so on the one hand you have
thousands of engineers working out the
best Rd corporate facilities there are
and on the other hand you have George
riding on top of open-source and George
was able to build an autonomous car that
completely works in fact one of the
people on my team named Kyle got into
the car with them and drove straight
down 101 no hands on the wheel with
George's autonomous driving system
so that tells you about the great
promise of the sophistication of this
deep learning system where one guy can
build a self-driving car on top of the
algorithms that are now in the
open-source community
let's talk about natural language
processing you'll remember that this was
one of the very first AI experiments
that got run trying to translate English
to Russian and Russian to English the
community has made huge strides since
then it remains a very very difficult
problem but deep learning and tons of
data right think about all the data
that's on the internet today that wasn't
in the 1950s Twitter streams and
Facebook feeds and blog posts and so on
we have this massive data source that we
can feed into these systems to perform
the complicated natural language
processing tasks that we need to perform
and it's a hard problem so as an example
of that this is a perfectly valid
English sentence Paris Hilton was in
Paris at the Paris Hilton listening to
Paris now you might need to spend a
second or two to try to parse that
sentence to figure out Paris Hilton the
celebrity is in Paris France the city in
Paris Hilton the hotel listening to
Paris the album and now computers are
getting very very good at parsing that
sentence in exactly the way you did
which is what is Paris Hilton in this
context here is another perfectly valid
English sentence and look this up on
Wikipedia because it has its own very
very own Wikipedia page Buffalo buffalo
Buffalo buffalo Buffalo buffalo Buffalo
buffalo it is actually a valid English
sentence that one's tough to parse but
go look up the Wikipedia page so
understanding natural language and the
context and what parts of speech words
are and what the sense of a sentence is
is very hard and deep learning is making
huge strides and helping us do that in
fact Google just released an open-source
project to do exactly this which is to
identify the parts of a sentence and
break it down into nouns and verbs and
subjects and direct objects and so on
and go find that yourself it's Google's
famously named Parsi Nick parks face
open-source project on perception we are
also making huge strides with deep
learning so what you see in this slide
is the view of what you want your
autonomous car to have which is looking
out in the street it's identified the
objects in that scene on the left side
here's a sedan probably moving 30 miles
an hour on the right side we've got a
biker probably moving 10 to 15 miles an
hour out of em we have another car then
we've got a bunch of stationary things
like trees and sidewalks and so you want
a system that can look out into a scene
like this and in real-time categorize
all the objects in it and make
predictions about where those objects
are going to be so that you can plan a
route that's safe and effective deep
learning is playing huge roles in an
autonomous cars ability to understand
and parse the scene in fact Nvidia
attributes a lot of its recent growth
and success as a business to this new
line of business which is providing deep
learning systems for autonomous cars
Jensen Vaughan the CEO and co-founder
said accelerating our business growth is
deep learning a new computing model that
uses the GPUs massive computing power to
learn artificial intelligence algorithms
it's adoption is sweeping one industry
after another driving demand for our
GPUs so as you can see this is touching
many many different parts of Silicon
Valley from the silicon literally the
chips that we use on up and we're just
getting started
another great example of the strides
that we're making in perception comes
out of research from Cornell University
where they also fed the learning
algorithms YouTube videos in this case
they said videos of people doing food
preparation tutorials it's one of the
most popular genres on YouTube and what
the researchers were able to do given
all of this input people preparing food
is basically break down the steps of the
recipe into discrete chunks so they
recognized when somebody was chopping
onions distinguished from when they were
mixing distinguished from when they were
running the blender and so you could
take a ten minute video tutorial on how
to make chicken tortilla soup
break it into the discrete chunks and
deep learning algorithms have gotten to
incredible accuracy being able to
accurately label the discrete steps of
preparing food now all we need to do is
marry that up to a robot that will
prepare the food and Riggle and so
here's the tantalizing prospect deep
learning is helping us make fundamental
progress on all of the original design
goals of artificial intelligence which
was to mimic the many many different
facets of human behavior and so maybe
this is it maybe this is the path
forward to generalized intelligence to
things like emotional intelligence to
things like creativity to things like
intuition and in fact you'll see a whole
genre of AI deep learning research that
is around creativity an example of that
it's deep jazz which can actually
compose original new jazz compositions
and so it's working this looks like the
start of a fundamental breakthrough in
AI research the way I think about it now
is that all the serious applications
from here on out need to have deep
learning and AI inside in exactly the
same way that all serious computing
systems needed to have Intel chips
inside them and Intel famously did their
Intel Inside advertising campaign to
drive this home I think now about the
startups that we see and the systems
that are getting built that deep
learning needs to be inside these
systems to make them better faster more
responsive easier to use and it's just
going to be a fundamental technique that
we expect to see in all serious
applications moving forward
so we're in an AI spring only time will
tell if this leads to the promise of
generalized intelligence but what is
absolutely non-controversial is that
deep learning is the most fundamental
advance in artificial intelligence
research since we started since that
summer of 1956 it's going to make every
application better and smarter and
faster more responsive to our needs and
I can't wait to see the startups that
lead us there so this is a super
exciting time and why we think that AI
and deep learning could be as
fundamental platform shifts technology
shifts as mobile and cloud were in the
last five to ten years