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Region Stockholm considers legislation as important but very difficult subjet.

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Region Stockholm considers legislation as important but very difficult subjet.

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KTH Digitalization platform works on data-sharing within KTH (infrastructure, incentives,...)

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KTH Digitalization platform works on data-sharing within KTH (infrastructure, incentives,...)

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SU/DSV collaboration within project to protect personal data

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SU/DSV collaboration within project to protect personal data

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Samples can move around fom hospital, but rather different from ML use cases . Have requirements very different from typical ML 

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Samples can move around fom hospital, but rather different from ML use cases . Have requirements very different from typical ML 

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Platform company has resources in the company to look into challenges

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Platform company has resources in the company to look into challenges

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KTH Digital Futures project DataLEASH works on mechanisms on how to share data in a privacy-preserving way

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KTH Digital Futures project DataLEASH works on mechanisms on how to share data in a privacy-preserving way

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Modulai only limited experience to work with Swedish Govt data. How to access data and develop services/ML products to Swedish govt.

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Modulai only limited experience to work with Swedish Govt data. How to access data and develop services/ML products to Swedish govt.

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IRI - prediciting legislative challenges when IT developed.

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IRI - prediciting legislative challenges when IT developed.

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Developed technology that can be used as a tool to solve regulatory challenges. Privacy by design and privacy by default.

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Designing network - KTH Innovation . AI-startups often stuck in their own garage, not knowing what's going on at the larger scene

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Designing network - KTH Innovation . AI-startups often stuck in their own garage, not knowing what's going on at the larger scene

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Stockholm University Digital Futures EXTREMUM project on explainable machine learning models. How to share models and explanations without compromising sensitive information

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Stockholm University Digital Futures EXTREMUM project on explainable machine learning models. How to share models and explanations without compromising sensitive information

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KTH - Aktiv inom olika områden av AI.
KTH Digital Futures (governments digitalisation initiative) gathers people from several fields and includes business partners.

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Marie Curie-project with 4 other universities and 15 doctorates 

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Marie Curie-project with 4 other universities and 15 doctorates 

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ML for the Tax Authority to find tax cheaters

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ML for the Tax Authority to find tax cheaters

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Precision health  strategic perspecitive, funding data lab, 

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Precision health  strategic perspecitive, funding data lab, 

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Interesting for Region Stockholm to together with the City of Stockholm begin to understand how to handle the GDPR challenge together on a larger scale and also start charing personnel data inbetween themselves seamlessly.

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Interesting for Region Stockholm to together with the City of Stockholm begin to understand how to handle the GDPR challenge together on a larger scale and also start charing personnel data inbetween themselves seamlessly.

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Lexplore Ability to measure reading ability based on eye-tracking.

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Lexplore Ability to measure reading ability based on eye-tracking.

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Now a service for improving reading capacity (Sweden, Austr. England, USA, Germany, UAE etc...)

Challenges to implement in the school-sector.

with several schools across a municipality, the municipality now can work with greater objectivity in measurement and evaluation.

Want to conduct studies on the data that they have gathered. - Pedagogical & organisational data.

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Spinout from Uppsala. Federated learning pertinent for this area. Especially as Uppsala is a hub for medtech . EU funded project with airlines . How do we collaborate with competitors 

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Spinout from Uppsala. Federated learning pertinent for this area. Especially as Uppsala is a hub for medtech . EU funded project with airlines . How do we collaborate with competitors 

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SU - all researchers would love to have patient data to "play" with. SU/DSV has the data bank with a lot of health data. Working do access it. Developed a tool to be able to see how sensible it actually is.

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SU - all researchers would love to have patient data to "play" with. SU/DSV has the data bank with a lot of health data. Working do access it. Developed a tool to be able to see how sensible it actually is.

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CITIVA? software for cancer treatment, calibration of software. Data heavily regulated at hospital level, not enough data from single hospitals. Build a use case around that . Monetise data at a granular level?

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CITIVA? software for cancer treatment, calibration of software. Data heavily regulated at hospital level, not enough data from single hospitals. Build a use case around that . Monetise data at a granular level?

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Peltarion - Make AI more accessible. Everyone would be able to use and implement AI.

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Peltarion - Make AI more accessible. Everyone would be able to use and implement AI.

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AI shouldn't be limited to few actors with large budgets or resources.
Help people implement AI and best-practice in implementation.

Two fronts -  DS groups
a) Applied AI
b) Research group - higher contact with AI Sweden, RISE, Vinnova, Universities, etc.

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Intersystems is also interested in sandbox. Works with hospitals with clinical test data. Even care units within hospitals has challenges with handling real data. Syntesized data would be very valuable.

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Intersystems is also interested in sandbox. Works with hospitals with clinical test data. Even care units within hospitals has challenges with handling real data. Syntesized data would be very valuable.

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At KI, 1000 patients monitored at millisecond level. TB generated every second . Problem is to sample , and find the gold nuggets .

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At KI, 1000 patients monitored at millisecond level. TB generated every second . Problem is to sample , and find the gold nuggets .

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Privacy not an issue at pre clinical level, as permits are processed.

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AI Sweden - we have contact with an AI startup who has specialized on syntesized data.

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AI Sweden - we have contact with an AI startup who has specialized on syntesized data.

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IRI - Digital twins. Legislation and sofware instruments to support on a system level. Developing ad hoc solutions for traffic data and other fast moving areas. 

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IRI - Digital twins. Legislation and sofware instruments to support on a system level. Developing ad hoc solutions for traffic data and other fast moving areas. 

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Stockholm Business Region, part of the City of Stockholm ( link between the city and the business community)

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Stockholm Business Region, part of the City of Stockholm ( link between the city and the business community)

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We supportlocal business development across many sectors. Within AI -  we try to package strength areas that can attract international talents, investments, establishments & collaborations. We also want to identifiy what 's missing the AI ecosystem so we can proactivly attract those players that will enrich and complement our AI landscape.

We work very broadly from supporting local meetups to providing advice to foreign companies to tap into the greater Stockholm region.

Work with internationalisation in order to attract capital and talent to Sweden & Stockholm.
What is the market?

Examples to attract certain events to Sweden: e.g. ACTIC 15 - Initiative.

Work with over 50+ municipalities across Sweden.

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Distributed learning and swarm learning. 

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Distributed learning and swarm learning. 

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Data pipeline issues. Need a data fabric that handles the data .
Data pipeline and security concerns are two different discussions

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The Swedish Tax Agency, long experience of advanced data analysis. We want to share more data but a lot of problematic laws make it difficult

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Two way pitching .. show them the landscape. 

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Two way pitching .. show them the landscape. 

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Make them aware of funding possibilities
Connecting the dots is very useful

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Project homepage with report as PDF download:
https://ki.se/en/lime/artificial-intelligence-at-karolinska-institutet

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Tradeoffs between: privacy, explainability. Directions to go forward: data anonymization/deidentification, model sharing via federated learning.

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Tradeoffs between: privacy, explainability. Directions to go forward: data anonymization/deidentification, model sharing via federated learning.