January 14, 2026 • 33 mins
NASA and IBM have developed advanced AI foundation models that analyze satellite data to reveal patterns across Earth and beyond. These tools are already driving real‑world impact, from helping Kenya plan the planting of 15 billion trees to enabling the UK to track harmful algae blooms. This collaboration provides strategic insights for climate action, environmental monitoring, and emergency response.
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Speaker 1 (00:00):
Hey everyone, it's Robert and Joe here. Today we've got
something a little bit different to share with you. It's
a new season of the Smart Talks with IBM podcast series.
Speaker 2 (00:09):
This season on Smart Talks with IBM, Malcolm Gladwell is back,
and this time he's taking the show on the road.
Malcolm is stepping outside the studio to explore how IBM
clients are using artificial intelligence to solve real world challenges
and transform the way they do business.
Speaker 1 (00:25):
From accelerating scientific breakthroughs to reimagining education. It's a fresh
look at innovation in action, where big ideas meet cutting
edge solutions.
Speaker 2 (00:34):
You'll hear from industry leaders, creative thinkers, and of course
Malcolm Gladwell himself as he guides you through each story.
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New episodes of Smart Talks with IBM drop every month
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This is a paid advertisement from IBM.
Speaker 3 (00:57):
If I were to go back, I don't know thirty
years in Kenya. What's the difference between then and now
in terms of tree cover. I'm talking to Philip thego
Special Technology envoid.
Speaker 4 (01:10):
To the Kenyan president, let's speak as if you think
about we aren elevent trol posts and previously we were
more than twenty percent. So we are cutting trees more
than we're planting them.
Speaker 3 (01:21):
In thirty years, Kenya last half its tree cover half.
And here's why that matters. Kenya is a mountainous country.
Dotted throughout the highlands are dozens of what canyons call
water towers natural reservoirs, densely forested areas capable of absorbing
the enormous amount of water that falls on the country
during the rainy seasons. The tree roots and undergrowth secure
(01:46):
and capture moisture, then slowly release it into the rivers
that flow down into the country's low lying coastal areas.
But in recent years the water towers have depleted, settlements
have encroached on them, trees have been chopped down, thousands
of acres cleared, the natural reservoirs ceased to hold nearly
(02:06):
as much water, so now Kenya is prone to extremes.
Too much water flowing down from the highlands in the
rainy season and too little water left during the dry season.
Speaker 4 (02:17):
So you have a couple of hours of water then
you have a couple of hours with no water that
it tops off to be dry by the city authority.
So that's the significance of the water towers we have
when they cannot hold water.
Speaker 3 (02:29):
Kenya desperately needed to restore its water towers by planting
as many trees as humanly possible. So in the fall
of twenty twenty three, the Kenyan government took action. It
started a national holiday, national Tree Growing Day, a day
to allow the citizens of Kenya to go out into
the forest to dominate the Kenyan countryside and plant as
(02:52):
many trees as they can, and the government decided on
a number.
Speaker 4 (02:58):
The presidents focus right around how to ensure that we
do not lose more forests was in this very ambitious
campaign around fifteen billion trees.
Speaker 3 (03:08):
That's right, fifteen billion with a B.
Speaker 4 (03:12):
So imagine that number will tell you the ambition, not
as he tells you the deficit. It has to be
fifteen billion in the next eight years.
Speaker 3 (03:20):
Fifteen billion trees over eight years averages out to more
than five million trees per day. That's a lot of trees.
But with such a massive goal, how can you track
your progress? How do you know where to plant those
trees so they'll have the most impact. How do you
monitor where older trees are still being cut down? Well,
(03:40):
the answer to those questions came from IBM and a
little space agency called NASA. That's right, folks, Smart Talks
is going to space. My name is Malcolm Glawell. You're
listening to the latest episode of Smart Talks with IBM,
where we offer our list a glimpse behind the curtain
(04:01):
of the world of technology. In this season, IBM has
gone inside elementary school classrooms, toured formulation labs at Loreel,
and spoken with the fan development team at Scuderia Ferrari HP.
In this episode, how IBM is partnering with NASA to
build geospatial models using data from satellites to better understand
(04:24):
our Earth and Solar system.
Speaker 5 (04:30):
Five four three two one zero all engine running liptoff.
We have a liptoff thirty two minutes past the hour
liftoff on Apollo eleven.
Speaker 3 (04:44):
IBM has worked on space related projects since before I
was even born.
Speaker 2 (04:49):
Im all for man.
Speaker 3 (04:52):
A team of four thousand IBM engineers helped create the
Saturn five rocket that took Neil Armstrong to the Moon.
Speaker 5 (05:00):
Buy up plate.
Speaker 3 (05:02):
And when I think of NASA. I tend to picture
the moon landing, or the team of people back in
Houston guiding the Apollo mission, or the Hubble telescope, or
astronauts aboard the International Space Station. What I didn't think
about until now are NASA's geographers.
Speaker 6 (05:20):
In order to go places, you need at map things.
Speaker 3 (05:23):
This is Kevin Murphy, chief Science Data Officer at NASA's
Science Mission Directorate.
Speaker 6 (05:29):
But I think that there's an assumption that NASAs all
about rockets and astronauts, and certainly that's a really large
part and important part of NASA.
Speaker 3 (05:38):
NASA sends people to space and looks out of the stars,
but NASA also looks down at the Earth. The agency
has about one hundred and fifty satellites that use radar, lightar, landset, Aquaterra, cloudset, AURA,
low Earth orbit, Medium Earth orbit, geostationary orbit, on and on.
(05:58):
In one sense, NASA makes hardware to build rockets and
spacecraft and all those satellites that circle the Earth. But
fundamentally NASA also collects data. It's scientists and engineers people
like Kevin want to make the best use possible of
all the information gathered by all those many dozens of instruments.
Speaker 6 (06:20):
Right now, we gather around twenty five petabytes of new
observational data per year. In the next couple months, we're
about to launch a high resolution global radar. When that launches,
will double how much we collect every year to about
fifty petabytes of information.
Speaker 3 (06:39):
Actually, since we recorded this conversation, NASA launched that global radar,
what they call NYSAR. So NASA is already generating new
data at the rate of fifty petabytes each year. To
put that in perspective, a single petabyte could hold about
five hundred billion pages of standard printed text. You know,
(07:00):
can anyone sort of apply to use this data.
Speaker 6 (07:02):
Is they don't even have to apply. It's free and
open data. It advances how we understand what we do
on Earth and how we see ourselves within the universe.
People can take it for so many different downstream applications.
So you can go to our websites today, you can
search through our tools and you can download information from
(07:23):
the Mars rovers, you can download information from the Lunar
Reconnaissance Orbiter or any of the Earth Science Data satellites.
Speaker 3 (07:29):
And give me an example of a really cool application,
a really cool use that someone I don't know in
academic or whatever has used your data for it is there?
