  Motivated by the Gestalt pattern theory, and the Winograd Challenge for
language understanding, we design synthetic experiments to investigate a deep
learning algorithm's ability to infer simple (at least for human) visual
concepts, such as symmetry, from examples. A visual concept is represented by
randomly generated, positive as well as negative, example images. We then test
the ability and speed of algorithms (and humans) to learn the concept from
these images. The training and testing are performed progressively in multiple
rounds, with each subsequent round deliberately designed to be more complex and
confusing than the previous round(s), especially if the concept was not grasped
by the learner. However, if the concept was understood, all the deliberate
tests would become trivially easy. Our experiments show that humans can often
infer a semantic concept quickly after looking at only a very small number of
examples (this is often referred to as an "aha moment": a moment of sudden
realization), and performs perfectly during all testing rounds (except for
careless mistakes). On the contrary, deep convolutional neural networks (DCNN)
could approximate some concepts statistically, but only after seeing many
(x10^4) more examples. And it will still make obvious mistakes, especially
during deliberate testing rounds or on samples outside the training
distributions. This signals a lack of true "understanding", or a failure to
reach the right "formula" for the semantics. We did find that some concepts are
easier for DCNN than others. For example, simple "counting" is more learnable
than "symmetry", while "uniformity" or "conformance" are much more difficult
for DCNN to learn. To conclude, we propose an "Aha Challenge" for visual
perception, calling for focused and quantitative research on Gestalt-style
machine intelligence using limited training examples.
