  Prediction in complex systems at criticality is believed to be very
difficult, if not impossible. Of particular interest is whether earthquakes,
whose distribution follows a power law (Gutenberg-Richter) distribution, are in
principle unpredictable. We study the predictability of event sizes in the
Olmai-Feder-Christensen model at different proximities to criticality using a
convolutional neural network. The distribution of event sizes satisfies a power
law with a cutoff for large events. We find that prediction decreases as
criticality is approached and that prediction is possible only for large,
non-scaling events. Our results suggest that earthquake faults that satisfy
Gutenberg-Richter scaling are difficult to forecast.
