Netherlands Cancer Institute, Elekta and UvA launch POP-AART Lab

The Partnership for Online Personalized AI-driven Adaptive Radiation Therapy (POP-AART) is the 28th lab to join ICAI. The lab will focus on the use of artificial intelligence for precision radiotherapy.

It is a major challenge to give patients the right dose of radiation, at the right spot with least damage to healthy tissue, and while the patient and the tumor move and change shape during radiation and over time. Within the POP-AART lab six PhD researchers will develop novel AI strategies for improving the images on which the radiation treatment is based, predicting changes over time of the tumor and incorporating them in automatic treatment planning and adaptation.

POP-AART will run for five years. Research topics range from improving CT images obtained just before radiation to the level of diagnostic quality CT images, predicting deformations and segmentations of the tumor and organs at risk and incorporate these data in online and automated treatment plan optimization for each patient individually at each radiation session.

The lab will be led by scientific directors Efstratios Gavves and Jan-Jakob Sonke. Gavves is assistant professor of Computer Vision and Deep Learning at UvA. Sonke is theme leader image guided therapy at the Netherlands cancer institute and Professor by special appointment of Adaptive Radiotherapy at the Faculty of Medicine at UvA. The Governing Board will consist of academic partners Lodewyk Wessels (NKI-AvL) and Mark de Graef (UvA) and industry partner Rui Lopes (Elekta).

About the Netherlands Cancer Institute

The Netherlands Cancer Institute, founded in 1913, is among the top 10 comprehensive cancer centers, combining world-class fundamental, translational, and clinical research with dedicated patient care. Their initiatives to promote excellent translational research have been recognized by the European Academy of Cancer Sciences, when they designated the institute ‘Comprehensive Cancer Center of Excellence in Translational Research’.

About Elektra

For almost five decades, Elekta has been a leader in precision radiation medicine. Their more than 4,000 employees worldwide are committed to ensuring everyone in the world with cancer has access to – and benefits from – more precise, personalized radiotherapy treatments. Headquartered in Stockholm, Sweden, Elekta is listed on NASDAQ Stockholm Exchange. 

Find out more about POP-AART lab.

Tilburg newest ICAI location with MasterMinds Lab

MindLabs recently joined the national Innovation Center for Artificial Intelligence (ICAI). This makes Tilburg one of eight locations with an ICAI lab. Together with the partners in the MasterMinds project, MindLabs collaborates in this public-private research lab, named MasterMinds AI Lab. The MasterMinds AI Lab is one of the many initiatives within MindLabs and is dedicated to the development and evaluation of new technologies, focusing on the cross-fertilization between artificial minds and human minds.

Artificial Intelligence and Human Behavior

The ICAI is a national collaboration of several universities, companies and the Dutch government with 27 labs on eight locations. The goal  of the Dutch government and universities is to remain at the forefront of AI by knowledge development and nurturing young talent.  MasterMinds collaborates in research at the intersection of robotics and avatars, serious gaming, decision making, and virtual and augmented reality. The results of these studies are applied to realize AI solutions for the benefit of society.

Max Louwerse

Professor Max Louwerse, scientific director of the MasterMinds AI Lab: “The future of AI increasingly lies at the intersection between artificial intelligence and human behavior. The MasterMinds project works at this interface to develop new technologies and will be able to apply them immediately. This is entirely in the nature of MindLabs.”

Five MasterMinds research projects

The MasterMinds project consists of five innovative research projects, aiming to develop breakthroughs with interactive AI technologies such as serious gaming, augmented and virtual reality, intelligent tutoring systems and natural language processing and data science. Research questions include: Can we train and improve complex decision-making using serious gaming? What is the learning effect of training pilots using virtual reality? How do we effectively design AR and VR training modules? How can we develop and use intelligent tutoring systems? The project is funded to stimulate regional ecosystems to develop a resilient, sustainable, and future-proof economy with a central role for SME’s.

The MasterMinds project brings together knowledge institutions, industrial partners, and governmental organizations to work together on AI solutions that can be readily used for the partners. The project aims to develop AI technologies combined with the impact on and input from human behavior, across multiple sectors in society in the field of aerospace, logistics, maintenance, and education focusing on robotics and avatars, serious gaming and learning, language and data science technologies and virtual and augmented reality solutions. The project provides a T-shaped profile of explainable AI solutions, where depth is achieved per subproject while breadth is achieved across the five projects. The project answers questions that have been formed by the industrial partners to prepare for the technology-driven future that lies ahead.

