Can AI predict (and control) the weather? w/ Dion Harris and Tapio Schneider

imagine fusing together massive amounts of data taken from thousands of satellit ground and sea level sensors that have been tracking the pulse of our planet's climate and weather for decades now throw in some physics and generative Ai and what do you get a digital replica of planet Earth a real life crystal ball that shows you exactly what any Street on earth looks like at any given time you can play around you can drill in you could understand what was happening in 1972 on this specific day what was really exciting is to see people who aren't climate researchers go and you know literally zoom in on their neighborhood and see what was happening or see what the foliage looked like at that time and and understand what's really you know happening this is Dion Harris from Nvidia the company that's been creating a complete digital twin of the planet appropriately called Earth 2 so I can use Earth 2 to travel back to my current neighborhood in Austin Texas in 1972 I can go all the way down a street level and see the oak trees lining the blocks the green expanse of Zilker Park but you can also see the specifics of what the climate looked like the air quality the weather systems the monsoon patterns and how they all interact but even more importantly we can use these complex climate models to travel into the future and visualize mult multiple outcomes in extremely high resolution and do it all really really fast something that traditional climate models were nowhere close to achieving roughly about 45,000 times faster in terms of a creating actual forecast it's not just one forecast that makes it interesting it's being able to do thousands of forecasts that can give you a much better representation of what the likely outcomes come out so just to give an example we are working with the central weather administration of Taiwan and they're often hit by by typhoons that are coming you know Inland and so what's interesting about Taiwan is it's an island and so you have to be able to to understand how and when you can relocate but their relocation options are somewhat limited and so by giving them more granular understanding of where and when typhoons will will hit landfall for example they can then model and quickly build you know re-evacuation or relocation um programs based on that data so having more resolution gives them more information quicker about specifically where and when you know errors are going to be impacted so it's safe to say that AI technology is ushering in a new era of powerful tools to predict and respond to climate change but it goes beyond that geoengineering is giving us the ability to intervene and control the weather does this mean that AI could actually solve climate change I'm baval sadu and this is the tedi show where we figure out how to live and thrive in a world where AI is changing everything want a website with unmatched power speed and control try Blu host Cloud the new web hosting plan from Blu host built for WordPress creators by WordPress experts with 100% upti incredible load times and 24/7 WordPress priority support your sites will be lightning fast with global reach and with Bluehost Cloud your sites can handle surges and traffic no matter how big plus you automatically get daily backups and worldclass security get started now at bluehost.com hi I'm belaval sadu host of Ted's newest podcast the Ted AI show where I talk with the world's leading experts artists journalists to help you live and thrive in a world where AI is changing everything I'm stoked to be working with IBM our official sponsor for this episode in a recent report published by the IBM Institute of business value among those surveyed one in three companies pause an AI use case after the pilot phase and we've all been there right you get hyped about the possibilities of AI spin up a bunch of these pilot projects and then crickets those Pilots are trapped in silos your resources are exhausted and scaling feels daunting What If instead of hundreds of Pilots you had a holistic strategy that's built to scale that's what IBM can help with they have 65,000 Consultants with generative AI expertise who can help you design integrate and optimize AI Solutions learn more at ibm.com Consulting because using AI is cool but scaling AI across your business that's the next level teams with big ideas start in jira the only project management tool you need to plan and track work across any team jira even helps our team here at Ted keeping us in sync to deliver the Big Ideas our listeners love and there's a lot more that teams will love about jira it keeps cross functional tasks organized with a Project's timeline that's always really key so that we make our deadlines and cross functional teams like Ted working in one tool gives leaders the important visibility they need to make Better Business decisions get started on your big idea today in jira hey listeners I'm excited to share with you a podcast I think you'll love called the next wave this show brings you fresh takes industry insights and a trustworthy perspective on how to implement AI to grow your business Matt wolf and Nathan Lans the hosts of the show do a great job of democratizing the expertise that is often reserved for the boardrooms of the biggest corporations out there and bringing that directly to you whether you're seeking to adapt your company to the AI era or simply curious about the future this podcast will equip you with the knowledge to thrive in