Extracting information from stochastic fields is a ubiquitous task in science. However, from cosmology to biology, it tends to be done either through a power spectrum analysis, which is often too limited, or recently the use of convolutional neural networks, which require large training sets and lack interpretability. I will present a new powerful tool called the “scattering transform”, which borrows ideas from both sides and stands nicely between the two extremes. I will use various examples in astrophysics and beyond to demonstrate its power, interpretability, and its advantage over traditional statistics.