The five MasterMinds reseach projects:

  1. Serious Games in logistics – Port of Rotterdam
  2. VR for air force simulations – Dutch Royal Airforce and MultiSIM
  3. AR & VR for production and maintenance – Actemium, Marel en CastLab
  4. Evidence-based prevention: predictive analytics – Interpolis en Gemeente Tilburg
  5. Virtual Reality in Education – WPG Zwijsen, Spacebuzz en Timeaware

MasterMinds brings together the “brightest minds” in artifical and human intelligence.

Read more on the MasterMinds Lab

ICAI Interview with Martijn Kleppe: gaining insights much quicker by combining AI and Humanities

Martijn Kleppe is a trained historian who collaborates with AI scientists. Kleppe: ‘When I was doing research as a historian, I could analyze twenty books in a month. For the computer this is a matter of seconds.’

Martijn Kleppe

Martijn Kleppe is one of the founding members of Cultural AI Lab and the head of the Research Department of the KB National Library of the Netherlands.

The Cultural AI Lab is a joint effort of the heritage institutions Rijksmuseum, Institute for Sound and Vision, the KB and knowledge institutions CWI, KNAW, UvA, VU and TNO.

What problems within the humanities can be solved with AI?

‘The biggest challenge that we face within the humanities is scale. There is a shift happening right now within historical research from close reading, which we have done for centuries, to distant reading, where we use a computer to detect patterns in all sorts of humanities data. In books, newspapers, television programs, artworks, social media outlets etcetera. This is the biggest opportunity that we have right now, but also the biggest challenge because we have to rely on other competences to make this kind of research possible.’

How is the interest in AI from the cultural world?

‘It is really gaining momentum right now. There is an ecosystem evolving with partners from the culture and media domain – like the Rijksmuseum, the National Archive – and the creative industry, that are interested in applying AI within their services or processes. And several members of our lab recently founded the working group ‘Culture & Media’ within the National AI Coalition with all sorts of cultural and media partners.’

Does the humanities also influence AI?

‘Yes, it creates new academic research questions. Most of the algorithms within the AI domain have been trained with new and high quality data. But in the datasets of the heritage institutions there is 200 year old data. Digitized newspapers from the end of the nineteenth century with a very low quality for example. And newspapers from colonial Indonesia and Suriname with a completely different vocabulary. Bringing in those kinds of datasets within the AI domain offers new perspectives and questions on polyvocal data. How can we handle these kinds of data and how can we improve them? My experience so far is that the computer scientists involved in our project love these new questions.’

At the ICAI meetup on July 8, the lab will speak about contentious words in cultural heritage collections. How does the lab approach this?

‘Handling issues like that, is the essences of our research. We try to answer the question: How can you detect bias in descriptions of artifacts of museum collections? And can you also help the museum by giving them suggestions for other kind of words? Especially last year, with the Black Lives Matter movement, we saw how relevant these questions were. How do we deal with the past? It is a technical, but also really a societal and ethical challenge.‘

Boek uit de KB collectie

As a historian, what attracts you in AI?

‘I have to ask new types of questions. Instead of doing source criticism on books, newspapers or television programs, now I have to do source criticism on algorithms. At the KB we have the Delpher.nl platform for example, where you can search in millions of digitalized texts from Dutch newspapers, books and magazines. In order to search efficiently, you have to understand the basis of the algorithm behind it. What I also really like about the AI research is the teamwork. Traditionally humanities scholars are more solitary researchers. But to be able to collaborate with other disciplines you have to be vulnerable and develop yourself.’

Does it happen that you get a new perspective on heritage collections because of the AI research?

‘Yes, for sure. During my PhD research I wrote an article about the first moment in time when a Dutch photograph was published in a Dutch newspaper. That research was based on manually going through a selection of newspapers. But then four years later I participated in a research project with a historian who was one of the firsts to start applying Computer Vision on historical newspapers. He run an algorithm that went through all the newspapers and immediately said: ‘Martijn, you were completely wrong. The first photograph was published earlier than the moment you mention in your paper.’ That was fantastic. Science is always about gaining new insights. When I did my research, those techniques did not exist yet. We now gain insights much quicker than we did before.’

