[0.0][S02] It sort of seems a bit bonkers to me[2.0] [2.0][S02]that we're in 2025 and this hasn't been done already.[5.97] [5.97][S02]I mean, do we not know where the forests are?[8.01] [8.01][S01] A lot of the information[8.78] [8.78][S01]that we want about the natural world has never been recorded[11.28] [11.28][S01]like that on paper necessarily-- where[13.61] [13.61][S01]all the different species of trees are[15.36] [15.36][S01]or where the habitats are or where[16.777] [16.777][S01]the boundaries between the rough grassland and wetland are.[20.423] [20.423][S01]So we need to actually create that information[22.34] [22.34][S01]for the first time.[23.06] [23.06][S02] Thing is, I can see the potential for this in terms[25.685] [25.685][S02]of scientific interest, but I do wonder whether,[28.49] [28.49][S02]let's say, we get to a point where you can understand what[31.55] [31.55][S02]dolphins are saying, communicate with the higher[33.62] [33.62][S02]animals on the planet.[34.62] [34.62][S02]Does it change how we view ourselves[36.32] [36.32][S02]and our place in the world?[37.59] [37.59][S01] I think yeah.[38.07] [38.07][S01]I think it absolutely has that potential.[39.87] [39.87][MUSIC PLAYING][42.323] [42.323][S02] Welcome back to "Google DeepMind--[44.24] [44.24][S02]The Podcast."[44.91] [44.91][S02]I'm Professor Hannah Fry.[46.19] [46.19][S02]Now, we focus a lot in this podcast on AI[48.98] [48.98][S02]and its interactions with humans.[50.94] [50.94][S02]But there is another story that's unfolding, one in which[54.38] [54.38][S02]AI could help protect our planet.[57.0] [57.0][S02]Think oceans and forests and deserts and fragile ecosystems[61.08] [61.08][S02]and the millions of animal species[63.21] [63.21][S02]that are crawling, swimming, and flying across this Earth.[66.88] [66.88][S02]AI has the potential to tackle one[69.66] [69.66][S02]of the biggest challenges of our time, the damage[72.63] [72.63][S02]to nature and ecosystems.[74.59] [74.59][S02]But with a problem that's this vast, where do you even begin?[78.58] [78.58][S02]Well, Drew Purves, Nature Lead at Google DeepMind,[81.25] [81.25][S02]is my guide through the rich terrain of AI for nature.[85.3] [85.3][S02]He has got two decades of experience[87.15] [87.15][S02]in ecological research and has been at Google DeepMind[89.61] [89.61][S02]for almost 10 years now.[91.36] [91.36][S02]Drew, thank you so much for joining me.[93.07] [93.07][S01] Oh, thanks.[93.52] [93.52][S01]It's great to be here.[94.45] [94.45][S02] I think most people probably[96.117] [96.117][S02]agree by now that the environment is[98.58] [98.58][S02]an important aspect of our future, something that deserves[102.03] [102.03][S02]preserving and looking after.[103.95] [103.95][S02]What's holding us back in this area?[106.48] [106.48][S02]What's making it a difficult problem?[108.37] [108.37][S01] The short answer to that[109.912] [109.912][S01]often is lack of information.[111.19] [111.19][S01]So you're right.[111.94] [111.94][S01]There's this groundswell now of agreement[113.97] [113.97][S01]about the importance of biodiversity, ecosystems,[117.03] [117.03][S01]and nature.[118.32] [118.32][S01]And then we have signs of action in different sectors,[122.02] [122.02][S01]in the private sector, in the government sector.[124.02] [124.02][S01]If you look at some of the numbers,[125.478] [125.478][S01]for example, there are 189 countries[127.05] [127.05][S01]around the world that have signed up to the 30[128.967] [128.967][S01]by 30 plan, which is to protect 30% of ecosystems on land[133.74] [133.74][S01]and in the oceans by the year 2030, things[136.343] [136.343][S01]that there's so much to feel good about.[138.01] [138.01][S01]But often, when it comes down to actually taking action[140.43] [140.43][S01]on the ground to either protect or restore biodiversity[143.76] [143.76][S01]and ecosystems, there's just a lack of basic information.[146.135] [146.135][S02] So what are the big questions that AI could[148.427] [148.427][S02]help answer in this space then?[150.002] [150.002][S01] I mean, there are a number of them, obviously.[152.46] [152.46][S01]But for example, if you're thinking about a protection[155.4] [155.4][S01]scenario, protecting biodiversity,[157.218] [157.218][S01]you need to know where the biodiversity is.[159.01] [159.01][S01]It might be focal species, or it might be biodiversity hotspots,[164.07] [164.07][S01]and so on.[164.77] [164.77][S01]It might be a particular endemic species.[166.72] [166.72][S01]If it's a restoration scenario, you[169.02] [169.02][S01]want to find particular places around the world that[172.83] [172.83][S01]have the highest potential for ecological restoration.[176.17] [176.17][S01]But for example, you might need to know which species of trees[179.48] [179.48][S01]to plant or which species of animals to reintroduce.[182.88] [182.88][S01]So down at this local level-- it's[184.58] [184.58][S01]because biodiversity is so place-specific.[186.93] [186.93][S01]You need that locally relevant information for communities[191.03] [191.03][S01]or whatever local action is happening[192.688] [192.688][S01]to guide the action down there.[193.98] [193.98][S01]And often, it's just missing.[195.188] [195.188][S02] Is that the big goal, then, of Google DeepMind,[198.02] [198.02][S02]using AI in nature to really fill[200.84] [200.84][S02]in the information gaps in the same way[203.09] [203.09][S02]that we have done for the human world?[205.05] [205.05][S01] Well, here at Google DeepMind,[207.09] [207.09][S01]we're growing a portfolio of work around AI for nature.[211.08] [211.08][S01]And there are at least three key categories of AI[214.4] [214.4][S01]for nature to explore.[215.73] [215.73][S01]So the first of those is AI for data.[218.028] [218.028][S01]And that can mean bringing in data from the field[220.07] [220.07][S01]from things like cameras, microphones,[221.75] [221.75][S01]and so on, or identifying and bringing[223.678] [223.678][S01]in data from the literature because there's[225.47] [225.47][S01]a huge amount of data there.[227.258] [227.258][S01]The second category, though, is taking all of that data[229.55] [229.55][S01]and combining it with lots of other sources of data,[231.717] [231.717][S01]like satellite data and so on, to create[233.57] [233.57][S01]that derived information that decision-makers need[236.6] [236.6][S01]to protect or enhance nature.[238.7] [238.7][S01]And then, the third category, which is easy to miss,[240.87] [240.87][S01]is that all the information in the world is fine,[243.0] [243.0][S01]but human beings can get overwhelmed by that amount[245.51] [245.51][S01]of data.[246.06] [246.06][S01]So then there's this key role for active deployment of AI[248.93] [248.93][S01]to help decision-making, to help people[251.42] [251.42][S01]make sense of all that data.[252.65] [252.65][S02] And I guess, in part because this[254.81] [254.81][S02]is quite a new area, right?[256.92] [256.92][S02]I mean, this hasn't been around for as long as some[259.07] [259.07][S02]of the other applications of AI.[260.73] [260.73][S02]It's not as obvious of how to solve biodiversity[264.56] [264.56][S02]as it might be to solve disease, for instance.[267.425] [267.425][S01] I think that's true.[268.8] [268.8][S01]It does feel like a growth area in AI.[270.57] [270.57][S01]It's kind of interesting, actually.[272.028] [272.028][S01]That ecology obviously has a long history.[274.17] [274.17][S01]But interestingly, mathematical ecology and statistical ecology[277.58] [277.58][S01]and even machine learning ecology has a long history, too.[280.52] [280.52][S01]Fisher, for instance, the famous Fisherian statistics,[283.092] [283.092][S01]a lot of that was developed in an ecological context.[285.3] [285.3][S01]I think that's because ecology is inherently quite challenging.[288.0] [288.0][S01]There's no obvious way to record and frame ecology.[291.0] [291.0][S01]You've got a vast variety of species.[292.56] [292.56][S01]You've got processes operating at different scales.[294.69] [294.69][S01]The temporal signals are always really noisy.[297.12] [297.12][S01]And so it's interesting.[298.27] [298.27][S01]It's always been a little bit of a challenging domain that[301.26] [301.26][S01]has led to innovation in statistics and machine learning.[304.24] [304.24][S01]And so we very much expect that, in the work[307.02] [307.02][S01]we're doing now, trying to apply this deep learning,[309.94] [309.94][S01]for instance, the recent AI revolution to ecology, that[313.05] [313.05][S01]also will be a way to innovate.