Speaker 6 (07:39):
It okay. So one of the really kind of cool
but unexpected observations that we had is that we launched
a pair of satellites in their early two thousands called Grace,
and these satellites orbit the Earth and they can measure
very precisely the distance that they're away from each other
as they orbit the Earth, and as you go into gravity,
(08:00):
you can actually see a satellite accelerate and the other
one accelerate after it, right, And using that information, we
were trying to map kind of the gravity fields of Earth.
What what they found is that they can actually map
below kind of the mass of Earth to where water
storage is. For instance, so aquifers, right, so you can
(08:20):
monitor through gravity how much water is being depleted or
added to an aquifer or the density of glaciers.
Speaker 3 (08:29):
So, just to back up for a moment, the presence
and density of water deposits below the Earth's surface have
an effect on gravitational fields that are being measured in space.
Speaker 6 (08:42):
Correct.
Speaker 3 (08:43):
Yeah, And so does that tell you presume you learn
things like where there's an aquifer where you didn't think
there was an aquifer.
Speaker 6 (08:50):
Or if it's being depleted faster.
Speaker 2 (08:53):
Yeah.
Speaker 3 (08:53):
Yeah, So who's using that kind of data?
Speaker 6 (08:56):
All sorts of different organizations, whether they're you know, NG
or government agencies or people that are planning a large
agricultural product.
Speaker 3 (09:05):
How did you Was that an intentional decisisse?
Speaker 6 (09:07):
It wasn't. It was accidental.
Speaker 3 (09:09):
It was accidental. NASA has assembled a historically unprecedented mountain
of data about the physical world, free and open to anyone,
and the possibilities for how that information can be used
are so vast that even NASA is still uncovering them.
When I was a kid, I loved legos. I had
(09:31):
a huge bin full of them. At the time, legos
were really just colored bricks of various sizes. They weren't
as complicated as they are today. And what I realized
even then was that there were more possibilities in a
box of legos than I could ever imagine on my own.
I played with my brother and he would show me
something that hadn't occurred to me, And I go to
my friend Bruce's and see that he was off on
(09:54):
some legos tangent that I'd never even thought of, like
a cool bridge or a castle or a truck. I
use legos one way. Bruce used his legos in a
completely different way. NASA's data treasure trove is like a
very very big box of Legos. And here's the question.
With so much data, containing so many possible connections, could IBM,
(10:19):
and specifically IBM's artificial intelligence help NASA scientists uncover patterns
and connect systems in a way they've never done before.
Speaker 7 (10:30):
Everything started with a question, right.
Speaker 3 (10:33):
I'm talking to one Bernabe Moreno, director of IBM Research
in Europe.
Speaker 7 (10:38):
As we advance AI, we have new tools to understand
the around this, understand the world, understand the language, and
understand our planets. And the question that we were asking
ourselves was all these new advances that we see in language.
It was a post GPT moment. Could we apply the
same idea and the same architecture and technology to a
(11:00):
dour planets?
Speaker 3 (11:01):
The advent of AI created a new opportunity. What if
all of NASA's mountain of data could be organized, analyzed,
understood by artificial intelligence. The original idea was to create
a geospatial foundation model for the Earth and from there
create additional specialized models for other scientific priorities of NASA,
(11:23):
and finally quit an AI system that can understand all
the data across those specialized models in order to uncover
hidden insights and relationships. Together, these models could unlock an
infinite number of potential applications. I asked Kevin Murphy at
NASA about the beginning of these Earth models.
Speaker 6 (11:45):
Has some colleagues, and we were investigating a number of
different avenues of using AI with our data, but also
kind of the management and stewardship of the data, so
not only like the observations, but how we make it
available to people, make it discoverable. And they said, hey,
we see these transform architectures. We think that they can
(12:05):
be applicable to some of the sequential observations that we make.
We'd really like to work with IBM on that. And
I was like, I'm really skeptical, but because I hadn't
seen those types of tools really produce results that were
commensurate with the amount of effort you put into them, right,
(12:26):
So we were getting some really good results and deep
learning approaches, but they took a lot of effort.
Speaker 3 (12:32):
But Kevin came around quickly.
Speaker 6 (12:35):
When we typically develop a new data product or an algorithm,
it takes anywhere from you know, twelve months, eighteen months,
twenty four months to go from data and hypothesis to
results which is validated. We were able to get approximately
the same precision for some well known types of benchmarks
(12:58):
with and I think it was about four four months. Oh,
instead of starting the work.
Speaker 3 (13:02):
Yeah, yeah, so was it happened faster than you thought,
much faster. In twenty twenty three, IBM and NASA launched
a foundation model trained on NASA's harmonized landset sentinel to
satellite data across the continental United States. They named the
model Prithvi, the Sanskrit word for Earth. The first version
(13:24):
of Prithvy used only Earth observation images and just that
was enough to totally change Kevin's idea of what foundation
models could do. But they didn't stop there. IBM and
NASA were encouraged at how well Prithvy worked for Earth
observation tasks, so they decided to create a more complex
(13:45):
version of Prithvy that could understand whether and climate data.
They hoped this new version of Prithvi would allow researchers
to answer new questions about the Earth, from short term
weather forecasting to longer term climate effects. Imagine you have
a map of all the different temperatures, pressures, clouds, rainfall,
(14:05):
and more from around the globe. With this map, IBM
and NASA could implement advanced tasks. They could track the
formation of El Nino or predict how the path of
a hurricane would change if the ocean temperature went up
by half a degree.
Speaker 7 (14:21):
I would always remember this moment was when we created
the Weather and Climate Foundational Model. The senior methodologist of NASA,
it was like, I cannot believe that it has changed
the way I think about the AI And ever since
he's been kind of preaching with this A samples.
Speaker 3 (14:38):
One and his team then took the model and decided
to test it, really tested. They took away ninety nine
percent of the data points and ran the experiment again.
What they were trying to figure out is if the
model had learned enough about the basic principles of the Earth,
the underlying physics of the way the planet works, to
fill in the blanks on its own with just one
(15:01):
percent of the original data, would it still be accurate
in its predictions. What happened The model crushed it so
it was able to extrapolate on the basis of one
percent of the data what the entire picture looked like yes,
because pre learned everything right, Yeah, it learned the kind
(15:23):
of principles of exactly. Yeah. Oh wow, that's very very impressive.
So at that moment when you realize you could do that,
and just curious about your emotional I mean, did you
jump up and down? What did you do that?
Speaker 7 (15:37):
So he's like, wow, it was a very emotional meeting
because you know, having this person say now I'm convinced right, Yeah,
it was kind of a quite a special moment. These
moments make your life as a researcher.
Speaker 3 (15:52):
Ibm And as a launch prith Fee for Weather and
Climate in twenty twenty four, and while ibm And as
a scientist could use Privy to run interesting experiments, they
were even more excited about how Prithy could help people
in the real world. So let's go back to Kenya
(16:14):
Ambassador Philip Diego and the country's great tree planting project.
Speaker 4 (16:19):
So on those initial months there was a massive effort,
including a couple of national holidays.
