Kategorier
Educational project

Software Infrastructures for Teaching at Scale

Project type: Educational Project

Software Infrastructures for Teaching at Scale

To teach many recent topics within digital technology at scale requires proper software infrastructures to support the teaching for lab exercises and projects. Some of these topics are data-driven systems, AI and cloud computing. Commercial providers are offering cloud computing and AI resources, however, in many situations these are ill fit for teaching activities as they are complex for early learners, are problematic due to GDPR, make teaching material obsolete by rapidly changing their UIs and when scaled add a significant cost. Therefore, there is an unmet need for better cloud-like infrastructures which can host sandbox software and datasets for teaching to improve onboarding and retention of students.

The universities have or are building new computing resources for research and teaching. So far, the primary focus has been on research in the form of e-science initiatives (e.g. SDU e-Science Center UCloud or AAU CLAUDIA) or e-infrastructure (e.g. DEIC) providing services with a general focus on all sciences. However, there is a need to complement or customise these services with offers for teaching in digital technologies.

The aim is to improve the Danish software infrastructure for teaching in digital technologies by coordinating cross-institutional development of software infrastructure for teaching including software toolkits, access to data sandboxes and access to the Danish national DeiC Type1 HPC for educational cloud computing.

The aim is to improve onboarding and retention of students result in an ability to teach more students and lower dropout rates.

February 1, 2021 – January 31, 2023 – 2 years.

Total budget DKK 5,63 million / DIREC investment DKK 0,88 million

Participants

Project Manager

Ulrik Nyman

Associate Professor

Aalborg University
Department of Computer Science

E: ulrik@cs.aau.dk

Project Manager

Jakob Lykke Andersen

Associate Professor

University of Southern Denmark
Department ofMathematics and Computer Science

E: jlandersen@imada.sdu.dk

Partners

Kategorier
Educational project

Learning Technology for Improving Teaching Quality at Scale

Project type: Educational Project

Learning Technology for Improving Teaching Quality at Scale

Teaching quality and student feedback is negatively impacted by lack of teachers and many students. There is a need to consider how learning technologies can help improve teaching quality and student feedback both in physical and digital learning environments. The resources for teaching at universities are being reduced and we experience challenges in current technology such as MOOCs (which is at scale).

Within the educational programs digital learning tools have been developed and utilised for many years, e.g. for video lectures, automated correction of exercises, and automated multiple-choice exams etc. However, it does not substitute direct teacher-to-student supervision and the need for teachers to constantly develop existing and new courses to meet the standards.

Scale teaching methods for both physical and digital teaching environments to higher number of students via digital learning technology and a combination of face2face learning, student driven learning and digital learning technology.

Increasing the teaching quality at scale with learning technology will enable educational programs to educate more students and improve retention.

February 1, 2021 – January 31, 2023 – 2 years.

Total budget DKK 5,52 million / DIREC investment DKK 0,84 million

Participants

Project Manager

Md Saifuddin Khalid

Associate Professor

Technical University of Denmark
DTU Compute

E: skhalid@sdu.dk

Niels Aske Lundtorp Olsen

Assistant Professor

Technical University of Denmark
DTU Compute

Partners

Kategorier
Educational project

Supporting Diversity via inclusive Teaching/Learning Activities

Project type: Educational Project

Supporting Diversity via inclusive Teaching/Learning Activities

The mix of students in digital technology is low in diversity (e.g. female students). This is a problem on a societal level which also impacts the study environment.

The partners have already implemented a range of initiatives to address the problem. ITU has several initiatives targeting both recruitment, onboarding, and retention of female students. In the project “Øget diversitet på de teknisk-naturvidenskabelige it-uddannelser” lead by It-vest, the three universities AU, AAU, and SDU are collaborating to implement new initiatives.

Establish inclusive teaching/learning activities that support diversity; e.g., by supporting recruitment, onboarding, and retention of female students. The initiatives will take inspiration from established programs (e.g., Boot-IT & IT-Camp at ITU, Open Innovation X, HealthTech), but – as a unique element – favor scenarios and domains with a stronger appeal to women as well as emphasize inclusion, interaction, and collaboration over competition.

Increase the diversity of students in the educational programmes.

January 1, 2021 – January 31, 2023 – 2 years.

