Physicists extend "glass" into "disordered systems". A system can be ordered or disordered in different way, e.g. order (crystal) or disorder (glass) in the positions of atoms, or the simpler case of ordered or disordered spins of atoms (either "up" or "down"). The same phenomena exist in both fields, e.g. glass transitions, so we prefer to study the simpler "implementation", namely spins in a magnetic field.
Computer analogy: we're trying to reverse-engineer an alien ASIC, which can operate either on an array of tuples of three reals, or on an array of bools. It works approximately the same in both cases. The latter case is much easier to analyze, so we prefer to do that.
Analogously Neural Network learning, i.e. learning modeled as a phase transition between disorder and order:
“... studies the connection between the highly non-convex loss function of a simple model of the fully-connected feed-forward neural network and the Hamiltonian of the spherical spin-glass model”
from "The Loss Surfaces of Multilayer Networks"
Choromanska, Henaff, Mathieu, Arous, & LeCun [1]
The other comment also led me to https://en.wikipedia.org/wiki/Spin_glass, which I didn't know about at all (I'd heard the term once but had no context for it).
Computer analogy: we're trying to reverse-engineer an alien ASIC, which can operate either on an array of tuples of three reals, or on an array of bools. It works approximately the same in both cases. The latter case is much easier to analyze, so we prefer to do that.