Some brief remarks about datasets are made, followed by a discussion of PIPs for large molecules, exemplified for 21-atom aspirin. The PIPs many-body representation of potentials for non-covalent interactions, notably exemplified by water, is described briefly. followed by the recent extension to covalent interactions, exemplified by linear alkanes. This is followed by a section on using PIPs as inputs to Gaussian Process and Neural Network regression and the very recent MB-PIPNet approach, and a short section on PIPs and mention of recent work using ∆-machine learning for PIPs potentials. Finally, a short section about assessing PIPs and other ML methods for the aspirin molecule is given.