Total budget DKK 6,77 million / DIREC investment DKK 1,01 million

Participants

Project Manager

Claus Brabrand

Associate Professor

IT University of Copenhagen
Department of Computer Science

E: brabrand.itu.dk

Bjørn Hjort Westh

Research Assistant

IT University of Copenhagen
Department of Computer Science

Aisha Umair

Associate Professor

University of Southern Denmark
The Maersk Mc-Kinney Moller Institute

Partners

Kategorier
Nyheder

Forklarlig kunstig intelligens skal øge sygehuses brug af AI

26. november 2021

Forklarlig kunstig intelligens skal øge sygehuses brug af AI

I nyt DIREC-projekt samarbejder AI-forskere med sygehuse om at skabe mere nyttig AI og AI-algoritmer, der er lettere at forstå.

AI (kunstig intelligens) vinder gradvist større plads i medicinske hjælpeteknologier såsom billedbaseret diagnose, hvor den kunstige intelligens med overmenneskelig præcision kan analysere skanningsbilleder. AI er derimod sjældent designet som en samarbejdspartner for lægepersonalet.

I et nyt human-AI-projekt EXPLAIN-ME – støttet af det nationale forskningscenter DIREC vil AI-forskere sammen med læger udvikle forklarlig kunstig intelligens (Explainable AI – XAI), der kan give klinikere feedback, når de uddanner sig på hospitalernes træningsklinikker.

”I den vestlige verden vurderes omkring hver tiende diagnose at være forkert, så patienterne ikke får den rette behandling. Forklaringen kan skyldes manglende erfaring og træning. Vores XAI-model vil hjælpe lægepersonalet med at træffe beslutninger og virke lidt som en mentor, der giver råd og respons, når de træner,” forklarer Aasa Feragen, projektleder og professor på DTU Compute.

I projektet samarbejder DTU, Københavns Universitet, Aalborg Universitet og Roskilde Universitet med læger på trænings- og simulationscentret CAMES på Rigshospitalet, NordSim på Aalborg Universitetshospital samt kræftlæger på Urologisk Afdeling på Sjællands Universitetshospital i Roskilde.

Ultralydsskanning af gravide 


På CAMES vil DTU og Københavns Universitet udvikle en XAI-model, der kigger læger og jordemødre over skulderen, når de i træningsklinikken ultralydsskanner ’gravide’ træningsdukker.

Inden for ultralydsskanning arbejder klinikere ud fra specifikke ’standardplaner’, som viser forskellige dele af fostrets anatomi, så man kan reagere ved komplikationer. Reglerne bliver implementeret i XAI-modellen, der bliver integreret i en simulator, så lægen får feedback undervejs.

Forskerne træner den kunstige intelligens på rigtige data fra Rigshospitalets ultralydsskanninger fra 2009 til 2018, og det er primært billeder fra de almindelige nakkefolds- og misdannelsesskanninger, som alle gravide tilbydes cirka 12 og 20 uger inde i graviditeten. Når XAI-modellerne om godt et års tid vil blive anvendt på træningsklinikken, skal man først tjekke, om modellen også virker i simulatoren, eftersom EAI-modellen er trænet på rigtige data, mens træningsdukken er kunstige data.

Ifølge læger afhænger kvaliteten af ultralydsskanninger og evnen til at stille rette diagnoser af, hvor megen træning lægerne har fået.

”Hvis vores model undervejs kan fortælle lægen, at der mangler en fod i billedet for, at billedet er godt nok, vil lægen muligvis kunne lære hurtigere. Hvis vi også kan få XAI-modellen til at fortælle, at sonden på ultralydsapparatet skal flyttes lidt for at få alt med i billedet, så kan det måske anvendes i lægepraksis også. Det ville være fantastisk, hvis XAI også kan hjælpe mindre trænede læger til at lave skanninger, der er på højde med de meget trænede læger,” siger Aasa Feragen.

Forskningslektor og leder af CAMES’ forskningsgruppe inden for kunstig intelligens Martin Grønnebæk Tolsgaard understreger, at mange læger er interesserede i at få hjælp af AI-teknologien til at fastlægge den bedste behandling for patienterne. Og her er forklarlig AI vejen frem.