the forthcoming wave of Change Plus I think you're going to enjoy the episode where I joined as a guest called why Google search isn't going anywhere anytime soon check out the next wave wherever you get your podcasts now I don't know if I buy that AI is a Panacea for the climate crisis but these models are an amazing example of how a AI will be transformative in the climate space so I'm feeling very hopeful and excited this is where we ought to be putting our AI technology to work but with every technology there comes great challenges and unintended consequences and because it's Earth we're talking about we have to tread carefully the stakes here are massive Our Guest today is climate physicist topio Schneider who's going to talk us through the promises and The Perils of this new era of climate modeling so topio you've been working in climate science for a solid 30 years now but I want to go all the way back to the beginning What attracted you to the climate space in the first place as a physics students I was always interested in in the physics of everyday life um you know how does a refrigerator work and how does a transistor work that kind of physics I found absolutely fascinating and as I progressed as a physics student I realized that the physics I was doing and learning was increasingly a bit further removed from daily life and I I decided I want to work on physics at the energy of sunlight by definition that's sort of the daily life physics and that's how I got got to climate as a part of physics I must say it did play a role in my decision- making that it it matters to people it was clear already then 30 years ago that global warming is happening and will impact all of our lives and that that was a factor as well but the primary motivation was wanting to understand this incredibly complex system and what a complex system it is indeed I'm curious what is the what has been the trajectory of AI into the work that you've done right obviously statistical analysis has been a thing we've had good oldfashioned AI as well and now of course we've got this generative AI deep learning wave that's happening I'm curious what's been the trajectory of AI I started out working in a biophysics group in the first wave of neural networks in the 199 so I had some early exposure to the early days of machine learning as it was then on but what really fundamentally changed for me was when I decided to work on more smaller scale processes which are the processes that are most uncertain in climate models and wanting to use data much more extensively than they have been used before I think that's when I really started to think about how we can use data I started collaborating with one of my colleagues at Caltech Andre Stewart trying to formulate ways we can use data well for climate purposes which is quite different from many other um applications of machine learning can you talk a little bit more about these data sources and how those have been evolving what's the kind of data that's most useful in your applications so the true age of satellite observations of Earth atmosphere oceans started around 1980 and since then data volume has has kept increasing increasing exponentially right now we're receiving from NASA alone about 50 terabytes of data every day from space alone and in addition we have sensors autonomous vehicles and oceans and the like and it's truly it has become a very data Rich field the the climate sciences and ways that they were not 30 40 years ago so the way the data that we have are most commonly used right now is for weather forecasting so whenever you get a weather forecast what has happened before you get it is that weather forecasting Center has assimilated all the data we have use that as initial condition for forecast this is that really to a big jump in the quality of weather forecast that many people don't quite appreciate it's about 25 years ago with something called 4D variational data simulation it led to a big jump in the quality of weather forecasts we have what's interesting is that in the climate space so when you think about now let's run a model that's a bit like a weather forecasting model but run it for decades or centuries and say what will happen decades from now their data have been used much much less extensively primarily to evaluate models to say this model is good or bad after the fact but not directly to inform the model and that's the piece that we want to change in in this climate modeling Alliance the clema project I'm leading is use the data directly to inform the model to achieve higher quality of predictions and projections for the future okay to make this a little bit clearer basically topio is advocating for using all this observational data that's just sitting around not merely to evaluate the models you know based on the predictions that they produce but to train the models themselves so they keep in mind all that historical observational data to do better climate modeling so I'm curious toio has the impact of these recent AI developments altered your expectations for what's going to be possible with these models that are specifically focusing on you know climate predictions on these longer time Horizons yeah definitely it's may be useful to talk a little bit about how these models are being developed and what sure what a day and my life used to look like these models are essentially solving Newton's laws and the laws of thermodynamics on a large scale on a global grid the problem is that these the meeses of this grid they have