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Hot-or-Not: Architecting new AI for high-tech systems

We are pushing the control of high-tech systems to the boundaries of what is physically possible. We are entering a world where reliable models are no longer available, and we need to take uncertainty as a given. AI comes to guide us in this new uncertain future.

In this Hot-or-Not, we will explore how new AI-techniques, based on Bayesian Learning can land in the realm of systems control.

From drones to complex systems

We will show you how broad the field of systems control can be. From unpredictable human interaction with razor sharp drones to the architecture of complex high-tech systems. We will explore the system design choices that Sioux engineers Ruben Kwant and Stijn Groen faced in preparation of the King’s Day performance. They will explain how they dealt with the uncertainties of the untight control that is so much part of working with drones.

TU/e prof. Bert de Vries of BIASlab will then take us into the future of AI and how it can support the high-tech industry. By using neuro-inspired techniques, we can use our beloved systems models and efficiently add self-learning mechanisms: introducing Bayesian learning. Unlike the current trends in deep learning this gives rise to a new AI that can be deployed in real-time systems and in the smallest devices.

Sioux tech director Bas van der Linden will put this new Bayesian hype in an industrial perspective. He will discuss which steps we need to take to incorporate the Bayesian methods into architectures of high-tech systems. Giving us the power to expand our material and production methods, let machines self-monitor, reduce complexity and increase precision.