”Mange af de AI-modeller, der findes i dag, giver ikke særlig god indsigt i, hvorfor de kommer frem til en bestemt beslutning. Det er vigtigt for os at blive klogere på. Hvis man ikke forklarer, hvorfor den kunstige intelligens kommer frem til en given beslutning, så tror klinikerne ikke på beslutningen. Så hvis man vil bruge AI til gøre klinikerne bedre, så er det ikke nok bare at få beslutningerne, men også behov for gode forklaringer, det vil sige Explainable AI.”

Løbende feedback ved robotkirurgi 


Robotkirurgi giver kirurger mulighed for at udføre deres arbejde med mere præcision og kontrol end traditionelle kirurgiske værktøjer. Det reducerer fejl og øger effektiviteten, og forventningen er, at AI vil kunne forbedre resultaterne yderligere.

I Aalborg skal forskerne udvikle en XAI-model, der støtter lægerne i træningscentret NordSim, hvor både danske og udenlandske læger kan træne kirurgi og operationer i robotsimulatorer med f.eks. grisehjerter. Modellen skal give løbende feedback til klinikerne, imens de træner en operation, og uden at det forstyrrer, fortæller Mikael B. Skov, professor på Department of Computer Science ved AAU:

”I dag er det typisk sådan, at man først får at vide, hvis man skulle have gjort noget anderledes, når man er færdig med at træne en operation. Vi vil gerne se på, hvordan man kan komme med den her feedback mere kontinuerligt, således, at man bedre forstår, om man har gjort noget rigtigt eller forkert. Det skal helst gøres sådan, at personerne lærer det hurtigere og samtidig begår færre fejl, inden de skal ud og lave rigtige operationer. Vi skal derfor se på, hvordan man kan komme med forskellige typer af feedback, som f.eks. advarsler, uden at det afbryder for meget”.

Billedanalyser ved nyrekræft


Læger skal ofte træffe beslutninger under tidspres, f.eks. i forbindelse med kræftdiagnoser, fordi man vil undgå, at kræften spreder sig. En falsk positiv diagnose kan derfor betyde, at patienten får fjernet en rask nyre og påføres andre komplikationer. Selv om erfaringen viser, at AI-metoder er mere præcise i vurderingerne end lægerne, har lægerne brug for en god forklaring på, hvorfor de matematiske modeller klassificerer en tumor som godartet eller ondartet.

I DIREC-projektet vil forskere fra Roskilde Universitet udvikle metoder, hvor kunstig intelligens analyserer medicinske billeder til brug ved diagnosticering af nyrekræft. Lægerne vil hjælpe dem med at forstå, hvilken feedback der er brug for fra AI-modellerne, så man finder en balance mellem, hvad der er teknisk muligt, og hvad der er klinisk nødvendigt.

”Det er vigtigt, at teknologien skal kunne indgå i hospitalernes praksis, og derfor har vi især fokus på at designe de her metoder inden for ’Explainable AI’ i direkte samarbejde med de læger, der rent faktisk skal bruge den i deres beslutningstagning. Her trækker vi især på vores ekspertise inden for Participatory Design, som er en systematisk tilgang til at opnå den bedste synergi mellem, hvad AI-forskeren kan komme op med af teknologiske innovationer, og hvad lægerne har brug for,” siger Henning Christiansen, professor i datalogi på Institut for Mennesker og Teknologi på Roskilde Universitet.

Om DIREC – Digital Research Centre Denmark

Det nationale forskningscenter DIREC skal sætte Danmark i front med de nyeste digitale teknologier gennem digital forskning i verdensklasse. For at tilfredsstille det store behov for højtuddannede it-specialister hjælper DIREC derudover med at udbygge kapaciteten inden for både forskning og uddannelse af dataloger. Centeret har et samlet budget på 275 millioner og er støttet af Innovationsfonden med 100 millioner. Partnerkredsen bag er et unikt samarbejde på tværs af de datalogiske institutter på landets otte universiteter og Alexandra Instituttet.

Centerets aktiviteter sker på baggrund af samfundsmæssige behov, hvor forskning løbende omsættes til værdiskabende løsninger i samarbejde med erhvervslivet og den offentlige sektor. Projekterne går på tværs af brancher og omhandler bl.a. kunstig intelligens, Internet of Things, algoritmer og cybersikkerhed.

