participation in the internship program at deepsense.ai gave me an opportunity to work on real-life projects with international clients. It was (and still is!) a significant boost for my skills and personal development. My teammates’ openness and eagerness to help are mind-blowing and thanks to that I can really feel the progress I’ve made”
said Natalia Czerep, a participant in the Summer Internship Program 2022, who is now working as a Junior Data Scientist at deepsense.ai.

Recruitment for the next Summer Internship Program at deepsense.ai will start in February. We invite all interested students to follow the information on our website and social media.

deepsense.ai Data Scientist Jakub Pingielski has won a gold medal in Kaggle’s competition HuBMAP + HPA – Hacking the Human Body. Jakub took 12th place out of 1175 participants, coming in only 1.5 points behind the winner.

The competition was connected with the Human BioMolecular Atlas Program (HuBMAP), working to create a Human Reference Atlas at the cellular level. Sponsored by the National Institutes of Health (NIH), HuBMAP and Indiana University’s Cyberinfrastructure for Network Science Center (CNS) have partnered with institutions across the globe for this endeavor. A major partner is the Human Protein Atlas (HPA), a Swedish research program aiming to map the protein expression in human cells, tissues, and organs, funded by the Knut and Alice Wallenberg Foundation.

The task for the contestants was to identify and segment functional tissue units (FTUs) across five human organs. Participants developed models using a dataset of tissue section images, with the best submissions segmenting FTUs as accurately as possible.

“In my opinion, participating in Kaggle competitions is a great opportunity to learn state-of-the-art machine learning models and techniques from leading practitioners. I decided to participate in this contest while working on a commercial project in a similar domain at deeepsense.ai. The lessons learned from this competition enabled me to provide best-in-class performance for our client. Moreover, competitions like this are very satisfying to participate in since you know that your solution will be used for a good cause,”
said Jakub.

The key to achieving the best results was the application of very strong data augmentations while training deep neural networks. By doing so, Jakub was able to train a model that was robust to domain shift and performed equally well on microscopic images from different medical databases. The core of the training pipeline was based on the Vision Transformer model which has proven to perform better than models based on Convolutional Neural Networks. Additionally, model ensembling and test time augmentation made it possible to squeeze out model performance.

The results of the competition were of significant value – with a better idea of the relationship between cells, researchers have obtained further insight into the function of cells that impact human health. Furthermore, the Human Reference Atlas constructed by HuBMAP will be freely available for use to researchers and pharmaceutical companies alike, potentially improving and prolonging human life.

It has become tradition for deepsense.ai to be a partner of the ML in PL conference. Every year, the event is focused on discussions around machine learning best practices in both academia and business. The 2022 edition, which will take place between 4-6 November, will feature top international ML experts.

On behalf of deepsense.ai, Robert Bogucki, CTO, will take part in the panel discussion “Industry and Academia”. During the panel, the wide range of implications of AI will be investigated and the guests will delve deep into both positive and negative impacts on science and industry.

Moreover, Wojciech Szmyd, Data Scientist at deepsense.ai, will share insights about the application of natural language processing techniques to the social media domain during the presentation “TrelBERT – a model that understands the language of social media.” In this talk Wojciech will explain what is so interesting in the language of social media and why this topic requires special attention.

“I’m super happy to have this opportunity to present the TrelBERT model –  deepsense.ai’s latest contribution to the advancement of Polish NLP.  Our model has achieved outstanding results in detecting cyberbullying and has brought us a great deal of satisfaction.”
– Wojciech Szmyd, Data Scientist at deepsense.ai.

The ML and PL conference is organized by the ML in PL Association – a non-profit organization devoted to fostering the machine learning community in Poland and promoting a deep understanding of ML methods.

With the growing importance and popularity of social media, the application of natural language processing techniques to this domain is becoming an increasingly trendy topic among NLP researchers. As part of an R&D project aiming to contribute to the advancement of Polish NLP, deepsense.ai experts have introduced TrelBERT, a language model for Polish social media which has achieved outstanding results in detecting cyberbullying.

“We actively follow the latest international developments in the field of NLP and, as part of the R&D project, our team has decided to take up the challenge and support the advancement of Polish NLP. Working on TrelBERT not only brought us a great deal of satisfaction, but also allowed us to learn how to handle the rather informal language used on social media. When examining the resulting model, we discovered that it knows a lot about Polish sports and politics – topics frequently discussed online by humans”
said Artur Zygadło, Lead Data Scientist at deepsense.ai.

The typical approach to training modern NLP models is so-called transfer learning, which assumes the development of one large NLP model that captures general knowledge about the language, and can then be trained to perform various specific tasks. The starting point for the experiments was HerBERT, a solution previously published by Allegro – the company behind the largest e-commerce platform in Poland. Their model already knew the general rules, words and their meanings in different contexts in the Polish language, which it learned by “studying” texts from various sources: CommonCrawl, the National Corpus of Polish, movie subtitles or Wikipedia. What it did not know about was the language of social media, which differs significantly from texts in the resources listed above. And this is what deepsense.ai’s team taught it.

The model created by deepsense.ai utilized HerBERT and was further trained using almost 100 million messages extracted from Polish Twitter. The decision was made to name the model TrelBERT, as “trel” is a Polish word describing the sound made by a bird (referring to “tweet”).

To evaluate TrelBERT against the tasks included in the Polish NLP, a benchmark called KLEJ (analogous to the famous English GLUE benchmark) was used. One of the tasks that was especially interesting was one which served the purposes of cyberbullying detection. The goal was to prepare a solution capable of determining whether a given Tweet is harmful or not. In this particular task, thanks to being trained on Twitter texts, the model surpassed all the other competitors and is currently holding the top position on the KLEJ leaderboard (in the “CBD” column – results for cyberbullying detection).

“It is no surprise to me that our model has achieved the best results in the cyberbullying detection task. This problem was born on social media and only exists there. We suspect that our model would beat the competition in other social media-related tasks, where content is spontaneous and generated by users, such as sentiment analysis or emotion recognition. Unfortunately, the Polish NLP community lacks such datasets”
said Wojciech Szmyd, Data Scientist at deepsense.ai.

Apart from submitting the model’s results to KLEJ, deepsense.ai made TrelBERT available in the popular Hugging Face repository of models. You can give the model a try and read more about it here.

neptune.ai – metadata store for MLOps – spun out of deepsense.ai, has raised $8 million in series A funding led by Almaz Capital with the participation of the existing investors: btov Partners, Rheingau Founders, and TDJ Pitango Ventures.

Led by Piotr Niedźwiedź, neptune.ai offers companies dedicated metadata storage that provides a central place to log, store, display, organize, share, compare, and query all metadata generated during the lifecycle of a machine learning model.

Since its launch in 2017, the store has brought in more than 20,000 ML engineers and 100 commercial customers, including Roche, NewYorker, Nnaisense and InstaDeep. The platform usage has grown eightfold over the past eight months while revenue has surged by four times.

“I am super proud of this, another great achievement for Piotr Niedźwiedź and his outstanding team. I have known Piotr since early 2011 when we established our first company together. Now, more than 11 years later, our passion has evolved into a strong group of cool tech companies – including, but not limited to: neptune.ai, deepsense.ai, CodiLime and Dealavo – with proven track records in delivery of professional IT services and products.  I feel this is still just the beginning of a fantastic adventure and our joint journey to the moon, as Piotr used to call neptune.ai. Dear Stakeholders – please fasten your seatbelts and keep your eyes open (and fingers crossed)!”
– Tomasz Kułakowski, CEO, deepsense.ai