  Large-scale imaging surveys have grown about 1000 times faster than the
number of astronomers in the last 3 decades. Using Artificial Intelligence
instead of astronomer's brains for interpretative tasks allows astronomers to
keep up with the data. We give a progress report on using Convolutional Neural
Networks (CNNs) to classify three classes of rare objects (galaxy mergers,
strong gravitational lenses and asteroids) in the Kilo-Degree Survey (KiDS) and
the Euclid Survey.