Læs mere på direc.dk

EXPLAIN-ME

Partnere i projektet EXPLAIN-ME: Learning to Collaborate via Explainable AI in Medical Education

  • DTU
  • Københavns Universitet
  • Aalborg Universitet
  • Roskilde Universitet
  • CAMES – Copenhagen Academy for Medical Education and Simulation på Rigshospitalet i København
  • NordSim – Center for færdighedstræning og simulation på Aalborg Universitetshospital
  • Urologisk Afdeling på Sjællands Universitetshospital i Roskilde.

Projektperiode: 1. oktober 2021 til 30. april 2025

Kontakt:
Aasa Feragen
Professor på DTU Compute
M: +45 26 22 04 98
afhar@dtu.dk

Anders Nymark Christensen
DTU Compute
+45 45 25 52 58
anym@dtu.dk

Kategorier
Explore project

Verifiable and Robust AI

Project type: Explore Project

Verifiable and Robust AI

The challenge to the research community is how to extend existing verification technologies to cope with software systems comprising AI components (see report of the Dagstuhl Seminar “Machine Learning and Model Checking join Forces” 2018). This is an unchartered territory and one of the most pressing research challenges in AI. The industrial importance of this topic is closely related to the question of liability in case of malfunctioning products. Over a 4-month period the explore project will provide a state-of-the-art survey and identify research directions to be followed.

Participants

Project Manager

Kim Guldstrand Larsen

Professor

Aalborg Universlty
Department of Computer Science

E: kgl@cs.aau.dk

Thomas Dyhre Nielsen

Professor

Aalborg Universlty
Department of Computer Science

Manfred Jaeger

Associate Professor

Aalborg Universlty
Department of Computer Science

Andrzej Wasowski

Professor

IT University of Copenhagen
Department of Computer Science

Rune Møller Jensen

Associate Professor

IT University of Copenhagen
Department of Computer Science

Peter Schneider-Kamp

Professor

University of Southern Denmark
Department of Mathematics and Computer Science

Jaco van de Pol

Professor

Aarhus University
Department of Computer Science

Thomas Hildebrandt

Professor

University of Copenhagen
Department of Computer Science

Alberto Lluch Lafuente

Associate Professor

Technical University of Denmark
DTU Compute

Flemming Nielson

Professor

Technical University of Denmark
DTU Compute

Thomas Bolander

Professor

Technical University of Denmark
DTU Compute

Thomas Asger Hansen

Head of Analytics and AI

Grundfos

Christian Rasmussen

Senior Manager Data Analytics

Grundfos

Malte Skovby Ahm

Research and business lead

Aarhus Vand

Partners

Kategorier
SciTech project

Privacy and Machine Learning

Project type: SCITECH Project

Privacy and Machine Learning

There is an unmet need for decentralised privacy-preserving machine learning. Cloud computing has great potential, however, there is a lack of trust in the service  providers and there is a risk of data breaches. A lot of data are private and stored locally for good reasons, but combining the information in a global machine learning (ML) system could lead to services that benefit all. For instance, consider a consortium of banks that want to improve fraud detection by pooling their customers’ payment data
and merge these with data from, e.g., Statistics Denmark.

However, for competitive reasons the banks want to keep their customers’ data secret and Statistics Denmark is not allowed to share the required sensitive data. As another example, consider patient information (e.g., medical images) stored at hospitals. It would be great to build diagnostic and prognostic tools using ML based on these data, however, the data can typically not be shared.

The research aim of the project is the development of AI methods and tools that enable industry to develop new solutions for automated image-based quality assessment. End-to-end learning of features and representations for object classification by deep neural networks can lead to significant performance improvements. Several recent mechanisms have been developed for further improving performance and reducing the need for manual annotation work (labelling) including semi-supervised learning strategies and data augmentation.

Semi-supervised learning  combines generative models that are trained without labels (unsupervised learning), application of pre-trained networks (transfer learning) with supervised learning on small sets of labelled data. Data augmentation employs both knowledge based transformations, such as translations and rotations and more general learned transformations like parameterised “warps” to increase variability in the training data and increase robustness to natural variation.

Researching secure use of sensitive data will benefit society at large. CoED-based ML solves the fundamental problem of keeping private input data private while still enabling the use of the most applied analytical tools. The CoED privacy-preserving technology reduces the risk of data breaches. It allows for secure use of cloud computing, with no single point of failure, and removes the fundamental cloud security problem of missing trust in service providers.

