Automated Transcript

Deep: Hi. There I'm Deep Dhillon. Welcome to Your AI Injection, the podcast where we discuss state of the art techniques and artificial intelligence with a focus on how these capabilities are used to transform organizations, making them more efficient, impactful, and successful.

Welcome back to your AI injection. This week, we're speaking with Anup Joshi. Anup teaches the application of LIDAR technology, satellite imagery, and field data to map and monitor carbon stocks in forests. Can't wait to talk with him and understand more about how's satellite imagery and LIDAR is used in carbon accounting.

Hi, thanks so much for being here. So to kick us off, tell us, uh, the greenhouse gas management Institute and your role there.

Dr. Joshi: Greenhouse gas management Institute is a nonprofit organization dedicated to building capacity for greenhouse gas. Many by management. We made, you know, we need a, to build a capacity in the countries, especially in developing countries to measure, monitor, and verify greenhouse gas emissions. And so we have a team of experts who deal with the different sectors of greenhouse gas management Institute. And then I look mostly on the land sector, so it, uh, greenhouse emissions from forest and other lands.

Deep: So tell me a little bit, like, you know, who are your customers and what do they engage you to do.

Dr. Joshi: Right now, trying to build a capacity in the developing countries. And we have a projects in the current one is in a 12 English speaking Caribbean countries. So the goal is to build a capacity in the region so that the countries, uh, don't have to depend on international consultants for their, uh, reporting purpose, like with the Paris agreement, every country who is in ator to Paris agreement has to, uh, report their greenhouse gas emissions from all the every two years. So to do that, we need to build a capacity in country if possible our goal is to build a hub where we bring the expertise from different countries. And so that the team could work to help all the countries in the region.

Deep: Tell us a little bit about what does it mean to, uh, have this capacity? Like what do you get the countries to do?

Dr. Joshi: Uh, with respect to their reporting requirements? Every country has to do their NA communication with where they show what the, and their greenhouse gas emissions are, what plants they have done to reduce it. And so to begin with, they first need to, uh, monitor, uh, greenhouse gas emissions from different sector. And let me put this more onto our land forest and auto land use sector. So the forestry sector itself is, uh, more complicated than auto sector because the forest acts both as a carbon source and sink. If we is burn, then you are releasing the carbon dioxide into the atmosphere, which is one of the greenhouse gases. So the trees two PS, if you use the carbon dioxide in the atmosphere to build a food and then store. So in, in that way, when trees grows more carbon is in a, in history. So, so in, in this process, what it does is, uh, it sucks into carbon dioxide from the atmosphere and then distorts in the body of the trees. So this will be a ne negative emissions, or we call it removals are sequestration. So this is the part of a carbon sequestration that we hear in, in the greenhouse gas emission lingo.

Deep: So just to kinda like understand a little bit better, you've got these countries, they're trying to report their emissions, so they need to figure out their negative emissions or their carbon Sans. What's the role of LIDAR and remote sensing here.

Dr. Joshi: So with the LIDAR, what do we, we will be able to do is to get the height of the forest. When you talk about the carbon sequestrations or, you know, uh, the carbon stock in a forest, we, uh, we can only measure indirectly. So there's no instrument to measure like as of now the carbon content in a free, so, so what does LIDAR help us is, you know, with the LIDAR techniques, you can build a model of tree Heights in the forest. So you have to measure a, a diameter of the tree at the breast height, and then height of the tree to calculate the amount of biomas in the tree. And then you project that into amount of the, um, biomas in a Hector of a lens. And the carbon is roughly 50% of a biomas. So what LIDAR does is with LIDAR, then you can have a wall to wall covers of a forest. And in that way you can more precisely measure the amount of a carbon in its forest. So you've got a country in a place like the Caribbean, which is a relatively small landscape. How are they getting their LIDAR imagery? How much of their national boundaries are they getting LIDAR imagery for? And, um. So the countries, the Caribbean haven't used the LIDAR so far because of the cost prohibit, it's very expensive UN until you got LIDAR on the satellite and can scan the globe.

Deep: So I just wanted clear. So, so today you can't buy LIDAR data from satellites. So is that what you're saying?

Dr. Joshi: Mostly a LIDAR data has been confined to the, you know, risk countries who can offer, for example, the us has a, you know, while to, while I, but in other countries it's hard. And I was involved in a project in Nepal in 2000, you know, 11, 12, where we had a budget from a donor, a Phoenix government to do a LIDAR, but we had a limited amount. So we had to do a strict sampling and then build a model out of that. So that's a lot better than, you know, trying to do a random sampling of a forest or stratify random sample and getting information. So with a LIDAR, it'll be faster and more accurate.