[314.598] [314.598][S02] OK, well, let's dig into some of this stuff then.[317.14] [317.14][S02]So in particular, in that middle layer of taking in data[320.88] [320.88][S02]and deriving something from it-- because I mean, a lot of this[324.93] [324.93][S02]involves mapping, that fundamental stage.[329.23] [329.23][S02]Tell us a little bit about mapping the biosphere.[331.74] [331.74][S01] So many of the decisions in environment[334.65] [334.65][S01]and ecology come down to place.[336.58] [336.58][S01]And that's because, in ecology and biodiversity,[339.062] [339.062][S01]each place is so different-- different species,[341.02] [341.02][S01]different habitats, and often different issues[342.75] [342.75][S01]that you need to deal with, whether it's agriculture[344.917] [344.917][S01]or wildfires, et cetera.[347.02] [347.02][S01]So all of that "missing information" I mentioned before,[351.13] [351.13][S01]most of the time, it's missing geo information.[354.0] [354.0][S01]And so, very simply, you can think of that as maps.[356.16] [356.16][S01]And so we need to map habitats.[358.15] [358.15][S01]That's the substrate over which ecology plays because substrate[361.2] [361.2][S01]is largely defined by plants.[362.74] [362.74][S01]So habitats and plants are closely related,[364.96] [364.96][S01]so whether that's forest, grasslands, et cetera.[367.57] [367.57][S01]But of course, then we want to map all the species ideally.[370.35] [370.35][S01]That's a very challenging problem[371.37] [371.37][S01]just because there are millions of them, and a lot of them[373.02] [373.02][S01]are very small and can't be seen, and so on.[374.8] [374.8][S01]Of course, there's maps for now, but also, we[376.29] [376.29][S01]might want to understand historical change.[378.37] [378.37][S01]Those baselines of change are very important.[380.68] [380.68][S01]And of course, being able to project maps[382.92] [382.92][S01]into the future, which is a whole different challenge--[385.44] [385.44][S01]all roads lead to maps in ecology.[387.94] [387.94][S02] OK, that's interesting.[389.44] [389.44][S02]I'm sort of surprised that this doesn't exist already, though.[391.75] [391.75][S02]I mean, can you not just get this from Google Earth?[393.44] [393.44][S01] [CHUCKLES] It's a really interesting question[395.19] [395.19][S01]because we got so used to-- and, of course, it's been amazing,[397.773] [397.773][S01]the geographic information we have[399.48] [399.48][S01]through things like Google Earth and Google Maps,[401.63] [401.63][S01]a real human achievement to bring all that together and make[404.13] [404.13][S01]it available.[404.8] [404.8][S01]But it's very human-centric so far, that data.[406.87] [406.87][S01]So of course, we've got roads, and you've[408.578] [408.578][S01]got every shopping center and every post office, everything[411.5] [411.5][S01]like that.[412.0] [412.0][S01]But actually, it's much less developed[413.79] [413.79][S01]in terms of mapping the natural world.[415.798] [415.798][S02] It's focused more on people than the environment.[418.34] [418.34][S01] Yeah, that's right.[419.06] [419.06][S01]A lot of the information that we want about the natural world[421.602] [421.602][S01]has never been recorded like that on paper,[423.46] [423.46][S01]necessarily, where all the different species of trees are[426.243] [426.243][S01]or where the habitats are or where[427.66] [427.66][S01]the boundaries between the rough grassland and wetland are.[431.623] [431.623][S01]So we need to actually create that information[433.54] [433.54][S01]for the first time.[434.48] [434.48][S02] It sort of seems a bit bonkers to me[436.48] [436.48][S02]that we're in, what, 2025, and this hasn't been done already.[440.72] [440.72][S02]There are attempts at this.[443.6] [443.6][S02]They exist.[444.32] [444.32][S02]Landsat was developed in the 1970s of taking satellite images[449.26] [449.26][S02]and categorizing different regions.[450.77] [450.77][S02]I think there was some stuff before that[452.437] [452.437][S02]for military purposes using satellite images.[454.88] [454.88][S02]But is this just a different scale to those things?[457.86] [457.86][S01] It amazes me too.[459.11] [459.11][S01]One thing that amazes me is that the satellites even exist.[461.87] [461.87][S01]I mean, that's incredible.[463.15] [463.15][S01]It just blows my mind that actually, for many decades,[465.82] [465.82][S01]we've basically had these floating high-res digital[468.34] [468.34][S01]cameras looking down at the Earth for decades.[470.652] [470.652][S01]That's absolutely incredible.[471.86] [471.86][S01]It's also incredible the amount of geographic information we[474.52] [474.52][S01]actually do have.[475.34] [475.34][S01]There are amazing data sets or amazing maps[477.5] [477.5][S01]of all kinds of things that are relevant to the environment.[480.0] [480.0][S01]And yet, at the same time, sometimes you[481.73] [481.73][S01]come in with the most basic questions like,[483.522] [483.522][S01]where are the forests?[484.53] [484.53][S02] Do we not know where the forests are?[486.27] [486.27][S01] That's right.[486.75] [486.75][S01]And honestly, it surprises me too.[488.37] [488.37][S01]But we don't have gold-standard accepted global maps[491.723] [491.723][S01]for most of the habitats that we need to understand,[493.89] [493.89][S01]including forests.[494.64] [494.64][S01]So there is no absolutely universally accepted gold[497.93] [497.93][S01]standard for forest/non-forest.[499.49] [499.49][S01]Now, having said that, there are some pretty good ones[501.74] [501.74][S01]nowadays appearing, but there's still room for improvement.[504.3] [504.3][S01]On the other hand, what we're looking[505.842] [505.842][S01]at is even if you had that to guide decision making,[508.43] [508.43][S01]you often need to distinguish different kinds of forest,[511.28] [511.28][S01]for example, the most basic split-- natural forest[514.039] [514.039][S01]from planted forest.[515.19] [515.19][S01]And there definitely isn't a gold-standard accepted map[517.52] [517.52][S01]for that.[517.95] [517.95][S01]And some of the work we're doing at the moment[519.867] [519.867][S01]is trying to do our best to provide the best yet[522.02] [522.02][S01]available map of that.[523.14] [523.14][S01]We call it the Natural Forests of the World Project.[525.03] [525.03][S02] Why are forest maps useful?[526.655] [526.655][S02]What can you use them for?[527.843] [527.843][S01] Well, forests are incredibly important,[530.01] [530.01][S01]harboring biodiversity and carbon.[531.6] [531.6][S01]And for that reason, there's a lot of government policy[535.11] [535.11][S01]and a lot of international regulation around forests.[537.38] [537.38][S01]And forests are a major focus of concern for conservation groups.[540.69] [540.69][S01]And in all cases, they need the best maps[542.417] [542.417][S01]so they can know where to protect if they're[544.25] [544.25][S01]looking to protect forests, where to potentially restore[546.89] [546.89][S01]forests, where to monitor for problems like forest diseases[550.98] [550.98][S01]and on and on.[551.67] [551.67][S02] Can I see it?[552.48] [552.48][S02]Have you got it?[552.99] [552.99][S01] Yes, I have.[554.032] [554.032][S01]So firstly, what we produced is a map for the whole globe[557.03] [557.03][S01]at 10-meter resolution.[558.63] [558.63][S01]So each 10-meter pixel is classified.[560.69] [560.69][S01]And we give it a probability that it's natural forest.[563.812] [563.812][S02] I should probably do a little audio description[566.27] [566.27][S02]for the people that are listening rather than watching.[568.83] [568.83][S02]We have essentially in a browser what looks superficially like--[573.86] [573.86][S02]well, exactly like Google Earth.[575.76] [575.76][S02]But overlaid on top are these teal colored pixels[579.11] [579.11][S02]that align but not exactly with what[581.9] [581.9][S02]you can see to be trees from the satellite images.[584.177] [584.177][S01] That's right.[585.26] [585.26][S01]So you can illustrate that, for instance, in--[587.177] [587.177][S01]and this is why it's so important but also[589.748] [589.748][S01]difficult to tell the difference between natural forest[592.04] [592.04][S01]and planted forest because the natural forest will typically[594.54] [594.54][S01]be much higher in biodiversity and often carbon[596.55] [596.55][S01]if it's somewhat old growth.