  X-ray free-electron lasers (XFELs) as the world's brightest light sources
provide ultrashort X-ray pulses with a duration typically in the order of
femtoseconds. Recently, they have approached and entered the attosecond regime,
which holds new promises for single-molecule imaging and studying nonlinear and
ultrafast phenomena such as localized electron dynamics. The technological
evolution of XFELs toward well-controllable light sources for precise metrology
of ultrafast processes has been, however, hampered by the diagnostic
capabilities for characterizing X-ray pulses at the attosecond frontier. In
this regard, the spectroscopic technique of photoelectron angular streaking has
successfully proven how to non-destructively retrieve the exact time-energy
structure of XFEL pulses on a single-shot basis. By using artificial
intelligence techniques, in particular convolutional neural networks, we here
show how this technique can be leveraged from its proof-of-principle stage
toward routine diagnostics even at high-repetition-rate XFELs, thus enhancing
and refining their scientific accessibility in all related disciplines.
