  The development of machines that {\guillemotleft}talk like
us{\guillemotright}, also known as Natural Language Understanding (NLU)
systems, is the Holy Grail of Artificial Intelligence (AI), since language is
the quintessence of human intelligence. The brief but intense life of NLU
research in AI and Natural Language Processing (NLP) is full of ups and downs,
with periods of high hopes that the Grail is finally within reach, typically
followed by phases of equally deep despair and disillusion. But never has the
trust that we can build {\guillemotleft}talking machines{\guillemotright} been
stronger than the one engendered by the last generation of NLU systems. But is
it gold all that glitters in AI? do state-of-the-art systems possess something
comparable to the human knowledge of language? Are we at the dawn of a new era,
in which the Grail is finally closer to us? In fact, the latest achievements of
AI systems have sparkled, or better renewed, an intense scientific debate on
their true language understanding capabilities. Some defend the idea that, yes,
we are on the right track, despite the limits that computational models still
show. Others are instead radically skeptic and even dismissal: The present
limits are not just contingent and temporary problems of NLU systems, but the
sign of the intrinsic inadequacy of the epistemological and technological
paradigm grounding them. This paper aims at contributing to such debate by
carrying out a critical analysis of the linguistic abilities of the most recent
NLU systems. I contend that they incorporate important aspects of the way
language is learnt and processed by humans, but at the same time they lack key
interpretive and inferential skills that it is unlikely they can attain unless
they are integrated with structured knowledge and the ability to exploit it for
language use.
