  Machine learning (ML) in its current form implies that an answer to any
problem can be well approximated by a function of a very peculiar form: a
specially adjusted iteration of Heavyside theta-functions. It is natural to ask
if the answers to the questions, which we already know, can be naturally
represented in this form. We provide elementary, still non-evident examples
that this is indeed possible, and suggest to look for a systematic
reformulation of existing knowledge in a ML-consistent way. Success or a
failure of these attempts can shed light on a variety of problems, both
scientific and epistemological.