On July 8, 2021, Cultural AI Lab will talk about making AI ‘culturally aware’ during the Lunch at ICAI Meeting. Want to join? Sign up here.

3 Reasons Why You Should Learn About AI

TU Delft and ICAI present the online program AI in Practice. A course developed for anyone interested in learning how AI technology can help their organizations. Why can AI be interesting for your company?

1. AI isn’t just hype

While the adoption of Artificial Intelligence (AI) was already on the increase before 2020, not surprisingly the COVID-19 pandemic has accelerated this trend. According to some studies, the ongoing crisis and lockdown measures have stepped up the digital transformation of many organizations and the adoption of AI. McKinsey’s Global Survey on AI in 2020 states that despite the economic challenges, most high-performing companies have increased their investment in AI amid the COVID-19 crisis.

The increasing use of digital tools and online activities are also uncovering new opportunities in areas as diverse as business, public administration, education and research. Elizabeth Aguado, a professional working on import operations in Peru, says:

“With the reset we are about to experience due to COVID-19, many young professionals see the necessity of upgrading their skills to stay competitive in an ever changing business world. There are definitely better career prospects for those who are skilled in new technologies and I realized that AI was something I needed to continue to be valued in the manufacturing and import/export industry.”

2. AI is for everyone

Regardless how large or small, simple or complex your organization may be, there are AI solutions available in all shapes and sizes. AI can help solve specific problems, drive efficiency and improve performance and decision-making. It can support all kinds of business needs, from automating business processes, handling and interpretation of data to engaging with customers and employees. Marian Stan, Manager Customer Support in Romania, says:

”I had been interested for some time to learn more about AI, especially to gain new ideas on how AI can be implemented in certain industries.”

AI is clearly a field that no longer concerns solely data scientists and computer programmers. With AI emerging as the most disruptive type of technology, a wide range of professionals also need to prepare for the application of AI in their organizations. This includes managers and business leaders who want to know what AI can do for their companies, or data analysts and consultants who want to understand how AI can be applied in their business processes. Zeinep Kechagia, Management Consultant in the financial sector, says:

“I am always very keen to explore the future of all complex decision-making processes in a structured way. What I have learned in the field of AI will help me to guide my clients through their transformation journey mostly by integrating their assets with state-of-the-art technology and solutions.”

3. Preparing for AI is key

AI is developing at such rapid pace that before investing in a new AI initiative, organizations need to fully understand the pros and cons of different AI technologies. Gerard Danjou, a Senior Director in a digital market place, says:

“After a career in consumer goods and traditional retail industry, I joined an E-commerce platform and became fascinated by improving search in the E-commerce industry. I wanted to understand the role AI would play in the future of E-commerce and the challenges and considerations that need to be taken into account when deploying E-commerce even further.”

Understanding the practical aspects of applying AI is key. From implementation challenges, lifecycle aspects, maintenance and management of AI applications, to potential implications such as compliance and ethical considerations. Think for instance of concerns about the potential misuse of data, security or privacy issues. Learning about the practicalities of AI can help you avoid wasting time and resources pursuing the wrong technology or taking unnecessary risks.

Month of AI

International and national initiatives, like the Month of AI, offer a wide range of options to learn about the latest in AI research and applications.  

Developed by the Innovation Center for Artificial Intelligence Academy (ICAI) and the Delft University of Technology, the online program AI in Practice presents a wide range of real cases and examples to illustrate how AI can be of value in all types of organizations. It includes more than 25 contributors from 14 different organizations, universities and companies.

Regardless of your professional background or job role, if you are, like many others, puzzled as to how to get involved with the technology, or how to apply it in your business processes and successfully integrate it in your organization, this online and self-paced program might be just what you need to be able to navigate this new technology and its integration.

UvA, TU Delft and Booking.com launch Mercury Machine Learning Lab

UvA, TU Delft and Booking.com collaborate on research into better recommendation systems

In the Mercury Machine Learning Lab, researchers from the University of Amsterdam (UvA) and Delft University of Technology (TU Delft) will be working together with Booking.com on various improved recommendation systems. The collaboration provides the unique opportunity to test AI techniques in the real world, allowing new machine learning methods to be safely developed for wide application, for example in mobility, energy or healthcare.