Deep: So let's, let's kind of dig in there a little bit, cause I wanna kind of understand how, how this works from the national ban. So the, so the country in this case, Nepal says, Hey, we need to understand our negative carbon emissions for this forest. So they hired you. You have a sponsor that helps pay the, the bill, you then sample the forest from a LIDAR advantage. So you don't have full LIDAR coverage of the entire forest. And then you need to still have somebody on the ground to actually measure the, the tree Heights and diameters. And then you construct a model, is that.

Dr. Joshi: So you do a, a sub sample of the LIDAR, right? LIDAR cover is a area. You do enough to build a model. And then you have a LIDAR coverage and you have a GPS score of a plot. And the model based on the ground data, and you train the model, you verify the model. And once you get up the verification as good or close to what you want, then you use a LIDAR to improve your sampling. So in that way, you can increase your in the algorithm more, right? So this gives you a carbon stock. How much carbon do you have in the forest? Uh, in case how much carbon stock you have in, in the different forest types in Nepal. So that's a half of occasion for getting a greenhouse gas emissions are sequestration. So that's called what we call is an emission factor. That that's how much carbon is in the ground. And the other part we need is activity data. So activity data is how much a land changes from one type to another. So for example, how much forest land changes into agriculture land or forest land, the settlement. So if, if the forest goes from, you know, forest to the settlement, what do we say, whatever carbon that's in the tree, we lost that. So that's.

Deep: I wanna understand a couple things. So like as a government, in this case, Nepal, they could have done no LIDAR, no AI, no machine learning, anything they could simply have sent down samplers like human samplers to, to sample various plots of the forest. And they could have come up with their technique. What's the relative cost difference between them going with a completely traditional manual sampling approach versus a hybrid approach where you have to random sample to teach the, to get more coverage and like what's the relative, um, benefit that you're getting. And what's the cost difference?

Dr. Joshi: Well, it's, it's a time. All right. And it's also cost because if you are doing a, a country, then you have to send your field crew to go out and measure it. So that's a lot of money and management and it's also time consuming.

Deep: So if you only sample manually, first of all, the number of places you manually sample is larger. And secondly, every year you have to keep assembling those teams out. But if you build a machine learning model, then the number of samples you need to get a reasonable model are lower in a given year. But then you can reapply that model across the years. Is that basically, is that, is that about right? That that's, that's the part, right?

Dr. Joshi: That that's right. And also what I was trying to come here is LIDAR is one part, right? That's, uh, expensive and, and not very commonly used. So the other part is general remote sensing data. So L satellite data, which is free now, and you have to train that. So that's a part of missing learning, right? So you got a random forest models, different algorithms in neural network models that can help analyze the forest better. And then you take the change in landscape. So how much forest is last, you know, forest can, uh, turn from forestland into grassland, our crop land to settlement. Yeah. So here here that, um, you know, mission learning or, you know, AI comes in the effect, you know, that's more used right now than LIDAR itself because of the cost.

Deep: So we recently had to get on that works for, um, pretty innovative company called Pachama. And one of the challenges that they had was access to training data. And I'm curious, when you go in as a, on the kind of consulting side and work directly with the government, are these governments opening up the data and making it publicly available for whatever light are or imagery that they purchase.

Dr. Joshi: Sharing data is still not, you know, as common as would like to see. So some governments are perfective. It really depends on country to country, but it, you know, what's happening right now is like, you know, this is a lot of paradises and you has had a mandate for them. So they are working on the global level and we got three global level Helan cover maps. So one is it's based on, uh, satellite data, uh, Lance satellite data, European S Sentinel data and Sentinel data is 10 meter resolution in optical data. Agile has they have our radar data Sentinel one. So what they're doing is they're combining their radar data where the cloud cover is high, just like in the carbon areas or in a tropical moist forest areas. They are often, you know, the cloud is hanging all around the year and it's hard to get a clear satellite map. So they are combining their radar data and optical data from Centennial and bringing a, a clear Moja of the, of the globe and then applying, you know, neural network, artificial missile, learning to classify those data into different land use categories. And their categories are pretty much aligned with, um, UN you know, IPCC. Um, so IPCC has a guidelines and they have a land use of six land use classes, forestland grassland, cropland, wetland, settlement, and other land. So they are classifying that at the global level. So that's going to provide the smaller countries, developing countries who are resource for, to use that data, to be able to monitor their greenhouse gas emissions from the land land use sector.

