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IRENE ALVARADO: Hi, everyone.
My name is Irene,
and I work at a place
called the Creative Lab,
a team inside of Google.
And some of us are
interested in creating
what we call
experiments to showcase
and make more accessible some
of the machine learning research
that's coming out of Google.
And a lot of our work
goes to this site
called the Experiments
with Google site.
Now, before I talk about some
of the products on the site,
let me just say
that we're really
inspired by pioneering AI
researcher Seymour Papert, who
wrote a lot about
learning theories
in humans and
essentially kind of how
to make learning not suck.
So this is one of
his great quotes.
"Every maker of video
games knows something
that the makers of curriculum
don't seem to understand.
You'll never see a video being
advertised as being easy.
Kids who do not like
school will tell you
it's not because it's too hard.
It's because it's boring."
So if there are some
parents in the room,
you might be agreeing
with this statement.
So I'll show you
some projects that
were inspired by this
thinking that learning should
be engaging, made
in collaboration
with the TensorFlow.js team
and many other research
teams at Google.
So this is the first one.
It's called Teachable Machine.
And essentially it's
a KNN classifier that
runs entirely in the browser.
And it lets you train
three classes of images
that trigger different kinds
of inputs, like GIFs and sound.
So I don't have time
to demo it, but I'll
show you what happens after you
train a model with the tool.
So can I get the video?
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See it choosing
between two classes.
Yeah, so, hopefully,
you get how it works.
Alex Chen, the creator,
he trained a class
to recognize the bird
origami and another class
to recognize the
spooky person origami.
OK, back to the slides.
Thank you.
So we released the
experiment online.
All the inference and training
is happening in the browser.
And we also released
the open source--
we open sourced the
boilerplate code that
went along with the experiment.
And what happened next was that
we were really kind of taken
aback by all the stories of
teachers around the world,
like this one, who started using
Teachable Machine to introduce
ML into the classroom.
Here's another example
of kids learning
about smart cities
and kind of training
the computer to recognize
handmade stop signs.
This was really amazing.
And finally, we heard from
another renowned and pioneering
researcher, Hal Abelson,
who teaches at MIT, that he
had been using Teachable
Machine to introduce ML
to policymakers.
And for a lot of them,
it was the first time
that they had ever
trained a model.
So needless to say, we're really
happy that although simple
in nature, Teachable
Machine ended up
being a really good tool
for educators and people
that were new to
machine learning.
So here's another example.
This one's called Move Mirror.
And the concept
is really simple.
You strike a pose in
front of a webcam,
and you get an image
with a matching pose.
And again, this is all
happening on the web.
So here's another example
of, actually, people using it
in the form of an installation.
People do really funny moves.
And again, this is
happening on a phone,
but on the phone's browser.
And so the story
for this one was
that in order to make the
experiment really accessible,
we had to take the
tech to the web,
so that we wouldn't
require users
to have a complicated tech setup
or to use IR cameras or depth
sensors, which can be expensive.
So PoseNet was born.
To our knowledge, it's
the first pose estimation
model for the web.
And it's open source.
It runs locally in your browser.
And it uses good
ol' RGB webcams.
So again, we were
really taken aback
by all the creative projects
that we saw popping up online.
Just to give you a sense,
the one on the left
is a musical interface.
The one in the middle
is a ping pong game
that you can use with your head.
I really want to play that one.
And the one on the right is
a kind of performative motion
capture animation.
But we also started
hearing from people
in the accessibility world
that they were using PoseNet.
So we decided to partner
with a bunch of groups
that work at the intersection
of disability and technology,
like the NYU Ability Project,
and musicians, artists, makers
in the accessibility world.
And out of that collaboration
came a set of creative tools
that we're calling Creatability.
And a lot of them
use PoseNet for users
who have motor impairments
to be able to interface
with a computer with
their whole bodies
instead of through a
keyboard and a mouse.
So again, I don't have
time to demo these.
But just give you a sense,
the one on the bottom left
is a visualization tool
made by a musician named
Jay Zimmerman, who's deaf,
and the one on the top right
is an accessible
musical instrument
made by a group
called Open Up Music.
And we just took their
designs and kind of moved it
to the web.
So again, all of
the components that
made this project are accessible
and they've been open sourced.
So just a step
back for a second,
if we were to think about what
made these projects successful
or at least useful
for other people,
we can see that they were all
interactive and accessible
through the browser.
So it really lowered the barrier
of entry for a lot of people.
They all had an
open-source component,
so that people could kind
of look under the hood,
see what's happening,
modify them, play with them.
And then, finally,
they're all free,
because the processing
is happening locally
in the browser
with TensorFlow.js.
And that gave us
privacy, so that we
didn't have to send
images of people's bodies
and faces to any servers.
So again, all the projects that
I went through kind of quickly,
they're on the
Experiments.withGoogle.com
site.
And even though these
were created in-house,
we actually feature work by
more than 1,700 developers
from around the world.
So if any of this
resonates with you,
this is really an open
invitation for you
to submit your work.
And I hope to have
showed that you never
know who you might
inspire or who
might take your work and
kind of innovate on top of it
and use in really creative ways.
Thank you.
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