  Statistical modeling of data sets by neural-network techniques is offered as
an alternative to traditional semiempirical approaches to global modeling of
nuclear properties. New results are presented to support the position that such
novel techniques can rival conventional theory in predictive power, if not in
economy of description. Examples include the statistical inference of atomic
masses and beta-decay halflives based on the information contained in existing
databases. Neural network modeling, as well as other statistical strategies
based on new algorithms for artificial intelligence, may prove to be a useful
asset in the further exploration of nuclear phenomena far from stability.
