so when I was a kid having a
conversation with your computer went a
little something like this
you would lie down on the couch and vent
about all the problems in your life or
whatever then you'd have to get up type
your own response press Enter and listen
back to the insights that it had to
offer pathetic right
fast-forward 25 years and
voice-recognition a technology kind of
like VR where someone is always claiming
that they've made a big breakthrough and
it turns out to still be kind of crap is
finally gaining widespread adoption so
early forms of voice recognition had
very limited vocabularies some of the
first systems from 1950s could only
recognize about 10 words and even about
30 years later that number had grown to
only around 20,000 which may seem like a
lot but remember the English language
has over 1 million words on top of that
early software couldn't predict what
words you were trying to say by using
context so to these programs it was just
as likely that you were trying to say
bacon legs
as bacon and eggs fortunately though we
finally got computers in the late 1980s
and early 1990s that had more storage
and processing power allowing them to
comprehend more natural speech instead
of forcing you to talk like this so they
could understand and nowadays
technologies like Siri and Cortana don't
rely on a limited dictionary or the
relatively weak processor in your device
at all instead they use huge cloud
databases that store millions of words
and phrases and have lightning-fast CPUs
to understand what you mean with much
more precision Google speech recognition
software even learns from real search
engine strings and can also recognize a
variety of accents so you can use it
whether you're from Eastern Canada or
southern Texas but can the power of the
cloud do more when you ask your phone
for a sports score how does it know to
tell you how your favorite team is doing
instead of where to find a spork store
well instead of listening to just one
word at a time and I alluded to this
before it listens to other
words as well for context and uses
probabilities to determine what you're
trying to say this is a pretty involved
process that uses complex mathematical
models Google for example uses an
artificial neural network that functions
similarly to your own brain using
digital neurons to learn what people are
saying you can actually see this in
action
as Google now often changes what it
thinks you said on the fly as you
continue speaking and it gets better
greater amounts of processing power have
enabled everything from real-time
translation to being able to talk to
gain characters with a VR headset to
emotion tracking in which a computer can
use the timing and pitch of your voice
to figure out how you're feeling we're
even seeing it deployed in fighter
aircraft so pilots can concentrate on
mission objectives instead of fiddling
with cockpit switches but although voice
recognition has come a really long way
its proliferation has presented us with
some new challenges one big concern has
been finding ways to filter out
background noise so you'll still get
correct results even if you're standing
in the middle of a busy street and
speaking of standing in public another
massive issue is privacy many types of
voice recognition software improve upon
themselves by learning user habits and
combined with cloud processing we've
already seen some real concerns such as
with Samsung Smart TVs earlier this year
which had a privacy policy which some
people believed allowed Samsung to
monitor your living room conversations
so while the tech has great potential we
also need to make sure it will leave us
all feeling like Winston Smith from 1984
and on the subject of 1984 you know what
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fast as possible all right so that's
pretty much it thanks for watching guys
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