I have recently been awarded France’s national order of merit, for my career, in science, in open source, and around AI.
The speech that I gave carries messages important to me (French below; it flows better).
Contents
Modern AIs acquire new capabilities by combining tools to perform a complex task, controlling them like an agent. Unlike traditional programming, they define the sequences of actions themselves.
Note
This post was originally published in French as part of my scientific chronicle in Les Echos.
Modern AIs are increasingly using …
The key to the most powerful conversational AIs is to reason by breaking down a complex task into simpler subproblems. Why is this crucial, and how does it work?
Note
This post was originally published in French as part of my scientific chronicle in Les Echos.
The recent release of …
I would like to take a brief moment to reflect on what drives me as an academic.
Academia’s root are in creating knowledge and sharing it. We, academics, have a role to play in shaping society. In computer science, we sometimes focus on the creation of technology. Here, creation …
Since 2017, an AI has been defeating the best Go experts, despite the game being particularly challenging. Such “super intelligence” is rare, but it could also emerge in fundamental mathematics.
Note
This post was originally published in French as part of my scientific chronicle in Les Echos.
Is unbiased data important to build health AI? Yes!
Can there be unbiased data? No!
The notion of bias depends on the intended use.
In medicine, we have seen the importance of tuning devices and decisions for the target population. The problem is not …
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For me, 2024 was full of back and forth between research, software, and connecting these to society. Here, I lay out some highlights on AI and society, as well as research and software, around tabular AI and language models.
As 2025 starts, I’m looking back on 2024. It …
Improving conversational artificial intelligences or simpler prediction engines involves overcoming biases, that is, going beyond the limits of data. But the notion of bias is subtle, as it depends on the goals.
Note
This post was originally published in French as part of my scientific chronicle in Les Echos.
In …
Despite their apparent intelligence, conversational artificial intelligences often lack logic. The debate rages on: do they reason or do they recite snatches of text memorized on the Internet?
Note
This post was originally published in French as part of my scientific chronicle in Les Echos. I updated it with new …
English summary
I have been appointed to the government-level panel of experts on AI, to set the national vision and strategy in France.
J’ai l’honneur d’être nommé au comité de l’intelligence artificielle du gouvernement Français.
La mission qui nous est confiée d’éclairer l’action publique …
A retrospective on last year (2022): I embarked on a new scientific adventure, assembling a team focused on developing machine learning for health and social science. The team has existed for almost a year, and the vision is nice shaping up. Let me share with you illustrations of where we …
The Mayavi Python software, and my personal history: A thread on Python and scipy ecosystems, building open source codebase, and meeting really cool and friendly people
I am writing today as a goodbye to the project: I used to be one of the core contributors and maintainers but have been …
Broad decoding models that can specialize to discriminate closely-related mental process with limited data
TL;DR
Decoding models can help isolating which mental processes are implied by the activation of given brain structures. But to support a broad conclusion, they must be trained on many studies, a difficult problem given …
The year 2020 has undoubtedly been interesting: the covid19 pandemic stroke while I was on a work sabbatical in Montréal, at the MNI and the MILA, and it pushed further my interest in machine learning for health-care. My highlights this year revolve around basic and applied data-science for health.
Highlights …
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A simple survey asking authors of two leading machine-learning conferences a few quantitative questions on their experimental procedures.
How do machine-learning researchers run their empirical validation? In the context of a push for improved reproducibility and benchmarking, this question is important to develop new tools for model comparison. We …
My current research spans wide: from brain sciences to core data science. My overall interest is to build methodology drawing insights from data for questions that have often been addressed qualitatively. If I can highlight a few publications from 2019 [1], the common thread would be computational statistics, from dirty …
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Given two set of observations, are they drawn from the same distribution? Our paper Comparing distributions: l1 geometry improves kernel two-sample testing at the NeurIPS 2019 conference revisits this classic statistical problem known as “two-sample testing”.
This post explains the context and the paper with a bit of hand …
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An important acknowledgement for a different view of doing science: open, collaborative, and more than a proof of concept.
A few days ago, Loïc Estève, Alexandre Gramfort, Olivier Grisel, Bertrand Thirion, and myself received the “Académie des Sciences Inria prize for transfer”, for our contributions to the scikit-learn project …
From a scientific perspective, 2018 [1] was once again extremely exciting thank to awesome collaborators (at Inria, with DirtyData, and our local scikit-learn team). Rather than going over everything that we did in 2018, I would like to give a few highlights: We published major work using machine learning to …
In my opinion the scientific highlights of 2017 for my team were on multivariate predictive analysis for brain imaging: a brain decoder more efficient and faster than alternatives, improvement clinical predictions by predicting jointly multiple traits of subjects, decoding based on the raw time-series of brain activity, and a personnal …