Every day, millions of travellers from all over the world make multiple decisions on Booking.com related to their upcoming travel plans. With all of these taps and clicks on property photos and scrolling through search results, Booking.com naturally has a wealth of data insights to help the company make changes on the platform to improve the customer experience.In addition to the responsibility of handling all of this information securely and ethically, how do you analyse all of this data properly and continue to make useful recommendations for customers? Is what works well for a Dutch traveller equally as relevant for a traveller from Japan? And how do you ensure that customers continue to receive interesting travel recommendations that are relevant to them without getting stuck in a filter bubble?

On the road to even better recommendations

One way to understand what constitutes a good recommendation is looking at what previous travellers have chosen and the experiences that their choices yielded. Machine learning techniques are well suited to learning such connections and preferences. However, the problem is that the connections and preferences found in the data are not only informed by the choices of other travellers, but also by the suggestions and selections the system showed them. In the Mercury Machine Learning Lab, researchers from the University of Amsterdam (UvA) and Delft University of Technology will work together with data scientists from Booking.com to develop methods that will ensure that this type of bias is avoided and that the learned connections remain accurate in a new or different context.

From the classroom to real-time e-commerce
Joris Mooij, scientific director of the Mercury Machine Learning Lab at the UvA: ‘It’s a huge opportunity for us as researchers to have access to a live dataset of global data and be able to experiment on Booking.com’s platform.’ Frans Oliehoek and Matthijs Spaan, scientific directors from TU Delft, agree. Oliehoek: ‘By testing AI techniques in the real world, we can better understand the limitations of current state-of-the-art methods in the field of reinforcement learning, as well as improve the application of AI in practice.’ Spaan: ‘The lab’s focus on developing better algorithms for recommender systems is highly relevant to our society as these systems guide many of our digital interactions. By addressing fundamental AI challenges, the results of the lab will also be valuable in other domains.’

Onno Zoeter, Principal Data Scientist and Booking.com scientific director adds: ‘Unlike in a hospital scenario for example, where experimenting with different types of data-driven recommendation systems can have real life-and-death consequences for patients and their suggested treatment protocols, testing approaches, models and hypotheses with travel data from Booking.com doesn’t come with the same public health implications. This means we can safely test and develop new machine-learning methods together that also have a potential impact far beyond the trips booked on our platform.’

Learnings from other languages

Artificial intelligence and natural language processing are already used to perform many important tasks in different languages, such as categorising reviews and fraud detection. The researchers will look for ways of creating a system with multiple languages in which the smaller languages can benefit from what has been learned in models and rounds of experimentation with languages that are spoken more widely. This should enable Booking.com to support all 44 languages and dialects in which their platform is available to customers in various new contexts, even more quickly.

Nurturing Dutch talent

The Mercury Machine Learning Lab will be part of ICAI, the Innovation Center for Artificial Intelligence. The Lab will provide world-class opportunities for Machine Learning graduates to stay in the Netherlands and lead innovative research, keeping important talent connected to Dutch universities and industry.

In addition to the existing researchers, the Mercury Machine Learning Lab will comprise six PhD candidates and two postdocs who will work on six different projects related to bias and generalisation problems over the course of the next five years. They will spend two days a week in the office at Booking.com doing research and actively participating in related streams of experimentation to test their hypotheses.

Find out more about Mercury Machine Learning Lab

Start of new ICAI lab: AI for Oncology

The AI for Oncology Lab is a collaboration between the Netherlands Cancer Institute and the University of Amsterdam. Both institutes join forces in the development of AI algorithms to improve cancer treatment. Join the opening on 24 June 2021.

Sign up for the opening on 24 June

The AI for Oncology lab will officially and festively start and you are invited! After the registration you will receive a link.

Date: Thursday 24 June 2021
Time: 16.00 to 17.00 hrs
Register here for the digital opening

Improved cancer treatment

The goal of the collaboration between the Netherlands Cancer Institute (NKI) and University of Amsterdam (UvA) is improved cancer treatment through the aid of Artificial Intelligence. A lot of complex information is acquired from patients during and prior to the treatment through medical imaging, pathology, DNA, and so on. AI solution can assist medical specialists finding and applying the right treatment based on all this information. Moreover, AI algorithms have the potential to guide medical interventions accurately to the location of the tumor without damaging surrounding healthy tissue.

