  Lample and Charton (2019) describe a system that uses deep learning
technology to compute symbolic, indefinite integrals, and to find symbolic
solutions to first- and second-order ordinary differential equations, when the
solutions are elementary functions. They found that, over a particular test
set, the system could find solutions more successfully than sophisticated
packages for symbolic mathematics such as Mathematica run with a long time-out.
This is an impressive accomplishment, as far as it goes. However, the system
can handle only a quite limited subset of the problems that Mathematica deals
with, and the test set has significant built-in biases. Therefore the claim
that this outperforms Mathematica on symbolic integration needs to be very much
qualified.
