November 25, 2025 • 17 mins
Welcome to "Stories Worth Hearing," where your host, John Quick, explores the intersection of technology and social impact. In this episode, we take a deep dive into the world of artificial intelligence with Laura McGorman from Meta. Laura, who leads the AI for Good team, introduces us to Meta's innovative AI tools, Llama and Segment Anything. These tools are revolutionizing disaster preparedness by automating emergency response processes and enhancing the accuracy of flood predictions. Through engaging discussions, Laura explains how these technologies are being deployed globally, in partnership with research institutions and local governments, to build more resilient communities and save lives.
Join us as we unravel the complexities of AI in disaster management, making this technical subject accessible to all listeners. Laura shares real-world examples of how AI is transforming emergency response efforts, from providing timely alerts to improving communication during crises. Whether you're a tech enthusiast or new to the field, this episode offers valuable insights into the future of disaster management and the pivotal role of AI in creating safer, more prepared communities. Tune in to discover how Meta's AI innovations are making a tangible difference in the world.
Check them out here: https://www.llama.com/ and here: https://ai.meta.com/sam3/
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:00):
Well, Laura, thank you so much for joining us.
This will be excited to talk to a Facebook expert.
Welcome to the show. Thank you so much for having me,
Jonathan. Well, this will be fun.
So first tell folks what it whatwhat it is that you do at
Facebook. And then we'll kind of hop into
some really cool tools that Facebook has and how maybe those
(00:21):
tools could help folks as they navigate, you know, crazy things
like weather disasters. Yeah, well, I love talking about
my job. I think I have one of the
coolest jobs in the world. So I lead A-Team called AI for
Good at Meta. And my job is to take all the
tools that Meta creates from a technological perspective.
That could be things like the Facebook app that everyone knows
(00:42):
and loves, or tools like our open source AI models that I'll
talk about today and try and figure out how they can be used
for social impact around the world.
And for this can be for applications like public health
that's been used for applications like climate change
on today the topic of commerce. That's awesome, man.
(01:04):
I bet that is a fun job. I bet you have a lot of fun, and
I'm sure you do tons of these types of interviews.
But today we get to kind of talkabout how Facebook and AI can
help prep somebody for disaster,disaster weather.
Disasters happen all over the US.
We just had one here in Alaska. Tell me about these tools that
(01:25):
Facebook has and how the averageperson could maybe utilize them
to help them prepare. Sure.
So before we dive into the topicof AI, which I think is really
the future of how disaster response is going to work in the
US and abroad. We have a lot of tools that are
available on the Facebook app right now that have actually
been in market for a number of years.
(01:45):
I'll chat about 3:00 that I think are the most important for
people to know about 1 is calledsafety check.
And this is a tool that if you're on Facebook and you're in
a disaster affected area, you'llessentially get a notification
to your Facebook account that allows you to mark yourself
safe. We think this is an important
tool because, you know, during disasters, it's really hard to
stay in touch with your broader community.
(02:06):
Maybe you only stay in touch with a couple close family
members. But this allows you to do a mark
yourself safe and let your wholesocial network know that you're
out of harm's way. The second floor is called local
alerts. This is really more about you
getting information from emergency response agencies in
your area. And this is a tool you can opt
into getting notifications essentially from either your
(02:26):
local Police Department or your local Emergency Management
department. If they have a Facebook page and
they use local alerts, you'll beable to get notifications
directly from them as the situation unfolds on the ground.
And we believe this is a really important tool that empowers
local governments and 1st responders to directly
communicate with affected communities.
And the last but not least is that many people don't know, but
(02:47):
Facebook also has a really robust set of fundraising tools
that allow for individuals who are out of a disaster affected
area to raise funds for those who are affected.