Deep: Is that where you think the overall system or industry is going, that there's gonna be a handful of entities that can cover the entire planet and have models that can then be applied to any new specific land coverage, visual, or LIDAR or radar information for a given country?

Dr. Joshi: Yeah, it's going that way. I mean, you know, at, at the global level, but then again, global level data, and if you want, uh, really country level, and even now, if you want a project level data, you need a more, more accuracy and more in a higher resolution. And I think that's where the, the private com companies are coming, because like when you go down to the project level and when you start trading a car, you know, which is, we think it's heading to, uh, you know, the goal is to have a carbon trade, like our stock market. At that point, it'll be very important to get the quality of data. So the high regulation, we have a very low errors or high confidence interval. And I think that's where the private companies are gear up. That's my personal reading.

Deep: So when we talk about, you know, the overall emissions monitoring problem monitoring, the natural carbon sinks is kind of one aspect of it. What do you think are some of the other areas that are gonna, you know, play a pretty significant role in, in terms of like reducing admissions.

Dr. Joshi: Monitoring and it's the speed, right? The accuracy and speed that's, what is lacking right now is, you know, we have the tools to do it, but it's not in, in near real time. So that's, that's where, you know, we need to move forward. We can do lot from satellite or from, uh, from, so there, there are models that, that are looking on, um, the water models, uh, the temperature models. And so this is, these are all where AI comes in effect and will be, you know, helpful, like trying to get data data from different systems into one list, and then analyzing those and, and getting results.

Deep: You're listening to your AI injection, brought to you by xyonix.com. That's x-y-o-n-i-x.com. Check out our website for more content, or if you need help injecting AI into your organization.

So let's fast forward a few years. Let's say we've got this system that you're describing speed to measure consistently you get high coverage across the spatial boundaries, maybe a monthly snapshot, um, from every place on the planet. And now you've got good accounting of forests growing and contracting. How do you get from that to carbon credit purchases that incentivize an individual farmer ultimately, or landowner or whatever, from not cutting down a tree so that they actually presumably get a check.

Dr. Joshi: Um, where you have projects, where you have, where they can sell their carbon credits. So, so this, this, uh, with the data, you can go back to this, especially explicit areas, right in the project area and the, with the data from, uh, data showing that what is happening there for us is getting growing, or it's been cut down. So, so how you, how it's growing and if it's keep on growing, and then it'll tell you how much carbon is sester per year in that area. And, and based on that, the projects would be paid on, you'll get the carbon credits, or, you know, you'll have, okay. If your project is a caus hectors and you have the carbon stock up, um, of hundred tons per Hector, then you have total carbon stock up on that times, the area. And if, if you are, if you are showing that if your project area from the satellite image is remaining, same, it's growing. So it's not cut down. Um, so what you do is the, uh, the trees keep on growing and as they grow on, they add X amount of carbon to the forest. So you are seeing the, how much carbon is adding onto that. And that will be your carbon credits.

Deep: How do you model risk into this equation? So, you know, if I have two forests, one that's sitting, you know, on the boundary and the Amazon, and has a high risk and exposure to being cut down next year, versus some other forest that's maybe sitting in a wealthy part of a Western nation that has a lower probability of being cut down. How does that get priced into the carbon?

Dr. Joshi: You have to build a model to project it, right. I think you need to get in your socioeconomic status a lot. Like what's it likely to do cut down. And also you have to think about the national co, right. So what, what is the chance of a forest fire, or what the chances of digs getting in the forest and killing the forest? Uh, we need to, uh, you know, comfort those scenarios and build a senior based models and, and build in enough cusin so that you can mitigate risk.

Deep: So how far are we away from a world where that farmer on the edge of the Amazon's burn is getting paid and what exactly do it's to happen to close the loop, cuz it's not happening and they're burning their forest down, not just there, but all over, um, for poultry amounts of money that they're getting, you know, they might put up a handful or maybe 20, 30 cows or something. So what's, what's not happening right now. And how many years away are we from them getting it check?

