  Creating learning models that can exhibit sophisticated reasoning skills is
one of the greatest challenges in deep learning research, and mathematics is
rapidly becoming one of the target domains for assessing scientific progress in
this direction. In the past few years there has been an explosion of neural
network architectures, data sets, and benchmarks specifically designed to
tackle mathematical problems, reporting notable success in disparate fields
such as automated theorem proving, numerical integration, and discovery of new
conjectures or matrix multiplication algorithms. However, despite these
impressive achievements it is still unclear whether deep learning models
possess an elementary understanding of quantities and symbolic numbers. In this
survey we critically examine the recent literature, concluding that even
state-of-the-art architectures often fall short when probed with relatively
simple tasks designed to test basic numerical and arithmetic knowledge.
