AI

Learning for AI

Learning for AI, and the other way too

A quote from neither Informator nor Tieturi, but from the Lancet medical journal, April 2021: “particularly deep learning, has changed our lives (…) deep learning systems capable of diagnosing skin cancer and fully autonomous AI approved for diabetic retinopathy screening (…) A key concern is how should clinicians be educated …

Covid

Silver Lining on Dark Covid Cloud: from cover stories in Science, Nature, SA, to AI success stories

Over the past two decades, global AI research output grew +600%, and its steep growth continues, according to the Stanford 2019 AI Index Report. The IDC Worldwide Semiannual Artificial Intelligence Tracker predicts global AI revenues to double over the first half of this decade, to $300 billion in 2024. The silver …

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 …

covid-19

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 …

covid

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 Most public authorities worldwide are using outdated analytics tools …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.

blog

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.”   

blog

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.