The Lab
We are actively exploring different approaches of integrity-preservation and distributed machine learning. We are particularly interested in privacy-preserving model construction, techniques for ensuring trust, manage incentives and verify contribution during computation, the business logic of setting up ML alliances, the governance of the alliances, the constructions and models, and the life cycle management of alliances.
FedML + Blockchain
A part of our platform for privacy-preserving federated machine learning using blockchain technology and smart contracts with support from Vinnova, Sweden's innovation agency.
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NDCP
In the pharmaceutical industry, where predictions on existing data (such as measured assays of various hazard endpoints for chemical compounds) constitute valuable assets and it would be desirable for all companies to have as good predictive models as possible; but sharing data between companies is usually not so easy.
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FedML Demo
A proof of concept, demonstrated to industry participants at GE Healthcare Testacenter in Uppsala, using incremental learning of linear models to do FedML.
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Scaleout AI Canvas
Our Lean AI canvas is a tool for identifying the best opportunities for AI pilot projects.
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