Through this collaboration, expertise in cancer research and AI technology blend together. The domains are represented by Clarisa Sánchez on behalf of the UvA, as AI&Health expert and Jonas Teuwen and Jan-Jakob Sonke on behalf of the NKI, specialized in oncology research and AI. The first goal is to conduct innovative scientific research. Subsequently, if successful, the lab aims to integrate the results in clinical practice.

The benefits of AI for cancer research

AI technology will play a bigger part in the future of cancer research. Machine learning algorithms can now perform certain tasks that used to require human intelligence. Algorithms based on artificial neural network are used in ‘Deep Learning’. Instead of relying on a programmer to explicitly tell the algorithm what to do, this network only requires an end goal and will learn how to achieve from many examples. The benefits of that is that this ‘Deep Learning’ algorithm can reach insights the programmer would never have considered.

In the case of image recognition for cancer detection, we can use a large database containing images of patients with and without cancer. The computer can use this data to learn and, if the database is sufficiently large, will be able to detect tumors even better than a specialist. In terms of personalized medicine, the images and other data can be used to better predict the most beneficial treatment option for every patient.

Image guided therapy aims to deliver therapeutic interventions with high accuracy. AI algorithms can be trained to automatically analyze medical images acquired during treatment and guide the intervention to the correct location.

In the lab, five research topics will be studied covering important aspects such as early diagnoses and image guided therapy.

Find out more about AI for Oncology lab

ICAI Interview with Sebastian Schelter: Tackling the data management questions for machine learning

One of the major problems in machine learning right now is managing real world data. Sebastian Schelter: ‘In the real world you have new data every day, every minute or every second. A small mistake is quickly made. And then that small mistake can have devastating consequences for the model.’

Sebastian Schelter is Lab Manager of AIRLab Amsterdam and has a Joint Appointment position. He is funded by both Ahold Delhaize and the University of Amsterdam. The AI for Retail (AIR) Lab Amsterdam is a joint UvA-Ahold Delhaize industry lab.

On the next Lunch-at-ICAI meeting on June 17, AIRLab will talk about the challenges in machine learning. What is the biggest challenge?

Schelter: ‘There are some classical machine learning problems in retail and e-commerce that we address at AIRLab, such as forecasting and recommendation. But by far the biggest problem right now, is the data management question for machine learning. A lot of these problems relate to handling the data and occur when you build real systems and real applications around machine learning algorithms. This is something that is often overlooked when people talk about AI. It’s one of the biggest issues in machine learning right now, but nobody likes to talk about it.’

Why don’t people talk about this?

‘Because it is a very difficult problem. It is hard to study in academia, because academics don’t have access to real systems. One of the advantages of AIRLab is that we can look at these problems because we collaborate with companies. That’s a unique situation. And another reason is that this problem lies at the intersection of the data management and the machine learning community. Problems at the intersections of fields are always more difficult to study because you have to bring together people from different expertise’s.’

How does AIRLab tries to tackle this problem?

‘In the real world you have new data every day, every minute or every second. And the data might change because the world changes and systems change. So very often, before you can actually feed data into a machine learning model, you have to preprocess it, join together different datasets, clean it, filter it, and convert the format. A small mistake is quickly made. And then that small mistake can have devastating consequences for the model. So what we are doing, is building tools to make it easier for data scientists to find these problems and fix them.‘

You have a Joint Appointment position since February 2020. How is this working out?

‘I’m one day per week at Ahold and four days at UvA. Within this setup I get the advantages from both worlds. I’ve been working in similar setups for a longer time already, going back and forth between academia and industries. I like research, but I also like to build things that get used in the real world. I think it is very valuable for computer scientists to take a step out of the lab. Then you find a lot of interesting problems that you wouldn’t have found otherwise.’

What are the challenges that come with this position?

‘You need to bring together the business and the academic side. You have to look for problems that are academically important and interesting, but that also have a business value in a certain amount of time.’

Where do you see Airlab in three years?

‘The PhD students have already started to write great research papers. We’ve had some really good results recently, which I can’t tell you yet. I’m convinced that by the end of the five-year-period of AIRLab, we will have developed a set of technologies and solutions that have real world impact and will be used by our partner companies.’

On June 17, 2021, AIRLab Amsterdam will address the data management challenges for machine learning during the Lunch at ICAI meeting. More info and sign up here.