The project will bring together leading experts in CoED and ML. It may serve as a starting point for attracting additional national and international funding, and it will build up competences highly relevant for Danish industry. The concepts developed in the project may change how organisations collaborate and allow for innovative ways of using data, which can increase the competitiveness of Danish companies relative to large international players.

October 1, 2020 – September 31, 2024 – 3,5 years.

Total budget DKK 4,7 / DIREC investment DKK 3,22

Participants

Project Manager

Peter Scholl

Assistant Professor

Aarhus University
Department of Computer Science

E: peter.scholl@cs.au.dk

Ivan Bjerre Damgaard

Professor

Aarhus University
Department of Computer Science

Christian Igel

Professor

University of Copenhagen
Department of Computer Science

Kurt Nielsen

Associate Professor

University of Copenhagen
Department of Food and Resource Economics

Partners

Kategorier
SciTech project

Machine Learning Algorithms Generalisation

Project type: SCITECH Project

Machine Learning Algorithms Generalisation

AI is radically changing society and the main driver behind new AI methods and systems is machine learning. Machine learning focuses on finding solutions for, or patterns in, new data by learning from relevant existing data. Thus, machine learning algorithms are often applied to large datasets and then they more or less autonomously find good solutions by finding relevant information or patterns hidden in the data. However, it is often not well understood why machine learning algorithms work so well in practice on completely new data – often their performance surpass what current theory would suggest by a wide margin.

Being able to understand and predict when, why and how well machine learning algorithms work on a given problem is critical for knowing when they may be applied and trusted, in particular in more critical systems. Understanding why the algorithms work is also important in order to be able drive the machine learning field forward in the right direction, improving upon existing algorithms and designing new ones.

The goal of this project is to research and develop a better understanding of the generalisation capability of the most used machine learning algorithms, including boosting algorithms, support vector machines and deep learning algorithms. The result will be new generalisation bounds, both showing positive what can be achieved and negative what cannot.

This will allow us to more fully understand the current possibilities and limits, and thus drive the development of new and better methods. Ultimately, this will provide better guarantees for the quality of the output of machine learning algorithms in a variety of domains.

Researching the theoretical foundation for machine learning (and thus essentially all AI based systems) will benefit society at large, since a solid theory will allow us to formally argue and understand when and under which conditions machine learning algorithms can deliver the required quality.

As an added value, the project will bring together leading experts in Denmark in the theory of algorithms to (further) develop the fundamental theoretical basis of machine learning. Thus, it may serve as a starting point for additional national and international collaboration and projects, and it will build up competences highly relevant for Danish industry.

October 1, 2020 – September 31, 2024 – 3,5 years.

Total budget DKK 2,41 / DIREC investment DKK 1,55

Participants

Project Manager

Kasper Green Larsen

Associate Professor

Aarhus University
Department of Computer Science

E: larsen@cs.au.dk

Allan Grønlund

Postdoc

Aarhus University
Department of Computer Science

Mikkel Thorup

Professor

University of Copenhagen
Department of Computer Science

Martin Ritzert

Postdoc

Aarhus University
Department of Computer Science

Partners

Kategorier
Nyheder

Fem nye digitale forsknings- og innovationsprojekter for 115 millioner kr 

18. november 2021

Fremtidens digitale arbejdsplads, samarbejdende robotter og AI på hospitaler:

Fem nye digitale forsknings- og innovationsprojekter for 115 millioner kr

Møder på Zoom og Teams er blevet hverdag under Corona, men hvordan ser den næste generation af digitale værktøjer ud, og hvordan kan de være med til at understøtte fremtidens hybride arbejdsplads? De spørgsmål har forskere og virksomheder sat sig for at udforske i et af de fem nye projekter, som det nationale forskningscenter for avancerede digitale teknologier (DIREC) netop har søsat.

Centret, der er støttet af Innovationsfonden, er et samarbejde, der går på tværs af de datalogiske institutter på landets otte universiteter og Alexandra Instituttet.

Samlet set er der igangsat projekter for 115 millioner kroner, hvoraf de 31,7 millioner kroner kommer fra DIREC. Andre projekter handler om, hvordan man styrer og programmerer flere robotter på en gang, sikrer IoT-enheder, accelererer brugen af kunstig intelligens på hospitaler og understøtter kunstig intelligens på meget små enheder, som smarte termostater, vinduer og garagedøre. 

