  Archaeologists, like other scientists, are experiencing a data-flood in their
discipline, fueled by a surge in computing power and devices that enable the
creation, collection, storage and transfer of an increasingly complex (and
large) amount of data, such as remotely sensed imagery from a multitude of
sources. In this paper, we pose the preliminary question if this increasing
availability of information actually needs new computerized techniques, and
Artificial Intelligence methods, to make new and deeper understanding into
archaeological problems. Simply said, while it is a fact that Deep Learning
(DL) has become prevalent as a type of machine learning design inspired by the
way humans learn, and utilized to perform automatic actions people might
describe as intelligent, we want to anticipate, here, a discussion around the
subject whether machines, trained following this procedure, can extrapolate,
from archaeological data, concepts and meaning in the same way that humans
would do. Even prior to getting to technical results, we will start our
reflection with a very basic concept: Is a collection of satellite images with
notable archaeological sites informative enough to instruct a DL machine to
discover new archaeological sites, as well as other potential locations of
interest? Further, what if similar results could be reached with less
intelligent machines that learn by having people manually program them with
rules? Finally: If with barrier of meaning we refer to the extent to which
human-like understanding can be achieved by a machine, where should be posed
that barrier in the archaeological data science?
