  Machine Learning (ML) is applicable to scientific problems, i.e. to those
which have a well defined answer, only if this answer can be brought to a
peculiar form ${\cal G}: X\longrightarrow Z$ with ${\cal G}(\vec x)$ expressed
as a combination of iterated Heaviside functions. At present it is far from
obvious, if and when such representations exist, what are the obstacles and, if
they are absent, what are the ways to convert the known formulas into this
form. This gives rise to a program of reformulation of ordinary science in such
terms -- which sounds like a strong enhancement of the constructive mathematics
approach, only this time it concerns all natural sciences. We describe the
first steps on this long way.