Speaker 3 (16:25):
For tree planting.
Speaker 4 (16:27):
Yes, where the entire cabinet was sent.
Speaker 3 (16:31):
Ah, did you plant trees as I did?
Speaker 4 (16:33):
Oh my god, I said, The entire cabinet plus someone
we have to be seen.
Speaker 3 (16:37):
Are you good at the planet two weeks ago?
Speaker 4 (16:39):
Well, it's very easy to go hole put a tree
in the ground show.
Speaker 3 (16:42):
Well wow, what planting a tree is easy? But remember
it has to happen fifteen billion times. IBM research has
been operating in Nairobi since twenty thirteen, and what Kenya wanted,
at least in the beginning was straightforward. Fee model that
IBM and NASA built could be used to essentially make
(17:04):
the world's greatest map, and Kenya, with IBM's help, could
use that model to make the world's greatest map of Kenya.
The first step was to lay a grid across the
topography of the country, break the forest into manageable bite
sized pieces, each of which could be analyzed separately.
Speaker 4 (17:23):
So, because our forest is massive when you look at
it in terms of green hite, but only lay it,
you're able to break it into pieces, like into boxes.
And for us that was important because then it's easy
to tackle it when it's in a greed system than
just as a massive forest. So that was also what
the model was able to do.
Speaker 3 (17:43):
Then the model painstakingly sorted through each of those boxes
and look for what Philip calls hotspots, so.
Speaker 4 (17:50):
You can see, for example, very quickly, which other areas
are being eroded very fast, and that you need to
quickly protect. Yeh, because you sometimes and that's where you
want to target, right, I mean it's not possible to
do everything at the same time.
Speaker 3 (18:02):
Do you have a definition of a hotspot? And how
many hotspots are there according to that definition? H, there
are a lot.
Speaker 4 (18:08):
So we have more than forty water towers, and I'll
tell you all of them have hotspots. And the hot
spots in my definition areas that are being degraded faster
and in a very unusual way. Right. You can literally
see how human activity is seriously degrading that particular area
that if you do not have a direct intervention, we'll
lose the entire forest. So that's the hot spot for us,
(18:32):
because you think about cutting one hundred trees a day
and cutting a million trees a day, So that's a
hot spot. You want to look at places where there's
just unusually high activity of deforestation.
Speaker 3 (18:43):
In a hot spot. The size of each box in
the grid was ten by ten meters, about half a
tennis court. That's how closely they were examining the forest,
so very crudely. The model ingests all of this satellite
data and it helps you answer some very specific questions
like where should we prioritize our tree planning efforts, which
(19:07):
areas down to an extraordinary level of specificity, are eroding
most quickly. You know, all those kinds of practical questions
about how to direct your strategy.
Speaker 4 (19:17):
So if you think about a smart forest, right, and
that's really for us according it smart fencing, smart forests,
everything that's smart because of AI. If you think about
your usual what you can see with your eyes and
then the satellite layer which just zooms in and you
see green. So what the model has been able to
do is to create a smart layer, right, and then
(19:37):
that smart layer you can actually see many things, from analytics,
to the greeds, to a dashboard, one a lot. So
able to layer to those blocks, you can quantify degradation
by blocks. You can match integrations, you can match reforestation.
Speaker 3 (19:53):
I asked Philip to imagine what it would have been
like to attempt the tree planting project in an era
before AI. His answer was, plant fifteen billion trees, restore
the water towers. Impossible. With Prithvie on Kenya's side, though,
it's really happening. What should be clear by now is
(20:13):
how versatile Prithvie can be. Want to know how to
combat deforestation, Prithvy can model that. Want to know when
the best time in the year to plant your crops is,
prithvy can help predict that too. Last year, six months
after IBM started helping Kenya with reforestation, Kenya needed Prithvy's
help on something else. And it was an emergency.
Speaker 4 (20:35):
So something was happening in the world that we sort
of had these flats that we didn't expect.
Speaker 3 (20:41):
In the spring of twenty twenty four, Kenya was hit
with thunderstorms and torrential rain, days and days of it.
Speaker 4 (20:49):
And so I got a call from the Red Cross,
the one of my friends, and they're like, Ambassador, we
need a little bit of help on how we deal
with response because what we see is unusual, right, because
normally you would only have one area. All of a sudden,
we had an entire country flooding. In April, we had
about three eight hundred kilometers square kind of total land flooded,
(21:12):
which is unusual for Kenyon. And so when I got
this call, we were like, Okay, there's someone could do
with IBM. We only did one function for the trees.
It was actually a climate model, and we said, can
we use this to help us better respond to floods
And so that was how we started having this discussion
(21:33):
with IBM in terms of repurposing the model to help
us deal with this new challenge around floods.
Speaker 3 (21:41):
Again, Prithvy is versatile. Prithe could use everything it knew
about the land, the forests and infrastructure to analyze how
and where and when floods would occur. The Kenyan government
could then use the model to help the Red Cross
organize its respet pants, show areas that needed to be
(22:02):
evacuated or safe places where the Red Cross could set
up camps. That information was invaluable.
Speaker 4 (22:11):
Historically, what has happened is that they would set up
camp based on population congregation right where people assembly is
where they set up a camp, not based on any data,
right simply because people are there, they will come there
to provide services and emergency response. What we realize is
that that model doesn't work. So what we've been able
(22:31):
to do with IBM is be able to to sort
of give Red Cause very specific locations or options where
to set up camps. So if people come here, just
tell them no, move here. That's the safe place you
really want to go. So I think for me that
was really amazing. So we're calling them were a very
funny word for it, flood assembly points. We always have
fire for assembly points, but now we can say we
(22:51):
have literally flat assembly points that are safe or citizens.
Speaker 3 (22:57):
That's fascinating. So the model has ingested this incredibly granular
picture later of the topography and weather patterns of Kenya.
It's just giving you a set of useful predictions about
how you should shape your response.
Speaker 4 (23:16):
Yes, and what we did remember is that, as I said,
it was a full multistate called capability. What IBM gave
us was a base map. We didn't have that before
and a base model. So you cannot have these layers
up on layers, up on layers to be able to
make intelligent decisions.
Speaker 3 (23:35):
Throughout my reporting on this episode, I've been really impressed
by what Prithvie can do. But it doesn't stop at
floods and reforestation. Prithvie has also been used to look
at wildfires and floods in the UK, and Kevin told
me that researchers in Africa have even used Prithvy to
identify locust breeding grounds, which could help them prevent swarms
(23:57):
that destroy crops. All these are issues on land.
Speaker 8 (24:02):
I mean, I always say to people, seventy percent of
our land mask is ocean.
Speaker 3 (24:07):
Kate Rice is the director of the Heart Tree Center,
which focuses on adopting AI into UK's public and private sectors,
and one of those sectors is the blue economy oceans, fish, shellfish.
But oceans are huge, and getting data formotions is difficult.
