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