Maybe they need supplies, maybe they need funding, maybe they
need housing assistance. Fundraising is a really powerful
tool in a way that you can support communities affected by
disaster. And you can create a nonprofit
(03:08):
fundraiser for everything from alocal animal shelter to a Red
Cross in your area. And we believe this, this is a
really powerful tool for people to know about as well.
That's cool. So the, the check, you know, the
check mark safe is not just a meme, it's an actual thing.
We've all seen the memes of like, you know, I've been, I'm
safe from whatever, you know, funny thing was happening that
(03:29):
week. But this is an actual thing
where people could go in and mark themselves safe.
Maybe they're in a hurricane or tornado or a flood or something
like that, and they could informloved ones that I'm alive.
And that's an easy way to do that.
Is that kind of the way it works?
That's exactly how it works. And, and I I find it to be a
really helpful tool. Sometimes things are happening
(03:50):
in other countries. I'll have a friend from Graduate
School that's in a country wherethere was a horrible earthquake
or typhoon. It's always a relief when I open
my Facebook account to see somebody who I know is in a
disaster affected area. You know, it doesn't require me
reaching out to them directly, but if you see that they've
marked themselves themselves safe, you know they're OK.
And that can be really a relief to their broader social network.
And this is probably something that if you can navigate
(04:13):
Facebook, you could easily be able to do this part of it.
It's not like you don't need like a degree in.
No. Coding or developing Or if
you're able to upload a Facebookphoto, you could probably do
this part. Yeah, you'll get a notification
essentially at the top of your news feed.
It's essentially one Click to mark yourself safe.
And so we've tried to make it aseasy as possible for people to
(04:33):
do that. And do people, I mean, I'm sure
you have data on this, but my guess is people are using this
pretty actively all over the globe when it comes to
disasters. Indeed, billions of people over
years have marked themselves safe.
And we, we believe it's a reallyimportant tool and we have kept
it going at launched over a decade ago and we wow.
OK, so let's talk about some of these new really cool AI tools
(04:57):
and what they are and maybe whatit means to being prepared for
disasters. Sure.
So many people maybe don't realize, but in addition to all
of the sort of social networkingtools that Meta offers, we also
are an AI company and we have open source AI tools, and I'll
talk about a few of them today. One is called Llama, which is a
(05:18):
large language model, similar toother models that power tools
like ChatGPT. We also have what are called
computer vision models. So these are AI models that work
on imagery, namely things like photographs, images, video.
And we've made a lot of these models open source, which means
anyone can download them and usethem free of charge.
(05:38):
And so in the context of disaster response, we have been
working with partners around theUnited States and around the
globe to figure out how artificial intelligence, whether
or not it's large language models like Llama or computer
vision models like Segment anything, can essentially
improve emergency response times, improve repairings, great
tools for first responders. And we're already seeing that
(05:59):
happen in collaboration with nonprofits and research
institutions across. That's awesome.
So if there's an agency, let's say in Alaska, where I live that
wanted to utilize these tools, how would they go about
utilizing these tools? Is it just simple sign up or do
they need to work with somebody on your team?
What are some of those steps that people could take?
(06:20):
Sure. Starting with some of the more
sort of on platform consumer ready tools that we talked about
at the very beginning, if you are a local response agency in
Alaska and you're not yet enrolled in local alerts,
there's a very straightforward process to do that.
If you have a Facebook page, youcan essentially see if you're on
the Facebook page. You can also Google the workflow
very easily. But for things like our AI
(06:42):
tools, there's a number of different ways you can have it.
So if you have an engineering team or if you have a team
that's familiar with artificial intelligence, these are things
you can download and use right out-of-the-box.
We have a website, ai.meta.com, and it has all of our open
source models there. Tools like Llama actually have
their own website, llama.com, again, which is our large
(07:02):
language model. But then there's another way you
could go about it. And that this is the way that we
think most productive is we think that in partnership with
your local research institution,you may already learn a lot.