Speaker 8 (24:26):
So you're dealing with something where there's not a lot
of people walking around collecting data. So the real difficulty
is understanding that collecting enough data to make anything makes sense.
And oceans are very complex in terms of their interaction
(24:46):
with our climate and how they interact with the climate,
so understanding the physics space models is pretty challenging too.
Speaker 3 (24:55):
Once again, enter IBM IBM created a new geospace to
help us better understand our oceans. Hartree and IBM, along
with the Plymouth Marine Laboratory, the UK Science and Technology
Facilities Council, and the University of Exeter have all partnered
to focus the model's power on the waters around the
(25:16):
United Kingdom, which ultimately will help the UK's blue economy.
Speaker 8 (25:21):
You get these major blooms in algae, so the ocean
goes green and you might see it in lakes as well. Now,
if you are shell fishing and that's what you're harvesting,
you can't harvest cockles muscles to be very colloquial, when
you have algae blooms because they're poisonous. So there are
(25:44):
certain times the year where you can harvest. In a
certain times of year, you can't if you keep having
the algal blooms. Just to put it on an economic terms,
that's a problem. So if we look at it that way,
that's an issue. We really do need to try and
understand where these algal blooms will happen, when they will happen,
(26:06):
and how to limit them, because obviously, if you're shell
fishing as your livelihood, that's going to really impact you.
Speaker 3 (26:14):
Kate told me that understanding these algal blooms, how they form,
why they form, and how they move would allow people
to better manage them.
Speaker 8 (26:24):
What is it you're putting in the water. Are you
putting fertilizers in the water in the near shore environment
that is causing those algal blooms? Is it because we
are heating up the oceans and particularly our near shore environments.
That is causing that. I don't know. I'm not a specialist,
but that's what you're trying to figure out. Is there
(26:47):
something we are doing that is creating those environments that
is causing those algal blooms or is it natural? And
natural is always a difficult one because I would say
we live in the very managed environment, and particularly in
the UK, very few landscapes are natural. Most of it
is managed in some way. Are we managing it in
(27:09):
an appropriate way? Is there changes in how we behave
that could make things better?
Speaker 3 (27:14):
Not that I needed more examples to sell me and
how useful the Prispian models are, but Kate gave me
a few more use cases that reinforced just how exciting
foundation models are for our oceans.
Speaker 8 (27:27):
These big brown seaweeds can really help with carbon sequestration.
Imagine if we could improve the environment enough so that
we could have more of that, so that we could
SEQUENTI more carbon. The other thing is wind power. In
the UK, we have a lot of offshore wind farms
and we're doing really well with our renewable energy resources.
(27:49):
So where do we put that and how does that
impact sand movements? So these sandbars and things aren't static,
they move, So understanding that is really important for where
you're going to put your suboceanic infrastructure. So you've got
cables going across the oceans. If we're going to use
our oceans more, we need to understand what that environmental
(28:13):
impact is going to be long term.
Speaker 3 (28:15):
The Ocean Model launched at the end of September twenty
twenty five. The research is only beginning. When I sat
down with Kevin Murphy at NASA, I wanted to understand
where all of this impressive work was going. And one
of the signature aspects of this work is that it's
(28:38):
not just for IBM and NASA researchers. Anyone can use
these models.
Speaker 6 (28:44):
So before, if you were a researcher, or let's say
you were a farmer or maybe a technology informed person
that was interested in something like this, you would have
to learn about how to do remote sensing, how to
calibrate the imagery, how to stitch it together, because you
know they come in kind of postage stamps that you
have to squash together, and then you'd have to learn
(29:06):
about the algorithms necessary to do all the processing right,
So a lot of work, and then you could actually
do the mapping that you were interested in. Today. What
you can do is you can go to hugging face,
which is where this model exists in the open using
kind of our open science principles, and you can apply
it to future or historical observations without having all of
(29:30):
that background information.
Speaker 3 (29:32):
And with the partnership between NASA and IBM, these foundation
models are multiplying. The new version of Prithvi I mentioned
launched in September twenty twenty four. Then in August twenty
twenty five, NASA and IBM launched another foundation model called Syria,
based on data from the Sun. Soria can help predict
solar flares which can disrupt communications and increase radiation for
(29:56):
high altitude flights. And then there's the Ocean model I
talk about with Kate Royce. So what does the future
look like for all these foundation models built from NASA data?
If I wanted to look five or ten years out
to understand erosion patterns in a coastal town, you could
give me.
Speaker 6 (30:14):
Eventually, I think we'll get there. Yeah, you know, we've
really only been doing this for the past few years.
There is a lot of I think capabilities to still
discover and uncover with how we use these models for
like especially long term predictions. Like you're talking about.
Speaker 3 (30:34):
What do you think you can't do and that you
really love to do. What's the kind of like great
white whale problem.
Speaker 6 (30:40):
We can't do this today, but I'd like to be
able to do it in the future, which is really
the linking of the models together. Right. So right now
we have these isolated areas where you know, we have
the harmonized Lansat Sentinel or gspatial model. We have the
weather model, which can look at short terms. We're building
(31:01):
out the heliophysics model to look at the sun dynamics.
But they're probably going to have to be additional models
built so that we can understand how they interact with
one another, right, And that is you know, kind of
towards a digital twin of kind of the Solar system
or Earth systems, which which I think is a big
(31:23):
Harry problem, but if we understand it, we might be
able to address some of the questions that you just
asked about prediction.
Speaker 3 (31:29):
So if you linked all of those models together, basically
what you're saying is, can I you say a digital twin.
You're essentially replicating holistically how our world works. And do
you think that is achievable.
Speaker 6 (31:45):
I don't think it's immediately achievable, but based on kind
of the progress that we've seen in the last three
or four years, I think it's more achievable today than
it was then.
Speaker 3 (31:55):
You think you'll see it in your Yeah, and I've
got a couple of years last. Smart Talks with IBM
(32:17):
is produced by Matt Ramano, Amy Gains McQuaid, Trina Menino,
and Jay Harper. Were edited by Lacy Roberts. Engineering by
Nina Bird Lawrence, mastering by Sarah Buguer, music by Gramoscope,
Strategy by Tatiana Lieberman, Cassidy Meyer and Sophia Derlin. Special
thanks to the team at NASA's Science Mission Directorate. Smart
(32:42):
Talks with IBM is a production of Pushkin Industries and
Ruby Studio at iHeartMedia. To find more Pushkin podcasts, listen
on the iHeartRadio app, Apple Podcasts, or wherever you listen
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(33:04):
IBM's positions, strategies or opinions. Since we recorded this episode,
(33:25):
IBM and NASA released Syria their solar weather model. In
early testing, it showed us sixteen percent improvement in solar
flare prediction accuracy. This is the kind of improvement that
helps protect our satellites, our power grids, and our GPS
systems from the Sun's unpredictable nature. And the next step
(33:46):
in this partnership another model coming in twenty twenty six.
