Artificial Intelligence and Improved Hearing – The Opening of FEPlab

From the first of November onwards, knowledge institution Eindhoven University of Technology (TU/e) and globally leading hearing aid manufacturer GN Hearing will join forces in FEPlab. The lab is dedicated to ameliorating the participation of hearing-impaired people in both formal and informal settings.

Research

FEPlab will focus its research on transferring a leading physics/neuroscience-based theory about computation in the brain, the Free Energy Principle (FEP), to practical use in human-centered agents such as hearing devices and VR technology. FEP is a general theory of information processing and decision-making in brains that is rooted in thermodynamics. The principle states that biological agents must take actions (or decisions) that minimize their (variational) free energy which is a measure of the amount of total prediction error in a system. Practically, by minimizing free energy, the agent takes actions that optimally balance information-seeking behavior (reduce uncertainties) against goal-driven behavior. Theoretical foundations for AI application of FEP-based synthetic agents have been produced by BIASlab at TU/e. In the current endeavor, FEPlab is focused to bring FEP-based AI agents to the professional hearing device industry. Professor Bert de Vries, the scientific director of FEPlab alongside Associate Professor Jaap Ham, believes FEP-based synthetic agents have much to offer to signal processing systems:

I believe that development of signal processing systems will in the future be largely automated by autonomously operating agents that learn purposeful (signal processing) behavior from situated environmental interactions.

Bert de Vries, Scientific Director FEPlab

Expertise and Focus

FEPlab will comprise experts from different fields of expertise such as Audiology, Autonomous Agents & Robotics, Decision Making, and Machine Learning to tackle the complex multidisciplinary challenges at hand. The lab will employ five PhD students at TU/e, of which four will join the BIASlab research group in the EE department and one PhD student will join the Human-Technology Interaction group at the IE&IS department. Key research topics include reactive message passing for robust inference, generative probabilistic models for audio processing, and interaction design for hearing aid personalization.

Sustainable Development Goals

FEPlab will focus on two SDGs. Firstly, the research goals of the lab resonate with SDG 3 focused on Good Health and Well-being since untreated hearing loss in the elderly increases the risk of developing dementia and Alzheimer’s disease as well as emotional and physical problems. Secondly, the lab’s research goals also support SDG 8 of achieving higher levels of economic productivity through technology upgrading and innovation as hearing loss is also shown to affect work participation negatively.

Partners

The ICAI FEPlab is a collaboration between Eindhoven University of Technology (TU/e) and GN Hearing.

Registration is open! ICAI Deep-Dive: Working with Medical Data

Working with medical data comes with many challenges, ranging from improving data usability to maintaining privacy and security. To outline some of these challenges, ICAI organizes the ICAI Deep-Dive: Working with Medical Data on the 3rd of November, 15:00-18:00. This hybrid event will be moderated by Nancy Irisarri Méndez and will take place on location at Radboud University and online.

Artificial intelligence solutions are rapidly transforming the world by automating tasks that for long have been performed by humans solely. Training on increasingly massive datasets is one of the enablers of this widespread use of robust and trailblazing models. However, due to socioeconomic and legal restrictions, the industry lacks large-scale medical datasets to enable the development of robust AI-based healthcare solutions. Therefore, there has been an increased interest in technical solutions that can overcome such data-sharing limitations while simultaneously maintaining data security and the privacy of patients.

We will open this ICAI Deep-Dive event with an introduction to two specific data-related challenges in the medical field. The first challenge will be introduced by Bram van Ginneken of the Radboud UMC, who will discuss FAIR (Findability; Accessibility; Interoperability; and Reusability) requirements for data sharing in practice. Thereafter, Gennady Roshchupkin of the Erasmus UMC will conclude part I of the event by discussing the challenges of using Federated Learning in genomics research.

The second part of the ICAI Deep-Dive event will be a panel discussion that centralizes the question “How do we tackle challenges in medical data usage by collaborating together?”. During the panel discussion, Nancy will moderate the discussion among Bram van Ginneken, Clarisa Sánchez, Gennady Roshchupkin, Johan van Soest, and is also open to everyone who is interested in the challenges mentioned in the previous two talks.

After the panel discussion, it is time for networking while enjoying some drinks.

