  In this paper, we aimed to help bridge the gap between human fluid
intelligence - the ability to solve novel tasks without prior training - and
the performance of deep neural networks, which typically require extensive
prior training. An essential cognitive component for solving intelligence
tests, which in humans are used to measure fluid intelligence, is the ability
to identify regularities in sequences. This motivated us to construct a
benchmark task, which we term \textit{sequence consistency evaluation} (SCE),
whose solution requires the ability to identify regularities in sequences.
Given the proven capabilities of deep networks, their ability to solve such
tasks after extensive training is expected. Surprisingly, however, we show that
naive (randomly initialized) deep learning models that are trained on a
\textit{single} SCE with a \textit{single} optimization step can still solve
non-trivial versions of the task relatively well. We extend our findings to
solve, without any prior training, real-world anomaly detection tasks in the
visual and auditory modalities. These results demonstrate the fluid-intelligent
computational capabilities of deep networks. We discuss the implications of our
work for constructing fluid-intelligent machines.