[597.64] [597.64][S01]So this will be an area that you would[598.71] [598.71][S01]tend to want to protect more.[600.43] [600.43][S01]And then equally, if you detect forest loss,[602.703] [602.703][S01]then the loss of natural forest will typically[604.62] [604.62][S01]be much more concerning than, for instance,[606.13] [606.13][S01]the loss of planted forest, many of which[607.838] [607.838][S01]are planted in order to cut them down, like timber.[610.33] [610.33][S01]So they can actually be sustainable.[611.83] [611.83][S01]But in this case, in the Southeast US, for example,[613.955] [613.955][S01]there are lots of loblolly pine plantations, for example.[616.548] [616.548][S01]So these are a type of planted forest[618.09] [618.09][S01]which is planted to give timber very quickly, after about[621.0] [621.0][S01]five years.[621.76] [621.76][S01]And so you can see there we've got a mix of areas[624.84] [624.84][S01]that we classify as natural and areas[626.97] [626.97][S01]that we classify as planted forest.[629.14] [629.14][S01]And if you zoom in and out, most of the time-- of course,[632.342] [632.342][S01]there are inaccuracies.[633.3] [633.3][S01]But we're over 90% accurate when we test it.[635.8] [635.8][S01]So you can see here, as we fade up our predictions there,[640.337] [640.337][S01]then if we look at those remaining parts, and you go in,[642.67] [642.67][S01]you'll find these neat rows.[644.0] [644.0][S02] Look at that.[644.52] [644.52][S01] And that's telling us it's planted forest.[645.81] [645.81][S02] That's extraordinary.[646.02] [646.02][S01] So to be able to do this at scale-- yeah.[647.61] [647.61][S02] So, OK, I notice in this example[649.443] [649.443][S02]that it's sort of natural forest/not natural forest.[653.247] [653.247][S02]But I also notice that you've got a confidence threshold[655.58] [655.58][S02]there.[656.1] [656.1][S01] Yes.[656.24] [656.24][S02] So are you classifying[658.1] [658.1][S02]this with a probability rather than just saying,[660.36] [660.36][S02]yes, it's natural forest, not?[661.34] [661.34][S01] Yes, we are.[661.83] [661.83][S01]And it's something we're quite passionate about, really,[664.163] [664.163][S01]because there is a tendency, if you[665.75] [665.75][S01]see a map, that is classified in a black and white way,[668.88] [668.88][S01]to believe that.[670.49] [670.49][S01]And even if it's relatively accurate most of the time,[673.23] [673.23][S01]it's really important to be aware of the inaccuracies.[676.237] [676.237][S01]And so that's why we much prefer to present things[678.32] [678.32][S01]as this uncertainty map.[681.11] [681.11][S01]Now, of course, one thing you'd often do[682.85] [682.85][S01]is to think, OK, I'll choose some threshold then[685.31] [685.31][S01]which I will use, for my purposes,[687.967] [687.967][S01]to distinguish forests and non-forest.[689.55] [689.55][S01]But depending on what you're after,[690.65] [690.65][S01]you may choose different thresholds.[692.01] [692.01][S01]So if you were really looking to make[693.65] [693.65][S01]sure you're protecting all of the remaining[695.63] [695.63][S01]natural forest in an area, you would[697.64] [697.64][S01]set your confidence threshold-- it's actually setting it low so[700.4] [700.4][S01]that you pick all of that up.[701.683] [701.683][S01]But of course, you'll also pick up[703.1] [703.1][S01]a whole load of planted forest, but you[704.725] [704.725][S01]don't mind along the way.[706.02] [706.02][S01]Whereas on the other hand, if you had very limited resources,[708.562] [708.562][S01]let's say, and you wanted to verify[710.09] [710.09][S01]that the loss of natural forest had occurred,[712.35] [712.35][S01]and you can only afford to visit certain places,[714.615] [714.615][S01]you may put a very high threshold on it[716.24] [716.24][S01]to make sure that you're really visiting the places where you're[718.907] [718.907][S01]most confident about and so on.[720.21] [720.21][S01]So we're providing that to downstream users[723.74] [723.74][S01]in this uncertainty form.[725.04] [725.04][S01]We're open sourcing all the maps.[726.51] [726.51][S01]We're open sourcing actually the data[728.06] [728.06][S01]and also the new models we've developed,[730.11] [730.11][S01]open sourcing out to the community.[732.21] [732.21][S01]And what it means for downstream users[734.09] [734.09][S01]is that they can get to their own high-quality,[737.81] [737.81][S01]remote-sense maps with much less compute than before,[742.85] [742.85][S01]lower data requirements, and, all importantly,[745.41] [745.41][S01]lower skills requirements as well.[746.88] [746.88][S01]So if we do it right, something like that[748.588] [748.588][S01]can really help to democratize remote sensing on the outside.[751.55] [751.55][S02] In terms of what's going on behind the scenes,[754.11] [754.11][S02]you're using a vision transformer, right?[756.905] [756.905][S02]How does that work?[758.16] [758.16][S01] So what, overall, we're doing[759.98] [759.98][S01]is bringing in this massive satellite information.[762.9] [762.9][S01]So these are enormous images for a start,[765.15] [765.15][S01]the sheer number of pixels.[766.38] [766.38][S01]And then for each pixel, it's not just RGB[768.38] [768.38][S01]like it would be with an image, but it's often[769.85] [769.85][S01]many different bands--[770.91] [770.91][S01]infrared, et cetera, all these different bands.[773.21] [773.21][S01]And those bands are going up through time.[774.96] [774.96][S01]Then you've got multiple satellites.[776.46] [776.46][S01]Each satellite is giving you records every few days.[778.98] [778.98][S01]You have a lot of missing data from things like clouds.[781.88] [781.88][S01]So you need to take this enormous amount of data[784.28] [784.28][S01]and somehow crunch it all down to pull out[787.0] [787.0][S01]the thing that you actually want to know, like, is it a forest?[789.625] [789.625][CHUCKLING][790.64] [790.64][S01]And the model in the middle that we're using[792.83] [792.83][S01]is this vision transformer model.[794.31] [794.31][S02] Transformers, people usually[796.01] [796.01][S02]associate them with large language models,[797.99] [797.99][S02]like paying attention to different bits of sentences[800.595] [800.595][S02]more important than others.[801.72] [801.72][S02]How does it feed in when it comes to maps?[803.58] [803.58][S01] That's right.[804.663] [804.663][S01]So transformers were mostly developed to work on language,[807.25] [807.25][S01]this idea of these attention heads and so on,[809.24] [809.24][S01]that were then adapted into vision transformers[811.82] [811.82][S01]to do similar things on images, attending[813.89] [813.89][S01]to different parts of images.[815.13] [815.13][S01]What we've done here is then expand that out[817.13] [817.13][S01]to a special vision transformer that's[819.47] [819.47][S01]set up to deal with the challenges of satellite data.[822.54] [822.54][S01]So it's actually now a multimodal, temporal, spatial[825.5] [825.5][S01]vision transformer.[826.527] [826.527][S01]But it's still a form of vision transformer.[828.36] [828.36][S01]It's a great story about how you can get this exchange of methods[831.09] [831.09][S01]between different areas, from language modeling[833.048] [833.048][S01]into vision and, in this case, into remote sensing[835.63] [835.63][S01]and out there into environmental policy.[837.85] [837.85][S02] So when it's working out whether it's forest or not[840.6] [840.6][S02]forest, it's paying attention, as it[843.09] [843.09][S02]were, to sometimes the infrared data, sometimes the image,[848.44] [848.44][S02]sometimes the topology of the area, that kind of thing.[852.847] [852.847][S01] That's right.[853.93] [853.93][S01]The transformer architecture is extremely expressive[857.43] [857.43][S01]architecture that gives the model a huge amount of freedom[860.19] [860.19][S01]to choose what it attends to and how it then[862.8] [862.8][S01]combines that information downstream into its prediction.[865.93] [865.93][S01]And that's particularly valuable in an area like remote sensing[868.87] [868.87][S01]where you've got such a wide variety of modalities coming in[871.86] [871.86][S01]and all the data sets are so huge.[874.3] [874.3][S01]And so it's an area that really benefits[876.3] [876.3][S01]from this kind of extra expressivity and flexibility[879.69] [879.69][S01]of the model.[880.36] [880.36][S02] But that expressivity, in the end,[882.277] [882.277][S02]comes down to forest/not forest.[883.643] [883.643][S01] That's what's funny, right?