Explainable AI promises to make black-box models easier to understand. But Gustau Camps-Valls argues that, in science, an explanation of a model can easily be mistaken for an explanation of the world itself. His alternative starts with a deceptively simple principle: think first, act second, model last. Gustau Camps-VallsProfessor in Electrical Engineering, Universitat de ValènciaHi! PARIS International Visiting […]
About the Hi!ckathon: The Hi!ckathon is a flagship AI and Data Science challenge organized by Hi! PARIS, the interdisciplinary center for data science and artificial intelligence. Open to students from Institut Polytechnique de […]
We are proud to highlight the strong presence of Hi! PARIS researchers at ICML 2026, one of the world’s leading conferences in machine learning. This year, 42 papers involving Hi! PARIS […]
Artificial intelligence has mastered language, vision, and even strategy games, but can it master mathematics? Amaury Hayat’s DESCARTES project explores one of the boldest frontiers in science: teaching machines not just to calculate, but to reason.
As machine learning systems become embedded in critical decisions, from finance to infrastructure, the need for trustworthy, interpretable predictions has never been greater. Aymeric Dieuleveut, Professor of Statistics and Machine Learning at École polytechnique and scientific co-director of the Hi! PARIS Center, believes the key lies not in the models themselves, but in how we communicate their uncertainty.