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In essence, it developed its own "interlingua" internal representation for

similar phrases or sentences. "This means the network must be encoding

something about the semantics of the sentence rather than simply memo-

rizing phrase-to-phrase translations," the researchers write. "We interpret
this as a sign of existence of an interlingua in the network."

In one experiment, for instance, the team merged 12 language pairs into a
model the same size as for a single pair. Despite the drastically reduced

code base, they achieved "only slightly lower translation quality" than

with a dedicated two-language model. "Our approach has been shown to

work reliably in a Google-scale production setting and enables us to scale

to a large number of languages quickly," the team says. Bear in mind that
it only started seriously working on AI for languages a short time ago, so

its rapid progress is Pretty scary -- especially if you're a professional

translator.

Not A Cloudy Day

We still see all the big corporations telling us that using their cloud ser-
vice is the way forward. This is all very well if it is a cloudy day but
sometimes it isn’t. Take last week when Amazon had a major outage at
one of it’s data centres.