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.

This post contains a list of the AI-related seminars that are scheduled to take place between 7 August and 30 September 2023. All events detailed here are free and open for anyone to attend virtually. Title to be confirmed Speaker: To be confirmed Organised by: I can't believe it's not better (ICBINB) Check the website nearer the time for instructions on how to join. Title to be confirmed Speaker: Jona Lelmi (University of California, Los Angeles) Organised by: University of Minnesota Check the website nearer the time for the Zoom link to join. Title to be confirmed Speaker: To be confirmed Organised by: Linköping University Check the website nearer the time for joining instructions.

This year's International Conference on Machine Learning (ICML) is taking place in Honolulu, Hawai'i from 23-29 July. The winners of the outstanding paper awards for 2023 have now been announced. This paper introduces an interesting approach that aims to address the challenge of obtaining a learning rate free optimal bound for non-smooth stochastic convex optimization. The authors propose a novel method that overcomes the limitations imposed by traditional learning rate selection in optimizing such problems. This research makes a valuable and practical contribution to the field of optimization.

No wonder some of them may be turning to tools like ChatGPT to maximize their earning potential. To find out, a team of researchers from the Swiss Federal Institute of Technology (EPFL) hired 44 people on the gig work platform Amazon Mechanical Turk to summarize 16 extracts from medical research papers. Then they analyzed their responses using an AI model they'd trained themselves that looks for telltale signals of ChatGPT output, such as lack of variety in choice of words. They also extracted the workers' keystrokes in a bid to work out whether they'd copied and pasted their answers, an indicator that they'd generated their responses elsewhere. They estimated that somewhere between 33% and 46% of the workers had used AI models like OpenAI's ChatGPT.