Participation to the Confiance.AI days

05 October 2022

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Glucose is (finally) on Github

(17 May 2023)

Glucose is finally (also) distributed on Github. The idea is simply to allow official forks from Glucose versions. We used to distribute it only via the archives and the [official...


Interview in the Regional Newspaper "Sud-Ouest" about Trustworthy AI

(03 May 2023)

A.I. has been the subject of many, many press articles in recent months. The exposure of tools such as chatGPT4 to the general public accelerates even more the need for...


Invitation to the Simons Institute (Berkeley)

(17 April 2023)

As part of the Simons Institute's SAT Program ([SAT Reunion Semester](https://simons.berkeley.edu/programs/extended-reunion-satisfiability)), I had the pleasure of being invited for a long stay in this famous research place about theoretical computer...


Lauch of the DIHNAMIC European Project

(20 March 2023)

The [Dihnamic](https://www.dihnamic.eu/) project has just been launched very officially. It is a large-scale European and regional project in which I act as a member of the advisory board. The project...


Dataquitaine 2023

(02 March 2023)

The sixth "Dataquitaine" (a regional scientific event organized in Nouvelle-Aquitaine) days were held on Thursday 2 March 2023 at [Kedge Business School](https://kedge.edu/l-ecole/les-campus/bordeaux), organised by [Digital Aquitaine](https://www.digital-aquitaine.com/) and especially the [Domex...

I am participating to the Confiance.ai days in Supelec, Paris-Saclay University. These days are a very efficient way of checking the progresses in trustworthy ai. The amount of money deployed to this topic of research is really impressive and some nice results are already there, guided by problems proposed by the industry.

Amongst the good stuff presented here, we can find a nice lexicon on what can be expected when seeking for trust. But what was even more impressive is the amount of work in order to deploy all the A.I. solutions! So many open questions!

What is interesting is how far some of the benchmarks used in the academia are miles away from real-life problems from the industry. Amongst all the presentations and posters I saw, industrials often have to revisit, by themselves, all the results obtained in well cited papers, simply because the classical measures used in academia does not apply in real life. Interesting.

(Last Modified date: 13 October 2022)
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