So when Meta releases these opensource models, you'll find that
the research community uses themsometimes the day that they're
open sourced. And so sometimes if you have a
local research institution or university that's doing research
(07:23):
and disaster response, they havemade already gotten their hands
dirty with some of Meta's open source models and collaboration
in that regard as well. That's cool.
So what about from like a practical standpoint, how do
these tools, the AI 1 specifically, how do they help
during, you know, a hurricane oran earthquake, let's say there
(07:44):
is a institution that's using them in a proactive way to, you
know, help with disaster preparedness.
What does that look like? Like, how are the tools actually
practically helping that, you know, average John and Joe out
in the world? Yeah.
So when I think we think about artificial intelligence, the
major thing people should be thinking is about making what
(08:06):
were previously manual workflowsmuch more automated and faster.
And so to give an example from the computer vision space, this
is a space that I spent a lot oftime working in.
Again, this is AI that's focusedon analyzing imagery and video,
more big issues across coastal communities in the United
States, Alaska included, in terms of actually predicting
(08:28):
floods. And so normally what you're
going to have to do in order to predict if an area is going to
flood is maybe you'll have some sort of automated monitoring.
This could be some sort of camera sensor that's placed at
the coastline. But in traditional workflows,
what you'll have to do is hire somebody back at some
headquarters to look at these photos coming in from the field
(08:49):
and do a bunch of manual measurements and figure out, OK,
the the sort of coast starts here and, and the water seems
moving at this rate. And we believe that the, you
know, the likelihood of floodingin the next week is X.
And it's all hyper manual and requires a lot of people to do a
lot of work. What you can do with the advent
of artificial intelligence and amodel like segment anything, a
(09:09):
segment anything basically can cut out any object from an
image. So in an automated fashion, I
can say this is where the coastline is.
This is how quickly and the calculations required become
much faster to then the teams actually sending notifications
for things like evacuation orders or other types of
preparedness efforts that can happen essentially without that
(09:29):
manual workflow needing to take place.
And so we think there's already again been a bunch of research
collaborations that will have used segment anything for
coastal flood monitoring in an automated fashion.
And we think what the next phaseof was going to look like is
essentially having those research collaborations be
brought into into Emergency Management workflows and and
(09:50):
being scaled up in a way that makes these these evacuations
and fairness efforts working. That's great.
So you know, lots of cities, probably almost every county or
borough across the US, includinghere in Alaska, they have
departments of Emergency Management.
And has Facebook found success in working with these types of
(10:11):
local Emergency Management entities and is there training
that comes along with it from Facebook side?
Sure. So we've done this in a lot of
states. Generally speaking, again, I
think it's a partnership model that spans things like Meta
research institutions are nonprofits and the public
sector. So we tend to work actually as
(10:32):
one big consortium of actors. And so Meta will be the
organization that supplies the open source AI model.
Again, we we make our many of our models available in a
fashion that's completely free and available to anybody who
wants to use them. And then what we often do will
partner with a local university.So to give an example of Texas,
we've been working with Texas A&M for a number of years.
(10:54):
They actually house a research institution called the Institute
for Disaster Resilient Texas that was formed after Hurricane
Harvey. They're the ones taking Meadows
Llama model and building custom tooling for the Texas set of use
cases and the Emergency Management set of use cases.
And then they're the ones sharing that with the Emergency
Management department in Harris County.
So I've gone to to Harris Countyand trained people.
(11:16):
So I've gone to California and trained people on our tools.
We just actually did a disaster response work work back in
Philadelphia that was really more a preparedness effort in
anticipation of the 2026 World Cup game.
So certainly work hand in hand with local government, but we
also try to bring our local partners who can really provide
that sustainability plan to be able to use our tools locally
(11:38):
for their own individual use over the long term.
Nice. And, and, you know, with most of
this being open source, there isnot a huge cost there to the to
the to the borough, to the Emergency Management side.
But, you know, often times that is a stumbling block for
government agencies or nonprofits.
