© Pint of Science, 2025. All rights reserved.
Science brings together people from all types of different backgrounds, cultures, and experiences, culminating in more innovative ideas and collaborative solutions. When diverse voices can be expressed freely and in a safe environment, scientific research and technological development become more representative and equitable, promoting accessibility and benefiting a wider range of communities. Join us for this edition of Pint of Science Utrecht where we will hear about a diverse range of topics from a diverse group of scientists, including the use of pronouns and how they are received by both …
Non-Binary Language, (Limited) World Tour: How Gender-Neutral Innovations Work Differently Across Languages
Hielke A.D. Vriesendorp
(Assistant Professor)
It's been hard to miss the increasing spread and visibility of non-binary language in English: from LinkedIn profiles, to bitterly fought political debates in newspapers, most will have come across they/them. But how does this work beyond English? I’ll discuss recent scientific research into non-binary language innovations in Dutch, Frisian, Swedish, Spanish, German, French, and Arabic. It might be that the English, pronoun-focused approach isn’t as obvious and universal as we might have assumed.
Mycelium materials across the (light) spectrum
Jara Salueña Martín
(PhD Candidate)
Mushrooms are just the fungus's tip—below ground, mycelium forms vast networks that we can turn into sustainable materials. Imagine chairs, home insulation, even jackets made from mycelium! While fungi can be strong and lightweight in their natural environment, we are still learning how to harness these properties into mycelium materials. By tweaking the growing conditions of fungi we can affect their material performance. My research explores the effect of light on mycelium growth to create more durable and tuneable fungal materials for a greener future.
Theoretical Physics for Artificial Intelligence
Ro Jefferson
(Assistant Professor)
Machine learning has become powerful and ubiquitous, driven mainly by empirical advances in large deep neural networks like ChatGPT. However, our understanding of how these systems really work lags worryingly behind the pace of industry. Perhaps surprisingly, techniques and ideas from theoretical physics -- such as quantum and statistical field theory -- turn out to be extremely well-suited to understanding deep neural networks on a mathematical level. In this talk, I will give an overview of recent cutting-edge developments in the rapidly emerging field of Theoretical Physics for AI.
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