Looking back on the ICAI Day

On Thursday, 22 April, JADS and ICAI hosted the first ICAI Day. The event gave participants a bird’s eye view of the state of affairs in the Netherlands around Artificial Intelligence (AI) and focused on the collaboration between academia and industry. Published on jads.nl at May 4, 2021

3rd anniversary of ICAI

The ICAI day marked the 3rd anniversary of the ICAI initiative. The National Innovation Center for Artificial Intelligence (ICAI) has the mission to keep the Netherlands at the forefront of knowledge and talent development in AI. ICAI acts as a national network on technology and talent development between universities, industries and government in the area of AI.

Connecting knowledge and practice in AI Labs

ICAI facilitates intensive collaborations between knowledge institutions and industry in the shape of AI Labs. “The ICAI labs give knowledge institutions the opportunity work closely on research questions that are relevant for industry and can yield practical results. Challenges and practical cases of organizations are the input for research of the academics. It is a collaboration with ongoing interaction and working together to gain new insights, experiment on site, and validate the outcomes. This approach proves to be very appealing: in the past 3 years, we’ve grown from 3 to 25 AI labs,” says Esther Smit, Business Director of ICAI.

Responsible AI lab at JADS

One such lab, the Responsible AI Lab, was established at JADS in 2020. The overall objective of this lab is to facilitate the use of AI technology by the industry in a responsible way. To achieve this, a team of JADS experts collaborates with KPN to develop transparent, privacy aware, and personalized AI solutions for businesses, that exploit the power of AI to create value from data.

ICAI Day: inspiring and diverse

In last Thursday’s ICAI event, the JADS/KPN Responsible AI lab was featured prominently. In an extensive interview, PhD candidates told about their challenges and findings within the Responsible AI Lab. Other elements of the program were breakout sessions in which six of the ICAI locations gave an impression of their AI labs, and the AI Startup Talkshow, that explored the intersection of AI and entrepreneurship and gave startups the opportunity to present themselves to the public. The afternoon was hosted by Nathan de Groot, online facilitator, day chair and trainer. “It was great to see all the work of the different locations being presented together in one place which gives an amazing overview of the work that is going on in AI research.”, says Esther Smit.

ICAI: propelling the Netherlands into the future of AI

Liesbeth Leijssen, director Business at JADS, adds: “We are proud to have hosted this valuable event. The partnership with ICAI is immensely valuable for JADS. We see a key role for ICAI in propelling the Netherlands into the future of AI and the digital transition. Not just in the field of education and AI, but also when it comes to supporting startups. This is a key collaboration for JADS, now and in the future.”

Event streams

Have you missed the event or do you want to watch again? You can find the video streams here.

Livestream for ICAI Day

The ICAI Day will start on April 22 at 14.00 hrs. To attend the plenary part, you can go to the livestream without registering. To take part in the breakout sessions you have to register. Registration is free and open to everyone!

Go to the livestream.

Register for the ICAI Day.

We look forward to see you there!

ICAI: Challenges and ambitions from two perspectives

Three years ago a couple of scientific researchers had an idea: stimulating AI talent in the Netherlands with bottom-up innovation and a lot of relevant stakeholders. ICAI was born. Now ICAI is growing significantly. It has 24 labs and is aiming at 40 to 50 labs by the end of 2022. What has ICAI achieved so far? And what are the challenges and ambitions? A view on ICAI from the perspective of lab manager Elvan Kula and scientific director Maarten de Rijke.

Elvan KulaLab Manager and PhD student at AI for Fintech Research lab (a collaboration of TU Delft and ING)

“It would be a nice

opportunity to perform studies

across ICAI labs.

Accomplishments

‘The first year of our lab was focused on bootstrapping AI for Fintech Research, which involved setting up the tracks, hiring people, organizing publicity and establishing awareness within ING. We have successfully set up a lab with 10 PhD students. In the beginning we had to make the stakeholders at ING aware or our lab and how we contribute to the company. Now, a year later, I feel like we have reached the sweet spot within the organisation where the stakeholders come to us with their own research ideas and ask if they can collaborate.’

Organizational challenge

‘As a lab manager I bring many different groups of collaborators together. I identify research opportunities, in close collaboration with engineers, researchers, students and professors from ING and TU Delft. One of the main challenges is managing important logistical aspects of the research projects in our lab. While research can be very unpredictable, our stakeholders at ING do want to have a clear plan on the deliverables and the timeline. As the lab manager, I plan and manage these expectations to deal with the unpredictability in research.’