(11:59):
Even if something's open source,it's tough to get a position
funded or whatever it is. Does Meta or Facebook do any
sort of grants to help kind of spur this work forward in a
county or a city or borough? Sure.
We have a program called the Llama Impact Grants program that
looks at sort of seed funding for exactly these kind of use
(12:19):
phases. We funded quite a number of
social impact ventures around the world essentially trying to
ensure their AI models are used in this regard.
So we do have those for grant programs in place.
But again, I also think that what's neat about partnering not
just directly with the public sector is it's really neat to
see the way research institutions can sometimes also
(12:40):
tap into things like private philanthropy.
If they studies that as open source models in an Emergency
Management capacity or in another social impact capacity,
there's often additional fundingopportunities for those projects
as well. Nice.
So, you know, this is your world.
You get to meet folks all over the US prior over the globe.
Do you have a moment in your jobin this capacity where you've
(13:03):
kind of looked back and said, man, we're making a we're
actually making a difference here.
Tell me about what that looked like for you.
I think the peak of that was actually probably during the
COVID pandemic, so a different set of use cases, not in in sort
of disaster response and extremeweather, but in this case public
health emergencies. So as you might imagine, before
(13:24):
the onset of things like the COVID vaccine, we were being
told that there were few things that we could do to keep
ourselves safe. One was to stay home, and there
was to wear masks. And I don't know if you're
familiar with most doctors offices or health surveillance
systems, but they tend to not have data on what percentage of
the population is wearing masks or staying home.
(13:44):
These are not things we've historically had to measure as
part of a global pandemic. And so the early version of my
program actually focused much more on open data.
We wondered if I would say everycountry I could name at the time
to share open source mobility data so that people could ask
they'll track whether or not populations were adhering to
(14:05):
stay at home owners. Facebook also did something
which was incredibly impressive is that we also launched a
global survey in 200 countries and 55 languages every single
day for almost 2 years to try tobetter understand at scale
whether or not people were wearing masks and other sort of
preventive measures relevant to the COVID pandemic.
(14:25):
And those tools actually became the go to data sources for
international forecasting institutions around the world
and research institutions aroundthe world.
There really wasn't any other data from traditional sources
that they could rely on. So I think what global
emergencies tell us is that, youknow, when we break down these
institutional barrier, we reallyjust try to solve problems
(14:46):
together. We can get in quarter of work
done and it's a period of my career I'm very proud of and
look back on. It was obviously horrible that
we were facing COVID at the timein terms of the level of
collaboration and just cooperation that took place
across public, private and and nonprofit sectors.
It was pretty exceptional in that regard.
Nice. Well, my last question to you is
(15:07):
this, Laura is what's it, what'sthe maybe end goal or big
success look like specifically with the AI disaster
preparedness tools that that meta has?
You know, let's say it's five years from now, What do you
consider is going to be the big win for you all looking back and
say, OK, we're doing something good here.
(15:28):
We're headed in the right direction And you know, your
team can sit back and say that'sa big W.
What? What's that look like for you?
Yeah. I mean, I think we would hope to
be able to get ahead of things as much as possible.
And this is really challenging. So I think for rural
communities, it's going to be how we work together with both
online and offline solutions. So if you think about a workflow
(15:51):
that involves artificial intelligence, how can something
like a flood siren that may havemaybe has to work in an offline
fashion still sort of get the right notification and the right
time to go off so that communities can remove
themselves from harm's way? I mean, I work on a global
program too. So I think a lot about, I would
love to be able to see what we can achieve in, you know, an
(16:13):
urban setting in New York City or in California.
I would want us to be able to beable to achieve that not only in
rural settings in the United States, but around the world.
And I think that's going to be, we don't have to partner with
the research community on. And then also to really think
about how we get these solutionsout of the very technical
(16:33):
communities and sometimes in which they're created and into
the hands of people on the ground.