Looking beyond the Earth and the Sun, the universe of
possibilities just keeps expanding.
Hey everyone, it's Robert and Joe here. Today we've got
something a little bit different to share with you. It's
a new season of the Smart Talks with IBM podcast series.
Speaker 2 (00:09):
This season on Smart Talks with IBM, Malcolm Gladwell is back,
and this time he's taking the show on the road.
Malcolm is stepping outside the studio to explore how IBM
clients are using artificial intelligence to solve real world challenges
and transform the way they do business.
Speaker 1 (00:25):
From accelerating scientific breakthroughs to reimagining education. It's a fresh
look at innovation in action, where big ideas meet cutting
edge solutions.
Speaker 2 (00:34):
You'll hear from industry leaders, creative thinkers, and of course
Malcolm Gladwell himself as he guides you through each story.
Speaker 1 (00:41):
New episodes of Smart Talks with IBM drop every month
on the iHeartRadio app, Apple Podcasts, or wherever you get
your podcasts. Learn more at IBM dot com slash smart Talks.
This is a paid advertisement from IBM.
Speaker 3 (00:57):
If I were to go back, I don't know thirty
years in Kenya. What's the difference between then and now
in terms of tree cover. I'm talking to Philip thego
Special Technology envoid.
Speaker 4 (01:10):
To the Kenyan president, let's speak as if you think
about we aren elevent trol posts and previously we were
more than twenty percent. So we are cutting trees more
than we're planting them.
Speaker 3 (01:21):
In thirty years, Kenya last half its tree cover half.
And here's why that matters. Kenya is a mountainous country.
Dotted throughout the highlands are dozens of what canyons call
water towers natural reservoirs, densely forested areas capable of absorbing
the enormous amount of water that falls on the country
during the rainy seasons. The tree roots and undergrowth secure
(01:46):
and capture moisture, then slowly release it into the rivers
that flow down into the country's low lying coastal areas.
But in recent years the water towers have depleted, settlements
have encroached on them, trees have been chopped down, thousands
of acres cleared, the natural reservoirs ceased to hold nearly
(02:06):
as much water, so now Kenya is prone to extremes.
Too much water flowing down from the highlands in the
rainy season and too little water left during the dry season.
Speaker 4 (02:17):
So you have a couple of hours of water then
you have a couple of hours with no water that
it tops off to be dry by the city authority.
So that's the significance of the water towers we have
when they cannot hold water.
Speaker 3 (02:29):
Kenya desperately needed to restore its water towers by planting
as many trees as humanly possible. So in the fall
of twenty twenty three, the Kenyan government took action. It
started a national holiday, national Tree Growing Day, a day
to allow the citizens of Kenya to go out into
the forest to dominate the Kenyan countryside and plant as
(02:52):
many trees as they can, and the government decided on
a number.
Speaker 4 (02:58):
The presidents focus right around how to ensure that we
do not lose more forests was in this very ambitious
campaign around fifteen billion trees.
Speaker 3 (03:08):
That's right, fifteen billion with a B.
Speaker 4 (03:12):
So imagine that number will tell you the ambition, not
as he tells you the deficit. It has to be
fifteen billion in the next eight years.
Speaker 3 (03:20):
Fifteen billion trees over eight years averages out to more
than five million trees per day. That's a lot of trees.
But with such a massive goal, how can you track
your progress? How do you know where to plant those
trees so they'll have the most impact. How do you
monitor where older trees are still being cut down? Well,
(03:40):
the answer to those questions came from IBM and a
little space agency called NASA. That's right, folks, Smart Talks
is going to space. My name is Malcolm Glawell. You're
listening to the latest episode of Smart Talks with IBM,
where we offer our list a glimpse behind the curtain
(04:01):
of the world of technology. In this season, IBM has
gone inside elementary school classrooms, toured formulation labs at Loreel,
and spoken with the fan development team at Scuderia Ferrari HP.
In this episode, how IBM is partnering with NASA to
build geospatial models using data from satellites to better understand
(04:24):
our Earth and Solar system.
Speaker 5 (04:30):
Five four three two one zero all engine running liptoff.
We have a liptoff thirty two minutes past the hour
liftoff on Apollo eleven.
Speaker 3 (04:44):
IBM has worked on space related projects since before I
was even born.
Speaker 2 (04:49):
Im all for man.
Speaker 3 (04:52):
A team of four thousand IBM engineers helped create the
Saturn five rocket that took Neil Armstrong to the Moon.
Speaker 5 (05:00):
Buy up plate.
Speaker 3 (05:02):
And when I think of NASA. I tend to picture
the moon landing, or the team of people back in
Houston guiding the Apollo mission, or the Hubble telescope, or
astronauts aboard the International Space Station. What I didn't think
about until now are NASA's geographers.
Speaker 6 (05:20):
In order to go places, you need at map things.
Speaker 3 (05:23):
This is Kevin Murphy, chief Science Data Officer at NASA's
Science Mission Directorate.
Speaker 6 (05:29):
But I think that there's an assumption that NASAs all
about rockets and astronauts, and certainly that's a really large
part and important part of NASA.
Speaker 3 (05:38):
NASA sends people to space and looks out of the stars,
but NASA also looks down at the Earth. The agency
has about one hundred and fifty satellites that use radar, lightar, landset, Aquaterra, cloudset, AURA,
low Earth orbit, Medium Earth orbit, geostationary orbit, on and on.
(05:58):
In one sense, NASA makes hardware to build rockets and
spacecraft and all those satellites that circle the Earth. But
fundamentally NASA also collects data. It's scientists and engineers people
like Kevin want to make the best use possible of
all the information gathered by all those many dozens of instruments.
Speaker 6 (06:20):
Right now, we gather around twenty five petabytes of new
observational data per year. In the next couple months, we're
about to launch a high resolution global radar. When that launches,
will double how much we collect every year to about
fifty petabytes of information.
Speaker 3 (06:39):
Actually, since we recorded this conversation, NASA launched that global radar,
what they call NYSAR. So NASA is already generating new
data at the rate of fifty petabytes each year. To
put that in perspective, a single petabyte could hold about
five hundred billion pages of standard printed text. You know,
(07:00):
can anyone sort of apply to use this data.
Speaker 6 (07:02):
Is they don't even have to apply. It's free and
open data. It advances how we understand what we do
on Earth and how we see ourselves within the universe.
People can take it for so many different downstream applications.
So you can go to our websites today, you can
search through our tools and you can download information from
(07:23):
the Mars rovers, you can download information from the Lunar
Reconnaissance Orbiter or any of the Earth Science Data satellites.
Speaker 3 (07:29):
And give me an example of a really cool application,
a really cool use that someone I don't know in
academic or whatever has used your data for it is there?
Speaker 6 (07:39):
It okay. So one of the really kind of cool
but unexpected observations that we had is that we launched
a pair of satellites in their early two thousands called Grace,
and these satellites orbit the Earth and they can measure
very precisely the distance that they're away from each other
as they orbit the Earth, and as you go into gravity,
(08:00):
you can actually see a satellite accelerate and the other
one accelerate after it, right, And using that information, we
were trying to map kind of the gravity fields of Earth.