Dr. Joshi: Well, I, I think there are two issues, right? And I mean, like from the scientific point of view, science is advancing and more tools are coming up for us to able to measure and monitor, but then there's a other side is the government. Somebody has to build the rules and enforce it because, uh, you know, we, uh, you know, scientists that we can, uh, build our models, predict this and this and tell them what's going to happen, but we won't ha we don't have a auto release to implement it. If the governments, uh, don't have a plan to enforce it. And, you know, there is a lot of illegal things going on, illegal logging, illegal burning. So they need to be reinforcement and there needs to be a, a legal policy framework. That's going to dictate what happens on the ground.

Deep: Right. So am I to take away that this is just not gonna happen because governments are just not gonna do it, even if we get the science, right.

Dr. Joshi: Uh, no, I think governments are going to do it, you know, as long as there's a public pressure. Right? So with the, with the tools that right now, like in a global forest was where they're suing that the governments are saying, no, we don't have any Def forest. No, we haven't cut down the forest, but the data is sewing. Forest is going down. So when you can bring those and keeping the table, you have a better chance of arguing and convincing them, or in other way, putting a pressure on top. For example, our government says, we, you know, we haven't, you know, our forest is intact. We haven't cut down or nothing is happening here. But if you can sort the satellite data from two period where it's burned or it's cut down and bring the other actors, general public, uh, media, and, and then, uh, that'll be another way of forcing the governments to get the work done. So are you seeing that happening today with the work you've done and the governments you are working with? You know, if you look to the Zel, you know, they have a very good system of monitoring and then presenting the government there far as, um, before it is come down. But again, but again, when there's a garment changes, there is always risk of you getting the, in a regular, um, a reverse. But what we have is we have these tools where we can put the pressure, and then I think the pressure, as well as other thing is education, right? So we, we need to make a, a general public understand what's going on. So if, if we have a data and, uh, true now social media or through the websites, if it can, can sort of people on the ground what's happening, I think there's a better on the people who are in the conservation field or who, who are in the advocacy field to bring this up. And, uh, and the pressure, the governments to get the things done, right.

Deep: Are we like way beyond all of that? Like, even if we decrease the rate of deforestation, we're still gonna exceed 1.5 degrees Celcius, like, why are we still talking about decreasing deforestation when we should really be talking about massive areas of land being reforested? And why are we talking about activists and like putting pressure instead of just writing checks?

Dr. Joshi: Well, I, I, I think, you know, just, uh, I mean the, the question is where is the money coming from, right? So somebody give them money. And if it is coming from governments, then do the government have a will to pay for that and a mechanism to go that and to farmers and.

Deep: Well, I mean, but we know, we know so many companies that are trying to buy high quality carbon credits, and there's not actually a lot of high quality carbon credits out there. So why don't we start there? We don't have to have political fights to have Microsoft buy quality carbon credits. We just need the mechanics in place so that they can swipe their corporate card and have the money flow.

Dr. Joshi: I mean, you know, that sounds good, but the problem here is the fart, most of the place is not owned by a farmer, right? So, so still the large amount of forest is owned by governments. Now I have another paper that we are working on, or we look to how much forest is left, know, um, the globe in, I mean, ter ecosystems. And we, you know, our estimate is about, so we, we still have about 50% forest. That's not, not under agriculture or other human use. And out of that, you know, significant portion of that is, uh, indigenous people's land. Okay. But still there's a, this issue of who wants to land and who get the diary benefit. So it's, it is so most of the, mostly it's controlled by the governments and that that's having been now one of the sticky points.

Deep: Aren't governments, just as likely to change their behavior if they get more money in their pocket.

Dr. Joshi: Okay. So that's the, that's another mechanism that's having, uh, some issues here, indigenous people, they are using it, but they don't have titles. They don't have land titles. And so, so paying is hard for the companies or, you know, for the people to get the money out of the.

Deep: Oh, I'm trying to understand this, but it's like, the ownership is not clear and whoever is burning it down, they're like leveraging the land, but they don't have clear title. So you can't necessarily put the money in the pocket of the person that's burning down the forest. Right?

Dr. Joshi: Like, like, you know, if you say Amazon forest, so it's, you know, most of the forest is still government land. So people are, you know, burning down to get a coal or burning down to do, uh, farming, but still, you know, title has, have been converted to them, right? So they're doing some illegal kind of stuff.

Deep: But presumably they're bribing the government officials can be computed. And this system could basically pay those government officials more than they get from the illegal bribes.

Dr. Joshi: Logically. That makes sense. And they, you know, it can be done, but that's not happening.