So at the end of the day, it's about, you know, saving more
lives from things like Rivera natural disasters.
And I think we it's a tall orderto get there for sure.
Well, this has been exciting folks.
I'll put the links to those new AI tools in the podcast
description. Laura, I run on.
I want to thank you. I appreciate you joining us here
(16:55):
on the show. Do you have any last minute
thoughts here before we head off?
The floor is yours. Oh well, I just thank you for
having me, Jonathan. Happy Thanksgiving and wishing
everybody a safe and happy holiday season.
Awesome. Well, thanks for joining us.
You're welcome back anytime.
Well, Laura, thank you so much for joining us.
This will be excited to talk to a Facebook expert.
Welcome to the show. Thank you so much for having me,
Jonathan. Well, this will be fun.
So first tell folks what it whatwhat it is that you do at
Facebook. And then we'll kind of hop into
some really cool tools that Facebook has and how maybe those
(00:21):
tools could help folks as they navigate, you know, crazy things
like weather disasters. Yeah, well, I love talking about
my job. I think I have one of the
coolest jobs in the world. So I lead A-Team called AI for
Good at Meta. And my job is to take all the
tools that Meta creates from a technological perspective.
That could be things like the Facebook app that everyone knows
(00:42):
and loves, or tools like our open source AI models that I'll
talk about today and try and figure out how they can be used
for social impact around the world.
And for this can be for applications like public health
that's been used for applications like climate change
on today the topic of commerce. That's awesome, man.
(01:04):
I bet that is a fun job. I bet you have a lot of fun, and
I'm sure you do tons of these types of interviews.
But today we get to kind of talkabout how Facebook and AI can
help prep somebody for disaster,disaster weather.
Disasters happen all over the US.
We just had one here in Alaska. Tell me about these tools that
(01:25):
Facebook has and how the averageperson could maybe utilize them
to help them prepare. Sure.
So before we dive into the topicof AI, which I think is really
the future of how disaster response is going to work in the
US and abroad. We have a lot of tools that are
available on the Facebook app right now that have actually
been in market for a number of years.
(01:45):
I'll chat about 3:00 that I think are the most important for
people to know about 1 is calledsafety check.
And this is a tool that if you're on Facebook and you're in
a disaster affected area, you'llessentially get a notification
to your Facebook account that allows you to mark yourself
safe. We think this is an important
tool because, you know, during disasters, it's really hard to
stay in touch with your broader community.
(02:06):
Maybe you only stay in touch with a couple close family
members. But this allows you to do a mark
yourself safe and let your wholesocial network know that you're
out of harm's way. The second floor is called local
alerts. This is really more about you
getting information from emergency response agencies in
your area. And this is a tool you can opt
into getting notifications essentially from either your
(02:26):
local Police Department or your local Emergency Management
department. If they have a Facebook page and
they use local alerts, you'll beable to get notifications
directly from them as the situation unfolds on the ground.
And we believe this is a really important tool that empowers
local governments and 1st responders to directly
communicate with affected communities.
And the last but not least is that many people don't know, but
(02:47):
Facebook also has a really robust set of fundraising tools
that allow for individuals who are out of a disaster affected
area to raise funds for those who are affected.
Maybe they need supplies, maybe they need funding, maybe they
need housing assistance. Fundraising is a really powerful
tool in a way that you can support communities affected by
disaster. And you can create a nonprofit
(03:08):
fundraiser for everything from alocal animal shelter to a Red
Cross in your area. And we believe this, this is a
really powerful tool for people to know about as well.
That's cool. So the, the check, you know, the
check mark safe is not just a meme, it's an actual thing.
We've all seen the memes of like, you know, I've been, I'm
safe from whatever, you know, funny thing was happening that
(03:29):
week. But this is an actual thing
where people could go in and mark themselves safe.
Maybe they're in a hurricane or tornado or a flood or something
like that, and they could informloved ones that I'm alive.
