General-purpose analogue machine learning has a history that predates digital computing with the first important results published by Shannon in 1941. In this talk, I discuss the thermodynamics constraints on analogue machine learning based on Hopf bifurcations in both classical and quantum systems. The latter case indicates that learning can proceed at zero temperature due entirely to quantum noise in the form of spontaneous emission and tunnelling.