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If I were to go back, I don't know, 30 years in Kenya was the difference between then and now in terms of tree cover. I'm talking to Philip Thego, special technology, and void to the canyon president. Let's speak, as if if think about, we are now 11th August, and previously we were more than 20% so we are cutting trees more than we are planting them.
In 30 years, Kenya lost half its tree cover, half, and here's where 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 and capture moisture,
then slowly release it into the rivers of 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 cease to hold nearly 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. So you have a couple of hours of water, then you have a couple of hours with no water, like a tap south to be dry by the city authority. So that's the significance of the water towers we have when they cannot hold water. Kenya desperately needed to restore its water towers by planting as many trees as humanly possible. So in the fall of 2023, 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 that dominate the Kenyan countryside and plant as many
“trees as they can. And the government decided on a number. The president's real focus, right?”
Around, how to ensure that we do not lose more forests, or in this very ambitious campaign,
around 15 billion trees. That's right. 15 billion with a bee. So imagine that number will
tell you the ambition. That also tells you the deficit. It has to be 15 billion in the next 80 years. 15 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, the answer to those questions came from IBM
and a little space agency called NASA. Let's write folks, smart talks is going to space. My name is Malcolm Globul. You're listening to the latest episode of smart talks with IBM, where we offer our listeners a glimpse behind the curtain of the world of technology. In this season, IBM has gone inside elementary school classrooms, toured formulation labs at L'Oreal, and spoken with a 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 our Earth and solar system. Five, four, three, two, one, zero, all-enchant running. Lift off, we have a lift off, 32 minutes past the hour, lift off in Apollo 11. IBM has worked on space-related projects since before I was even born.
A team of 4,000 IBM engineers helped create the Saturn V rocket that took Neil Armstrong to the moon.
“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.
In order to go places, you need a map of things.
This is Kevin Murphy, Chief Science Data Officer at NASA's Science Mission Directorate.
“But I think that there's an assumption that NASA's all about rockets and astronauts,”
and certainly that's a really large part of the important part of NASA. NASA sends people to space and looks out of the stars, but NASA also looks down at the Earth. The agency has about 150 satellites that use radar, lightar, lens-set, aquatera, cloud-set, or a low-Earth orbit, medium-Earth orbit, geostationary orbit, on and on. In one sense, NASA makes hardware.
They build rockets and spacecraft and all those satellites at circle the Earth. But fundamentally, NASA also collects data. It's scientists and engineers, people at Kevin, want to make the best use possible of all the information gathered by all those many dozens of instruments. Right now, we gather around 25 petabytes of new observational data per year.
“In the next couple of months, we're about to launch a high-resolution global radar.”
When that launch is, we'll double how much we collect every year to about 50 petabytes of information. Actually, since we recorded this conversation, NASA launched that global radar, what they call "nicear." So NASA is already generating new data at the rate of 50 petabytes each year to put that in
perspective. A single petabyte could hold about 500 billion pages of standard printed text.
Now, can anyone sort of apply to use this data? Is it-- 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 the Mars Revers. You can download information from the Lunar Reconnaissance Orbiter or any of the Earth Science data satellites. And give me an example of a really cool application, a really cool use that someone, I don't know, an academic, or whatever, has used your data for. Is there a--
OK, so one of the really kind of cool, but unexpected observations that we had is that we launched a pair of satellites in their really 2000s 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 wells, 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 would 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-- We monitor through gravity how much water is being depleted or added to an aquifer or the density of glaciers. 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. Correct.
Yeah. And so does that tell you, presumably you learned things like where there's an aquifer, where you didn't think there was an aquifer? Or if it's seeing depleted faster. Yeah. Yeah. So who's using that kind of data? All sorts of different organizations, whether they're, you know, NGOs or government agencies or people that are planning a large agricultural product. How did you was that an intentional decision? It wasn't. It was accidental. 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 a huge bin full of them. At the time, Legos were really just color bricks of various sizes. They weren't as complicated as they are today. And when I realized even then, was that they were more possibilities in a box
of Legos than I could ever imagine on my own. I'd play 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
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 and specifically IBM's
Artificial intelligence help NASA scientists uncover patterns and connect sys...
done before. Everything is started with a question, right? I'm talking to one Bernabe Morano,
“director of IBM Research in Europe. As we advance AI, we have new tools to understand.”
There's around this. I've missed on the wall on the stunt the language and understand our planet. 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 add data about our planet? 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.
And finally, create 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. As 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 and make it discoverable. And they said, hey,
we see these transform architectures. We think that they can 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 why don't you use skeptical? Because I hadn't seen those types of tools really produce results that were commensurate with the amount of effort you put into them. So we were getting some really good results in deep learning approaches, but they took a lot of effort.