And that's an easy way to do that.
Is that kind of the way it works?
That's exactly how it works. And, and I I find it to be a
really helpful tool. Sometimes things are happening
(03:50):
in other countries. I'll have a friend from Graduate
School that's in a country wherethere was a horrible earthquake
or typhoon. It's always a relief when I open
my Facebook account to see somebody who I know is in a
disaster affected area. You know, it doesn't require me
reaching out to them directly, but if you see that they've
marked themselves themselves safe, you know they're OK.
And that can be really a relief to their broader social network.
And this is probably something that if you can navigate
(04:13):
Facebook, you could easily be able to do this part of it.
It's not like you don't need like a degree in.
No. Coding or developing Or if
you're able to upload a Facebookphoto, you could probably do
this part. Yeah, you'll get a notification
essentially at the top of your news feed.
It's essentially one Click to mark yourself safe.
And so we've tried to make it aseasy as possible for people to
(04:33):
do that. And do people, I mean, I'm sure
you have data on this, but my guess is people are using this
pretty actively all over the globe when it comes to
disasters. Indeed, billions of people over
years have marked themselves safe.
And we, we believe it's a reallyimportant tool and we have kept
it going at launched over a decade ago and we wow.
OK, so let's talk about some of these new really cool AI tools
(04:57):
and what they are and maybe whatit means to being prepared for
disasters. Sure.
So many people maybe don't realize, but in addition to all
of the sort of social networkingtools that Meta offers, we also
are an AI company and we have open source AI tools, and I'll
talk about a few of them today. One is called Llama, which is a
(05:18):
large language model, similar toother models that power tools
like ChatGPT. We also have what are called
computer vision models. So these are AI models that work
on imagery, namely things like photographs, images, video.
And we've made a lot of these models open source, which means
anyone can download them and usethem free of charge.
(05:38):
And so in the context of disaster response, we have been
working with partners around theUnited States and around the
globe to figure out how artificial intelligence, whether
or not it's large language models like Llama or computer
vision models like Segment anything, can essentially
improve emergency response times, improve repairings, great
tools for first responders. And we're already seeing that
(05:59):
happen in collaboration with nonprofits and research
institutions across. That's awesome.
So if there's an agency, let's say in Alaska, where I live that
wanted to utilize these tools, how would they go about
utilizing these tools? Is it just simple sign up or do
they need to work with somebody on your team?
What are some of those steps that people could take?
(06:20):
Sure. Starting with some of the more
sort of on platform consumer ready tools that we talked about
at the very beginning, if you are a local response agency in
Alaska and you're not yet enrolled in local alerts,
there's a very straightforward process to do that.
If you have a Facebook page, youcan essentially see if you're on
the Facebook page. You can also Google the workflow
very easily. But for things like our AI
(06:42):
tools, there's a number of different ways you can have it.
So if you have an engineering team or if you have a team
that's familiar with artificial intelligence, these are things
you can download and use right out-of-the-box.
We have a website, ai.meta.com, and it has all of our open
source models there. Tools like Llama actually have
their own website, llama.com, again, which is our large
(07:02):
language model. But then there's another way you
could go about it. And that this is the way that we
think most productive is we think that in partnership with
your local research institution,you may already learn a lot.
So when Meta releases these opensource models, you'll find that
the research community uses themsometimes the day that they're
open sourced. And so sometimes if you have a
local research institution or university that's doing research
(07:23):
and disaster response, they havemade already gotten their hands
dirty with some of Meta's open source models and collaboration
in that regard as well. That's cool.
So what about from like a practical standpoint, how do
these tools, the AI 1 specifically, how do they help
during, you know, a hurricane oran earthquake, let's say there
(07:44):
is a institution that's using them in a proactive way to, you
know, help with disaster preparedness.
What does that look like? Like, how are the tools actually
practically helping that, you know, average John and Joe out
in the world? Yeah.
