We propose visual creations that put differences in algorithms and humans \emph{perceptions} into perspective. We exploit saliency maps of neural networks and visual focus of humans to create diptychs that are reinterpretations of an original image according to both machine and human attentions. Using those diptychs as a qualitative evaluation of perception, we discuss some crucial issues of current \textit{task-oriented} artificial intelligence.

This note explained a way to look at reproducing kernel Hilbert space for regression problems. It consists in expressing kernel regresssion solutions with simple integral operators algebra, which we can approximate consistently from empirical data, providing the corresponding estimators of the solutions. Let's consider the classical regression problem arg min ‖f(x) y‖ In practice we are going to restrict the search for a solution f F, over a simpler function space f H. Let's associate to it the canonical RKHS, see Aronszajn (1950) H It is good to find function f from X to R, but what if Y is a real Hilbert space. Indeed, it is natural to extend the theory of RKHS to vector valued functions Schwartz (1964). Once again we can build an Hilbert space of functions from X to Y, let's first define γ Those are going to be the building element of H Definition 1 (The RKHS H).

We propose a new form of human-machine interaction. It is a pictorial game consisting of interactive rounds of creation between artists and a machine. They repetitively paint one after the other. At its rounds, the computer partially completes the drawing using machine learning algorithms, and projects its additions directly on the canvas, which the artists are free to insert or modify. Alongside fostering creativity, the process is designed to question the growing interaction between humans and machines.