Deep; Yeah. I guess that's what I'm. That's not happening. It's how, how do we get beyond well, corruption in this part of the world? Cause I think I understand the problem a little bit more, if you have illegal actors going in and burning down forests so that they can just graze their animals on the land, then that's a different problem because yeah, you don't have a database of owners to go write a check to, but presumably whoever owns the land, if you give them a check, then they're incentivized in order to get the next check and the next check and the next check they're incentivized to maintain the, the forest, right.

Dr. Josh: I mean, you know, that's the whole, the UN stuff is having government understand staff, uh, on them so that they can, uh, maintain forest as a forest.

Deep: So going back to the AI roles, are there other areas that you see kind of machine learning and AI helping out in the bigger picture of global emissions reductions that maybe is not just LIDAR and imagery and like monitoring, like, are there other areas that are maybe emerging that you know about?

Dr. Joshi: Uh, I, I, I think, you know, the mission learning or AI is going to be a critical part because so the amount of data that you needed from the premise side is so high, uh, at the, you know, you, you know, just trying to do a piece by piece is we don't have a time to do that. So we need to have a, a more accurate models that, you know, that can be done faster and then presented information out there, get down into a more accurate information.

Deep: What can you say is just somebody who's on the ground talking to these governments, working with this state all the time, is this gonna work? Are we gonna be able to reduce our admissions to the level we need to and extract enough carbon from the atmosphere?

Dr. Joshi: Yeah, I am hopeful because you know, the amount of, well, the understanding on the ground, the people talking about the, um, whole climate change issue and how, how the forest can help them is, you know, is, is, uh, is increasing. And, you know, when I used to travel and talk to the people around, more people are aware of it because people are directly in know, have been suffering from the climate related issues. Now we, we, you know, unpredictable rainfalls or, you know, months in having. Wildfires, you know, everything. Uh, yeah, the hurricanes, a higher impact in a high trend, more forest fires. And so people are getting more aware of it. And, uh, and, and then other side is the corporate war, right? That's another driving force when public and car corporate starts paying at instant to it. You know, uh, now with, um, with more information, they're looking at the carpet are looking through their supply chains and seeing how, you know, are there supply chains clean is the virus has been cut while Palm, uh, supplies are sustainable. So, so there's a hope. And, uh, so we are running out of time, but I, I have a positive attitudes. So, you know, it won't be easy, but I think we are moving in the direction, but we need to get more speed.

Deep: So ANU, tell us a little bit about your teaching and how it, it ties into carbon sequestration and, and AI.

Dr. Joshi: So I do teaching at the university of Minnesota. I do research on the side. So what, what we do is we are looking at, uh, looking at the effect of climate changes. Um, not only in terms of greenhouse gas emissions, but also the biodiversity. So the biodiversity and other aspect of it, you know, how climate change, let's say in case of vegetation, how vegetations are changing, um, changing weight and with the change of it, vegetation, it's going to affect, uh, biodiversity or even the animals. Right. And so what we are seeing here in Minnesota is there's a, you know, uh, more wildlife diseases is, um, is related to the climate chains. Like, you know, we have, we used to have our very, very cold winters where we, uh, and now we are having those lesser number of those per year. And then we are having like Al ASPO on the trees. That's a and killing the trees. And we are all also saying, as it's getting more warmer, the, the, the, in the colder climates are moving north, I know slightly nerd. So, um, so that's a one point that climate change is a having effect. And, and, uh, and what we're trying to do is trying to bring all more services to the students so they can use, um, uh, you know, a Google or engine platform or where they have a free satellite data and collaborate. And, you know, look at more specifically spec, uh, especially explicit way of seeing how, uh, how the vegetations are changing, um, in, in a, uh, in a landscape based on, based on climate, uh, parameters, like, you know, temperature, rainfall, um, and, uh, looking.

Deep: Got it. So one of the things you mentioned was, you know, the importance of biodiversity, and it got me thinking, if we're talking about like carbon, uh, markets, where you can buy offsets, how do you think about like the role of pricing in biodiversity into those credits? Is that how we should think about it?