So when I think we think about artificial intelligence, the
major thing people should be thinking is about making what
(08:06):
were previously manual workflowsmuch more automated and faster.
And so to give an example from the computer vision space, this
is a space that I spent a lot oftime working in.
Again, this is AI that's focusedon analyzing imagery and video,
more big issues across coastal communities in the United
States, Alaska included, in terms of actually predicting
(08:28):
floods. And so normally what you're
going to have to do in order to predict if an area is going to
flood is maybe you'll have some sort of automated monitoring.
This could be some sort of camera sensor that's placed at
the coastline. But in traditional workflows,
what you'll have to do is hire somebody back at some
headquarters to look at these photos coming in from the field
(08:49):
and do a bunch of manual measurements and figure out, OK,
the the sort of coast starts here and, and the water seems
moving at this rate. And we believe that the, you
know, the likelihood of floodingin the next week is X.
And it's all hyper manual and requires a lot of people to do a
lot of work. What you can do with the advent
of artificial intelligence and amodel like segment anything, a
(09:09):
segment anything basically can cut out any object from an
image. So in an automated fashion, I
can say this is where the coastline is.
This is how quickly and the calculations required become
much faster to then the teams actually sending notifications
for things like evacuation orders or other types of
preparedness efforts that can happen essentially without that
(09:29):
manual workflow needing to take place.
And so we think there's already again been a bunch of research
collaborations that will have used segment anything for
coastal flood monitoring in an automated fashion.
And we think what the next phaseof was going to look like is
essentially having those research collaborations be
brought into into Emergency Management workflows and and
(09:50):
being scaled up in a way that makes these these evacuations
and fairness efforts working. That's great.
So you know, lots of cities, probably almost every county or
borough across the US, includinghere in Alaska, they have
departments of Emergency Management.
And has Facebook found success in working with these types of
(10:11):
local Emergency Management entities and is there training
that comes along with it from Facebook side?
Sure. So we've done this in a lot of
states. Generally speaking, again, I
think it's a partnership model that spans things like Meta
research institutions are nonprofits and the public
sector. So we tend to work actually as
(10:32):
one big consortium of actors. And so Meta will be the
organization that supplies the open source AI model.
Again, we we make our many of our models available in a
fashion that's completely free and available to anybody who
wants to use them. And then what we often do will
partner with a local university.So to give an example of Texas,
we've been working with Texas A&M for a number of years.
(10:54):
They actually house a research institution called the Institute
for Disaster Resilient Texas that was formed after Hurricane
Harvey. They're the ones taking Meadows
Llama model and building custom tooling for the Texas set of use
cases and the Emergency Management set of use cases.
And then they're the ones sharing that with the Emergency
Management department in Harris County.
So I've gone to to Harris Countyand trained people.
(11:16):
So I've gone to California and trained people on our tools.
We just actually did a disaster response work work back in
Philadelphia that was really more a preparedness effort in
anticipation of the 2026 World Cup game.
So certainly work hand in hand with local government, but we
also try to bring our local partners who can really provide
that sustainability plan to be able to use our tools locally
(11:38):
for their own individual use over the long term.
Nice. And, and, you know, with most of
this being open source, there isnot a huge cost there to the to
the to the borough, to the Emergency Management side.
But, you know, often times that is a stumbling block for
government agencies or nonprofits.
(11:59):
Even if something's open source,it's tough to get a position
funded or whatever it is. Does Meta or Facebook do any
sort of grants to help kind of spur this work forward in a
county or a city or borough? Sure.
We have a program called the Llama Impact Grants program that
looks at sort of seed funding for exactly these kind of use
(12:19):
phases. We funded quite a number of
social impact ventures around the world essentially trying to
ensure their AI models are used in this regard.
So we do have those for grant programs in place.
But again, I also think that what's neat about partnering not
just directly with the public sector is it's really neat to
see the way research institutions can sometimes also
(12:40):
tap into things like private philanthropy.
