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EMILY FORTUNA: Joining us now is Yossi Matias, Vice President
of Engineering at Google and the head of the Google R&D
Center in Israel.
Thanks for being here, Yossi.
YOSSI MATIAS: Thank you very much for inviting me.
TIMOTHY JORDAN: Yossi, you're involved
with AI for social good.
And you've worked on an impressive number
of projects surrounding that.
How did you get into that area?
How did it start up here?
YOSSI MATIAS: So in a way, we were working on social good
from the early days in Google.
It's part of the DNA of the company
to try and use technology to solve problems for people.
And obviously, in the last couple of years, where AI
is getting a lot of--
we became an AI first company.
And AI had all these advancements.
So suddenly, we started looking into how to leverage on AI,
how to use it to solve those societal problems that can help
out people in a profound way.
EMILY FORTUNA: And specifically, you've
worked on flood prediction.
And in my experience, predicting the future
is not a task best left to mere mortals.
So I'm curious.
You could tell us how you got started on that
and how it actually works.
YOSSI MATIAS: Sure.
Yeah.
There's an old saying that prediction is hard, especially
about the future.
So I've been leading a team which
is working on crisis response, where the mission of the team
is to develop technologies to help people during crisis--
natural disasters and other crisis--
by providing the right information to people.
And in fact, my involvement in that,
going back 8 and 1/2 years, where as a user,
I had the experience.
We had a devastating fire in Israel.
And it was near our Haifa office.
We have offices in Tel Aviv and Haifa.
I was visiting that day.
And I didn't know what I should be doing.
I saw this huge smoke.
I didn't find any information on the internet.
And this was actually the first time
that I experienced the need for information.
And it was not until I actually called the mayor's office
that I got the phone number of actionable information.
And in fact, at that time, I asked my team
to see how to make that information
available to everybody.
And within a few hours, anybody searching for fire in Israel
could get that information.
So this was the first example where
we gave kind of actionable information to people.
Now, fast forward-- now we integrated
into Search something we call SOS Alerts,
which essentially provides information about major crisis
when they happen.
And one thing we discovered is that we can only
give information which we have.
But one of the most devastating natural disaster are floods.
They impact up to 250 million people per year.
And there are thousands of fatalities because of them.
And in many cases, we don't have information about where floods
exactly is going to occur.
So we knew this is important problem.
But it wasn't clear whether we can actually
do anything about it.
There was an engineer that just joined.
And he said he wants to work on a project that
could help save lives.
And I asked him, well, why don't you spend part of your time
seeing how much we can advance using AI and other technologies
in order to help out with flood forecasting?
And within a few months, he made enough progress
that we actually built a team around it.
And in less than a year, actually, it
was ready as a pilot in the partner area in India.
And that's how actually the flood forecasting effort
came to life.
TIMOTHY JORDAN: And when you first
started looking at that problem, did you think it was solvable?
YOSSI MATIAS: So actually, when I had the discussion with him,
I told him that I'm not sure if we're
going to be able to solve it, because in order to really--
and we were focusing on really what we call riverine floods.
These are floods where the river starts rising because of water.
Then you know then there's likely going to be some floods.
But the important information to know
is exactly where the flood is going to occur,
because it's only then that people will take action.
It's only then that people would evacuate,
or it's only then where government
can take action and evacuate the right people
from the right places.
Now, in order to get these accurate predictions,
there's a lot going on.
You need to have relatively fresh information about where
the water already is in order to make the predictions.
We are leveraging on the fact that we
have better elevation models for many more
places around the world.
And that elevation model itself is
computed with some of the best technologies.
Machine learning on aerial imagery and the other data
that we have, it's using a lot of computational power
to do hydraulic simulations and complemented also
with machine learning techniques.
And it's using information that we're
getting through a partnership with the Indian government,
where they're actually measuring the water level in quite
a few rivers and trying to actually make
sense of what's going on there.
So this is really a true partnership
with the Indian government on that.
And as it turns out, we could actually get into prediction
a few hours before the floods actually get into the level
that it did of over 90% accuracy, which was actually
quite impactful.
TIMOTHY JORDAN: Yeah, that's hugely impactful.
EMILY FORTUNA: Yeah.
How were you able to connect with the Indian government
and get these people involved in the project?
YOSSI MATIAS: The beauty is that once we
started working on that, there's a lot of interest
to collaborate.
And I would say that on many of the societal problems,
the notion of collaboration between companies
and communities and entrepreneurs and governments,
I think this is one of the areas that we
need to see more of, because these are big problems.
And we do need to have this kind of community
effort and global effort, in order to really tackle them.
TIMOTHY JORDAN: That's cool.
EMILY FORTUNA: Thank you so much, Yossi.
And thank you for helping create this environment
to be able to make these products possible.
YOSSI MATIAS: It's a privilege to be
able to have this kind of environment
and to work with people who care about how to use technologies
to solve big problems.
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