[885.31] [885.31][S01]All of that gets crunched down into just,[887.41] [887.41][S01]in this case, that single map.[888.97] [888.97][S02] Well, OK, I'm just picking up on something[891.22] [891.22][S02]that you said in your answer there--[892.992] [892.992][S02]because you were talking about how you have these satellite[895.45] [895.45][S02]images over time.[896.84] [896.84][S02]So then rather than just mapping where the forests are or aren't,[900.65] [900.65][S02]does that mean that you can look at how the forests are changing?[903.71] [903.71][S01] Yeah, that's a very important point.[905.15] [905.15][S01]Yeah, indeed.[905.75] [905.75][S01]So the interesting thing with remote sensing and satellite[909.04] [909.04][S01]data is, because the satellites have been up for years,[911.39] [911.39][S01]if you can do this mapping, say you[913.6] [913.6][S01]take one year's worth of satellite data,[915.77] [915.77][S01]say for this year, and we can see[917.26] [917.26][S01]where the forests are this year, we can automatically[919.468] [919.468][S01]redo that from the past to estimate the picture of change.[922.82] [922.82][S01]And this is really important.[924.31] [924.31][S01]So there's a project called Global Forest[927.16] [927.16][S01]Watch, which Google has supported before,[928.87] [928.87][S01]that does this.[929.54] [929.54][S01]And so each year, the latest map comes out,[931.6] [931.6][S01]and you can look at the changes in forest cover through time.[934.58] [934.58][S01]More recently, we've created this stack of maps[938.02] [938.02][S01]that we call deforestation drivers.[940.36] [940.36][S01]And what this is actually looking at[941.86] [941.86][S01]is it's taking each unit of forest loss[944.56] [944.56][S01]and categorizing the cause of that loss[946.72] [946.72][S01]in terms of logging, agricultural expansion,[949.58] [949.58][S01]and so on and so on.[950.64] [950.64][S01]But we've applied that for the last 20 years.[952.74] [952.74][S01]So we have 20 years of not just deforestation but 20 years[956.12] [956.12][S01]of the causes of deforestation.[958.02] [958.02][S01]So in collaboration with our colleagues at the World[960.32] [960.32][S01]Resources Institute, at WRI, we've[962.51] [962.51][S01]worked up this global map of the causes of deforestation[966.02] [966.02][S01]for each year from the year 2000 to today.[968.807] [968.807][S02] And what do you find when you do that?[970.89] [970.89][S01] It's really interesting[972.39] [972.39][S01]to look at these patterns.[973.79] [973.79][S02] Go on, show me, show me.[975.29] [975.29][S02]I know you've got it.[975.87] [975.87][S02]I know you've got it.[976.53] [976.53][S02]I want to see it.[977.1] [977.1][S01] So the first thing you[977.75] [977.75][S01]can see when you look across the world[978.86] [978.86][S01]is that there's forest loss everywhere.[980.37] [980.37][S02] Yeah.[980.93] [980.93][S02]Quite a lot in Northern Europe.[982.32] [982.32][S01] Yes, for example.[983.22] [983.22][S01]So it's so easy to think that all of these problems[985.46] [985.46][S01]are just occurring in the tropics or in the Global South.[987.998] [987.998][S02] Brazil.[988.79] [988.79][S01] Right, exactly.[989.28] [989.28][S01]And the second thing, though, is the causes[991.28] [991.28][S01]of the loss does change between different regions.[993.65] [993.65][S01]So if you have a look at-- zoom in on Brazil here in South[997.25] [997.25][S01]America, you can see--[998.56] [998.56][S01]again, you can go right in and see these--[1000.31] [1000.31][S02] But lots of permanent agriculture.[1001.88] [1001.88][S01] That's right.[1002.42] [1002.42][S01]Yeah, so this sort of darker yellow color.[1004.252] [1004.252][S01]And you'll find when you fade in and out,[1005.96] [1005.96][S01]you'll often find that this is associated with clearings[1008.5] [1008.5][S01]and so on.[1009.08] [1009.08][S01]So you can see how that's working.[1010.497] [1010.497][S02] You can literally see[1011.872] [1011.872][S02]how it picks out the edges of fields effectively.[1014.28] [1014.28][S01] That's right.[1015.363] [1015.363][S01]Because all that fine-grain data is actually in the satellite[1017.97] [1017.97][S01]signals.[1018.47] [1018.47][S01]There have been previous maps, but this one[1020.262] [1020.262][S01]is 10 times finer resolution.[1021.703] [1021.703][S01]And that's what really enables you to pull out[1023.62] [1023.62][S01]these local patterns.[1025.21] [1025.21][S01]And that's where a lot of the decision-making happens,[1027.46] [1027.46][S01]is at local scales.[1028.295] [1028.295][S01]And so understanding things at local scale is really important.[1030.92] [1030.92][S02] Putting all of this together, long into the future,[1036.349] [1036.349][S02]is there a real-time aspect to this?[1038.69] [1038.69][S02]Could you prevent illegal logging[1040.359] [1040.359][S02]from happening before it did?[1042.143] [1042.143][S01] It's a really good question[1043.81] [1043.81][S01]because the work that we've been talking about up to now[1046.96] [1046.96][S01]tends to be taking satellite data from an extended period[1051.67] [1051.67][S01]to estimate how things were either right now[1054.618] [1054.618][S01]or looking into the past.[1055.66] [1055.66][S02] Retrospectively.[1056.63] [1056.63][S01] Right.[1057.422] [1057.422][S01]Whereas this idea of rapid, real-time alerts[1059.86] [1059.86][S01]and then also longer-term future forecasting[1062.98] [1062.98][S01]is some of the real growth areas for us.[1064.81] [1064.81][S01]There are already actually existing organizations[1068.96] [1068.96][S01]that are using satellites to try to pick up[1071.36] [1071.36][S01]deforestation alerts live.[1072.96] [1072.96][S01]So that actually exists.[1074.49] [1074.49][S01]One of the challenges, though, is[1075.89] [1075.89][S01]that in order to pick up most of the real deforestation,[1079.178] [1079.178][S01]they've had to use a methodology that[1080.72] [1080.72][S01]picks up a lot of false positives at the same time.[1082.86] [1082.86][S02] I see.[1083.19] [1083.19][S01] So it's actually really challenging.[1084.27] [1084.27][S01]So there's the so-called deforestation alerts.[1085.83] [1085.83][S01]It's an incredible achievement.[1087.3] [1087.3][S01]But at the same time, it feels like that could be improved.[1089.798] [1089.798][S01]So obviously, if we could get it down[1091.34] [1091.34][S01]to the point where the alerts were much more accurate,[1094.74] [1094.74][S01]then it would be much more easy for people to act on them.[1097.167] [1097.167][S02] I guess this goes beyond just satellite data then.[1099.75] [1099.75][S02]You have to start working in other types of data,[1101.97] [1101.97][S02]wouldn't you?[1102.89] [1102.89][S01] Yes.[1103.85] [1103.85][S01]Well, I think so.[1104.61] [1104.61][S01]I mean, the interesting thing is that, you[1106.43] [1106.43][S01]can produce a model of change.[1108.53] [1108.53][S01]And you can train that on past data,[1110.61] [1110.61][S01]and you can do that very scientifically,[1112.29] [1112.29][S01]and you do all of your held out data and your validation and all[1115.032] [1115.032][S01]that clever machine learning stuff.[1116.49] [1116.49][S01]The problem is, on the other hand,[1117.907] [1117.907][S01]that the whole thing around climate change[1120.543] [1120.543][S01]and environmental change is the future is different to the past.[1123.21] [1123.21][S01]So how can we be confident that we can reapply[1125.84] [1125.84][S01]that model into the future?[1127.4] [1127.4][S02] This isn't like predicting the weather a week[1130.2] [1130.2][S02]in advance.[1131.43] [1131.43][S02]The whole underlying model is different.[1133.267] [1133.267][S01] That's right.[1134.35] [1134.35][S01]And when you look at, say, this process of deforestation[1137.16] [1137.16][S01]that we've been talking about, that is a human-driven process.[1139.93] [1139.93][S01]And humans can change behavior very, very quickly in responses[1143.28] [1143.28][S01]to things like changes in the market.[1145.39] [1145.39][S01]Perhaps the price of soybeans goes up,[1148.41] [1148.41][S01]and then that encourages more rapid deforestation.[1150.642] [1150.642][S01]On the other hand, government regulation and policies[1152.85] [1152.85][S01]or local regulation and policies and so on-- so[1156.36] [1156.36][S01]traditional machine learning approaches[1158.012] [1158.012][S01]are going to really struggle to deal with that because they[1160.47] [1160.47][S01]can only deal with numbers and they can only[1162.303] [1162.303][S01]deal with the past.