If they studies that as open source models in an Emergency
Management capacity or in another social impact capacity,
there's often additional fundingopportunities for those projects
as well. Nice.
So, you know, this is your world.
You get to meet folks all over the US prior over the globe.
Do you have a moment in your jobin this capacity where you've
(13:03):
kind of looked back and said, man, we're making a we're
actually making a difference here.
Tell me about what that looked like for you.
I think the peak of that was actually probably during the
COVID pandemic, so a different set of use cases, not in in sort
of disaster response and extremeweather, but in this case public
health emergencies. So as you might imagine, before
(13:24):
the onset of things like the COVID vaccine, we were being
told that there were few things that we could do to keep
ourselves safe. One was to stay home, and there
was to wear masks. And I don't know if you're
familiar with most doctors offices or health surveillance
systems, but they tend to not have data on what percentage of
the population is wearing masks or staying home.
(13:44):
These are not things we've historically had to measure as
part of a global pandemic. And so the early version of my
program actually focused much more on open data.
We wondered if I would say everycountry I could name at the time
to share open source mobility data so that people could ask
they'll track whether or not populations were adhering to
(14:05):
stay at home owners. Facebook also did something
which was incredibly impressive is that we also launched a
global survey in 200 countries and 55 languages every single
day for almost 2 years to try tobetter understand at scale
whether or not people were wearing masks and other sort of
preventive measures relevant to the COVID pandemic.
(14:25):
And those tools actually became the go to data sources for
international forecasting institutions around the world
and research institutions aroundthe world.
There really wasn't any other data from traditional sources
that they could rely on. So I think what global
emergencies tell us is that, youknow, when we break down these
institutional barrier, we reallyjust try to solve problems
(14:46):
together. We can get in quarter of work
done and it's a period of my career I'm very proud of and
look back on. It was obviously horrible that
we were facing COVID at the timein terms of the level of
collaboration and just cooperation that took place
across public, private and and nonprofit sectors.
It was pretty exceptional in that regard.
Nice. Well, my last question to you is
(15:07):
this, Laura is what's it, what'sthe maybe end goal or big
success look like specifically with the AI disaster
preparedness tools that that meta has?
You know, let's say it's five years from now, What do you
consider is going to be the big win for you all looking back and
say, OK, we're doing something good here.
(15:28):
We're headed in the right direction And you know, your
team can sit back and say that'sa big W.
What? What's that look like for you?
Yeah. I mean, I think we would hope to
be able to get ahead of things as much as possible.
And this is really challenging. So I think for rural
communities, it's going to be how we work together with both
online and offline solutions. So if you think about a workflow
(15:51):
that involves artificial intelligence, how can something
like a flood siren that may havemaybe has to work in an offline
fashion still sort of get the right notification and the right
time to go off so that communities can remove
themselves from harm's way? I mean, I work on a global
program too. So I think a lot about, I would
love to be able to see what we can achieve in, you know, an
(16:13):
urban setting in New York City or in California.
I would want us to be able to beable to achieve that not only in
rural settings in the United States, but around the world.
And I think that's going to be, we don't have to partner with
the research community on. And then also to really think
about how we get these solutionsout of the very technical
(16:33):
communities and sometimes in which they're created and into
the hands of people on the ground.
So at the end of the day, it's about, you know, saving more
lives from things like Rivera natural disasters.
And I think we it's a tall orderto get there for sure.
Well, this has been exciting folks.
I'll put the links to those new AI tools in the podcast
description. Laura, I run on.
I want to thank you. I appreciate you joining us here
(16:55):
on the show. Do you have any last minute
thoughts here before we head off?
The floor is yours. Oh well, I just thank you for
having me, Jonathan. Happy Thanksgiving and wishing
everybody a safe and happy holiday season.
Awesome. Well, thanks for joining us.
You're welcome back anytime.
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