#CampusInsight Patrick Perez

Campus Insight inédit sur l'intelligence artificielle avec comme invité Patrick Perez, CEO de kyutai open-science AI lab.

Kyutai is a non-profit laboratory entirely dedi?cated to open research in artificial intelligence (AI). Its objective is to tackle the main challenges of modern AI, particularly by developing large multimodal models – using text but also sound, images, etc. – and by inventing new algorithms to enhance their capacities, reliability and efficiency. To do this, the laboratory will use the computing power made available to it by Scaleway, an iliad Group subsidiary. Scaleway’s supercomputer has the highest-performance computing power for AI applications deployed to date in Europe. Resolutely committed to the democratization of AI, Kyutai is positioning itself as a leading player in AI open science. Its ambition is to share its ad?vances with the entire AI ecosystem – the scien?tific community, developers, companies, society at large and decision-makers in democracies. Kyutai will also contribute to the training of future AI experts, by offering internships to students on Master’s programs and supervising PhD students and postdocs.

Patrick Perez

A graduate in applied mathematics from École Centrale Paris, Patrick has more than thirty years’ experience in computer vision and machine learning, shared between public and private labs: Brown University as a postdoc and Inria as a PhD student, researcher and senior scientist in the public sector; and in the private sector, Microsoft Research as a research scientist, Technicolor as Distinguished Scientist and Valeo as Scientific Director and VP of AI. During his career, Patrick has explored various fields of applications (medical, cinema and media, automotive), processing and analyzing signals from all types of sensors (cameras, MRI, microphones, radar, laser scanners), and has built up and supported long-term research teams. Some of his most recent projects with the Valeo.ai team led to embarking transformer?based 360-degree visual perception in cars and generating visual counterfactual explanations for camera-only autonomous driving systems