Future challenge

‘One of the main challenges of our lab is scaling AI’s impact across the bank. The current research projects in our lab focus on standalone use cases that create impact for a specific team or department at ING. In the upcoming years, we want to work towards diffusing and scaling AI throughout the bank. Achieving results at scale requires us to deal with some technical challenges related to legacy systems and the fragmentation of data.’

Innovation

‘The main advantage of doing research within industry is that you get access to real world problems and large amounts of real world data. It allows us to do research that has practical applications and that is truly impactful. In the context of ING there are close to 14 million customers, 15.000 engineers in more than 600 teams. We have the opportunity to do research that helps thousands of people. Another benefit is that we work closely with a lot of people at ING that have much experience in the world of industry and business. We can learn a lot from them and they learn a lot from us.’

Expectations

‘Advances in AI are redefining the way the financial services sector is using data analytics and new technologies. With millions of customers and thousands of employees worldwide, the expectation is that AI will play an increasingly important role in ING’s business and operations. As the lab manager, I want to continue to strengthen the partnership between ING and TU Delft to support the ongoing transformation of the bank.’

Focus of ICAI

‘It would be a nice opportunity to perform studies across ICAI labs. The PhD students in our lab work on a range of topics, such as software analytics, data integration and fairness in machine learning, that are relevant to other companies as well. It would be very interesting to replicate our research in other ICAI labs and see how our findings relate to the results obtained at other companies.’


Maarten de RijkeCo-founder and Scientific Director of ICAI

“What I want to leave behind is

the attitude that making relevant

technological progress is a

shared responsibility.

Accomplishments

‘I’m really proud of the energy that ICAI has been able to generate and continues to generate. We are a supersmall team and we want to stay small. But by now, there are 24 labs and over 150 researchers throughout the country involved. And it’s all their initiative. We just facilitate it. The labs have created new ways of working, new ways of tackling problems and new types of teams. ICAI has a minimal but important set of requirements. First: Take care of your talent, the PhD students. And second: Take care of your environment, so share the knowledge and publish openly. And people do that in really creative ways. With training programs for professionals for example. Or with big industrial lab setups.’

Organizational challenge

‘Our dream, based on bottom-up innovation happening, has been an experiment. You think up a format and you adjust it as you go along. The challenges had to do with: how big should this be? How can we manage this? How do we organize communication? How do we make sure that it’s as open as we want it to be, while also providing enough benefits for early stage investors?’

Future challenge

‘The first step within ICAI was to get the resources so that we can attract and train talent. Now we’re at the stage that we need to think about how we can retain the talent. As our first labs begin to graduate their PhD students, we want those PhD students to find their next step somewhere in the country. For that we have created the Launch Pad. We want to open up the window so that they all see that plenty of interesting opportunities are nearby: in industry, at start-ups, NGO’s, government, academia. Their talent and expertise are needed everywhere.’

Innovation

‘The new thing that we do with ICAI, is making innovation and high-risk investment a shared responsibility. What we want to change is that companies too are investing in high-risk early stage research that is in a very low technology readiness level. Innovation is not just something governments need to think about. We all need to think about this. This whole development is also about making sure we have enough capability and capacity so that we can build and come up with innovative solutions to tough problems in the Netherlands. It’s about building and maintaining a decent level of technological autonomy. And that goes against big developments of the last decade that were focused on outsourcing. But I think that for this kind of AI technology where so many answers are still unknown, you have to experiment yourself. Because you have to have enough knowhow and talent. Otherwise you’re going to end up making big mistakes.’

Focus of ICAI

‘We have always had a strong focus on technological and economic impact. We still continue to have that focus, but in the long run our goals aren’t technological. They aren’t economic. They are societal. Our technological ambitions are aligned with the United Nation Sustainable Development Goals (SDG’s). These societal goals are extremely hard and require high-risk investments by all stakeholders involved: government, industry, knowledge institutes, society. This has been in the ICAI DNA from the start. Our labs on AI and health or AI for retail or agriculture obviously contribute to these SDG’s. But the same holds true for our labs that focus on AI for better machine perception, with less data and higher precision. And we recently opened our Responsible AI Lab, the Civic AI Lab and the Cultural AI Lab. That’s where we’re heading. Empowering teams of private and public partners to help address those big challenges with the knowledge and talent that we develop.’

Text: Reineke Maschhaupt