IMPORTANT : you'll be ask to show an ID at the entrance
6:45 Doors open
7PM - 9PM Talk-talk
9PM - 10PM Cocktail - Networking
As usual, there is NO waiting list or reserved seat
First come first served
(the room has 100 seats)
This meetup will be streamed (details later)
AI is booming in photo editing.
Editing may seem like a distortion of the reality of the light capture in the camera, and the work of editing as kind of a cheating.However, cameras are capturing a reality quite different from the one the human eye catches, and editing is a way to recreate what the eye of the photographer saw.
Battista Biggio, Machine Learning Security
Data-driven AI and machine-learning technologies have become pervasive, and even able to outperform humans on specific tasks. However, it has been shown that they suffer from hallucinations known as adversarial examples, i.e., imperceptible, adversarial perturbations to images, text and audio that fool these systems into perceiving things that are not there.In this talk, I will quickly describe threats against machine learning, and identify possible countermeasures.
Christophe Denis, Explainable and convivial AI tools for healthcare
The lack of explainability of machine learning techniques poses operational, legal and ethical operational problems, in particular for healthcare applications.Our research project, presented in this talk, consists of providing and evaluating explanations of machine learning methods considered as a black box.The application should not become a radical monopoly restricting the users choice and freedom, in particular to manage individually some therapeutic dilemmas.Francis Bacon and Ivan Illich are back !
José Sanchez, Axionable, Machine Learning in production, the challenges to create value
We observe that many entreprises don’t pass the POC stage of their Machine Learning projects, leading to frustration for both technical and business teams.In this talk we will briefly present the hidden part of the Iceberg of Machine Learning projects (organisation, process and technology), and then show an example of a CI/CD deployment of a ML project on the cloud.
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