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Iniziamo la nostra escursione in una tipica valle svizzera: campi verdi ben curati, alcuni accoglienti chalet, ma soprattutto una mandria di mucche curiose che ci danno il benvenuto.

Nella nostra nuova serie di approfondimenti, lasciate che Wouter Jongeneel vi guidi alla scoperta sia della Svizzera che della regolazione automatica.

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.

Tutti hanno sentito parlare del cosiddetto "Algoritmo di Google" e di altri algoritmi dei social media utilizzati per trovare i contenuti più interessanti per ogni persona. Ma cosa significa in realtà? Come funziona e cosa succede all'interno del suo computer?

Cosa significa stabilità nella teoria del controllo e cosa c'entrano le forme? Prendete una penna, un rotolo di carta igienica vuoto e un paio di forbici: Wouter Jongeneel ha qualcosa da insegnarvi.

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.