Styrken i projekterne er, at samarbejdet foregår på tværs af universiteterne og industrien, forklarer Thomas Riisgaard Hansen, direktør for DIREC:

”De mest innovative løsninger kommer altid, når vi sætter folk sammen på tværs. Det kan være på tværs af universiteter, fagligheder og på tværs af forskning og industri. Således er det et krav i alle de projekter, vi søsætter, at vi altid har flere partnere involveret med forskellige kompetencer”.

Det handler de fem forsknings- og innovationsprojekter om:

AI på hospitaler
Det meste forskning i medicinsk AI, når aldrig klinikerne. Målet er i projektet “Explain Me” er at undersøge, hvordan man kan skabe mere nyttig AI på hospitaler og lave AI-algoritmer, som er lettere at forstå for brugerne. Algoritmerne vil i projektet blive testes af i medicinske træningssimulatorer blandt læger og sygeplejersker og på patientdata inden for urologi.

Projektet er et samarbejde mellem Københavns Universitet, Aalborg Universitet, DTU, Rigshospitalet og Sjællands Universitetshospital.
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Samarbejdende robotter
Hvordan styrer og programmerer man robotter, når det ikke kun handler om én robot, men fx otte robotter, der skal samarbejde. Det scenarie er fx relevant inden for landbruget eller ved store ulykker, hvor mange biler er kørt sammen på motorvejen. I projektet HERD vil forskere og industri se på, hvordan man styrer og programmerer dem.

Projektet er et samarbejde mellem Syddansk Universitet, Aalborg Universitet, CBS, Teknologisk Institut, Roboto og Agro Intelligence.
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Fremtidens hybride arbejdsplads
Vi kender alle Zoom og Teams, som er blevet hverdag under corona, men hvordan bliver næste generation af digitale værktøjer, som kan understøtte fremtidens hybride arbejdsplads? I projektet ReWork er forskere gået sammen med en række virksomheder om at designe og udvikle processer og værktøjer, der kan forbedre det gode samarbejde på distancen. De vil både se på, hvordan man digitalt kan understøtte fremtidens arbejdsgange, men de vil også se på, hvordan man kan bruge digitalisering til at gøre det lettere at kombinere familieliv med arbejdsliv.

Projektet er et samarbejde mellem Aarhus Universitet, Københavns Universitet, IT-Universitetet og Roskilde Universitet, Alexandra Instituttet, Arla, Bankdata, kunstnerkollektivet Catch, Microsoft, Zimulate, Keyloop, LTI, Cadpeople, Khora, LEAD+ og BEC Financial Technologies.
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Embedded AI
Hvordan arbejder man med avanceret AI, når det ikke foregår i de store systemer, men ude i edge´n på mindre enheder? Men modsat skyen er ressourcerne begrænsede på små enheder, og derfor vil man i projektet udvikle metoder og værktøjer, der optimerer programmering på små enheder.

Projektet er et samarbejde mellem DTU Compute, Aarhus Universitet, Københavns Universitet, CBS, Grundfos, MAN Energy Solutions, Velux og Indesmatech.
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Sikre IoT-enheder
I projektet “Secure Internet of Things” ser man på, hvordan vi håndterer sikkerhed, efterhånden som vi putter mere intelligens ind i alt fra smarte termostater til garagedøre.

Projektet er et samarbejde mellem Aarhus Universitet, Aalborg Universitet, DTU Compute, CBS, Alexandra Instituttet, Terma, Develco Products, Beumer Group og Logos Payment Solutions.
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Kategorier
Explore project

Explainable AI

Project type: Explore Project

Explainable AI

Artificial Intelligence brings the promise of technological means to solve problems that previously were assumed to require human intelligence, and ultimately provide human-centered solutions that are both more effective and of higher quality in a synergy between the human and the AI system than solutions that are provided by humans or by an AI system alone.

However, compared to traditional problem solving based on logical rules and procedures, some artificial intelligence systems, in particular systems based on neural networks (e.g. as in deep learning), do not offer a human-understandable explanation to the answers given. Lack of explanation is not necessarily a problem, e.g. if the correctness of an answer can be easily validated, such as automatic character recognition subsequently validated by a human. However, in some situations, a lack of explanation may pose severe problems, and may even be illegal as it is the case for governmental decisions.