a size of somewhere between 10 and 100 kilometers as typical today and there's a lot of stuff that are much smaller in scale than the mesh size of a climate model okay so what toio is saying here is important so let me break it down imagine the entire Earth as a giant puzzle climate scientists are trying to solve this puzzle but they only have very large pieces to work with each piece representing an area of 10 to 100 km they use these pieces to build a picture of how these climate systems work fitting them together and seeing how they interact the problem is there are many tiny but important details like clouds that are way smaller than a single puzzle piece it's kind of like trying to see a butterfly in a puzzle where each piece is the size of a car in other words the resolution of traditional climate models is way too fuzzy to be able to discern these tiny important details in Clarity thus creating a lot of uncertainty and as you'll hear from topio AI is able to mitigate this problem and so what you have to do is find some empirical way of representing what clouds do given what you know on larger scales on on the mesh size of the climate model and that was pretty tedious process and it has been reasonably successful but in this process and the lack of complete success of this process lie pretty much all uncertainties and climate predictions and what machine learning tools change is that for these small scales now we can learn what they do from data a lot of companies that are delving into machine learning are are experiencing this issue of they're not really grounded in the physics of the real world right I mean just to give you an orthogonal example of video generation you know person's running backwards on on a treadmill right or a glass is breaking and it's behaving like plastic CU you know this model doesn't understand sort of the cause and effect relationships and sort of the the rules of physics that govern that environment what's the typee of work that yall are doing to Anchor these these predictions that you do uh into the physics of the real world yeah that that's it's a good analogy actually I mean what you don't want is a climate model that hallucinates physics right then and for for video or even for a weather forecast if there's something wrong well it looks funny and you correct it right for a weather forecast if it's wrong to often we don't trust it and go to a different Source the additional challenge of course for climate is that you do not have an easy validation case you do not immediately know when something goes wrong if it takes years or even decades for these changes to become manifest so what do we do to deal with this problem what we do is we use the laws of physics that we know and embed machine learning tools inside the laws inside conservation law and that gives us if you wish an insurance policy that what we produce is is reasonable and even more to the point what we want to predict is something we have no data for we don't have data for the future and the future can be entirely different from what we're currently seeing this is known as the outof distribution Challenge and machine learning so by using physics as far as we can it helps with this outof distribution challenge as well that's a really good point um obviously the future is unknown but this is like as close as we can get to a real crystal ball so with advances in you know uh we're obviously uh sensing the world in Greater Fidelity in Greater frequency we've got these we've got this beautiful tool that is machine learning and you're anchoring it in the laws of of the real physics that govern the real world I'm curious what are your hopes for what these models will be able to do uh in the very near future yeah so maybe let's start from what we need and then say how we get there I think what we is is assessments of risks extreme weather extreme climate risks for the next few decades climate is changing uh we need to mitigate as much climate change as we can but some climate change is unavoidable we need to get ready for what is coming and build our infrastructure so that they're right sized and cost effective for the world whe all inhabit 10 20 years from now so you need two things you need to reduce model errors and you want to quantify uncertain these errors so that we can take them into account and planning decisions if you're an engineer building stormw management system you don't want to just know the mean rainfall or any kind of expected values but you want uncertainty ranges you want risks the biggest uncertainties and climate modeling come from these small scale processes for example clouds dominate the uncertainties and climate predictions we don't quite know what clouds will do under global warming that dominates uncertainties and how climate will change and the Machine learning tools we're talking about I think have huge potential there one concrete example that turns out to be important is how does a cloud exchange air with its surroundings through turbulence turns out to be it's one of the key controlling factors for how clouds behave in the global warming that process is hard to measure um it's even hard to simulate precisely and infer from simulations but there are machine learning tools that allow you to indirectly infer what that process looks like that you can then use in a climate model and Achieve large error reductions our colleagues at MIT have developed an ocean model similar story there small scale turbulence in an ocean you can you can similarly learn