Artificial Intelligence has become the synonym for Machine Learning.
As a result concepts have moved from mostly algorithmic (e.g, BFS search into a state graph) to geometric and probabilistic (e.g, minimizing a projection over a linear decision boundary or maximizing the posterior likelihood of training data).
It is therefore important to build demos and interactive tools to exhibit and illustrate the main concepts that may be hidden under equations.
This is precisely the aim of this website, to propose (interactive) tools and documents concerning the main steps or algorithms that an AI expert how to understand.
One key interest in exhibit such underlying properties is to illustrate the limitations of these algorithms and therefore to understand when and where they are relevant.