UvA and Bosch extend collaboration with new ICAI research lab

The UvA and world-leading technology company Bosch have agreed to extend their established collaboration with the launch of a new public-private research lab. Delta Lab 2 – the follow-up to the successful collaboration Delta Lab 1 – will focus on the use of artificial intelligence and machine learning for applications in computer vision, generative models and causal learning. Delta Lab 2 will form part of ICAI, the national Innovation Center for AI, headquartered on the Amsterdam Science Park. The lab will be headed by the UvA’s Dr Jan-Willem van de Meent and Prof. Theo Gevers. Dr Eric Nalisnick will be the daily lab manager.

‘For Bosch, collaboration and close exchange with academic institutions is an essential component of our efforts in the development of safe, robust, and explainable AI. By expanding and realigning the previously successful collaboration in the UvA-Bosch Delta Lab, we are realizing our ambition of combining cutting-edge research with high application potential,’ says Michael Fausten, Senior Vice President and Head of the Bosch Center for Artificial Intelligence (BCAI).

In the Delta 2 lab, ten PhD students, one postdoc and one lab manager will work on projects over the next five years with a total budget of €5.2 million. aiming for new research on deep (causal and partial differential equation-based) generative models; certainty and causality in machine learning; and 3D computer vision.

Building on success

Gevers: ‘The collaboration between UvA and Bosch in Delta Lab 1 has been a great success. We want to build on that success with new fundamental research into machine learning and computer vision technologies. We are grateful to Bosch for continuing to invest in fundamental research. We also thank the previous directors and researchers for all their efforts, and we will continue to work enthusiastically on unexplored areas of AI.’

Van de Meent: ‘We are excited to continue our productive collaboration. Working with Bosch is a win win. It not only facilitates uptake of AI innovations in industry, but also provides a wealth of use cases that can inspire new innovations, such as incorporating physical knowledge into models, reasoning about their causal structure, and evaluating the level of confidence that can be attributed to predictions.’

More information can be found here.

Civic AI Lab on UNESCO’s top 100 list of AI solutions worldwide

Civic AI Lab’s proposal for the UNESCO’s International Research Centre on Artificial Intelligence (IRCAI) has been rated early stage with great potential and therefore made it to the top 100 projects list of AI solutions worldwide. IRCAI is releasing a list of 100 projects solving problems related to the 17 United Nations Sustainable Development Goals with the application of Artificial Intelligence, from all five geographical regions: Africa, Europe and Americas, Asia and the Pacific, and the Middle East. Civic AI Lab is one of ICAI’s 29 labs.

IRCAI’s list of 100 projects

Nine Veni grants for AI researchers

The Dutch Research Council (NWO) has awarded nine Veni grants to researchers involved in groundbreaking AI research. The recipients can use the grants – up to a maximum of 280,000 per researcher – to further develop their research ideas over the next three years. ICAI wants to congratulate these colleagues for their achievements!

The AI recipients of a Veni grant:

  • Continual Learning under Human Guidance
    dr. E.T. Nalisnick (M), Universiteit van Amsterdam
    Artificial intelligence (AI) systems need to adapt to new scenarios. Yet, we must ensure that the new behaviours and skills that they acquire are safe. The researcher will develop AI techniques that allow autonomous systems to adapt but to do so cautiously, under the guidance of a human.
  • Intelligent interactive natural language systems you can trust and control
    dr. V. Niculae (M), Universiteit van Amsterdam
    Artificial intelligence agents are seemingly approaching human performance in natural language tasks like automatic translation and dialogue. However, deployed in the wild, such systems are out of control, learning to produce harmful language even unprompted. Using recent machine learning breakthroughs, the researcher rethinks language generation for trustworthiness and controllability.
  • Efficient AI with material-based neural networks
    dr. H.C. Ruiz Euler (M), Universiteit Twente
    The unprecedented success of artificial intelligence (AI) comes at the price of unsustainable computational costs. This project will research the potential of a novel technology for highly efficient AI hardware: “material-based neural networks”. This technology will enable the next generation of efficient AI systems for edge computing and autonomous systems.
  • Helping computers say what they mean to say
    dr. J.D. Groschwitz (M), Universiteit van Amsterdam
    When a computer talks to us, for example when answering a question, it must translate that answer from its inner computer representation to fluent human language. This project combines linguistics and state-of-the-art machine learning to create a language generation system in which the output text expresses exactly what the computer meant to say.
  • The life and death of white dwarf binary stars
    dr. J.C.J. van Roestel (M), Universiteit van Amsterdam
    Double white dwarf stars are a rare but important type of binary star. They are potential supernova progenitors, some merge to form massive rotating white dwarfs, and they also emit gravitational wave radiation. I will combine data from the Dutch BlackGEM telescope with multiple other telescope surveys and use novel machine learning methods to uncover the population of short-period eclipsing white dwarf binary stars across the entire sky. By comparing the observed population and characteristics with binary population synthesis models, I will determine how these double white dwarfs end their life.
  • Personalizing radiotherapy with Artificial Intelligence: reducing the toxicity burden for cancer survivors
    Lisanne van Dijk PhD, University Medical Center Groningen (UMCG)
    Many head and neck cancer patients suffer from persistent severe toxicities following radiotherapy. As survival rates increase, toxicity reduction has become more pivotal. This project uses Artificial Intelligence techniques to predict toxicity trajectories, which can facilitate personalized decision-support to guide physicians in finding optimal strategies to reduce these severe toxicities.
  • Towards realistic models for spatiotemporal data
    dr. K. Kirchner (V), Technische Universiteit Delft
    Many environmental factors, such as temperature or air pollution, are recorded at several locations and dates. Because of limited computing power, a realistic analysis of the resulting large datasets is often unachievable. This project develops computational approaches which enable efficient accurate data analysis and reliable forecasts for phenomena with uncertainty.
  • Explainable Artificial Intelligence to unravel genetic architecture of complex traits
    Gennady Roshchupkin PhD, Erasmus MC Medical Center
    While we have learned that most diseases have a genetic component, we are still far away from understanding the underlying processes. Using Artificial Intelligence, I will investigate the complex relationship between DNA mutations and human health. This will be the basis for development of novel diagnostic, prognostic and therapeutic tools.
  • Neural networks for efficient storage and communication of information
    dr. J. Townsend (M), Universiteit van Amsterdam
    The brain is an extremely efficient system for storing and communicating information. This research will study the use of artificial neural networks, inspired by the mechanisms in the brain, for data compression, enabling faster internet communication and more efficient storage of computer files.

Find the complete list of Veni grants 2021 here.

Limburg welcomes first ICAI Lab for the development of AI for patient care

Knowledge institutions from Limburg will join forces with local parties in healthcare and IT. The first ICAI Lab in the region – the ICAI Brightlands Smart Health Lab – is committed to making breakthroughs in patient care by developing and deploying artificial intelligence.

ICAI Brightlands Smart Health Lab

With the arrival of the ICAI Brightlands Smart Health Lab, Limburg joins the national network of ICAI Labs. In this new lab, researchers from Maastricht University and Zuyd University of Applied Sciences will collaborate with healthcare providers, the Netherlands Comprehensive Cancer Organisation, medical IT service provider ilionx, and several Brightlands campuses and organisations.

The activities of the new lab span the entire process from the development to the deployment of artificial intelligence in healthcare. Among other topics, the lab focuses on preparing medical data so that it can be used anonymously for ‘training’ artificial intelligence. Other key activities of the ICAI Brightlands Smart Health Lab include the development of artificial intelligence and putting the technology into practice. The lab specifically focuses on artificial intelligence that can predict how a patient’s disease will develop. In this way, the technology can help map out treatment plans for diseases like cancer.

Ecosystem

Professor Andre Dekker, one of the lab’s academic directors alongside Dr. Rianne Fijten and Dr. Alberto Traverso, considers the collaboration between all parties as crucial for the success of their joint vision for the future. Dekker: “Bringing together knowledge and data from very diverse parties in the Brightlands ecosystem is precisely what allows us to conduct relevant research, develop real solutions and put these into practice.”

Responsibly connecting data

Dekker and his team bring their expertise in responsible sharing of privacy-sensitive medical data. The ICAI Brightlands Smart Health Lab thereby builds on their work on the Personal Health Train: a network that allows healthcare providers to learn from each other’s data, but in which data never leaves the host institution’s management and through which the privacy of patients is preserved.

Dr. Rianne Fijten: “Data is often an abstract concept for researchers, but it comes from real people. That is why careful handling of privacy-sensitive medical data is very important to us.”

