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