[1163.21] [1163.21][S01]And I think that's one of the reasons why we're[1164.31] [1164.31][S01]really excited about hybridizing,[1166.03] [1166.03][S01]in a sense, the best of the scientific tradition[1168.03] [1168.03][S01]of simulation modeling, with the machine[1169.92] [1169.92][S01]learning, data-driven approach to forecasting with actually[1173.565] [1173.565][S01]things like the large language models, like Gemini,[1175.69] [1175.69][S01]which potentially could pick up on changes in government policy,[1179.59] [1179.59][S01]news, media, perhaps integrate together things[1182.13] [1182.13][S01]like agricultural prices and so on to help[1185.25] [1185.25][S01]us at least to slightly adjust and identify[1188.43] [1188.43][S01]some of the uncertainty scenarios[1190.32] [1190.32][S01]around those future predictions.[1192.255] [1192.255][S02] If that's vegetation,[1193.63] [1193.63][S02]though, if that's trees and hedgerows and shrubs,[1196.15] [1196.15][S02]what about--[1196.71] [1196.71][S02]I mean, we also care quite a lot about the creatures[1198.54] [1198.54][S02]that are living inside them.[1199.81] [1199.81][S02]How do you bring that in?[1201.22] [1201.22][S01] So habitats is one thing.[1202.81] [1202.81][S01]But then most species are not visible from space,[1205.71] [1205.71][S01]and they don't form a habitat in that sense.[1207.88] [1207.88][S01]Instead, it's all the insects, the fungi, the birds, et cetera.[1211.12] [1211.12][S01]And so much of our concern around biodiversity and so much[1214.32] [1214.32][S01]of the action around conservation[1216.03] [1216.03][S01]is actually around those species.[1217.69] [1217.69][S01]And so you look surprised earlier[1219.42] [1219.42][S01]when you said there isn't an agreed map of forest,[1222.333] [1222.333][S01]and that's true.[1223.0] [1223.0][S01]But that's also true for most of the species in the world.[1225.94] [1225.94][S01]Now, again, it's really important to realize everything[1228.54] [1228.54][S01]we're doing in ecology and biodiversity builds on[1231.72] [1231.72][S01]this huge foundation of organizations and hard work[1234.447] [1234.447][S01]from the past.[1235.03] [1235.03][S01]So there's an amazing organization called the IUCN[1237.24] [1237.24][S01]that does provide maps for 140,000 species worldwide.[1240.85] [1240.85][S01]It's amazing.[1242.16] [1242.16][S01]On the other hand, those maps are very coarse.[1245.1] [1245.1][S01]They're driven by expert opinion.[1246.77] [1246.77][S01]Those experts, they really know what they're talking about.[1249.228] [1249.228][S01]But necessarily, they're quite coarse maps, so 50 kilometer[1252.7] [1252.7][S01]or so cells.[1254.28] [1254.28][S01]And they're only refreshed about every 10 years.[1256.28] [1256.28][S02] Oh, wow.[1256.88] [1256.88][S01] But there's an obvious potential role there[1259.213] [1259.213][S01]for producing, in theory, much better maps[1262.18] [1262.18][S01]using the latest in AI and remote sensing, et cetera.[1265.18] [1265.18][S01]So we're exploring that as well.[1266.78] [1266.78][S02] OK, but then how do you do it, though?[1268.97] [1268.97][S02]Because you haven't got the equivalent of satellites[1272.92] [1272.92][S02]in space photographing insects.[1275.147] [1275.147][S01] That's right.[1276.23] [1276.23][S01]You could view it as a sort of prediction problem.[1277.79] [1277.79][S01]What you're saying is the signals[1279.165] [1279.165][S01]I do have, like the satellite signals and other input data,[1283.73] [1283.73][S01]can help me produce a sort of probabilistic estimate of how[1287.08] [1287.08][S01]likely something would be then.[1288.735] [1288.735][S01]And there's probably always quite[1290.11] [1290.11][S01]a large residual uncertainty on that.[1292.78] [1292.78][S01]So how do you go about doing that?[1294.23] [1294.23][S01]Interestingly, at the absolutely coarsest level,[1296.9] [1296.9][S01]it's the same old, in a sense, machine learning approach.[1300.607] [1300.607][S01]In other words, you take your training data.[1302.44] [1302.44][S01]And you take your input data, like the satellites.[1304.58] [1304.58][S01]And you bring in a special model, and you fit the model,[1306.44] [1306.44][S01]and you get the output, and you evaluate it.[1308.273] [1308.273][S01]It's the classic stuff.[1309.24] [1309.24][S01]But in this case, it's really challenging[1311.18] [1311.18][S01]because the data we're bringing in mostly[1313.01] [1313.01][S01]is citizen science data.[1314.6] [1314.6][S01]So this is people out in the field noticing--[1316.475] [1316.475][S02] Who've spotted something, yeah.[1317.4] [1317.4][S01] That's right.[1317.88] [1317.88][S01]It's an incredible platform called iNaturalist.[1320.04] [1320.04][S01]It's on your phone now.[1321.33] [1321.33][S01]And if you see something, you can take a snapshot of it,[1324.33] [1324.33][S01]and then you can upload that to iNaturalist.[1326.562] [1326.562][S01]And iNaturalist actually have their own machine[1328.52] [1328.52][S01]learning models so they can classify that image[1330.478] [1330.478][S01]to species, which is fantastic.[1332.43] [1332.43][S01]And this is now the main source of[1334.91] [1334.91][S01]these on-the-ground observations of species[1337.468] [1337.468][S01]globally, is coming from this amazing phone[1339.26] [1339.26][S01]app called iNaturalist.[1340.29] [1340.29][S01]So that's fantastic.[1341.34] [1341.34][S01]But actually, that data is still very biased and patchy.[1343.903] [1343.903][S02] Why?[1344.57] [1344.57][S01] So, for example, people typically[1346.94] [1346.94][S01]are close to their home when they take a picture.[1349.73] [1349.73][S01]And so where people go, when they go,[1352.01] [1352.01][S01]they'll typically go on a nice day.[1353.85] [1353.85][S01]And also what they choose to look at and take pictures[1356.12] [1356.12][S01]of-- so what you'll find is you'll[1357.62] [1357.62][S01]get lots of pictures of brightly colored birds close[1359.96] [1359.96][S01]to cities on sunny days.[1361.918] [1361.918][S02] Right, I see.[1362.96] [1362.96][S01] And what you don't get[1364.418] [1364.418][S01]is many pictures of small brown mushrooms in remote places[1368.24] [1368.24][S01]when it's raining.[1370.04] [1370.04][S01]And although I'm joking about that,[1371.64] [1371.64][S01]there's actually a much more serious element to this,[1373.52] [1373.52][S01]too, which is when you look globally[1375.08] [1375.08][S01]at the distribution of citizen science data-- there are[1376.88] [1376.88][S01]major areas of the world with very little-- sub-Saharan[1378.83] [1378.83][S01]Africa, for example.[1379.98] [1379.98][S01]Because actually, iNaturalist depends[1381.68] [1381.68][S01]on having a certain amount of leisure time,[1383.472] [1383.472][S01]really, to do these sorts of activities.[1385.14] [1385.14][S02] Yeah, and also speed of internet connection[1387.8] [1387.8][S02]and quality of your smartphone camera.[1390.0] [1390.0][S01] That's right.[1390.33] [1390.33][S01]And this is a real irony, then, for two reasons, in a way.[1392.747] [1392.747][S01]One is from the fundamental ecology.[1394.743] [1394.743][S01]If you weren't careful, if you naive about it,[1396.66] [1396.66][S01]you would conclude that all species like[1398.69] [1398.69][S01]to live near to middle-class neighborhoods[1400.85] [1400.85][S01]on the edge of cities, right?[1402.5] [1402.5][S01]Because that's where most of the data is.[1404.63] [1404.63][S01]And then, globally, there's a real irony there,[1407.04] [1407.04][S01]which is we're lacking data in many of the places that[1410.09] [1410.09][S01]have the most biodiversity, in many of the places where[1413.372] [1413.372][S01]the current threats to biodiversity are the greatest,[1415.58] [1415.58][S01]and where people's livelihoods depend most on biodiversity,[1418.14] [1418.14][S01]which is just kind of awful.[1419.43] [1419.43][S01]And so the magic, in a sense, that we're[1421.46] [1421.46][S01]looking for from deep learning in this space[1423.96] [1423.96][S01]is to see if we can somehow generalize[1425.91] [1425.91][S01]on this very patchy, biased data that we[1427.83] [1427.83][S01]do have to ecologically plausible distributions for all[1432.66] [1432.66][S01]of the species worldwide.[1433.87] [1433.87][S01]And that's a big challenge.[1435.01] [1435.01][S01]And at the moment, I think you would say the jury is out as[1437.468] [1437.468][S01]to whether it's possible.