  In the cognitive sciences, it is common to distinguish between crystal
intelligence, the ability to utilize knowledge acquired through past learning
or experience and fluid intelligence, the ability to solve novel problems
without relying on prior knowledge. Using this cognitive distinction between
the two types of intelligence, extensively-trained deep networks that can play
chess or Go exhibit crystal but not fluid intelligence. In humans, fluid
intelligence is typically studied and quantified using intelligence tests.
Previous studies have shown that deep networks can solve some forms of
intelligence tests, but only after extensive training. Here we present a
computational model that solves intelligence tests without any prior training.
This ability is based on continual inductive reasoning, and is implemented by
deep unsupervised latent-prediction networks. Our work demonstrates the
potential fluid intelligence of deep networks. Finally, we propose that the
computational principles underlying our approach can be used to model fluid
intelligence in the cognitive sciences.
