Edge AI: Architects, Keep Edging! Even after the Skiing Season

The current slalom between insecurity, logistics, and volatile energy/material/component supplies, is adding momentum to resource-efficient computing and green AI. Moreover, a recent video presentation by Amazon points out that up to 90% of the infrastructure costs for developing and running ML apps is inference. Train it sometimes, run it a lot of times In plain […]

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 EU and UN, your IT-architects […]

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 Most public authorities worldwide are using outdated analytics tools in forecasting and warning. Canadian […]

What’s wrong with repairs?

That’s a question, let go just a rhetoric one. Copy-pasting most of what I wrote in this Swedish article in Ny Teknik, 7½ years back, would still work: since then, rather few business-to-consumer industries have replaced the disposable-product mentality, poor quality, and short product lifecycles. But, it’s finally turning.  

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.

Leveled up: Auditability of AI and Machine Learning

Architects and many others remember the tightrope walk between flexibility/performance and testability/predictability/V&V in systems with many run-time parameters, or parallelism, or late binding time ranging from polymorphism to SOA-UDDI and ad-hoc computing. Now, it’s leveled up by ML (machine learning).

Architects beware: 60 years since Dartmouth

Many R&D-intensive industries experienced an initial period of teething troubles, about six decades between their seminal events and their commercial breakthrough, followed by exponential growth.