What what they found is that they can actually map
below kind of the mass of Earth to where water
storage is. For instance, so aquifers, right, so you can
(08:20):
monitor through gravity how much water is being depleted or
added to an aquifer or the density of glaciers.
Speaker 3 (08:29):
So, just to back up for a moment, the presence
and density of water deposits below the Earth's surface have
an effect on gravitational fields that are being measured in space.
Speaker 6 (08:42):
Correct.
Speaker 3 (08:43):
Yeah, And so does that tell you presume you learn
things like where there's an aquifer where you didn't think
there was an aquifer.
Speaker 6 (08:50):
Or if it's being depleted faster.
Speaker 2 (08:53):
Yeah.
Speaker 3 (08:53):
Yeah, So who's using that kind of data?
Speaker 6 (08:56):
All sorts of different organizations, whether they're you know, NG
or government agencies or people that are planning a large
agricultural product.
Speaker 3 (09:05):
How did you Was that an intentional decisisse?
Speaker 6 (09:07):
It wasn't. It was accidental.
Speaker 3 (09:09):
It was accidental. NASA has assembled a historically unprecedented mountain
of data about the physical world, free and open to anyone,
and the possibilities for how that information can be used
are so vast that even NASA is still uncovering them.
When I was a kid, I loved legos. I had
(09:31):
a huge bin full of them. At the time, legos
were really just colored bricks of various sizes. They weren't
as complicated as they are today. And what I realized
even then was that there were more possibilities in a
box of legos than I could ever imagine on my own.
I played with my brother and he would show me
something that hadn't occurred to me, And I go to
my friend Bruce's and see that he was off on
(09:54):
some legos tangent that I'd never even thought of, like
a cool bridge or a castle or a truck. I
use legos one way. Bruce used his legos in a
completely different way. NASA's data treasure trove is like a
very very big box of Legos. And here's the question.
With so much data, containing so many possible connections, could IBM,
(10:19):
and specifically IBM's artificial intelligence help NASA scientists uncover patterns
and connect systems in a way they've never done before.
Speaker 7 (10:30):
Everything started with a question, right.
Speaker 3 (10:33):
I'm talking to one Bernabe Moreno, director of IBM Research
in Europe.
Speaker 7 (10:38):
As we advance AI, we have new tools to understand
the around this, understand the world, understand the language, and
understand our planets. And the question that we were asking
ourselves was all these new advances that we see in language.
It was a post GPT moment. Could we apply the
same idea and the same architecture and technology to a
(11:00):
dour planets?
Speaker 3 (11:01):
The advent of AI created a new opportunity. What if
all of NASA's mountain of data could be organized, analyzed,
understood by artificial intelligence. The original idea was to create
a geospatial foundation model for the Earth and from there
create additional specialized models for other scientific priorities of NASA,
(11:23):
and finally quit an AI system that can understand all
the data across those specialized models in order to uncover
hidden insights and relationships. Together, these models could unlock an
infinite number of potential applications. I asked Kevin Murphy at
NASA about the beginning of these Earth models.
Speaker 6 (11:45):
Has some colleagues, and we were investigating a number of
different avenues of using AI with our data, but also
kind of the management and stewardship of the data, so
not only like the observations, but how we make it
available to people, make it discoverable. And they said, hey,
we see these transform architectures. We think that they can
(12:05):
be applicable to some of the sequential observations that we make.
We'd really like to work with IBM on that. And
I was like, I'm really skeptical, but because I hadn't
seen those types of tools really produce results that were
commensurate with the amount of effort you put into them, right,
(12:26):
So we were getting some really good results and deep
learning approaches, but they took a lot of effort.
Speaker 3 (12:32):
But Kevin came around quickly.
Speaker 6 (12:35):
When we typically develop a new data product or an algorithm,
it takes anywhere from you know, twelve months, eighteen months,
twenty four months to go from data and hypothesis to
results which is validated. We were able to get approximately
the same precision for some well known types of benchmarks
(12:58):
with and I think it was about four four months. Oh,
instead of starting the work.
Speaker 3 (13:02):
Yeah, yeah, so was it happened faster than you thought,
much faster. In twenty twenty three, IBM and NASA launched
a foundation model trained on NASA's harmonized landset sentinel to
satellite data across the continental United States. They named the
model Prithvi, the Sanskrit word for Earth. The first version
(13:24):
of Prithvy used only Earth observation images and just that
was enough to totally change Kevin's idea of what foundation
models could do. But they didn't stop there. IBM and
NASA were encouraged at how well Prithvy worked for Earth
observation tasks, so they decided to create a more complex
(13:45):
version of Prithvy that could understand whether and climate data.
They hoped this new version of Prithvi would allow researchers
to answer new questions about the Earth, from short term
weather forecasting to longer term climate effects. Imagine you have
a map of all the different temperatures, pressures, clouds, rainfall,
(14:05):
and more from around the globe. With this map, IBM
and NASA could implement advanced tasks. They could track the
formation of El Nino or predict how the path of
a hurricane would change if the ocean temperature went up
by half a degree.
Speaker 7 (14:21):
I would always remember this moment was when we created
the Weather and Climate Foundational Model. The senior methodologist of NASA,
it was like, I cannot believe that it has changed
the way I think about the AI And ever since
he's been kind of preaching with this A samples.
Speaker 3 (14:38):
One and his team then took the model and decided
to test it, really tested. They took away ninety nine
percent of the data points and ran the experiment again.
What they were trying to figure out is if the
model had learned enough about the basic principles of the Earth,
the underlying physics of the way the planet works, to
fill in the blanks on its own with just one
(15:01):
percent of the original data, would it still be accurate
in its predictions. What happened The model crushed it so
it was able to extrapolate on the basis of one
percent of the data what the entire picture looked like yes,
because pre learned everything right, Yeah, it learned the kind
(15:23):
of principles of exactly. Yeah. Oh wow, that's very very impressive.
So at that moment when you realize you could do that,
and just curious about your emotional I mean, did you
jump up and down? What did you do that?
Speaker 7 (15:37):
So he's like, wow, it was a very emotional meeting
because you know, having this person say now I'm convinced right, Yeah,
it was kind of a quite a special moment. These
moments make your life as a researcher.
Speaker 3 (15:52):
Ibm And as a launch prith Fee for Weather and
Climate in twenty twenty four, and while ibm And as
a scientist could use Privy to run interesting experiments, they
were even more excited about how Prithy could help people
in the real world. So let's go back to Kenya
(16:14):
Ambassador Philip Diego and the country's great tree planting project.
Speaker 4 (16:19):
So on those initial months there was a massive effort,
including a couple of national holidays.
Speaker 3 (16:25):
For tree planting.
Speaker 4 (16:27):
Yes, where the entire cabinet was sent.
