This last installment introduces Hybrid Processing and maps it together with Active Learning and Transductive & Inductive Semi-Supervised Learning in a novel framework.

In this post, I finally found personal closure on the topic of how AL translates to some real-world scenario and introduce Hybrid Processing as a new paradigm.

People are starting to realize that some techniques that were previously regarded as Unsupervised Learning should more aptly be named Self-Supervised Learning. Let’s expand on that.

A guide to successfully putting AI into practice

Do we need to understand our models in order to trust them?

Data privacy - one of machine learning’s biggest challenges and how to tackle it

Despite countless research papers demonstrating proof-of-concept and high performance on validation sets, AI adoption by the healthcare industry is slow and scarce.

Conversations about AI are difficult. This is partially due to people having different definitions, or not even consistently standing behind one definition, but rather forming their opinion based on some fluctuating intuition of what AI is.

It has been over half a year since I published the article Mythbusters: 7 common myths about AI. On my quest to demystify AI, I have held many workshops and talks. In this follow-up post, I have collected and debunked seven more frequently encountered myths.

Every problem worth solving needs great tools for support. Deep learning is no exception. If anything, it is a realm in which good tooling will become ever more important over the coming years.

Digitalization has not only affected our private lives but also the vocational world. In her book “The New Education”, university professor Davidson predicts that 65% of jobs that will be available for students in 15 years do not exist yet.

At Luminovo, we are convinced that AI will primarily complement human work by automating repetitive tasks, rather than substituting it. Discover why you should see AI as your future best friend and what it looks like beyond the hype.

Artificial Intelligence is one of the most hyped topics of recent years. Naturally, a lot of superficial knowledge has found its way into the press and the minds of the general public. In this post, I want to uncover a few of those misconceptions.

With the advent of deep learning there has been a major shift in how AI engineers work. Now it looks like there might be another shift on the horizon: Neural Architecture Search.

If you’re like me, you enjoy throwing CNNs at every pictorial problem that comes your way. If you’re like me, then you have heard of BatchNorm.