  The present paper reviews and discusses work from computer science that
proposes to identify concepts in internal representations (hidden layers) of
DNNs. It is examined, first, how existing methods actually identify concepts
that are supposedly represented in DNNs. Second, it is discussed how conceptual
spaces -- sets of concepts in internal representations -- are shaped by a
tradeoff between predictive accuracy and compression. These issues are
critically examined by drawing on philosophy. While there is evidence that DNNs
able to represent non-trivial inferential relations between concepts, our
ability to identify concepts is severely limited.