Partners in the ICAI Brightlands Smart Health Lab

The ICAI Brightlands Smart Health Lab is a collaboration between Maastricht University, Zuyd University of Applied Sciences, Maastro Clinic, Maastricht UMC+, ilionx, Netherlands Comprehensive Cancer Organisation (IKNL), Brightlands Institute for Smart Society, Brightlands Smart Services Campus, Brightlands Maastricht Health Campus and AI Hub Brightlands.

Future Impact joins ICAI Launch Pad

Future Impact, a tech-job focused platform for young talent, and ICAI will collaborate in the Launch Pad programme. Launch Pad is initiated by ICAI to connect young AI talent to the Dutch ecosystem. Together with Future Impact and the Dutch AI Coalition, ICAI wants to further expand Launch Pad into a pro-active support programme for AI careers in the Netherlands.

ICAI believes that the Netherlands offers great opportunities in the field of AI, but that AI talent and the organizations that need this talent are not yet able to find each other. The aim of Launch Pad is to connect AI talent to the Dutch ecosystem by providing a matchmaking process between AI-PhD students and Dutch companies looking for AI talent. Future Impact will fulfill the role of matchmaker.

Partnership

All partners in the Launch Pad collaboration have a specific expertise and role in the matchmaking process. ICAI will use its network of ICAI labs to connect with PhD students who are about to enter the labor market. Future Impact will focus on the recruitment and matching of young AI talent. And the Dutch AI Coalition (NLAIC) will provide a network of organizations who are involved with AI in the Netherlands.

Awareness of AI opportunities

The Launch Pad programme wants to create awareness amongst students of the possibilities in the Dutch ecosystem. At the moment, many trained PhD students go (back) abroad, without being aware of the possibilities and favorable living conditions in the Netherlands. ICAI wants more students to stay in the Netherlands after pursuing their PhD.

Future Impact as matchmaker

Future Impact will act as matchmaker and introduce AI talent to Dutch based partners. The platform has a broad network of relevant organizations looking for AI talent. In addition to the matchmaking role, Future Impact offers support throughout the whole matchmaking and application process. This includes coaching candidates in finding their areas of interest, helping them to prepare for job interviews and negotiating contracts.

ICAI mission

One of the missions of ICAI is to create and nurture a national AI knowledge and talent ecosystem. ICAI Launch Pad is the next practical step in carrying out this mission.

Registration open now! ICAI Day: A Deep Dive into AI


On the 27th of October ICAI organizes the ICAI Day: A Deep Dive into AI. This hybrid event will take place on location in Den Bosch and online. The focus of this ICAI event will be on the technological side of AI. Registration for the event is now open for everybody who is interested. You can register here.

Together with the ELSA Labs community of NLAIC, we organize a lunch event as part of the ICAI day for all the labs. In small table settings we will deep dive into specific area’s and how to work on AI with trustworthiness integrated in the technology. Introductions will come from the Police Lab and ELSA Lab. You must be physically present to attend this part of the programme.

We have speakers from outside and inside the labs who will dive into the latest technologies of AI and show work on Geometric Deep Learning: from Euclid to drug design (Michael Bronstein, Imperial College & Twitter) and graph convolution networks (Xie Weiyi, Radboud MC/Thira Lab). Finally our experts in the labs, from academia and industry, will share insides on lessons learned from the collaborations in the ICAI labs.


The event will take place in two parts:

12:00 – 13:30 Part 1: Lunch table discussions
With introductions of:
Heleen Rutjes (TU Eindhoven, ELSA Labs)
Maurits Bleeker (UvA, Police Lab)
Sarah Ibrahimi (UvA, Police Lab)

Topics of the lunch table discussions:
– Inclusive society with data engineering
– Autonomous systems in mobility
– Robotics & autonomous agents
– Computer vision in healthcare
– Using AI in education and governmental org.
– Online personalization and impact

13:45 – 17:00 Part 2: ICAI plenary event
13:45 – 14:00 Welcome by chair Nathan de Groot and director of ICAI Maarten de Rijke
14:00 – 14:45 Keynote Michael Bronstein Geometric Deep Learning: from Euclid to drug design (Twitter, Imperial College)

14:45 – 15:00 Break

15:00 – 15:30 Lecture Xie Weiyi (Thira Lab) – Graph Attention Networks for airway labeling
15:30 – 15:35 Short videos of different ICAI labs
15:35 – 16:20 Discussion table ICAI labs –Lessons learned in collaboration:
Elvan Kula (ING, AI for Fintech Lab),
Georgios Tsatsaronis (Elsevier, Discovery Lab),
Cees Snoek (UvA, QUVA, AIM & Atlas Lab)
16:20 – 16:30 Closing words

16:30 – 17:30 Drinks


We look forward welcoming you at our event!

