NASA and AI: Decoding Our Universe

Welcome to Tech Stuff, a production from iHeartRadio. This season on Smart Talks with IBM, Malcolm Glabwell 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, from accelerating scientific breakthroughs to reimagining education. It's a fresh look at innovation in action,

If I were to go back, I don't know, thirty years in Kenya, what's the difference between then and now? Intern of tree Cover, I'm talking to Philip, thego Special Technology envoid to the Kenyan President.

Let's speak as if you think about we arena elevent trol perscs and previously we were more than twenty percent. So we are cutting trees more than we're planting them.

In thirty years, Kenya lost 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 and capture moisture, then slowly release it into the rivers that flow down into the country's low lying coastal areas.

So you have a couple of hours of water, then you have a couple of hours with no water that taps off 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 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 many trees as they can, and the government decided on a number.

The presidents really focus right around how to ensure that we don't lose more for us was in this very ambitious campaign around fifteen billion trees.

That's right, fifteen billion with a bee.

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.

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, the answer to those questions came from IBM and a little space agency called NASA. That's right, folks, Smart Talks

Five four three two one zero all engine running liptoff. We have a liptoff thirty two minutes past the hour liftoff on Apollo eleven.

IBM has worked on space related projects since before I was even born. Im all for Man, A team of four thousand IBM engineers helped create the Saturn five rocket that took Neil Armstrong to the Moon. Arm 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 at map things.

This is Kevin Murphy, chief Science Data Officer at NASA's Science Mission Directorate.

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 in NASA.

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, aquatera, cloudset AURA, 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 delights that circle

Right now, we gather around twenty five 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 launches, will double how much we collect every year to about fifty petabytes of information.

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. Now can anyone sort of apply to use this data 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 the Mars rovers, 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 in academic or whatever has used your data for. It is there?

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

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 presuming you learn things like where there's an aquifer where you didn't think there was an aquifer.

Or if it's being 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 desis?

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 colored bricks of various sizes. They weren't

Everything started with a question, right.

I'm talking to one Bernabe Moreno, director of IBM Research in Europe.

As we advance AI, we have new tools to understand this, 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 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 quit an AI system that can understand all the data across those specialized models in order to uncover

Has some colleagues, and we were investigating in 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 architecture. We think that they can be applicable to some of the sequential observations that we make.

But Kevin came around quickly.

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 with and I think it was about four months of starting the work.

Yeah, yeah, so 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 of Prithvi used only Earth observation images and just that was enough to totally change Kevin's idea of what foundation models could do.

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 sample.

One and his team then took the model and decided to test it, really tested it. 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 percent of the original data, would it still be accurate

Yes, because pre learned everything right.

Yeah, it learned the kind 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?

So it'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.

Ibm And as a launch prithe for Weather and Climate in twenty twenty four and while ibm And as a scientist could use Prithvy 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 Ambassador Philip Diego 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.

Ah, did you plant trees.

As I did?

Oh my god, I said, the entire cabinet plus someone we have to be seen.

Are you good at the planet? Two weeks ago?

Well, it's very easy to go hole put a tree in the ground.

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 ken you wanted, at least in the beginning was straightforward. The prith Fee model that IBM and NASA built could be used to essentially make the world old's greatest map, and Kenya, with IBM's help, could use that model to make the

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.

Then the model painstakingly sorted through each of those boxes and look 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. Yeah, 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 and how many hotspots are there according to that definition?

Oh, there are a lot.

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 hotspot for us, because you think about cutting one hundred trees a day and cutting a million trees a day, So that's a

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 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.

So if you think about a smart forest, right, and that's really for us, we're calling 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 that smart layer you can actually see many things, from analytics, to the greeds, to a dashboard, one a lot. So

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 Prithvy on Kenya's side, though it's really happening. What should be clear by now is how versatile Prithvie can be. I want to know how to combat deforestation. Prith vy can model that I want to know when the best time in the year to plant your crops is. Prithvy can help predict that too.

So something was happening in the world that we sort of had these flats that we didn't expect.

In the spring of twenty twenty four, Kenya was hit with thunderstorms and torrential rain, days and days of it.

And so I got a call from the Red Cross then 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 are seeing is unusual, right because no man, you would only have one area. All of a sudden, we had an entire country flooding. In April, we had about three hundred kilometers square kind of total land flooded, which is unusual for Kenyon. And so when I got this call, we were like, Okay, there's someone

Again, prithvy is versatile. Prithvie 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 response, show areas that needed to be evacuated or safe place is 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 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 to do with IBM is be able to to sort of give Red Cause very specific locations or options where

That's fascinating. So the model has ingested this incredibly granular picture later of 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.

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.

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 that destroy crops. But all these are issues on land.

I mean, I always say to people. Seventy percent of our landmask is ocean.

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 from motions 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 space 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 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 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.

Certain times the year where you can harvest, and there 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. So we really do need to try and understand where these algore blooms will happen, when they will happen, and how to limit them, because obviously, if you're shell fishing 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?

Not that I needed more examples to sell me and how useful the Prithvian 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 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. So where do we put that and how does that

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 not just for IBM and NASA researchers. Anyone can use these models.

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 squashed, and then you'd have to learn about the algorithms necessary to do all the processing right, So a lot of work and then you could actually do

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 turing 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 high altitude flights. And then there's the Ocean model I

Could give me. 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 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.

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 geospatial model. We have the weather model which can look at short term predictions. We're building out the heliophysics model to look at the dynamics.

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.

Yep?

And do you think that is achievable?

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.

Do you think you'll see it in your Yeah, sure, I'm hopeful, and I've got a couple.

Of years left.

Smart Talks with IBM is produced by Matt Ramano, Amy Gains, McQuaid, Trina Menino, and Jake Harper. Were edited by Lacy Roberts. Engineering by Nina Bird Lawrence, mastering by Sarah Buguerer, music by Gramoscope, Strategy by Tatiana Lieberman, Cassidy Meyer and Sophia Derlin. Special thanks to the team at NASA's Science Mission Directorate. Smart Talks with IBM is a production of Pushkin Industries