What challenges arise at high-scale training that you couldn’t imagine, having only experienced lower scale? How can conditional diffusion be brought out to domains where being exact is more important than being creative? Why is it so important to consider uncertainty in modelling?

And more crucially: could Heilbronn, a city of 100,000 inhabitants in Germany that I’m almost sure you never heard of, become the next AI research and development centre of Europe?   

At this very moment, I’m the one taking the lead on this investigation, so let me briefly introduce myself.

Hi, I am Loric Herbé, a regular master student in Data science at ETH Zurich, and for the next few minutes, you will have the opportunity to step into my shoes and discover the backstage of an event that could very well be our most tangible piece of evidence.

Let us first draw some context, so that the investigation board gets clearer in your heads.

On this end of June 2025, a soberly titled “Interdisciplinary School on Machine Learning and Artificial Intelligence for Science” was hosted at the emerging ETH Zürich Campus in Heilbronn. Supported by a range of sponsors, it’s part of a broader effort by ETH to establish a serious research and innovation presence in the region. The goal of this event: gathering talents from around the globe, from master to industry, to deepen their intuition and understanding of the latest applications of machine learning in science. How? By alternating between theoretical lectures, talks, and hands-on sessions that polished their mastery, while it was still hot.

My role regarding this event? With a few other colleagues, we were tasked with coming up with hands-on material, under the shape of Jupyter Notebooks. A challenging experience, as we had to find the right balance between tackling High Performance Computing (HPC) problems, along with giving the right amount of theory and intuition, all the while fitting into time constrained blocks.

What I just described took place mostly prior to the event, which means that on-site, I almost became a regular participant, and this is under this prism that I’m willing to unveil with you the fantastic colours of this five-day-long life fragment.

It would be an absolute crime not to start by giving a glimpse of the talks that we have been given. Without going into details, these talks were pure gold from a Data Science student’s perspective. The amount of insights and creative solution tracks to common problems we face in the field was just surprisingly high, especially given the duration of the talks. And this applies to a very broad spectrum of interest, such as the double-edged sword that is given by constrained optimization, large scale training techniques effects on stochasticity, or even techniques to bring in model interpretability for fields that can’t accept treating a result as a black box’s answer, and this is only to name a few.

Among all these thought-provoking talks, I want to give a special shoutout to the one of Professor Andreas Krause, which gave an overview of the active learning and reinforcement learning fields, notably through the lenses of optimism in the face of uncertainty. The reason I’m mentioning this talk is that as a Data science student, the in-depth lecture “Probabilistic Artificial Intelligence” given by Professor Krause was probably one of the most valuable perspectives on Machine Learning that I was ever given, and I’ll even go the extent that this also changed some of my perception and modeling assumptions on my life in general.

On another note, I can’t forget to mention what it felt like to have such brilliant minds buzzing around you for a week. And there I’m referring both to the organisers as well as the participants. To give my words some concrete pictures, imagine that in the first 10 minutes sitting in the bus to Heilbronn, I was discovering the current state of the art quantization technique for LLMs at inference time through some talk with another master student whose thesis built around that, and that my first meal revolved around learning a new perspective on model validation through Posterior Agreement with the eminent Professor Joachim Buhmann.  However, don’t get me wrong, although this could be a brain racking experience at some times, this also retained the social development aspect that you could find in a regular summer camp, where you have good laughs around a nice diner and talk about the near future under a nice sunset.

Taking back half-seriously my investigator costume, I would say that it’s hard to answer whether Heilbronn will truly become tomorrow’s AI centre of Europe, but it’s worth pointing out that it’s through such efforts, organising such events, that it will definitely earn some key position in this fast-paced field. 

What a journey we made together! Now, I believe that the most acute readers will feel that there is a missing piece to conclude this investigation. And they would be right, what happened to the first few questions? Sadly, I don’t yet pretend to be able to answer those questions at the same level those eminent professors did. However, I think I might have a solution… can’t wait to walk, alongside you this time, in a future second edition!