But Kevin came around quickly. When we typically develop a new data product or an algorithm, it takes anywhere from, you know, 12 months, 18 months, 24 months to go from data and hypothesis to result, which is validated. We were able to get approximately the same precision for some well-known
“types of benchmarks with, and I think it was about four months instead of starting the work. Yeah,”
yeah. So it happened faster than you thought. Much faster. In 2023, IBM and NASA had launched a foundation model trained on NASA's Harmonized Landsat Sentinel II satellite data across the continental
United States. The name of the model, Prithvi, the Sanskrit word for Earth. The first version of Prithvi
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 Prithvi worked for Earth observation tasks. So they decided to create a more complex version of Prithvi that could understand weather 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, 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. I will always remember
this moment. Was when we created the weather and climate from the Sharmolov, the senior
“methodologist of NASA? I was like I kind of believed that has changed the way I think of”
all day and ever since he's been kind of breaching with these assumptions. One and his team then took the model and decided to test it. Really tested. It took away 99% 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 1% of the original data, would it still be accurate in its predictions?
What happened? The model crushed it. It was able to extrapolate on the basis of 1% of the data what the entire picture looked like. It was pre-learned to everything. It pre-learned the principles
Of the Earth.
and just curious about your emotional, did you jump up and down? What did you do? It was a very emotional meeting because having this person say, "Now I'm convinced." It was kind of quite special moment. This moment made your life as a researcher. IBM and NASA launched Prithfi for weather and climate in 2024. While IBM and NASA scientists could use Prithfi to run interesting experiments they were even more excited about how Prithfi could help people in the real world.
So let's come back to Kenya and faster to fill up the go and the country's great tree planting project. So on those initial months there was a massive effort, including a couple of national holidays for tree planting. Yes, where the entire cabinet was sent. Did you plant trees? Yes, I did. Oh my
“good. I said the entire cabinet plus someone else. We have to be seen. Are you good at that?”
I wanted to eat the go. Well, it's very easy to go home. Put our tree in the ground.
That's all well. Wow, what a planting tree is easy. But remember, it has to happen 15 billion times.
IBM research has been operating in Nairobi since 2013 and what Kenya wanted at least in the beginning was straightforward. The Prithfi model that IBM and NASA built could be used to essentially make 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 in the manageable bite-sized pieces, each of which could be analyzed separately.
So because I thought as this massive, when you look at it in terms of green light, but when you 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 grid system than just as a massive forest. So that was also what the model was able to do. Then the model paints takingly sorted through each of those boxes and looked for what Philip calls hotspots. So you can see, for example,
“very quickly, which other areas are being eroded very fast and that you need to quickly protect”
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. Do you have a definition of a hotspot in how many hotspots are there according to that definition? Oh, there are lots. So we're more than 40 water towers and I'll tell you all of them have hotspots and the hotspots in my definition are 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 them to our forest. So that's the hotspot for us because think about cutting 100 trees a day and cutting
a million trees a day. So that's our hotspot. You want to look at places where there's just
an unusually high activity of different stations. In a hotspot, the size of each box in the grid
“was 10 by 10 meters, about a 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 areas down to an extraordinary levels of specificity are eroding most quickly, you know, all those kinds of practical questions about how to direct your strategy. So if you think about a smart forest, right? And that's really for us. We're calling it smart fencing smart forest, everything
nice smart because of AI. So 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 reduce to create a smart layer, right? And in that smart layer, you can actually see many things from analytics to the grids to a dashboard when a lot. So able to layer, put those blocks, you can quantify the gradation by blocks, you can match interventions, you can match the
representation. 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 15 billion trees, restore the water
towers, impossible. With Griffey on Kenya's side, though, it's really happening. What should be clear by now is how versatile Griffey can be. Want to know how to combat deforestation? Griffey can model that. Want to know when the best time in the year to plant your crops is? Griffey can help predict that too. Last year, six months after IBM started helping Kenya with
Reforestation, Kenya needed Griffey's help on something else, and it was an e...
So something was happening in the world that we sort of had these floods that we didn't expect.
“In a spring of 2024, Kenya was hit with thunderstorms and torrential rain, days and days of it.”
And so I got a call from the Red Cross, the one of my friends and the like ambassador, we need a little bit of help on how we deal with response because what we are seeing 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 3,800 kilometers square kind of total land flooded, which is an usual Kenya. And so when I got this call, we were like, okay, there's some work we did 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 with IBM in terms of repurposing the model to help us deal with this new challenge around
“floods. Again, Prithvi is versatile. Prithvi could use everything at new about the land,”
the forest, 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 response. Show areas that needed to be evacuated or safe places with the Red Cross could set up camps. That information was invaluable. Historically, what has happened is that they would set up camp based on population congregation, right, where people assemble 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 to do with IBM is be able to sort of give Red Cross various specific locations or options where to set up camps. So if people come here, just tell them no move. Yeah, that's the safe place. You really want to go. So I think for me that was really amazing. So we are calling them
a very funny word for it. Flood assembly points. We always are fire.