[1438.55] [1438.55][S01]But we are seeing some evidence of it.[1440.08] [1440.08][S02] But does the things that you've learned and that you[1442.747] [1442.747][S02]now "know" in inverted commas about the vegetation then feed[1445.47] [1445.47][S02]into where you expect the species to be?[1448.2] [1448.2][S02]Does one help the other?[1449.2] [1449.2][S01] Firstly, that's a really good point,[1451.242] [1451.242][S01]that we know enough about ecology to know[1453.6] [1453.6][S01]that, actually, things like climate and elevation[1455.97] [1455.97][S01]and habitat are very strong predictors of species.[1458.785] [1458.785][S01]So you're absolutely right.[1459.91] [1459.91][S01]We've already talked about the fact[1461.368] [1461.368][S01]that we can classify habitats from space down to a fine grain.[1464.52] [1464.52][S01]So that itself we would expect to be highly predictive[1466.77] [1466.77][S01]of which species are there.[1468.51] [1468.51][S01]And so then bringing in all this observation data,[1471.163] [1471.163][S01]then we can look at the overlap between these in the areas[1473.58] [1473.58][S01]where we do have data and hopefully pick up[1476.01] [1476.01][S01]a general enough understanding to then be[1478.08] [1478.08][S01]able to reapply that globally.[1480.497] [1480.497][S01]Because there is another challenge here, which is,[1482.58] [1482.58][S01]you have these long-tail distributions in ecology[1485.228] [1485.228][S01]all over the place.[1486.02] [1486.02][S01]You have a few common species and then lots of very rare ones.[1489.16] [1489.16][S01]So we need to do the same thing across the taxonomic tree[1492.1] [1492.1][S01]of life.[1493.112] [1493.112][S01]Can we learn the key relationships for the more[1495.07] [1495.07][S01]common species that are data-rich,[1497.15] [1497.15][S01]enabling us to make the best predictions we can for species[1500.24] [1500.24][S01]actually fairly poor, data-poor?[1502.25] [1502.25][S01]So if we can learn about--[1504.85] [1504.85][S01]so, for example, if we bring in species trait data,[1507.29] [1507.29][S01]we might find out that, in this part of the tree of life,[1510.23] [1510.23][S01]let's say, it's the larger-bodied species that[1513.76] [1513.76][S01]tend to live in the colder environments.[1516.02] [1516.02][S01]And we might be able to discover laws like that in places[1519.762] [1519.762][S01]where we do have lots of data like Europe[1521.47] [1521.47][S01]and the US in a way one day where[1523.488] [1523.488][S01]we can apply that with a certain level of confidence in areas[1526.03] [1526.03][S01]where we have much less data, like the Global South.[1529.702] [1529.702][S01]Not that we'd ever have a blind guess.[1532.34] [1532.34][S01]But what it would really do is lower the data requirements[1534.91] [1534.91][S01]in those areas.[1535.54] [1535.54][S01]Once we've learned some of the more general rules,[1537.59] [1537.59][S01]it would mean that actually a relatively small amount[1539.798] [1539.798][S01]of observational data may be enough to anchor[1541.9] [1541.9][S01]the distribution of that species.[1543.45] [1543.45][S01]Such an exciting space.[1544.62] [1544.62][S02] I do wonder about other types of data here.[1546.96] [1546.96][S02]You're talking about data about the landscape, about elevation,[1550.08] [1550.08][S02]about the vegetation.[1551.94] [1551.94][S02]But are there other elements of data like photographs,[1555.63] [1555.63][S02]for instance, that you can also bring in here?[1557.703] [1557.703][S01] Yes, there are.[1558.87] [1558.87][S01]And in general, you could think of that[1560.495] [1560.495][S01]as this trend towards multimodal models and multimodal AI.[1564.21] [1564.21][S01]And that particularly lights up, I[1565.9] [1565.9][S01]think, in the world of ecology and biodiversity science[1569.66] [1569.66][S01]because you've got such a wide variety of organisms[1572.0] [1572.0][S01]at different scales, and so different modalities suit[1575.78] [1575.78][S01]monitoring different kinds of species.[1578.39] [1578.39][S01]So talking there, you've got images,[1581.137] [1581.137][S01]let's say, humans take deliberately with a camera.[1583.22] [1583.22][S01]They're the ones that, say, iNaturalist specializes in.[1586.107] [1586.107][S01]Often, though, say in the Serengeti Projects and others,[1588.44] [1588.44][S01]we're talking about so-called camera traps.[1590.37] [1590.37][S01]These are cameras that are motion-activated cameras.[1592.802] [1592.802][S01]And a lot of the time, they've got infrared lights on them.[1595.26] [1595.26][S01]So they're actually taking infrared images at night.[1597.51] [1597.51][S01]So there are different kinds of images you can take.[1600.75] [1600.75][S01]You've then got aerial imagery, let's say,[1602.6] [1602.6][S01]from drones or from planes.[1604.245] [1604.245][S01]You've got the remote-sensing satellite[1605.87] [1605.87][S01]imagery we're talking about.[1607.17] [1607.17][S01]And then also, you've got bioacoustics,[1609.21] [1609.21][S01]so-called bioacoustics, is where you're deploying microphones[1611.87] [1611.87][S01]in the field.[1613.31] [1613.31][S01]That might be familiar to a lot of people watching this,[1615.745] [1615.745][S01]for example, around the apps that[1617.12] [1617.12][S01]identify birdsong, Merlin and eBird and similar.[1620.113] [1620.113][S02] Because those already exist.[1621.78] [1621.78][S02]I've played with them a little bit.[1622.88] [1622.88][S02]You're outside.[1623.7] [1623.7][S02]You turn it on.[1624.42] [1624.42][S02]It tells you what birds you're listening to.[1626.253] [1626.253][S01] That's right.[1627.337] [1627.337][S02] But that's supervised learning, is it,[1629.6] [1629.6][S02]that manages to do that?[1631.037] [1631.037][S01] Yeah, that's right.[1632.37] [1632.37][S01]So in all of these areas, you need[1635.94] [1635.94][S01]what we call a corpus of labeled data to start with.[1638.848] [1638.848][S01]So you need a picture with a label along with it.[1640.89] [1640.89][S01]This is a monarch butterfly, or this is a--[1644.47] [1644.47][S02] A crow, crow squawking.[1645.945] [1645.945][S01] Yeah, exactly.[1647.07] [1647.07][S01]All of this sort of stuff.[1647.97] [1647.97][S01]And with enough of that, you can then[1648.92] [1648.92][S01]train a model that generalizes.[1650.217] [1650.217][S01]So that's what's called supervised learning.[1652.05] [1652.05][S01]That's right.[1652.55] [1652.55][S01]And that's the power of most of these developments in each[1654.29] [1654.29][S01]of the individual modalities, whether that's[1656.42] [1656.42][S01]images, remote sensing, et cetera.[1658.8] [1658.8][S01]Yeah.[1659.3] [1659.3][S02] You've got a project, though, called Perch,[1661.8] [1661.8][S02]right?[1662.3] [1662.3][S02]Tell me about Perch.[1663.77] [1663.77][S01] So Perch is in this space of bioacoustics.[1666.23] [1666.23][S01]And the idea of deploying microphones in the field[1668.02] [1668.02][S01]is really attractive because there's almost always[1670.103] [1670.103][S01]something making a sound.[1671.152] [1671.152][S02] Yeah.[1671.86] [1671.86][S01] And it's incredibly rich,[1673.1] [1673.1][S01]the information we can extract from sound, actually.[1675.41] [1675.41][S01]And you can leave the microphones out for days on end[1678.7] [1678.7][S01]to record all that data.[1680.282] [1680.282][S01]And of course, sound is not necessarily just species,[1682.49] [1682.49][S01]but it can be behavior.[1684.02] [1684.02][S01]We might be able to distinguish juveniles from adults.[1686.27] [1686.27][S01]You might be able to pick up warning signals.[1688.33] [1688.33][S01]Insects make sounds.[1689.51] [1689.51][S01]Reptiles make sounds.[1690.77] [1690.77][S01]Birds make sounds.[1691.79] [1691.79][S01]Mammals make sounds.[1692.78] [1692.78][S01]And you pick all of that up.[1693.947] [1693.947][S01]So you don't have to have line of sight to the organism either.[1697.73] [1697.73][S01]It can work at night.[1698.75] [1698.75][S01]So it's an incredibly promising technology for pulling out data[1702.82] [1702.82][S01]from the field.[1703.71] [1703.71][S02] But if it's already been done, if you do already[1706.21] [1706.21][S02]have these apps that can tell if you're listening to a crow,[1708.71] [1708.71][S02]then what else is there to do?[1710.81] [1710.81][S01] Perch is taking a slightly different approach.[1713.268] [1713.268][S01]It's a foundational model for natural sound.