When a fish holds its position against a current in a river, its brain must figure out how fast to swim and how to steer to offset the water flow. Most fish use vision to register the world sliding past, detect optic flow speed and direction, and their brains turn these signals into compensatory swimming. The retina captures signals of optic-flow direction, central pretectal neurons interpret direction, and spinal nerves drive muscle contractions. The catch is that one cannot easily change the living brain to test how these circuits work. Although advances in imaging now allow detailed recording, and even manipulation, of neurons alongside behavior, rewiring connections to ask what a particular link actually does remains almost impossible in the living, complex animal.

Loons, gulls, puffins, and petrels are some of the 100 species of birds that can both fly and swim. These diving birds can plunge in water to swim after prey, and then leap back into the air to fly away. Now, inspired by these naturally aquatic aviators, engineers at EPFL and MIT have designed a robot that can swim underwater, and flap out of the water to continue flying through air, much like diving birds. The "flapping-wing aerial-aquatic vehicle," or FAAV, weighs less than 300 grams and is designed to help scientists study the mechanics that enable diving birds to fly through air and water. The robot has a central body, or fuselage, two flexible, flapping wings, and a steerable tail.

This research from the NeuroAI Lab of Martin Schrimpf, part of EPFL's Schools of Computer and Communication Sciences and Life Sciences, uses AI models to predict exactly where to stimulate the brain to evoke images of faces and specific objects in the users instead of simply evoking spots of light. The models developed at EPFL were used by Dutch researchers for live trials on sighted monkeys. The preliminary results, presented in April at the International Conference on Learning Representations, show very promising implications for vision in humans as well. "The motivation for this project is that there are many people with visual deficits that are irreparable, in the sense that somewhere along the visual processing stream, starting with the retina, there is a deficit which cannot be repaired," says Johannes Mehrer, a scientist in the NeuroAI lab who led the research. "One way of tackling this problem is to develop a visual prosthesis."

When we want to understand how matter behaves, the real action happens at the atomic scale. Heating of water, a chemical reaction in a battery, the way proteins fold in our cells, or how a catalyst works to convert carbon dioxide into useful fuels, all of these processes are governed by the motions and interactions of atoms. Atomic-scale simulations give us a way to explore the microscopic behavior of matter, by tracking how atoms move under the laws of quantum mechanics. These simulations have become essential across physics, chemistry, biology, and materials science. They test hypotheses that experiments cannot easily probe and help design new materials before they are synthesized and tested in the lab.

Designing a new drug often means inventing molecules that have never existed before. Chemists represent molecules as graphs, where atoms are the "nodes" and chemical bonds the "edges," capturing their connections. This graph representation expands far beyond chemistry: a social network is a graph of people and friendships, the brain is a graph of neurons and synapses, and a transport system is a graph of stations and routes. From molecules to social networks, graphs are everywhere and naturally capture the relational structure of the world around us. Therefore, for many applications, being able to generate new realistic graphs is a central problem.

Have you ever wondered how wild animals behave when no one's watching? Understanding these behaviors is vital for protecting ecosystems--especially as climate change and human expansion alter natural habitats. But collecting this kind of information without interfering has always been tricky. Traditionally, researchers relied on direct observation or sensors strapped to animals--methods that are either disruptive or limited in scope. Camera traps offer a less invasive alternative, but they generate vast amounts of footage that's hard to analyze.

This is what Aïcha – a fictional Master's student in AI – and her friend Félix discover in Utop'IA an educational (French language) graphic novel developed in association with author and illustrator Herji as part of a project initiated by LEARN. "Exploring AI through an environmental lens brings its physical, tangible side into sharp focus," says Sonia Agrebi, an expert in digital sociology and a LEARN projects manager. "Utop'IA examines how AI can make both a positive and negative impact on the environment. As a society, we use AI without realizing the repercussions. Our aim isn't to moralize or point the finger of blame, but rather to challenge perceptions and explain concepts to raise awareness of the issues surrounding AI." Utop'IA is backed by solid scientific reasoning and evidence, since every detail was reviewed by a committee of EPFL experts in AI, sustainability and learning science. "AI is playing an increasingly important role in our everyday lives, but I find it alarming that so little is said about its environmental impact. Utop'IA offers digestible insights into this complex subject."

A robot that can jump into flight like a bird could eliminate the need for runways for small fixed-winged drones. Birds use the powerful explosive force generated by their legs to leap into the air and start flying, but building a robot that can withstand the strong acceleration and forces involved in doing that has proved difficult. Now, Won Dong Shin at the Swiss Federal Technology Institute of Lausanne (EPFL) and his colleagues have built a flying propellered robot called RAVEN that can walk, hop and jump into the air to start flying, with legs that work like a bird's. "Fixed-wing vehicles, like airplanes, always require a runway or a launcher, which is not found everywhere. It really requires designated infrastructure to make an airplane take off," says Shin. "But if you see a bird, they just walk around, jump and take off. They don't need any external assistance."

The geometric transformer samples the sequence space of the beta-lactamase TEM-1 enzyme (in grey) complexed a natural substrate (in cyan) to produce new well folded and active enzymes. Designing proteins that can perform specific functions involves understanding and manipulating their sequences and structures. This task is crucial for developing targeted treatments for diseases and creating enzymes for industrial applications. One of the grand challenges in protein engineering is designing proteins de novo, meaning from scratch, to tailor their properties for specific tasks. This has profound implications for biology, medicine, and materials science.

Title to be confirmed Speaker: Olga Mula (TU Eindhoven) Organised by: University of Lisbon Register here.