Dutch Summer of AI

Students: apply now to join the Dutch Summer of AI (last call)

Dutch Summer of AI, an annual programme focussed on driving artificial intelligence (AI) adoption in the Netherlands, is back for its 2022 edition. The Dutch Summer of AI offers an unique opportunity for students to experiment with data and AI in practice, meet peer and top employers in the industry.

If you are in the final years of bachelor or master’s programs (universities and universities of applied sciences) in data science, artificial intelligence, computer science, econometrics, mathematics (and other relevant studies) with a keen interest in AI, this is the summer for you to shine!

The concept of the Summer of AI is one where students work in teams with major organisations on AI projects for seven weeks. The programme is designed in such a way that students can participate in Dutch Summer of AI during their summer break. If you are a student interested in AI and solving real problems then you can apply by clicking on this link.

Make your summer break artificially intelligent

Artificial Intelligence is the next biggest thing in the world of computer science and it combines a number of technologies. AI fields include the likes of machine learning, computer vision, natural language processing, speech recognition, and robotics.

The programme, Summer of AI, sees students work as a group on projects provided by participating organisations. A total of seven organisations will offer projects to students, who will be mentored by experts from the organisations sponsoring their projects. In some ways, the Dutch Summer of AI is similar to competitions held by organisations on Kaggle, which was acquired by Google in 2017.

The real differentiator between Dutch Summer of AI, initiated by ABN AMRO and DEUS, and many other competitions is that students get to solve real challenges within a business and develop “valuable connections” in the process.

One of the things about artificial intelligence is that the technology is evolving rapidly and students need a real avenue to test their knowledge. The Summer of AI allows students to put their AI knowledge into practice, develop experience and relationships that can help them in their future.

In the past, the Summer of AI has acted as a test bed for a number of students to become entrepreneurs and this year is not expected to be any different. “Participating students stay part of the AI ecosystem,” the website reads in proclamation of its theme.

Dutch Summer of AI: the timeline

  • May 15: Deadline to apply as a student.
  • May 16 to June 4: Review of the proposals
  • July 4: The Kick-off Event (live)
  • July 27: Halfway Event (live)
  • August 19: Final Event (live)

Click here to apply.

Dutch Summer of AI: List of participating organisations

  1. ABN AMRO
  2. DSM
  3. Company.info
  4. Tata Steel
  5. KPN
  6. Shell
  7. AEGON
  8. South Pole
  9. NS (Nederlandse Spoorwegen)

Case: Shell’s case study for 2022

During the Summer of AI, Shell is trying to find answers to some of the pressing questions related to climate change. The company is making it clear that the transition of our energy systems towards renewable sources cannot be handled by one company alone. To solve this problem, Shell is opening the doors of its Energy Transition Campus Amsterdam (ETCA) to start-ups, scale-ups, academia, and mature companies.

The goal is to reduce our CO2 emissions and Shell’s project for Summer of AI focuses on various aspects of ETCA’s electricity consumption. This is a pure data science project answering questions like the kind of power storage that will be required to shave off peak demand from the grid and offering recommendations on energy efficiency measures.

Students joining Shell’s project will be answering these questions and will need to commit at least four days per week. This will include at least one day at Shell ETCA in Amsterdam per week and virtual during the rest of the week. Students will need permission to work in the Netherlands and DS/AI/technical students with experience or strong interest can apply.

Case: KPN’s future of marketing case study

Marketing is increasingly carried out by data-driven decision making and that requires the ability to quickly experiment with various types of offers and communication channels. KPN is making that possible by working with reinforcement learning, namely contextual Multi-Armed Bandit models (MAB).

The case study from KPB will allow students to improve MAB models and ensure that customers get the best offer at the right time. Students are required to dedicate four days per week (non-weekend) and at least two days a week in the Amsterdam office. The programme will prefer master students only and KPN will offer compensation and travel expenses will be reimbursed.

Case: Tata Steel’s unsupervised learning challenge

Tata Steel is participating in Summer of AI with a computer vision challenge. As one of the leading steel producers, Tata Steel faces the challenge of providing products with zero defects and it does that using a large number of automatic camera inspection systems installed in several places in the process.

The current defects classification model used by Tata Steel is supervised and it works by showing the algorithm what a scratch will look like and then refining the definition. However, Tata Steel says the ideal model will be able to prevent defects from reaching customers automatically.