Fire assembly points, but now we can say we have literally flood assembly points that are safe or citizens. That's fascinating. So the model has ingested this incredibly granular picture later of the topography and weather patterns of Kenya. This is giving you a set of
“useful predictions about how you should shape your response. Yes, and what we did remember is that”
as a city was a full multi-stacle capability, what IBM gave us was a base map. We didn't have that before. And a base model. So you can have these layers up on layers up on layers to be able to make intelligent decisions. Throughout my reporting on this episode, I've been really impressed by what Prithvi can do, but it doesn't stop at floods and reforce station. Prithvi has also been used to look at wildfires
and floods in the UK. And Kevin told me that researchers in Africa have even used Prithvi to identify locust breeding grounds, which could help them prevent swarms that destroy crops.
But all these are issues on land. I mean, I always say to people, 70% of our land
mass is ocean. Kate Royce 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 from Oceans is difficult. 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 with our climate and how they interact with the climate. So understanding the physics-based models is pretty challenging, too. Once again, enter IBM. IBM created a new geospatial model to help us better understand our oceans. Heart Tree 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 United Kingdom, which ultimately will help the UK's blue economy.
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 shellfishing and that's what you're harvesting, you can't harvest
Cockles, muscles to be very colloquial when you have algae blooms because the...
So there are certain times the year where you can harvest and are certain times the year you can't.
If you keep having the algal blooms, just to put it on any economic terms, that's a problem. So if we look at it that way, that's an issue. So we really do need to try and understand where these algal blooms will happen when they will happen and how to limit them because obviously if you're shellfishing as your livelihood, that's going to really impact you. 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. 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 something we are doing that is creating those environments that is causing those algal blooms?
Or is it natural? And natural's always a difficult one because I will say we live in a very
managed environment, particularly in the UK, very few landscapes on natural. Most of it is managed in some way. Are we managing it in a appropriate way? Is there changes in how we behave that could make things better? Not that I needed more examples to tell me on how use for the Prithvi models are, but Kate gave me a few more use cases that reinforced just how exciting foundation models are for our oceans. 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 sequencher 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. So where do we put that? And how does that impact sand movements? So these sand bars and things aren't static, they move. So understanding that is really important for where you're going to put your sub-oceanic 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 impact is going to be long term.
The ocean model launched the end of September 2025. 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 not just for IBM and NASA researchers. Anyone can use these models. So before, if you are 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 they come in kind of postage stamps that you have to”
squash together. And then you'd have to learn about the algorithms necessary to do all the processing. 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 with how having all of that background information. And with the partnership between NASA and IBM,
these Foundation models are multiplying. The new version of Prithvi I mentioned launched in September 2024. Then in August 2025, NASA and IBM launched another Foundation model called Suria, based on data from the Sun. Suria can help predict solar flares, which can disrupt communications and increase radiation for high altitude flights. And then there's the ocean model I talked 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 can't give me. Eventually, I think we'll get there. 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. What do you think you can't do and that you really love to do? What's the kind of, like,
Great, white, way I'll problem?
future, which is really the linking of the models together. So right now, we have these isolated
areas where, you know, we have the harmonized Landsat Sentinel or Prithvi G.A.A. spatial model. We have the weather model, which can look at short-term predictions. We're building out the heliophysics model to look at the sun dynamics, but there are 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 I think is a big, hairy problem, but if we understand it, we might be able to address some of the questions that you just asked about prediction. Right? So if you linked all 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. Yeah. And do you think that is achievable? I don't think it's immediately achievable. Yeah. 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.”
Do you think you'll see it in your, I sure, in your vision. Yeah. I'm sure I'm hopeful, and I've got a couple of years left.
Smart talks with IBM is produced by Matt Romano, Amy Gaines McQuade, Trinna Manino,
and Jay Carper, were edited by Lacey Roberts, engineering by Nina Bird Lawrence, mastering by Sarah Brugher, music by Grammyscope, strategy by Tatyana Lieberman,
“Cassidy Meyer, and Sophia Durlan, special thanks to the team at NASA's Science Mission”
Directorate. Smart talks with IBM is a production of push-in industries and Ruby Studio at IHeart Media. To find more push-in podcasts, listen on the IHeart Radio app, Apple podcasts, or wherever you listen to podcasts. I'm Malcolm Gladwell. This is a paid advertisement from IBM. The conversations on this podcast don't necessarily represent IBM's positions, strategies, or opinions.
Since we recorded this episode, IBM and NASA released Syria, their solar weather model. In early testing, it showed a 16% 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 in this partnership, another model coming in 2026,
looking beyond the Earth and the Sun. The universe of possibilities just keeps expanding.
