How are two layer feed-forward neural networks universal?
I think they are talking about the universal approximation theorem which states that given a continuous function $f$ over an n-dimensional input vector $\vec{x}$, then a neural network with a single hidden layer can approximate $f(\vec{x})$ arbitrarily closely.