Register here

LTP Program ROBUST wins NWO support

ICAI is very proud to be able to expand the network of ICAI with the LTP ROBUST program “Trustworthy AI systems for sustainable growth”, supported by NWO in the new Long Term Program with €25 million.

AI technology promises to help with many tough societal challenges. For the technology to be adopted and benefit everyone, it is essential that the AI systems that we develop are trustworthy. The ROBUST Long-Term Program addresses this challenge and gets the opportunity to build the program.

First and foremost, ROBUST focuses on attracting talent to work on the challenges of trustworthy AI. Talent is the core of any AI ecosystem. Second, it makes trustworthy AI research and innovation a shared responsibility between knowledge institutes, industry, governmental organizations, and other societal stakeholders. And third, it practices learning by doing in the Dutch context, through use-inspired research, connections with startups and SMEs, and an extensive knowledge sharing efforts.

17 new ICAI labs

The ROBUST program builds on ICAI, the Innovation Center for Artificial Intelligence. It intends to add 17 labs to ICAI’s current ecosystem of 30 labs, in areas as diverse as health, energy, logistics, and services. The labs that make up ROBUST are driven by economic opportunities and contributions to the UN’s sustainable development goals. They will develop AI-algorithms that advance the state of the art in accuracy, reliability, repeatability, resilience, and safety of AI algorithms – all essential hallmarks of trustworthy AI.

ROBUST is a collaboration of 21 knowledge institutes, 23 companies, and 10 societal organizations. ROBUST is supported by the Netherlands Organisation for Scientific Research (NWO) and the AiNed National Growth Fund Investment Program.

Project team

The project leader for the ROBUST program is prof. Maarten de Rijke of the University of Amsterdam and ICAI. The co-applicants are prof. Mark van den Brand (Technical University Eindhoven), prof. Arie van Deursen (Delft University of Technology), prof. Bram van Ginneken (RadboudUMC), dr. Eva van Rikxoort (Thirona), prof. Clarisa Sánchez Gutiérrez (University of Amsterdam), and prof. Nava Tintarev (Maastricht University).

Contact: Esther Smit, esmit@icai.ai

Read the NWO press release here.

LUMO Labs makes first TTT-AI pre-seed investment in LUMC innovation partner Autoscriber

LUMO Labs announces a TTT-AI investment in Autoscriber, a Dutch health tech software startup developed in a clinical setting. Autoscriber is developing AI-supported voice recognition software to capture and summarize healthcare professional-patient consultations. TTT-AI is part of the ICAI Venture Program.

Autoscriber’s value proposition is at the intersection of three crucial healthcare trends:

  • affordable and accessible healthcare for all
  • growing importance of structured/discrete data capture to support data-driven healthcare initiatives
  • increasing desire for understanding and self-determination among patients

Health professionals access Autoscriber as a subscription based software-as-a-service (SaaS) solution to large hospitals and practices, smaller practices and General Practitioners. LUMO Labs’ pre-seed funding will allow Autoscriber to go live in multiple hospitals during the next 12 months.

The technology, which promises to streamline clinical interactions into a seamless experience for patients and caregivers, was developed in collaboration with the Clinical Artificial Intelligence and Research Lab (CAIRELab) at Leiden University Medical Center.

“We are very excited to work so closely with the LUMC. Every design choice we make we validate with the physician in a clinical setting.” said Koen Bonenkamp, co-founder and CTO.

Autoscriber software records, transcribes and extracts clinical concepts during consultations. It allows for automated summaries and integration in the patient’s Electronic Health Record that can be easily edited by the physician, providing real-time support for diagnostics and personalized care.

LUMO Labs is investing because Autoscriber meets and/or exceeds their investment fundamentals, including a strong entrepreneurial founding team with profound expertise, proof-of-concept and the promise to dramatically improve the lives of caregivers and patients.

“The problem Autoscriber is solving is universal: reducing time and money spent on repetitive, administrative tasks by physicians while increasing transparency, comprehensibility and human interaction in deeply personal treatment situations,” said LUMO Labs founding partner Andy Lürling. “Their solution is dynamic and highly scalable because of the strong technological and human-centered set-up.”