Speaker 3 (16:31):
Ah, did you plant trees as I did?
Speaker 4 (16:33):
Oh my god, I said, The entire cabinet plus someone
we have to be seen.
Speaker 3 (16:37):
Are you good at the planet two weeks ago?
Speaker 4 (16:39):
Well, it's very easy to go hole put a tree
in the ground show.
Speaker 3 (16:42):
Well wow, what planting a tree is easy? But remember
it has to happen fifteen billion times. IBM research has
been operating in Nairobi since twenty thirteen, and what Kenya wanted,
at least in the beginning was straightforward. Fee model that
IBM and NASA built could be used to essentially make
(17:04):
the world's greatest map, and Kenya, with IBM's help, could
use that model to make the world's greatest map of Kenya.
The first step was to lay a grid across the
topography of the country, break the forest into manageable bite
sized pieces, each of which could be analyzed separately.
Speaker 4 (17:23):
So, because our forest is massive when you look at
it in terms of green hite, but only lay it,
you're able to break it into pieces, like into boxes.
And for us that was important because then it's easy
to tackle it when it's in a greed system than
just as a massive forest. So that was also what
the model was able to do.
Speaker 3 (17:43):
Then the model painstakingly sorted through each of those boxes
and look for what Philip calls hotspots, so.
Speaker 4 (17:50):
You can see, for example, very quickly, which other areas
are being eroded very fast, and that you need to
quickly protect. Yeh, because you sometimes and that's where you
want to target, right, I mean it's not possible to
do everything at the same time.
Speaker 3 (18:02):
Do you have a definition of a hotspot? And how
many hotspots are there according to that definition? H, there
are a lot.
Speaker 4 (18:08):
So we have more than forty water towers, and I'll
tell you all of them have hotspots. And the hot
spots in my definition areas that are being degraded faster
and in a very unusual way. Right. You can literally
see how human activity is seriously degrading that particular area
that if you do not have a direct intervention, we'll
lose the entire forest. So that's the hot spot for us,
(18:32):
because you think about cutting one hundred trees a day
and cutting a million trees a day, So that's a
hot spot. You want to look at places where there's
just unusually high activity of deforestation.
Speaker 3 (18:43):
In a hot spot. The size of each box in
the grid was ten by ten meters, about half a
tennis court. That's how closely they were examining the forest,
so very crudely. The model ingests all of this satellite
data and it helps you answer some very specific questions
like where should we prioritize our tree planning efforts, which
(19:07):
areas down to an extraordinary level of specificity, are eroding
most quickly. You know, all those kinds of practical questions
about how to direct your strategy.
Speaker 4 (19:17):
So if you think about a smart forest, right, and
that's really for us according it smart fencing, smart forests,
everything that's smart because of AI. If you think about
your usual what you can see with your eyes and
then the satellite layer which just zooms in and you
see green. So what the model has been able to
do is to create a smart layer, right, and then
(19:37):
that smart layer you can actually see many things, from analytics,
to the greeds, to a dashboard, one a lot. So
able to layer to those blocks, you can quantify degradation
by blocks. You can match integrations, you can match reforestation.
Speaker 3 (19:53):
I asked Philip to imagine what it would have been
like to attempt the tree planting project in an era
before AI. His answer was, plant fifteen billion trees, restore
the water towers. Impossible. With Prithvie on Kenya's side, though,
it's really happening. What should be clear by now is
(20:13):
how versatile Prithvie can be. Want to know how to
combat deforestation, Prithvy can model that. Want to know when
the best time in the year to plant your crops is,
prithvy can help predict that too. Last year, six months
after IBM started helping Kenya with reforestation, Kenya needed Prithvy's
help on something else. And it was an emergency.
Speaker 4 (20:35):
So something was happening in the world that we sort
of had these flats that we didn't expect.
Speaker 3 (20:41):
In the spring of twenty twenty four, Kenya was hit
with thunderstorms and torrential rain, days and days of it.
Speaker 4 (20:49):
And so I got a call from the Red Cross,
the one of my friends, and they're like, Ambassador, we
need a little bit of help on how we deal
with response because what we see is unusual, right, because
normally you would only have one area. All of a sudden,
we had an entire country flooding. In April, we had
about three eight hundred kilometers square kind of total land flooded,
(21:12):
which is unusual for Kenyon. And so when I got
this call, we were like, Okay, there's someone could do
with IBM. We only did one function for the trees.
It was actually a climate model, and we said, can
we use this to help us better respond to floods
And so that was how we started having this discussion
(21:33):
with IBM in terms of repurposing the model to help
us deal with this new challenge around floods.
Speaker 3 (21:41):
Again, Prithvy is versatile. Prithe could use everything it knew
about the land, the forests and infrastructure to analyze how
and where and when floods would occur. The Kenyan government
could then use the model to help the Red Cross
organize its respet pants, show areas that needed to be
(22:02):
evacuated or safe places where the Red Cross could set
up camps. That information was invaluable.
Speaker 4 (22:11):
Historically, what has happened is that they would set up
camp based on population congregation right where people assembly is
where they set up a camp, not based on any data,
right simply because people are there, they will come there
to provide services and emergency response. What we realize is
that that model doesn't work. So what we've been able
(22:31):
to do with IBM is be able to to sort
of give Red Cause very specific locations or options where
to set up camps. So if people come here, just
tell them no, move here. That's the safe place you
really want to go. So I think for me that
was really amazing. So we're calling them were a very
funny word for it, flood assembly points. We always have
fire for assembly points, but now we can say we
(22:51):
have literally flat assembly points that are safe or citizens.
Speaker 3 (22:57):
That's fascinating. So the model has ingested this incredibly granular
picture later of the topography and weather patterns of Kenya.
It's just giving you a set of useful predictions about
how you should shape your response.
Speaker 4 (23:16):
Yes, and what we did remember is that, as I said,
it was a full multistate called capability. What IBM gave
us was a base map. We didn't have that before
and a base model. So you cannot have these layers
up on layers, up on layers to be able to
make intelligent decisions.
Speaker 3 (23:35):
Throughout my reporting on this episode, I've been really impressed
by what Prithvie can do. But it doesn't stop at
floods and reforestation. Prithvie has also been used to look
at wildfires and floods in the UK, and Kevin told
me that researchers in Africa have even used Prithvy to
identify locust breeding grounds, which could help them prevent swarms
(23:57):
that destroy crops. All these are issues on land.
Speaker 8 (24:02):
I mean, I always say to people, seventy percent of
our land mask is ocean.
Speaker 3 (24:07):
Kate Rice is the director of the Heart Tree Center,
which focuses on adopting AI into UK's public and private sectors,
and one of those sectors is the blue economy oceans, fish, shellfish.
But oceans are huge, and getting data formotions is difficult.
Speaker 8 (24:26):
So you're dealing with something where there's not a lot
of people walking around collecting data. So the real difficulty
is understanding that collecting enough data to make anything makes sense.
