  GW170817 has led to the first example of multi-messenger astronomy with
observations from gravitational wave interferometers and electromagnetic
telescopes combined to characterise the source. However, detections of the
early inspiral phase by the gravitational wave detectors would allow the
observation of the earlier stages of the merger in the electromagnetic band,
improving multi-messenger astronomy and giving access to new information. In
this paper, we introduce a new machine-learning-based approach to produce
early-warning alerts for an inspiraling binary neutron star system, based only
on the early inspiral part of the signal. We give a proof of concept to show
the possibility to use a combination of small convolutional neural networks
trained on the whitened detector strain in the time domain to detect and
classify early inspirals. Each of those is targeting a specific range of chirp
masses dividing the binary neutron star category into three sub-classes: light,
intermediate and heavy. In this work, we focus on one LIGO detector at design
sensitivity and generate noise from the design power spectral density. We show
that within this setup it is possible to produce an early alert up to 100
seconds before the merger for the best-case scenario. We also present some
future upgrades that will enhance the detection capabilities of our
convolutional neural networks. Finally, we also show that the current number of
detections for a realistic binary neutron star population is comparable to that
of matched filtering and that there is a high probability to detect GW170817-
and GW190425-like events at design sensitivity.
