Explainers

In this second stage we leave the valley and move a bit higher up. Of course, we could walk or climb, but let us enjoy a remarkable piece of engineering and catch a gondola (cable car).

We start our hike in a typical Swiss valley: tidy green fields, some cozy chalets, but most importantly, a herd of curious cows that welcomes us.

In our new series of explainers, let Wouter Jongeneel walk you through both Switzerland and automatic control.

This time host Alberto Padoan takes us on a deep dive into some of the main pillars of modern control theory. From Kalman's paradigm-shifting state-space approach to the space race, complex networks, and more...

You've probably heard of reinforcement learning (or RL for short) if you've spent any time reading about technology or control theory, but what does it really mean? Let us explain.

Everyone has heard of the so-called “Google Algorithm” and other social media algorithms used to find the most compelling content for each person. But what does that actually mean? How does it work and what is going on inside your computer?

What does stability mean in control theory, and what do shapes have to do with it? Grab a pen, an empty toilet roll and a pair of scissors: Wouter Jongeneel has something to teach you.

If you've heard about game theory but don't really know what it is or what it means, this game by Nicky Case is a great starting point. (please note that Nicky Case is not associated with NCCR Automation - we just think this is a great resource).

The distinction between “convex” or “non-convex” optimization isn’t just a technicality. Convexity makes for calculations that can guarantee the best result in an automated process – here’s how.

Humans have been trying to do more with less for a very long time. We’d like cars and planes that go further but use less fuel and have a smaller carbon footprint. We’d like food that is optimally nutritious but also affordable, and we’d like energy solutions for our house, cities and countries that are effective but also green and affordable.

While neural networks often achieve impressive performance, they sometimes fail to capture simple concepts that humans understand intuitively – a gap that may even put lives at risk. But can we transfer our human knowledge to neural networks to help them learn more meaningful solutions? NCCR Automation researcher Loris Di Natale has a plan to make sure automated machines don’t freeze us out.