Posts in 'science'

Do AIs reason or recite?

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.

Conversational AI, or large language …

Comité de l’intelligence artificielle: vision et stratégie nationale

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 …

2022, a new scientific adventure: machine learning for health and social sciences

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 …

2021 highlight: Decoding brain activity to new cognitive paradigms

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 …

2020: my scientific year in review

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 …

Survey of machine-learning experimental methods at NeurIPS2019 and ICLR2020

Note

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 …

2019: my scientific year in review

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 …

Comparing distributions: Kernels estimate good representations, l1 distances give good tests

Note

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 …

2018: my scientific year in review

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 …

Our research in 2017: personal scientific highlights

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 …