from data how to model that in the context of this large physical model and then we are still talking about perhaps order 10 kilm scale resolution you still need to get to this kilometer scale and here's another really good use of of AI tools it's for downscaling or super resolution if you wish that you fill in the details that a climate model does not produce using data we have for the present climate climate projections for a future climate to produce the localized uh climate risk assessments that we ultimately need so you you brought up really two points one is rather than getting this like macro scale picture we need to give decision makers and local authorities this micr scale picture so they can it can be a lot more actionable and then as we reduce the error the the quality of predictions the accuracy of the predictions will obviously go up will there be a feedback loop there where basically machine learning will allow us to get better at predicting the future as we collect actual data or will that window keep moving out and the future will always be unpredictable no we will get better and better at predicting what will happen at least say for the next 20 to 50 years or so I think that's a good time Horizon to focus on we should be able to provide good predictions the reason I choose this time scale is that if you think longer term um uncertainties in what we as humans do will start to dominate how much CO2 will we Emit and that's obviously not something you can model from first principles so that becomes a conditional for providing scenarios um but for the next few decades the uncertainties we have are dominated by model uncertainties and then by just the natural chaotic variability of the atmosphere and oceans so the model on certainties we should be able to reduce dramatically and then there just no way to do this locally alone the globe it's all interconnected but to get the local information then you need these downscaling tools risk assessment tools so you need to build a value chain of models that are interl and in the end you want people to use all that information so that's one thing to say I have a fantastic diffusion model for downscaling but it's quite a different story now to give this in the hands of people who need this information a small town planner so you need to build good user interfaces that make it very easy for stakeholders to access this information in their decision workflow support for the show comes from LinkedIn if you are a B2B marketer you know how noisy the ad space can be a lot of noise if your message isn't targeted to the Right audience it'll just disappear but with LinkedIn ads you can be a lot more precise you can reach the professionals who are more likely to find your ad relevant because LinkedIn has targeting capabilities to help you reach Folks by job title industry company and more you can stand out with LinkedIn ads and start converting your B2B audience into high quality leads right away I've learned so much about the vastness of LinkedIn because it does seem like everybody is on there so it's helping me find some leads or think of connections that I wouldn't have otherwise thought of without the technology it's helping me stay informed and stay educated start converting your B2B audience into highquality leads today we will even give you a $100 credit on your next campaign go to linkedin.com AUD to claim your credit that's linkedin.com audio terms and conditions apply LinkedIn the place to be to be Cana presents a work love story like no other meet productivity she's all business the canva do is done creativity is more of a freethinker whiteboard brainstorm their world's apart but sometimes Opposites Attract thanks to canva the data is in the deck and now it's an animated graph canva where productivity meets creativity now showing on computer screens everywhere love your work at canva.com I love your point about good user interfaces and it actually begs the question um how do we translate these insights that you're getting from these models Into Climate action at that local level can you paint a picture of how those local decision makers would engage with this type of data so climate action I think we need to distinguish two pieces there is mitigation so reduce emissions and there's adaptation adapt to whatever is coming I think for the mitigation part that's largely policy problem and the technological progress problem and somewh we know enough about the climate system additional information is probably not going to change the picture there very much but for the adaptation part any public private sector organization that makes any decision of of reach of a few decades will have to adapt to climate change a municipal planner will want to plant stor stor Water Management infrastructure that's one type of information they need uh information on precipitation extremes decades from now um an architect building designer will want to build a building in which it's still comfortable to be inside a few decades from now so they want to know um temperature probability of temperature extremes prolonged temperature extremes for some time at the downstream end of this value chain chain what you need is an ecosystem of tools that caters to different sectors specifically and meets people in their decision-making process um I think once you have those tools available that will Trigger action right flood protection is of course another good good example um rising sea level increased risk of storm surges with rising sea level you want