The amazing part is that their system seems to be adaptable to any language with
a minimum of human effort.<p><pre><code> > One of the reasons deep learning has been so valuable is that it has converted
> researcher time spent on hand engineering features to computer time spent on
> training networks.
[...]
> We can now train a model on 10,000 hours of speech in around 100 hours on a
> single 8 GPU node. That much data seems to be sufficient to push the state of the
> art on other languages. There are currently about 13 languages with more than one
> hundred million speakers. Therefore we could produce a near state-of-the-art
> speech recognition system for every language with greater than one hundred
> million users in about 60 days on a single node.</code></pre>
Facebook disallows some images, based on the personal standards of whoever happens to be in charge of image disallowing that day.
Google controls what you see based on your own past, limiting your exposure to opinions you might not like.
Companies comply with oppressive government requests for control and surveillance.<p>If we surrender our ability to communicate with people speaking in foreign languages in this fashion, we will literally become unable to talk about things that we "shouldn't", and everything we do talk about will be on permanent record and monitored in real-time for dissent and to target adverts at us.
I keep reading about these algorithms that are "better than humans". Perfect image recognition, perfect speech recognition, parsing plain text-queries and answering questions, etc, etc. So where are the practical implementations?<p>All the speech recognition engines I've interacted with so far were awful. Not just bad, awful.<p><i>>Collecting such data sets could be very difficult and prohibitively expensive.</i><p>Uh, movie subtitles?