Students selected by Tata Steel for Summer of AI will be required to create a defects classification system that detects different types of defects by outperforming its current system. In other words, the Summer of AI student will be required to build an unsupervised computer vision model.

Tata Steel is expecting students to spend four days per week and one of the days is expected to be spent at the Tata Steel IJmuiden. The company is preferring master students in STEM related fields with experience with ML/AI and compensation will be provided. Students selected for the challenge will also be reimbursed for their travel.

Case: ABN AMRO’s sustainability challenge

During Summer of AI, ABN AMRO is trying to tackle sustainability by using AI to identify and size opportunities for solar panels. This will be done across retail and SMEs in the Netherlands. The students selected for this sustainability challenge will join the AI team of the Strategy and Innovation Department and experience life as a data scientist at the bank.

The students are expected to spend four days per week with a minimum one day in ABN AMRO’s office at the Zuidas Amsterdam. This programme is also accepting master students in STEM related fields with some experience with Python programming and ML/AI. ABN AMRO will provide internship compensation and travel expenses.

Case: Company.info’s entity recognition model challenge

Company.info is coming to Dutch Summer of AI with a rather interesting challenge. At Company.info, one of the challenges is to know if companies are mentioned in the news and it is solving that problem using entity linking and recognition. However, this current setup is language dependent, meaning it works well in Dutch but not in German.

It has also been noticed that the Dutch system doesn’t always work well in formal or legal texts. The students joining the challenge are expected to investigate on how to improve the system in scenarios like different language or legal text. The students can accomplish this by making the current model more flexible or by training models specifically for different use cases.

Students will work closely with Company.info’s data science team and will get a dedicated supervisor. The preferred students will have masters in AI, Data Science, Computer Science or another relevant field. They are expected to work four days a week from the Amsterdam office. Students will be reimbursed for their travel expenses.

Case: South Pole’s AI impact challenge

At Dutch Summer of AI, South Pole is entering with an impact challenge designed to monitor the impact of its AFOLU (agriculture, forestry and land use) projects. The students joining the project will need to investigate whether it is possible to estimate the amount of carbon sequestered in a forest purely based on remotely measured data.

They will also need to investigate the possibility to synthetically increase the resolution of historic satellite imagery to improve the estimation of the carbon baseline. The investigation will also result in fusing optical and radar satellite imagery for improving the reliability of digital monitoring solutions.

For its AFOLU projects, South Pole is offering a stipend of €500 and students will need to be available for at least four days a week. They will be required to join the office in Amsterdam at least two days per week. A MSc degree student or a student having finished an MSc degree with at least some experience of applying AI/ML to remote sensing can join.

Case: Aegon’s DIP product challenge

At Summer of AI, Aegon is entering with a DIP product challenge. The students will have the opportunity to create a state-of-the-art AI model capable of predicting the conversion of the DIP product. The students will get access to a database with competitive price information and access to datalabs where the data is available.

At the end of the summer, students will have learned how to create a proof of concept and bring their model to production. This ability to bring a model to production is an experience not easy to get in the industry. Aegon is offering internship compensation and students will need to spend four to five days of a week with at least two or three days in Aegonplein The Hague.

Case: NS’s challenge to study impact of events

Nederlandse Spoorwegen is looking to study the impact of events on the number of passengers. It wants to predict the crowdedness of trains by correlating the data associated with the time when an event is taking place. For this, students will have to factor in data like when people travel to the event and travel back home.

Each event is different and at Summer of AI, students will need to work with experts in the field of crowdedness predictions to predict the impact of events. Students are required to commit at least four days a week on this challenge and are seeking four to six master students with Python and AI experience.

Case: DSM’s fish derived oils challenge

At Summer of AI, DSM wants to use AI to uncover drivers for fish oil purchase recommendations. The company says this particular oil is available mostly during the fishing season and the price of the oil changes depending on the season’s progress. The price will go up with scarcity of fishes and if more fishes are caught, the price goes down.

A business using the fish oil needs to make decisions about the best time to buy it and this is possible with an enriched data set. DSM already has a number of structured and especially unstructured data sources providing this information and students will be required to make the data better using new data sources and thus improve the accuracy of the forecast.

The students are expected to commit two to three days per week and create a machine learning model by taking the available information as input. The final model should be capable of predicting the right time to purchase fish oil.

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