  The validation of a theory is commonly based on appealing to clearly
distinguishable and describable features in properly reduced experimental data,
while the use of ab-initio simulation for interpreting experimental data
typically requires complete knowledge about initial conditions and parameters.
We here apply the methodology of using machine learning for overcoming these
natural limitations. We outline some basic universal ideas and show how we can
use them to resolve long-standing theoretical and experimental difficulties in
the problem of high-intensity laser-plasma interactions. In particular we show
how an artificial neural network can "read" features imprinted in laser-plasma
harmonic spectra that are currently analysed with spectral interferometry.
