Machine learning

Machine Learning

Federated (Machine) Learning spins off to Med apps

Telcos, Med, Pharma, and on it goes. Looking back, the old saying “AI is what was impossible yesterday” fits on FL. I wrote about AI in health in my posts before this summer; now, those seem to converge in health and care, thanks to the CORD-19 AI Challenge and even …


What CoViD-research can learn from IT architects & AI, and vice versa

Microcommunities (by WHO and ECDC) and Microservices (in SOA). Spread of Corona virus and Internet virus. Public safety and data security. Organizational learning and machine learning. An ever-growing list. The devil is in the detail That is, the Joker is the combination of cross-cutting issues and (unpredictable) behavior. So, dear …


ML in Health: Allen alliance fights Corona

Since my last post on Health and (Federated) Machine Learning, the tech sector’s involvement has multiplied, almost day by day. MS Research & AI 2 (the Allen Institute for AI) partnered with leading research groups to prepare and distribute for free the COVID-19 Open Research Dataset (CORD-19). Mining useful insights …

Machine learning

3 x ML in Public Health and Care

Health, pharma, and care expose Machine Learning to yet another stress test in practice, which adds another vital quality attribute to architects’ QA-list (along with explainability, security, safety, accuracy, etc.)  – privacy-friendly ML. Three current examples from the health realm 1. Most public authorities worldwide are using outdated analytics tools …

Intelligible Intelligence

Intelligible Intelligence: Deep XAI still more R&D than toolbox

Most architectural tradeoffs are hard. So is the one between the accuracy of Deep Machine Learning (ML) and explainability/transparency of explainable AI (XAI). Therefore, DARPA’s initial XAI program is expected to run through 2021.


Twin examples of multiple trees: 1. UML models, 2. Machine Learning

“Today, professionals get trained in using tools…  there’s a lack of education of fundamentals like modeling, architecture, methods, or concepts… Getting value out of data needs professionalization based on education and practical experience.”   


6 Things AI and Machine Learning Reshape in SW

In my recent post, I mentioned that AI and ML challenge architecture, but also offer tools to tackle the challenges. Needless to say, automation of repetitive tasks will change our job descriptions, just like those of our end users.


On Smart Configurators, for IT Architects

Manufacturing is the key player in applied intelligent configuration, but software businesses are catching up too. Factoring out dependencies between components (or classes, services etc.) to separate configuration issues from application logic, is a well-tried SW-variability principle likely to evolve further in the era of machine learning (ML).   


Konsument-IT som liknar företags-IT

Christer Norströms presentation under SICS Open House 2018 handlade om hans intelligenta träningstjänst Racefox. Den stödjer individanpassad uthållighetsträning för löpare och längdåkare. Arkitekturen och kraven på Intelligenta träningsassistenter ligger ungefär halvvägs mellan ”vanlig” konsument-IT (gadgets) och företags-IT (mission critical).Expertanvändare i gränslandet