  There is no denying the tremendous leap in the performance of machine
learning methods in the past half-decade. Some might even say that specific
sub-fields in pattern recognition, such as machine-vision, are as good as
solved, reaching human and super-human levels. Arguably, lack of training data
and computation power are all that stand between us and solving the remaining
ones. In this position paper we underline cases in vision which are challenging
to machines and even to human observers. This is to show limitations of
contemporary models that are hard to ameliorate by following the current trend
to increase training data, network capacity or computational power. Moreover,
we claim that attempting to do so is in principle a suboptimal approach. We
provide a taster of such examples in hope to encourage and challenge the
machine learning community to develop new directions to solve the said
difficulties.