[1715.78] [1715.78][S01]And what that does is, like other foundational models,[1718.25] [1718.25][S01]it's not out of the box, it's not just[1719.92] [1719.92][S01]providing you with the ability to identify[1722.738] [1722.738][S01]lots of different birds, for example.[1724.28] [1724.28][S01]It does do that.[1725.45] [1725.45][S01]But what it's really designed to do[1727.75] [1727.75][S01]is to allow people to rapidly create their own detectors[1730.48] [1730.48][S01]for things that haven't been in the detector already.[1733.173] [1733.173][S01]So for instance, if you're working[1734.59] [1734.59][S01]on a particularly rare species, it won't be in one of these apps[1737.35] [1737.35][S01]already.[1737.69] [1737.69][S01]It's too rare.[1738.273] [1738.273][S01]They haven't put it in there.[1739.55] [1739.55][S01]But you might also be working on a more familiar species,[1742.07] [1742.07][S01]but you'd like to divide the juveniles[1743.653] [1743.653][S01]from the adults or a local accent, for instance,[1746.24] [1746.24][S01]of a birdsong.[1747.745] [1747.745][S02] Because birds do have accents, do they?[1749.87] [1749.87][S01] Oh, [LAUGHS] well, yeah, yeah.[1751.45] [1751.45][S01]I think that's right.[1752.03] [1752.03][S01]They certainly have local variants on their songs.[1754.26] [1754.26][S01]So you'd be able to track these sorts of things.[1756.26] [1756.26][S02] So how does bioacoustic modeling actually[1757.78] [1757.78][S02]work then?[1758.692] [1758.692][S01] So the way Perch works[1760.15] [1760.15][S01]is it brings in these audio data files,[1762.91] [1762.91][S01]and they're huge, so lots and lots of data.[1765.23] [1765.23][S01]And it crunches all that down into a much smaller amount[1767.89] [1767.89][S01]of data, which is this so-called embedding.[1770.93] [1770.93][S01]And then that embedding, then, can[1772.78] [1772.78][S01]have all these downstream uses, the most obvious of which[1775.63] [1775.63][S01]is developing new detectors.[1777.243] [1777.243][S01]So you can think of it as a big kind of process that[1779.41] [1779.41][S01]takes huge amounts of data in, and it crunches it down[1781.66] [1781.66][S01]to a small amount of data that retains[1784.09] [1784.09][S01]all of the salient signal that you'd[1786.04] [1786.04][S01]want for understanding ecology and identifying species[1790.382] [1790.382][S01]and has thrown away as much of the noise as possible.[1792.59] [1792.59][S01]However, there's also, for example,[1794.12] [1794.12][S01]at least one other important use case, which is called Search.[1796.703] [1796.703][S01]So if you take different audio clips and crunch them down here,[1799.64] [1799.64][S01]you can also then easily go and find similar audio clips.[1802.015] [1802.015][S01]Because often, one of the main barriers[1803.64] [1803.64][S01]is, let's say, you're looking to monitor some rare species,[1806.2] [1806.2][S01]and you put the microphone out for hours.[1808.64] [1808.64][S01]Well, where's the chirp?[1810.56] [1810.56][S01]You've got to listen to the thing.[1813.02] [1813.02][S01]But if you can just find one or two here, then you can say,[1816.2] [1816.2][S01]right now, I can find the similar ones[1818.5] [1818.5][S01]because I'm looking for the ones that have got a similar Perch[1821.11] [1821.11][S01]embedding to these.[1822.0] [1822.0][S01]And even a third one, which I don't mind speculating around,[1824.5] [1824.5][S01]an idea I've been talking with a lot of colleagues about[1826.833] [1826.833][S01]is, if you're looking to do something verify[1829.57] [1829.57][S01]ecological restoration in the field, you can imagine,[1832.33] [1832.33][S01]if I put microphones in a good place, like a nature reserve[1835.083] [1835.083][S01]and in some other place like my garden--[1836.75] [1836.75][S01]I've been rewilding my garden--[1838.73] [1838.73][S01]then through time, you can see whether the Perch embeddings,[1842.0] [1842.0][S01]say, in my garden become more similar to the local nature[1844.4] [1844.4][S01]reserve.[1844.98] [1844.98][S02] OK, so wait, wait, let me understand that then.[1846.69] [1846.69][S02]So let's say you're doing a project where[1848.398] [1848.398][S02]you want to increase the occupancy of a particular type[1851.66] [1851.66][S02]of species.[1852.66] [1852.66][S02]You can put a microphone in there[1854.3] [1854.3][S02]and work out whether your intervention is[1856.28] [1856.28][S02]increasing the population of whatever it might be.[1858.363] [1858.363][S01] That's absolutely right.[1859.905] [1859.905][S01]So there's at least two scenarios to think about.[1862.118] [1862.118][S01]One is, yes, if you're focused on one[1863.66] [1863.66][S01]or more particular species, then Perch[1865.513] [1865.513][S01]would be massively helpful because you could develop[1867.68] [1867.68][S01]detectors for those species.[1869.04] [1869.04][S01]And we also know it's not just the species, yes/no,[1871.165] [1871.165][S01]but the frequency of the calls and even some[1872.998] [1872.998][S01]of the variation in the calls.[1874.26] [1874.26][S01]So in some cases, you can tell individuals apart, by the way,[1876.802] [1876.802][S01]here.[1877.35] [1877.35][S01]So we can say, is it one bird singing all day long,[1880.372] [1880.372][S01]or do we have four or five birds singing?[1882.08] [1882.08][S01]And Perch can even help with that.[1884.27] [1884.27][S02] That specific bird--[1885.845] [1885.845][S01] Specific bird.[1886.97] [1886.97][S02] Wow.[1887.285] [1887.285][S02]OK.[1887.6] [1887.6][S01] Exactly, yeah.[1888.44] [1888.44][S01]It's really important to know I've got lots of blackbird song,[1891.09] [1891.09][S01]but is it 1 blackbird, or is it 10 blackbirds?[1893.04] [1893.04][S01]So in this way, you can use bioacoustics in combination[1896.54] [1896.54][S01]with Perch to increase your ability[1898.23] [1898.23][S01]to measure not just the presence or absence of species[1900.882] [1900.882][S01]but the abundance and then the change in that through time.[1903.34] [1903.34][S01]It's getting more abundant.[1904.465] [1904.465][S01]So that's one scenario, very species-focused view.[1907.0] [1907.0][S01]And this is how most people currently approach biodiversity[1910.35] [1910.35][S01]and ecosystems, is through this species[1912.0] [1912.0][S01]lens, which is very important.[1913.45] [1913.45][S01]But it's also interesting to me--[1914.92] [1914.92][S01]and I'm sharing an idea here that I've[1916.59] [1916.59][S01]shared with lots of colleagues, which[1917.73] [1917.73][S01]is in some areas of the world where you've got a really high[1920.37] [1920.37][S01]biodiversity and much less data, much less understanding, what[1923.91] [1923.91][S01]you could do instead is almost view the Perch[1927.0] [1927.0][S01]distillation as a distillation of the audio ecology of a place,[1930.1] [1930.1][S01]even if you can't identify all the species.[1932.08] [1932.08][S01]And then if I pick a good place, like a local nature reserve,[1935.74] [1935.74][S01]and I pick a place that I'm looking to restore,[1938.092] [1938.092][S01]then I could just say on aggregate,[1939.55] [1939.55][S01]are the Perch embeddings becoming more similar?[1941.34] [1941.34][S01]And I may not know which species are involved.[1943.257] [1943.257][S01]I've got all these things going, pops and clicks and whistles[1947.745] [1947.745][S01]and et cetera.[1950.55] [1950.55][S01]Because what Perch, in a sense, is[1953.25] [1953.25][S01]a kind of deep learning numerical answer[1955.89] [1955.89][S01]to the question, what does the nature sound like in this place?[1959.84] [1959.84][S01]And so if, through time, I could say[1961.54] [1961.54][S01]my place is becoming, in terms of that sound,[1965.157] [1965.157][S01]more similar to some reference place--[1966.74] [1966.74][S02] Well, let me ask though, because--[1968.657] [1968.657][S02]OK, so Perch, given the name, sounds quite a lot like it's[1971.29] [1971.29][S02]mainly focused on birds.[1972.305] [1972.305][S02]But can you put this into other environments?[1974.18] [1974.18][S02]What about underwater?[1975.218] [1975.218][S02]Does it work there?[1976.01] [1976.01][S01] So you're right.[1977.218] [1977.218][S01]Perch started by just classifying very large numbers[1979.96] [1979.96][S01]of bird species.[1981.798] [1981.798][S01]But amazingly, it turned out that then when[1983.59] [1983.59][S01]they tried it underwater on these hydrophones,[1985.782] [1985.782][S01]it seems to work really well.[1986.99] [1986.99][S02] What, the same model?[1987.95] [1987.95][S01] Exactly the same model worked really well.[1989.8] [1989.8][S02] What?