to know what those risks are and then proactively um design your levies your built infrastructure accordingly I think that's that's a kind of climate action that will be triggered with better science and better information on the risks again there's the whole mitigation side which is hugely important of course where the scientific information you know it's important but in some ways we have what we need we know we need to reduce emissions exactly I mean it's almost like we know what action we have to take to prevent climate catastrophe and we have to do it now but we still have a hard time sort of visualizing the impact of that action or really even the lack of that action in a very concrete way and so climate change obviously has become really politicized or polarizing as a topic um but maybe when you have that model in front of you it might become more clearcuts so do you see a world in which these models and applications built on top of them would make these risks seem more tangible and more real to people and thus more important to address as a society I I want these risks to be tangible and and easily accessible to people having say apps for consumers where people can contextualize what's currently happening and put it in context of future risks I think it would be tremendously helpful in in raising awareness for for what is happening you mentioned polarization I have to say as far as the climate change questions are concerned it it is still clearly polarized but I would say polarization is decreasing um there's just a reality that climate is changing at this point if you're a business and you make any decision that has a reach of a few years to decades you would just lose money if you don't think about climate change and that becomes the lowest common denominator that people can agree upon that they have to worry about it I expect the polarization to further decrease on this issue simply because you can't deny that climate change is happening and now it's just a question what do we do about it it's amazing that economic incentives are aligned here right and like to your point 10 and 20 years are a time scale which impacts all of us it's not this nebulous hundreds of years from now thing that we're planning for which makes a a huge difference I I think before we get into mitigation I do want to talk very quickly about like AI is like the Panacea for everything these is like well how do we solve this problem well AI of course and then AGI will magically come along and solve everything for all of us um in your mind what can AI not help us do from a climate perspective I think the potential for modeling and the like is huge for anything involving software but it's important to keep in mind that we live in a very Material World nitrogen fertilizers are produced by primarily by the harbor bu method which relies on natural gas and has a large source of emissions steel production cement production right these are all enormous Industries putting out massive amounts of material those things are not so easy to change quickly there of course Aviation is also a good bit harder Aviation is 2% of global emissions it's it's important but if you can deal with everything else that's already pretty good but everything else is there's still a number of Fairly recal problems there so I think AI may not be the Magic Bullet for all of that but that being said it it can play a role in finding Solutions now will the next large language model find us a way of producing nitrogen fertilizers that doesn't involve using natural gas and lead to greenos gas emissions we are definitely not there yet there are folks out there that are advocating for um you know more it's like stronger intervention right and so there's been uh recent popularity in cloud seeding in fact I'm aware of uh um you know like 23-year-old kids who are doing startups that are going gungho about this in Elsa gundo and so I'm kind of curious you know you you outlined the complexity of this problem of modeling this phenomena weather phenomena at this like it's very hard to model some a system this complex but does that end up having some like ripple effect in some other part of the world like do we understand that I'm kind of curious what you think about these other forms of climate intervention the clate interventions on different levels from speculative to things that can actually be done um seating clouds to make it rain in a given place that has been tried since the Advent of weather forecasting in fact that was the original hope when John fyman and other started weather forecasting programs in the wake of World War II that it would lead to weather modification by and large these programs have not been successful the other way in which you can see clouds is is under this heading of geoengineering where you just change the cloud cover of Earth especially low clouds over the oceans that will reflect more sunlight and that will um offset some of the Waring that comes from increasing greenhouse gases um there are various ways of off setting warming from increasing greenhouse gases seaing clouds it's fairly speculative it may work what almost certainly is possible is put aerosols so little particles could be sulfate or silica particles into the Strat spere with rockets um they would reflect sunlight it would lead to cooling offsetting some warming that seems technologically feasible it's uh relatively clear that that that it would be doable to do this um you would you could offset the warming what you could not offset easily are