[MUSIC PLAYING]
PAIGE BAILEY: I'm Paige Bailey, a TensorFlow developer
advocate.
And I'm here today with Carla Bromberg, who's
going to tell us all about our AI for social good efforts
at Google.
So tell us a little bit more about it, Carla.
CARLA BROMBERG: Thanks, Paige.
So for AI for social good, we have--
we aim to accelerate the application of machine learning
to the world's biggest humanitarian
and environmental issues.
So today I'm going to tell you about some
of the core research and engineering work
we're doing at Google, more specifically,
the flood forecasting initiative.
PAIGE BAILEY: Excellent.
So is this the flood forecasting initiative here?
CARLA BROMBERG: Yes.
So this is our demo.
And so what this shows you is how we're using machine
learning to help forecast and predict,
with better accuracy, where floods are.
So that way, we can tell users when
they're affected by floods.
So earlier today in the keynote, we
announced that we're rolling this out
across a big portion of India.
And I'm going to actually show you
the simulation that we have for the demo here today.
So this simulation is over the course of 48 hours.
So if you look, you can see the water filling in.
So this is without using machine learning.
You can see that the accuracy is not very high.
And you can see, also, that there's
a lot of regions that are predicted to have floods.
Now with Google's flood forecasting effort,
you can see, as you both use the elevation maps that we've
been working on that involve machine learning as well
as the models that we've made, how much more accurate it is.
So if you're a person who lives in this region, like earlier,
it would have said it was flooding.
And then you would have to evacuate--
or you would think that you would need to evacuate.
But actually, when you look at the more precise models,
you'll actually be safe.
So what we're hoping is--
through this work, you can see that accuracy
has gone up to 79%.
And then when people get the alerts,
they can have the information that they need.
So as you can see on the phone, what they'll
see moving into this monsoon season
is, when you search on Google, or when
you go into Google Maps, and you're
in an affected region, it will give you some more
information so that you know how to keep yourself
and your family safe, and whether or not you
should evacuate, and when you get an alert
from Google public alerts.
PAIGE BAILEY: This is fantastic.
My academic background is very focused
on quantitative hydrogeology and geospatial reasoning.
And it's amazing to be able to see the kind of accuracy uptick
that you get just by using machine learning
with your elevation maps.
This is really, really cool.
CARLA BROMBERG: Thank you.
We're really excited.
And we hope we can save a lot of lives ahead of this monsoon
season.
And we're hoping to also expand this work across more
countries.
And for this, we're partnering really
closely with the Indian government,
and many others on the ground, and first responders.
So hopefully we can help get the word out
so that people can evacuate to safety.
PAIGE BAILEY: Thank you so much for telling me about this work.
And let's go and take a look at the other demo
that we have from AI for social good
that's very focused on whale detection.
CARLA BROMBERG: Yeah, absolutely.
EMILY FORTUNA: Hi, I'm here with Julie Cattiau.
And she's here to tell me about this really cool whale demo.
JULIE CATTIAU: Yeah, nice to meet you.
So this is a demo that's related to the bioacoustics project
at Google.
So we're a small team, part of the AI for social good effort.
And so we've been training machine
learning model to detect the presence of animals
just by listening to the audio sound.
EMILY FORTUNA: Very cool.
JULIE CATTIAU: And so this project in particular
is a partnership with NOAA, the National Oceanic
and Atmospheric Administration, which is part
of the American government.
NOAA, in particular, has a network of microphones.
These microphones have been set up in 2005.
And they're in different spots in the Pacific Ocean.
And so in the end, it adds up to a lot of data.
And so NOAA-- the data set that NOAA, in particular, was
interested in getting our help to analyze
is close to 200,000 hours of audio.
So as you can imagine, nobody can listen to the whole thing.
It's just way too long.
And that's where machine learning can be very helpful.
Because with enough examples of sounds,
machine learning can be applied to recognizing
a particular species of animals just by listening to the sound.
And actually, if you think about animals that live in the ocean,
almost all of them produce some sort of sounds, even fish,
which can be surprising.
Boats definitely make a lot of noise.
They're not animals, but they make noise.
Whales make sounds, dolphins do too.
And so the question for us was, can we
identify which species are present in a audio data set
and classify them using artificial intelligence?
So this demo here that you can see represents three
months' worth of data from that data set I was telling you
about.
And what you can do is, if you slide
this tablet along the data, you can see that this is actually
a spectrogram.
So it's a visual representation of a sound.
This is frequency over time for a particular sound.
And so NOAA provided some examples of humpback whale
sounds.
And then we trained a model to recognize those sounds
automatically.
And we used it on the whole pile of data
that they had to tell them where the whales were
and at which point in time.
When the color is light blue, it means that our classifier
has detected a whale.
Would you like to listen to the whale songs?
EMILY FORTUNA: Absolutely.
JULIE CATTIAU: OK.
[WHALE CALL]
EMILY FORTUNA: So I'm getting lots of ocean sounds right now.
Oh, I hear the whale.
JULIE CATTIAU: You do?
Awesome.
EMILY FORTUNA: That's wonderful.
Well, thank you so much.
This is lovely.
JULIE CATTIAU: Yeah, thank you.
EMILY FORTUNA: And It's--
I am-- it's great that you're able to track whale migration
patterns.
JULIE CATTIAU: Yeah, thank you for your interest.
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