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TODD KERPELMAN: Hey, there I/O Live.
Todd Kerpelman here, and I am here
with Jen Tong, who is apparently using the power of the cloud
to find new planets.
No big deal.
JEN TONG: Yeah, why not?
TODD KERPELMAN: Sure.
So, Jen, what is going on here at this booth?
JEN TONG: So behind us, we see Project PANOPTES,
which is an open source project that includes open source
hardware that is composed entirely
of commercial, off-the-shelf components.
And it is intended to be low-cost enough
for educational institutions and hobby
astronomers to build these robotic telescopes
and contribute to the project.
TODD KERPELMAN: And so what happens once they
build these robotic telescopes?
How are they discovering stars--
or planets, I should say?
JEN TONG: So the way they discover
planets around other stars is by combining their efforts using
the cloud.
So what happens is, each night, the telescopes
will all wake up and look at the sky.
And they'll get a bunch of great images of the sky.
And then, when the day comes, they'll
go to sleep but not really.
They're going to start uploading all their data to Cloud
Platform.
And from there, we can combine all the data together,
aggregate it, and then, from that,
we can infer the existence of planets around other stars.
TODD KERPELMAN: And how do you infer the existence of planets?
JEN TONG: So planets are very hard to see directly,
because their stars are very bright,
and there's lots of glare, and they're very far away.
In fact, a star only looks like one pixel on the camera.
So we have to use some trickery to do that.
So instead of looking for the planet directly,
we look for a dimming of the star
when the planet moves between us and the star,
kind of like an eclipse.
Or we call it a transit in the more general case.
TODD KERPELMAN: And so the general idea
is you've got hundreds of telescopes, all
around the world, taking pictures of the night sky.
They combine all those images up to Google Cloud, which
analyzes them all and looks for a star that
dims on a regular enough basis that you
think it must be because there's a planet passing
in front of it.
JEN TONG: That is exactly correct.
So we're able to infer that just by having
a whole bunch of samples from having a very large fleet
of telescopes.
TODD KERPELMAN: Wow, that's very interesting.
Now, if I remember from being three years old,
stars do twinkle.
How can you tell that a star is dimming because
of a planet passing in front of it versus normal star
twinkling?
Which is totally a technical term.
JEN TONG: Totally technical.
And that's a great question, because that's
part of the stuff that makes PANOPTES special.
Because we're using commercial, off-the-shelf cameras instead
of specialized astronomy sensors,
we have to compensate for the fact
that those cameras are designed to take color photos.
Because when a camera takes color photos,
it filters some of the light out using a thing
called a Bayer filter.
And when the star twinkles, it moves around that Bayer filter.
And it makes it much harder to count the number of photons.
Because we don't know how much are getting
filtered out by the Bayer filter.
So the way we compensate for that is we
look for another star, in the same picture,
that has the exact same amount of twinkle.
And from that, we can do a relative brightness
measurement, because we know how bright those stars should be,
because we can identify the star.
And that's how we kind of cancel out the twinkle.
TODD KERPELMAN: And I'm contractually obligated to ask,
what awesome Google Cloud Platform features are you using
to power Project PANOPTES?
JEN TONG: PANOPTES definitely kind of illustrates
that boring uses of the cloud can enable really cool stuff.
So we are using some simple stuff.
We're using some of the simple security controls.
We're putting a service account on each one of the devices,
so we can control access to a specific telescope,
can access a specific part of our storage buckets.
And then we're using Google Cloud Storage
to store all of that data.
And then we're aggregating it on Google Compute Engine.
TODD KERPELMAN: And so the general idea
is anybody can get involved.
They can build their own telescope.
And they can be part of this project.
And then, if they find a star, they
can name it after themselves.
Is that basically it?
JEN TONG: Well, naming stars is a more complicated issue.
And individual PANOPTES telescopes
don't actually discover a star themselves.
It's kind of like a team that collaboratively accomplishes
the goal.
But yeah, anybody can get involved.
We especially like to work with hobbyist astronomers
and educational institutions, because we
want to kind of inspire a love of astronomy in the youth
around the world.
TODD KERPELMAN: That's good.
And how would I get started if I wanted to?
JEN TONG: So I encourage you to go check
out projectpanoptes.org.
TODD KERPELMAN: All right, so you heard it here.
Go to projectpanoptes.org, and you too
can name a star after you.
Jen totally promised that you can do that.
I'm here with Sara Robinson.
Sara, what is this that we're looking at here?
SARA ROBINSON: We're looking at a demo of our Cloud Machine
Learning APIs, specifically highlighting
our Video API, Speech API, and Translation API.
So what these let you do is they let
you access a pre-trained machine learning model,
with a single REST API request, so you don't
need to know anything about how machine learning works
to use them.
TODD KERPELMAN: Awesome.
And what is this game that we have set up?
SARA ROBINSON: So we're going to see how
we compare to our Video API.
Shall we take a look?
TODD KERPELMAN: You mean like me against the computer?
SARA ROBINSON: You against our pre-trained model.
TODD KERPELMAN: All right, well, let's see how it goes.
SARA ROBINSON: Are you up for the challenge?
TODD KERPELMAN: I think so.
What are we going to do in this game?
SARA ROBINSON: So what we're going to do
is we're going to play a video.
And I'm going to have you try to annotate the video
as it's going.
And then, when it's done, we'll compare
the annotations you came up with, with our Video API.
TODD KERPELMAN: All right, I'm feeling confident.
Let's see if I can beat this computer.
SARA ROBINSON: All right.
TODD KERPELMAN: All right, here we go.
SARA ROBINSON: So let's take a look at this video.
And we're going to play against the APIs.
We're going to play against the Video API.
So when I hit Play, type what you see.
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TODD KERPELMAN: Keys.
Book.
Keys.
A door, books, plane-- my typing is terrible.
Woman, room, lost, suitcase, goat.
She's still lost.
Wait, no, now she has Google Trips.
SARA ROBINSON: So we can skip to the end of the video.
TODD KERPELMAN: But wait-- aw, OK.
SARA ROBINSON: We won't play the whole thing.
But we saw about half of the video.
So we can see that found 13 items.
Video API found 89 items.
But it was a valiant effort.
TODD KERPELMAN: Once again, beaten by a machine.
SARA ROBINSON: Do you want to take a look at the API response
in a little bit more detail?
TODD KERPELMAN: Yeah, let's take a look at what we got here.
SARA ROBINSON: So this is everything
that the Video API found.
So we can actually skip to the points in the video
where it found these things.
And then, if we want to look at the JSON response from the API,
we can do that here.
So we can see all of the different entities it picked up
in the video.
And we can also take a look at what the Speech API transcribed
from this video.
So this is an entire transcription
of the audio from that video using our Speech-to-Text API.
And we can even use this to skip to various points in the video.
And then finally, I'll show our translation API.
So let's say you're a user somewhere else in the world,
and you want to translate a video into your own language.
You can go over here.
And we can try it out in French, for example.
You can also translate the Video API entities there.
So that's an overview of our Machine Learning API demo.
TODD KERPELMAN: Hey, did you like this video?
Want to see more like it?
Head on over to g.co/io/guide to see all of our I/O guide
videos.
Come on, let's go.
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