And oceans are very complex in terms of their interaction
(24:46):
with our climate and how they interact with the climate,
so understanding the physics space models is pretty challenging too.
Speaker 3 (24:55):
Once again, enter IBM IBM created a new geospace to
help us better understand our oceans. Hartree and IBM, along
with the Plymouth Marine Laboratory, the UK Science and Technology
Facilities Council, and the University of Exeter have all partnered
to focus the model's power on the waters around the
(25:16):
United Kingdom, which ultimately will help the UK's blue economy.
Speaker 8 (25:21):
You get these major blooms in algae, so the ocean
goes green and you might see it in lakes as well. Now,
if you are shell fishing and that's what you're harvesting,
you can't harvest cockles muscles to be very colloquial, when
you have algae blooms because they're poisonous. So there are
(25:44):
certain times the year where you can harvest. In a
certain times of year, you can't if you keep having
the algal blooms. Just to put it on an economic terms,
that's a problem. So if we look at it that way,
that's an issue. We really do need to try and
understand where these algal blooms will happen, when they will happen,
(26:06):
and how to limit them, because obviously, if you're shell
fishing as your livelihood, that's going to really impact you.
Speaker 3 (26:14):
Kate told me that understanding these algal blooms, how they form,
why they form, and how they move would allow people
to better manage them.
Speaker 8 (26:24):
What is it you're putting in the water. Are you
putting fertilizers in the water in the near shore environment
that is causing those algal blooms? Is it because we
are heating up the oceans and particularly our near shore environments.
That is causing that. I don't know. I'm not a specialist,
but that's what you're trying to figure out. Is there
(26:47):
something we are doing that is creating those environments that
is causing those algal blooms or is it natural? And
natural is always a difficult one because I would say
we live in the very managed environment, and particularly in
the UK, very few landscapes are natural. Most of it
is managed in some way. Are we managing it in
(27:09):
an appropriate way? Is there changes in how we behave
that could make things better?
Speaker 3 (27:14):
Not that I needed more examples to sell me and
how useful the Prispian models are, but Kate gave me
a few more use cases that reinforced just how exciting
foundation models are for our oceans.
Speaker 8 (27:27):
These big brown seaweeds can really help with carbon sequestration.
Imagine if we could improve the environment enough so that
we could have more of that, so that we could
SEQUENTI more carbon. The other thing is wind power. In
the UK, we have a lot of offshore wind farms
and we're doing really well with our renewable energy resources.
(27:49):
So where do we put that and how does that
impact sand movements? So these sandbars and things aren't static,
they move, So understanding that is really important for where
you're going to put your suboceanic infrastructure. So you've got
cables going across the oceans. If we're going to use
our oceans more, we need to understand what that environmental
(28:13):
impact is going to be long term.
Speaker 3 (28:15):
The Ocean Model launched at the end of September twenty
twenty five. The research is only beginning. When I sat
down with Kevin Murphy at NASA, I wanted to understand
where all of this impressive work was going. And one
of the signature aspects of this work is that it's
(28:38):
not just for IBM and NASA researchers. Anyone can use
these models.
Speaker 6 (28:44):
So before, if you were a researcher, or let's say
you were a farmer or maybe a technology informed person
that was interested in something like this, you would have
to learn about how to do remote sensing, how to
calibrate the imagery, how to stitch it together, because you
know they come in kind of postage stamps that you
have to squash together, and then you'd have to learn
(29:06):
about the algorithms necessary to do all the processing right,
So a lot of work, and then you could actually
do the mapping that you were interested in. Today. What
you can do is you can go to hugging face,
which is where this model exists in the open using
kind of our open science principles, and you can apply
it to future or historical observations without having all of
(29:30):
that background information.
Speaker 3 (29:32):
And with the partnership between NASA and IBM, these foundation
models are multiplying. The new version of Prithvi I mentioned
launched in September twenty twenty four. Then in August twenty
twenty five, NASA and IBM launched another foundation model called Syria,
based on data from the Sun. Soria can help predict
solar flares which can disrupt communications and increase radiation for
(29:56):
high altitude flights. And then there's the Ocean model I
talk about with Kate Royce. So what does the future
look like for all these foundation models built from NASA data?
If I wanted to look five or ten years out
to understand erosion patterns in a coastal town, you could
give me.
Speaker 6 (30:14):
Eventually, I think we'll get there. Yeah, you know, we've
really only been doing this for the past few years.
There is a lot of I think capabilities to still
discover and uncover with how we use these models for
like especially long term predictions. Like you're talking about.
Speaker 3 (30:34):
What do you think you can't do and that you
really love to do. What's the kind of like great
white whale problem.
Speaker 6 (30:40):
We can't do this today, but I'd like to be
able to do it in the future, which is really
the linking of the models together. Right. So right now
we have these isolated areas where you know, we have
the harmonized Lansat Sentinel or gspatial model. We have the
weather model, which can look at short terms. We're building
(31:01):
out the heliophysics model to look at the sun dynamics.
But they're probably going to have to be additional models
built so that we can understand how they interact with
one another, right, And that is you know, kind of
towards a digital twin of kind of the Solar system
or Earth systems, which which I think is a big
(31:23):
Harry problem, but if we understand it, we might be
able to address some of the questions that you just
asked about prediction.
Speaker 3 (31:29):
So if you linked all of those models together, basically
what you're saying is, can I you say a digital twin.
You're essentially replicating holistically how our world works. And do
you think that is achievable.
Speaker 6 (31:45):
I don't think it's immediately achievable, but based on kind
of the progress that we've seen in the last three
or four years, I think it's more achievable today than
it was then.
Speaker 3 (31:55):
You think you'll see it in your Yeah, and I've
got a couple of years last. Smart Talks with IBM
(32:17):
is produced by Matt Ramano, Amy Gains McQuaid, Trina Menino,
and Jay Harper. Were edited by Lacy Roberts. Engineering by
Nina Bird Lawrence, mastering by Sarah Buguer, music by Gramoscope,
Strategy by Tatiana Lieberman, Cassidy Meyer and Sophia Derlin. Special
thanks to the team at NASA's Science Mission Directorate. Smart
(32:42):
Talks with IBM is a production of Pushkin Industries and
Ruby Studio at iHeartMedia. To find more Pushkin podcasts, listen
on the iHeartRadio app, Apple Podcasts, or wherever you listen
to podcasts. I'm Malcolm Glawell. This is a paid advertisement
for my The conversations on this podcast don't necessarily represent
(33:04):
IBM's positions, strategies or opinions. Since we recorded this episode,
(33:25):
IBM and NASA released Syria their solar weather model. In
early testing, it showed us sixteen percent improvement in solar
flare prediction accuracy. This is the kind of improvement that
helps protect our satellites, our power grids, and our GPS
systems from the Sun's unpredictable nature. And the next step
(33:46):
in this partnership another model coming in twenty twenty six.
Looking beyond the Earth and the Sun, the universe of
possibilities just keeps expanding.
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