Participants

Project Manager

Thomas Hildebrandt

Professor

University of Copenhagen
Department of Computer Science

E: hilde@di.ku.dk

Irina Shklovski

Professor

University of Copenhagen
Department of Computer Science

Naja Holten Møller

Assistant Professor

University of Copenhagen
Department of Computer Science

Hugo Lopez

Assistant Professor

University of Copenhagen
Department of Computer Science

Boris Düdder

Associate Professor

University of Copenhagen
Department of Computer Science

Tijs Slaats

Associate Professor

University of Copenhagen
Department of Computer Science

Henrik Korsgaard

Assistant Professor

Aarhus University
Department of Computer Science

Susanne Bødker

Professor

Aarhus University
Department of Computer Science

Lars Kai Hansen

Professor

Technical University of Denmark
DTU Compute

Thomas Bolander

Professor

Technical University of Denmark
DTU Compute

Kim Guldstrand Larsen

Professor

Aalborg University
Department of Computer Science

Thomas Dyhre Nielsen

Professor

Aalborg University
Department of Computer Science

Alessandro Tibo

Assistant professor

Aalborg University
Department of Computer Science

Manfred Jaeger

Associate Professor

Aalborg University
Department of Computer Science

Anders Lyhne Christensen

Professor

University of Southern Denmark
The Maersk Mc-Kinney Moller Institute

Sebastian Risi

Professor

IT University of Copenhagen
Digital Design Department

Lars Rune Christensen

Assistant professor

iT University of Copenhagen
Department of Business IT

Arisa Shollo

Associate Professor

Copenhagen Business School
Department of Digitalization

Rasmus Larsen

AI Specialist

The Alexandra Institute

Peter C. Damm

Applied Research Director

KMD

Mathias Niepert

Manager & Chief Research Scientist

NEC Labs Europe
Heidelberg

Tobias Jacobs

Senior Researcher

NEC Labs Europe
Department of Computer Science

Morten Marquard

Founder & CEO

DCR Solutions

Partners

Kategorier
Bridge project

REWORK – The future of hybrid work

Project type: Bridge Project

REWORK – The future of hybrid work

The recent COVID-19 pandemic, and the attendant lockdown, have demonstrated the potential benefits and possibilities of remote work practices, as well as the glaring deficiencies such practices bring. Zoom fatigue, resulting from high cognitive loads and intense amounts of eye contact, is just the tip of an uncomfortable iceberg where the problem of embodied presence remains a stubborn limitation. Remote and hybrid work will certainly be part of the future of most work practices, but what should these future work practices look like? Should we merely attempt to fix what we already have or can we be bolder and speculate different kinds of workplace futures? We seek a vision of the future that integrates hybrid work experiences with grace and decency. This project will focus on the following research question: what are the possible futures of embodied presence in hybrid and remote work conditions?

There are a multitude of reasons to embrace remote and hybrid work. Climate concerns are increasing, borders are difficult to cross, work/life balance may be easier to attain, power distributions in society could potentially be redressed, to name a few. This means that the demand for Computer Supported Cooperative Work (CSCW) systems that support hybrid work will increase significantly. At the same time, we consistently observe and collectively experience that current digital technologies struggle to mediate the intricacies of collaborative work of many kinds. Even when everything works, from network connectivity to people being present and willing to engage, there are aspects of embodied co-presence that are almost impossible to achieve digitally.

We argue that one major weakness in current remote work technologies is the lack of support for relation work and articulation work, caused by limited embodiment. The concept of relation work denotes the fundamental activities of creating socio-technical connections between people and artefacts during collaborative activities, enabling actors in a global collaborative setting to engage each other in activities such as articulation work. We know that articulation work cannot be handled in the same way in hybrid remote environments. The fundamental difference is that strategies of awareness and coordination mechanisms are embedded in the physical surroundings, and use of artefacts cannot simply be applied to the hybrid setting, but instead requires translation.

Actors in hybrid settings must create and connect the foundational network of globally distributed people and artefacts in a multitude of ways.

In REWORK, we focus on enriching digital technologies for hybrid work. We will investigate ways to strengthen relation work and articulation work through explorations of embodiment and presence. To imagine futures and technologies that can be otherwise, we look to artistic interventions, getting at the core of engagement and reflection on the future of remote and hybrid work by imagining and making alternatives through aesthetic speculations and prototyping of novel multimodal interactions (using the audio, haptic, visual, and even olfactory modalities). We will explore the limits of embodiment in remote settings by uncovering the challenges and limitations of existing technical solutions, following a similar approach as some of our previous research.

