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Dans un récent article dans l’Equipe le journaliste Sacha Nokovitch signale les contenus « deepfake » dans le sport. Bien que suivant les tendances générales, je discute aussi les spécificités : contenu moins violent/ inquiétant et plus désirable et au même temps techniquement moins soigné, etc.
Dans le podcast « ResponsibleAI » diffusé en avril 2026 par Karine Majdalani, Anjuli Manche et Maël Machane nous discutons de la relation que les objects générés par IA entretiennent avec la réalité et comment ceux-ci peuvent influencer l’humain.
In a recent interview with Mathilde Texier from France Televisions we discuss how video deepfakes mixes with societal and political news to generate misinformation.
This is a talk presented at Nanmat conference held Nov 3-6 2026 at ICTP, Cluj.
Talk materials: 
the slides of the presentation. and here the 
Youtube video version.
Took part recently at a round table on AI in medecine within the AI4-MED conference. Several subjects were touched including the concerns, the safeguards, the trust in complex situations. More detailed reproduction of the discussion will follow on another outlet.
During the FAAI 2025 conference I presented a recent work with Pierre Brugière on . See here the paper (arxiv version) and here the slides.
Executive summary: we introduce a deep-learning framework for hedging derivatives in markets with discrete trading and transaction costs, without assuming a specific stochastic model for the underlying asset. Unlike traditional approaches such as the Black–Scholes or Leland models, which rely on strong modeling assumptions and continuous-time approximations, the proposed method learns effective hedging strategies directly from data. A key contribution is its ability to perform well with very limited training data—using as few as 256 simulated price trajectories—while outperforming classical hedging schemes in numerical experiments under a geometric Brownian motion setting. This makes the approach both robust and practical for real-world applications where data and model certainty are limited.
In a recent interview with Alexandre Boero from Clubic we discuss how recent technologies rendered possible a growing network of fake online media sites and journalists entirely generated by AI, designed to appear credible and manipulate audiences and advertisers, raising serious concerns about misinformation and the erosion of trust in digital content.
Deepfakes, AI-generated articles, fake websites mimicking the media… in a recent interview with Marie-Eve Frénay from Les Echos and we discuss the new generation of manipulative content flooding the web.
This joint work with Laetitia LAGUZET has been presented at the 5th Computational Methods for Multi-scale, Multi-uncertainty and Multi-physics Problems Conference held in Porto, 1-4 July 2025.
Abstract Physics-Informed Neural Networks (PINNs) are a type of neural network designed to incorporate physical laws directly into their learning process. These networks can model and predict solutions for complex physical systems, even with limited or incomplete data, often using a mathematical formulation of a state equation supplemented with other information.
Introduced by Raissi et al. (2019), PINNs find applications in fields like physics, engineering, and fluid mechanics, particularly for solving partial differential equations (PDEs) and other dynamic systems. In this contribution we explore a modification of PINNs to multi-physics numerical simulation involving radiation transport equations; these equations describe the propagation of a Marshak-type wave in a temperature dependent opaque medium and is considered a good benchmark for difficult multi-regime computations.