precipitation changes that go along with global warming so the real challenge with these kinds of scenarios becomes who essenti charge of controlling climate globally you know some warming in some parts of the world may be quite desirable for agriculture in the in the Arctic say um so people may not want that warming not to happen um of course by and large warming is not desirable um has all sorts of attendant risks but then if you do geoengineering you might change uh the monsoon rainfall in India and that will have severe implications that people there obviously they wouldn't like so there is a governance problem and an ethical problem who's in church um there's the obvious moral hazard problem so suppose we find ways of of setting some warming does it give us Kurt blanch to keep emitting there might the risk if you do this for a while any kind of geoengineering is that you're offsetting the warming say you do it for 30 years you offset all the warming that would have happened in 30 years prob something at 0.6 de Centigrade or so but for one reason or another you miss the fix of injecting aerosols into the stratosphere or seeding the clouds and then you'll get all the warming that you offet over a time scale of a few months in one bang that's which is What's called the termination shock so for those of you who haven't heard the term before or read the book termination shock is kind of like putting the planet on a climate change painkiller if we suddenly stop the treatment all that warming that we've been masking hits us all at once kind of like withdrawal but for the whole planet it's the shock of terminating our Quick Fix hence the name so there is another governance ethics hazard in that as well I think it's good to have that discussion on on a society level cloud seeding as a research program I I I support because it's really one of the big uncertainties in climate projections is what will happen to clouds or how does pollution affect clouds more broadly and at the very least these programs elucidate that question whether we should do this on a global scale well my personal take is at least to be extremely cautious right we are messing with a system we don't fully understand whatever we do will have Global implications we don't have effective governments mechanisms it seems very difficult you could find a globally optimal solution if you can globally agree on the loss function to minimize um then I think that would be possible that but that's the problem right I mean we won't globally agree on the loss function the the objectives for different countries different stakeholders will be very different case in point china and India are already saber rattling over China's weather modification program India's worried it's messing with their monsoons and the rivers that they share across their borders which no surprise could have an impact on agriculture and food security well I certainly hope that we come up with a globally optimal solution and doesn't take an eccentric billionaire to just go you know have cart blanch and just start doing this um without the consent of this like planet that we inhabit together I'd love to change gears a little bit just on um the impact of AI systems right like there is on in terms of power consumption how do you think about this race towards more data more compute equals more intelligence we must do this and the climate impact that this uh uh this sort of race is Happ having yeah the climate Sciences have been big uses for supercomputer since they exist so we are big electricity users by implication and sometimes people joke we should just take the the uh attendant CO2 emissions that come with climate simulations into account straight in the simulation which it's a joke it's tongue and Sheek I mean it's not that big in a global scale right um but of course it's a serious concern I mean the the several lining here perhaps is that we're getting ever more compute for the same amount of electricity energy used so that's the good news of course training AI models right now is incredibly expensive given that it is so expensive you can't simply scale it with bigger computers indefinitely so we probably have to find more energy efficient more data efficient ways of achieving what today's big mod models achieve and I think that's the new frontier built built models that are more energy efficient smaller um and just as good and I think that's where we'll see we will see a lot of progress in the coming years that would be my expectation not to get to science fictiony here but like you know data centers are already very resource intensive we're going to keep producing more data too as a society uh that's certainly not going away how do you see sort of like this this industrialization the next evolution of industrial ization techn technology getting built into the type of climate models that you're building like are you accounting for this already like or um you know how does that how does that work when we do climate simulations we take emission scenarios as given so there are some economists social scientist scientists and the like getting together and mapping out several plausible futures for emissions how much technological progress is expected and the like and what we take from that are these scenarios and then we make climate projections conditional on those scenarios the unambiguously good news is the rapid decrease of the cost of electricity that is renewably produced the cost of solar power has decreased by almost a factor 10 and roughly