Dr. Joshi: Absolutely. Our team, what we're trying looking to is it's not one with the carbon credits, but the quality of the carbon credits. So we want to bring in the biodiversity component of it. So if you have a forest land that has higher biodiversity, let's say, so you have a forest that has tires in it, And to have a, in it, you need to have enough price. So you need more deer in it. So in a variety, uh, animals. So if you look, come up with a metrics, so that put an additional value added to the wildlife or biodiversity component on the top of a carbon. So in that way, you, as an individual who want to invest your money, you might want to buy, I by carbon stocks are carbon credits in the area where it is high biodiversity, because what you'll be doing in turn is you'll be supporting biodiversity and also maintaining greenhouse gas emissions. So we need to integrate this so that you can just like you in a, in a stock marketing. So you can, you can have a higher rise of your carbon in an area where it has high biodiversity.

Deep: Yeah. And I could also even see an argument for not caring about biodiversity for some percentage of your portfolio, with respect to a carbon offset. So like there's a company that basically grows a bunch of vegetation and turns it into an oil and injects it underground. There's no biodiversity benefit, but at least I know for sure that this much carbon got taken outta the atmosphere. So people may choose to put their money into different types of credits and maybe allocate them in different ways. It, it feels like priority one is don't exceed one point 5g CELs, or really bad things happen, including biodiversity laws and then priority too, is like all of the other decisions that get us there. Would you agree with that? Or do you think there's like a different way to.

Dr. Joshi: No. Uh, well, I, I mean, like if you don't think of a forest and you think about monoculture, if you don't think of a biodiversity, those forests are more prone to risk from thes. Share. Sure. So going to risk assessment of air forest, then what you're investing, uh, for your carbon, then I think the divide diversity comes into the equation.

Deep: So if you, if you grow a bunch of crops and then burn them, then clearly you didn't sequester any carbon other than the duration, the crops were alive. Right. But if you take those crops and turn them into some kind of oil and inject it deep underground, then you've sequestered carbon.

Dr. Joshi: We need to look at that on the what's the efficiency of it, right. And then how much carbon, if are talking about market, you're buying a external of carbon. So we need to see how much carbon actually is produced from the agri agriculture crop, and then, and it's injected on the ground. So you need to go down on the, not, not the whole crop or its value, but you, you need to look at how much carbon is injected on the ground. I, it, right.

Deep: And there's, there's other techniques I wanted to ask you about another, uh, you know, there's, there's a lot of mechanical systems being pursued right now. And there's a, a gates back company in Vancouver that has these big machines that basically try to pull carbon out of the air and sequester them in the form of some kind of fuels or things to be buried. Um, I was curious what your thoughts are around the efficiencies and those kinds of oh, mechanical approach is.

Dr. Joshi: And the cherry looks good, but what's the practical and what efficiency is that that needs to be seen. So right now, the best option I see is like planting more trees are not cutting down. What's there in a way. I, I sometimes worry that in a way it gives up also for general public, like, you know, you'll be able to something carbon from the air efficiently with data so that we can keep on burning. We can keep on, you know, driving monster trucks. Uh, there are no new techniques coming. And then, you know, unless they come in, become a viable commercial option, um, still we cannot come on them right now. I mean, if, if we done, uh, or then you're not going anywhere. So, so there should be a research there should be research and development for, for these projects and who we should encourage them to get it, but unless, um, they can prove it on a commercial scale. It can be done so far. I think it's, um, you know, in my book, it's on a lower prior right now, because it's still in the academic still is where you, people are. Trying. And we know, I mean, we know forest sequester carbon, there's no right. There's no question whatsoever there, so. Right. Right. And then, you know, and yeah, and also the, the biggest, you know, thing is to men in the forest, that's already forest, like, you know, yeah. The Amazon forest, uh, are the mature for us because this, some of those trees are like, you know, over a hundred years old. So they have, they have a lot of problems goes to once we, once those have been destroyed, it's going to take a lot long time to get that carbon, you know, restart.

Deep: Well, thanks so much for coming on and you it's been very enlightening to hear your words. 

That's all for your AI injection as always. Thanks so much for tuning in. If you've enjoyed this episode, we've got a two part article series digging in deep on AI. It's up on our website xyonix.com. That's xyonix.com/articles. As always, please feel free to tell your friends about us, give us a review and check out our past episode at podcast.xyonix.com or on your favorite podcast platform. That's all for this episode. I'm Deep Dhillon, your host saying, check back soon for your next AI injection. In the meantime, if you need help injecting AI into your business, reach out to us at xyonix.com. That's x-y-o-n-i-x.com. Whether it's text, audio, video, or other business data, we help all kinds of organizations like yours automatically find and operationalize transformative insights.