Scientific value
REWORK will develop speculative techniques and ideas that can help rethink the practices and infrastructures of remote work and its future. REWORK focuses on more than just the efficiency of task completion in hybrid work. Rather, we seek to foreground and productively support the invisible relation and articulation work that is necessary to ensure overall wellbeing and productivity.

Specifically, REWORK will contribute:

  1. Speculative techniques for thinking about the future of remote work;
  2. Multimodal prototypes to inspire a rethink of remote work;
  3. Design Fictions anchoring future visions in practice;
  4. Socio-technical framework for the future of hybrid remote work practices;
  5. Toolkits for industry.

The research conducted as part of REWORK will produce substantial scientific contributions disseminated through scientific publications in top international journals and conferences relevant to the topic. The scientific contributions will constitute both substantive insights and methodological innovations. These will be targeting venues such as the Journal of Human-Computer Interaction, ACM TOCHI, Journal of Computer Supported Cooperative Work, the ACM CHI conference, NordiCHI, UIST, DIS, Ubicomp, ICMI, CSCW, and others of a similar level.

The project will also engage directly and closely with industries of different kinds, from startups that are actively envisioning new technology to support different types of hybrid work (Cadpeople, Synergy XR, and Studio Koh) to organizations that are trying to find new solutions to accommodate changes in work practices (Arla, Bankdata, Keyloop, BEC).

Part of the intent of engagement with the artistic collaboratory is to create bridges between artistic explorations and practical needs articulated by relevant industry actors. REWORK will enable the creation of hybrid fora to enable such bridging. The artistic collaboratory will enable the project to engage with the general public through an art exhibit at Catch, public talks, and workshops. It is our goal to exhibit some of the artistic output at a venue, such as Ars Electronica, that crosses artistic and scientific audiences.

Societal value
The results of REWORK have the potential to change everybody’s work life broadly. We all know that “returning to work after COVID-19” will not be the same – and the combined situation of hybrid work will be a challenge. Through the research conducted in REWORK, individuals that must navigate the demands of hybrid work and the organizations that must develop policies and practices to support such work will benefit from the improved sense of embodiment and awareness, leading to more effective collaboration.

REWORK will take broadening participation and public engagement seriously, by offering online and in-person workshops/events through a close collaboration with the arts organization Catch (catch.dk). The workshops will be oriented towards particular stakeholder groups – artists interested in exploring the future of hybrid work, industry organizations interested in reconfiguring their existing practices – and open public events.

Capacity building
There are several ways in which REWORK contributes to capacity building. Firstly, by collaborating with the Alexandra Institute, we will create a multimodal toolbox/demonstrator facility that can be used in education, and in industry.

REWORK will work closely with both industry partners (through the Alexandra Institute) and cultural (e.g. catch.dk)/public institutions for collaboration and knowledge dissemination, in the general spirit of DIREC.

We will include the findings from REWORK in our research-based teaching at all three universities. Furthermore, we plan to host a PhD course, or a summer school, on the topic in Year 2 or Year 3. Participants will be recruited nationally and internationally.

Lastly, in terms of public engagement, HCI and collaborative technologies are disciplines that can be attractive to the public at large, so there will be at least one REWORK Open Day where we will invite interested participants, and the DIREC industrial collaborators.

January 1, 2022 – December 31, 2024 – 3 years.

Participants

Project Manager

Eve Hoggan

Professor

Aarhus University
Department of Computer Science

E: eve.hoggan@cs.au.dk

Susanne Bødker

Professor

Aarhus University
Department of Computer Science

Irina Shklovski

Professor

University of Copenhagen
Department of Computer Science

Pernille Bjørn

Professor

University of Copenhagen
Department of Computer Science

Louise Barkhuus

Professor

IT University of Copenhagen
Department of Computer Science

Naja Holten Møller

Assistant Professor

University of Copenhagen
Department of Computer Science

Nina Boulus-Rødje

Associate Professor

Roskilde University
Department of People and Technology

Allan Hansen

Head of Digital Experience and Solutions Lab

The Alexandra Institute

Mads Darø Kristensen

Principal Application Architect

The Alexandra Institute

Partners