  We present here the latest development of a machine-learning pipeline for
pre-merger alerts from gravitational waves coming from binary neutron stars.
This work starts from the convolutional neural networks introduced in our
previous paper (PhysRevD.103.102003) that searched for three classes of early
inspirals in simulated Gaussian noise colored with the design-sensitivity
power-spectral density of LIGO. Our new network is able to search for any type
of binary neutron stars, it can take into account all the detectors available,
and it can see the events even earlier than the previous one. We study the
performance of our method in three different types of noise: Gaussian O3 noise,
real O3 noise, and predicted O4 noise. We show that our network performs almost
as well in non-Gaussian noise as in Gaussian noise: our method is robust w.r.t.
glitches and artifacts present in real noise. Although it would not have been
able to trigger on the BNSs detected during O3 because their signal-to-noise
ratio was too weak, we expect our network to find around 3 BNSs during O4 with
a time before the merger between 3 and 88 s in advance.
