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 Most public authorities worldwide are using outdated analytics tools …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: 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.
Yet another AI language you miss in your CV? 4 reasons why it will matter less and less.
It never hurts, but it varies how helpful a (fairly) new programming or script language is. From more or less a prerequisite in R&D and platform-vendor firms, to a nice-to-have CV footnote in mainstream businesses that rather emphasize extended SQL, analytics, data architecture, and automated ML platform/s.
3 AI seminarier som Angick Arkitekter
Tre bara i Stockholm, på knappt två veckor. En maraton, eller rentav på väg till marigt? Utbudet av seminarier kring AI (snarare än inom) lever upp till standarddefinitionen ”AI är sådant som var omöjligt igår”. Under 90-talets kalla ”AI-vinter” hade nämligen även tre seminarier på två år varit rena miraklet.
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).
Auditability and V&V in the era of Machine Learning are worth a close review…
Developers, more often than architects, tend to get frustrated by declarative programming, because it boosts expressive power at the cost of less testability.
5 ”inte” som gjort AI 2.0 het
I mars gjorde tillämpad AI stora rubriker igen. Den här gången var det machine learning (ML) med neurala nät i poker, Deep Stack.