  For fifty years astronomers have been searching for pulsar signals in
observational data. Throughout this time the process of choosing detections
worthy of investigation, so called candidate selection, has been effective,
yielding thousands of pulsar discoveries. Yet in recent years technological
advances have permitted the proliferation of pulsar-like candidates, straining
our candidate selection capabilities, and ultimately reducing selection
accuracy. To overcome such problems, we now apply intelligent machine learning
tools. Whilst these have achieved success, candidate volumes continue to
increase, and our methods have to evolve to keep pace with the change. This
talk considers how to meet this challenge as a community.