over the last 15 years or so so what we need is renewably produced electricity and that's increasingly feasible and that then can power large data centers so I think it is a science fiction scenario where we would have to worry that that the data centers are kind of eating our climate future I think I'm more optimistic there that in fact they'll be renewably powered uh getting more efficient and we can sustainably compute for whatever we need in the next few decades what is your vision for the future right um you know on one hand a lot of people view AI itself as a very polarizing subject some people are extremely optimistic about it to the point where people are like we must keep accelerating accelerate or die on the other hand people are like oh no we got to pump the brakes are we deploying and proliferating this technology far too quickly how do you think about this conundrum and on the Spectrum I think my own work is it's certainly not representative but I think it is indicative of how AI can be hugely beneficial I think in in the in the long run for many for us it really takes a lot of a judgy out of the day-to-day ver we can learn functions from data that before we had to guess by hand and it was tedious it's increasingly becoming a very efficient tool so I think I'm extremely optimistic and I think in the long run this will increase productivity and I I see some of that in my daily life now and I'm very optimistic about that part um of course it does mean you know some jobs will become less important others more important they will be winners and losers just as were at the beginning of the Industrial Revolution that will happen again I think I see huge potential I think I see huge risk not using that potential and setting us back I'm less worried about say computers taking over those kind of scenarios they're they're not the large concern for me right now what are you looking forward to I mean for me personally of course is I want an amazing climate model that reduces uncertainties quantifies UNC certainties and then build verticals on top of it that goes to local scale information that goes to to apps that consumers can use to to assess climate risks to their own properties to to their weekend plans and the like um I I want climate information to be permeating economic decisions in a rational and and effective way and I think it's achievable one easy one is anyone who purchases property right you like to know what the risk of flooding and wildfires in that area are and uh like to notice this granular accuracy and in a way that you can trust and I think that's achievable um and just simply you know when you talk with your friends about the weather today it's an unusually hot day have contextual information how unusually hot is it in the past what is this going to be like 10 20 years from now just informed daily discourse with with that type of information I would find that incredibly helpful too tapio thank you so much for your time it was a pleasure talking to you and I'm really really really really enjoy this conversation and me too thank you thank you [Music] Bill so after my conversation with Dion and toio there's a couple things I just want to stop and Marvel at number one our planet is covered in sensors let's appreciate that for a second it's not just satellites and space and under underwater drones it's lar measuring aerosols and clouds it's a radar measuring ice sheet thickness the boatload of sensor data out there constantly monitoring this planet blows my mind and only now are we starting to fuse it all together and number two we're building some predictive climate models using the same AI technology we use for silly or whimsical things like AI art generators or 3D video games that's just cool and a big reminder that it actually matters what we do with technology speaking of Technology if these models are accurately predicting climate disasters we may be tempted to use geoengineering to modify the weather but oh boy does something like this require Global coordination in lockstep otherwise I see a new kind of geopolitical Crisis brewing in the future literally the weaponization of weather instead we should think of this Crystal Ball as a Sandbox for scenario planning generating a model of what the Earth could look like in the future if we make a set of decisions and put them in motion we can use it like a canvas for Global coordination so if we continue with the current path of excessive emissions and energy blindness the models will map out just how dire the consequences will be but hey the future isn't fixed and these models can also show what other Futures are possible if we take intensive coordinated action to mitigate some of the the harm caused by the climate crisis that is if we shift away from fossil fuel towards clean energy sources if we work to reduce emissions and preserve biodiversity these next Generation models allow us to travel forward in time and visualize the impact of our actions to make a more tangible vision of the world we actually want to live in [Music] the Ted AI show is a part of the Ted audio Collective and is produced by Ted with Cosmic standard our producers are Ella feder and Sarah McCrae our editors are Ben bang and Alejandra Salazar our showrunner is Ivana Tucker and our associate producer is Ben Montoya our engineer is Asia polar Simpson our technical director is Jacob winnink and our executive producer is Eliza Smith this episode was fact checked by Dana Kachi and I'm your host baval sadoo see y'all in the next one