[1990.11] [1990.11][S01] And I think what this[1990.85] [1990.85][S01]teaches us is that there's something[1992.44] [1992.44][S01]about natural sound, the way that that's evolved,[1995.24] [1995.24][S01]that actually has--[1996.41] [1996.41][S01]now, of course, it won't be perfect,[1997.91] [1997.91][S01]but it has a surprisingly high degree of transfer[2000.27] [2000.27][S01]into underwater.[2002.05] [2002.05][S01]There's this whole area of acoustic ecology,[2004.145] [2004.145][S01]and we know that-- well, we think[2005.52] [2005.52][S01]there's evidence for the fact that species have evolved away[2008.4] [2008.4][S01]from each other in terms of their use of the audio spectrum[2011.43] [2011.43][S01]because it's easy to forget that most of the sound you're picking[2014.25] [2014.25][S01]up in bioacoustics is deliberately[2017.19] [2017.19][S01]produced by organisms.[2018.298] [2018.298][S01]It's communication.[2019.09] [2019.09][S01]That's what's amazing.[2019.87] [2019.87][S01]They're communicating with each other.[2021.453] [2021.453][S01]And just like us, they need to think about the spectral bands.[2024.12] [2024.12][S02] They want to be heard.[2024.91] [2024.91][S01] Yeah, audio spectral bands.[2025.87] [2025.87][S01]So they're differentiated.[2027.18] [2027.18][S01]So it looks like--[2027.93] [2027.93][S02] So some use low sounds,[2029.14] [2029.14][S02]some use high sounds because you want[2030.81] [2030.81][S02]to make sure that you're communication is successful.[2033.337] [2033.337][S01] Yes, exactly.[2034.42] [2034.42][S01]So yeah, overall high and low.[2035.8] [2035.8][S01]And then you think about the temporal thing.[2037.71] [2037.71][S01]Is it like da, da, da, da, da, or is it ou, ou, ou thing?[2041.17] [2041.17][S01]So you can imagine that this is somehow[2043.26] [2043.26][S01]evolved as an interaction not just within species,[2045.82] [2045.82][S01]communication, but between all the species.[2047.98] [2047.98][S01]They've all responded to each other somehow[2049.29] [2049.29][S01]and settled down on these different patterns.[2051.19] [2051.19][S02] And what do you find when[2052.139] [2052.139][S02]you put microphones underwater?[2053.469] [2053.469][S02]What do you get from these hydrophones?[2055.179] [2055.179][S01] So we've cued up actually a couple[2057.36] [2057.36][S01]of sound recordings from coral reefs[2059.245] [2059.245][S01]that I'd like to listen to.[2060.37] [2060.37][S02] OK, go for it.[2061.75] [2061.75][CRACKLING, GURGLING][2062.969] [2062.969][S02]I mean, oh, some grumbles going on, some squeaks.[2069.0] [2069.0][S02]Quite crackly as well.[2070.302] [2070.302][S01] Yeah, and then here's another one.[2072.26] [2072.26][CRACKLING][2073.71] [2073.71][S02] Much quieter.[2075.67] [2075.67][S02]No grumbles.[2078.4] [2078.4][S02]I mean, what does that say about this coral reef, though?[2081.032] [2081.032][S01] So maybe now it won't[2082.449] [2082.449][S01]surprise you to hear that the first one of those[2084.449] [2084.449][S01]was a much healthier coral reef than the second.[2086.872] [2086.872][S02] Oh, really?[2087.83] [2087.83][S01] So that diversity and richness[2089.889] [2089.889][S01]of ecological sound, animal communication,[2092.889] [2092.889][S01]is a strong indication of a healthy ecosystem, you see?[2095.872] [2095.872][S01]So even as humans, we can hear that difference.[2097.83] [2097.83][S02] Wow.[2098.497] [2098.497][S02]So you really have that richness of understanding[2101.77] [2101.77][S02]the environment itself just from the audio signal.[2103.853] [2103.853][S01] That's right.[2104.937] [2104.937][S01]And that's an illustration of how even just that overall--[2107.39] [2107.39][S01]if we can embed that overall signal in a way,[2110.04] [2110.04][S01]that can just give us a general indication[2111.79] [2111.79][S01]of the health of ecosystems.[2113.48] [2113.48][S01]Of course, with more specific species data,[2115.58] [2115.58][S01]we can actually start to tease out individual species,[2118.16] [2118.16][S01]so if we're particularly concerned[2119.89] [2119.89][S01]with an endangered species, maybe even invasive species.[2123.34] [2123.34][S01]And we can pull out seasonal and daily temporal patterns[2127.24] [2127.24][S01]in animal behavior.[2128.99] [2128.99][S01]And we can start to understand geographic differences[2132.22] [2132.22][S01]in where species live, all just by dropping these microphones,[2135.2] [2135.2][S01]in this case, into the ocean.[2136.62] [2136.62][S02] But this is an explosion in possibilities.[2138.87] [2138.87][S02]Because if, previously, you stuck a microphone underwater,[2141.703] [2141.703][S02]you'd have to listen through hours[2143.12] [2143.12][S02]and hours and hours of tapes and have no real way of--[2146.067] [2146.067][S02]I mean, it's basically the human brain[2147.65] [2147.65][S02]trying to pick up on patterns.[2149.09] [2149.09][S02]But now, if you can single out not just individual species[2153.93] [2153.93][S02]but individual voices, as it were,[2155.88] [2155.88][S02]from particular individuals, that's gigantic, right?[2159.32] [2159.32][S02]Gigantic potential.[2160.112] [2160.112][S01] Yeah, it's absolutely huge.[2161.778] [2161.778][S01]That's why so many of us are so excited by the power[2164.21] [2164.21][S01]of bioacoustics combined with deep learning in this way[2166.913] [2166.913][S01]and especially with this foundational approach[2168.83] [2168.83][S01]to bioacoustics that Perch is bringing.[2170.84] [2170.84][S01]And it has at least two advantages[2172.263] [2172.263][S01]that you've hinted at there.[2173.43] [2173.43][S01]One is that it can just massively accelerate[2175.765] [2175.765][S01]what humans could do.[2176.64] [2176.64][S01]So you could take a problem that humans could do.[2178.682] [2178.682][S01]It's just that you'd have to have[2180.2] [2180.2][S01]hundreds of people listening to thousands of hours of audio.[2183.21] [2183.21][S01]So it was very, very inefficient.[2184.932] [2184.932][S01]But you're right.[2185.64] [2185.64][S01]There are also areas where you're going[2187.265] [2187.265][S01]go beyond human capabilities.[2189.24] [2189.24][S01]Humans are not necessarily all that[2191.06] [2191.06][S01]good at parsing out natural sounds[2193.04] [2193.04][S01]or identifying species from sound.[2194.49] [2194.49][S01]It's quite hard skill to learn.[2195.66] [2195.66][S01]And there may be cases like pulling out[2197.285] [2197.285][S01]different individuals might eventually be even too[2199.4] [2199.4][S01]subtle for humans to do.[2200.637] [2200.637][S02] Staying underwater for a moment,[2202.47] [2202.47][S02]there are some species--[2203.47] [2203.47][S02]I'm thinking about whales and dolphins[2205.25] [2205.25][S02]here-- where there is some evidence[2207.44] [2207.44][S02]that their communication is sophisticated, right?[2210.39] [2210.39][S02]Can you use these ideas for that?[2212.76] [2212.76][S02]Can you-- I don't know-- learn to speak dolphin?[2216.51] [2216.51][S01] [CHUCKLES] I like to think[2218.27] [2218.27][S01]that we will be able to talk to animals using AI at some point[2221.01] [2221.01][S01]and that these embedding foundational modeling approaches[2223.88] [2223.88][S01]like Perch will be really important in that.[2225.95] [2225.95][S01]But, of course, you'd need some other elements there.[2228.207] [2228.207][S01]Interestingly, some colleagues of mine[2229.79] [2229.79][S01]have been involved in producing this thing called DolphinGemma,[2233.58] [2233.58][S01]which is a large language model that's[2236.87] [2236.87][S01]been adapted to be suitable for beginning to decode dolphin[2240.8] [2240.8][S01]communication.[2242.04] [2242.04][S01]It takes the sounds, separates them out, tokenizes them,[2245.76] [2245.76][S01]and basically brings it into the world of large language[2248.17] [2248.17][S01]modeling.[2248.67] [2248.67][S01]So that's an example of it's early days,[2250.54] [2250.54][S01]but that's an example of AI actively being[2252.29] [2252.29][S01]used to study animal communication at a level[2254.91] [2254.91][S01]that we really couldn't do before.[2256.45] [2256.45][S02] The thing is, I can see[2257.04] [2257.04][S02]the potential for this in terms of scientific interest.[2259.87] [2259.87][S02]But I do wonder whether--[2261.93] [2261.93][S02]let's say we get to a point where[2263.91] [2263.91][S02]you can understand what dolphins are saying, communicate[2266.76] [2266.76][S02]with the higher animals on the planet.[2269.07] [2269.07][S02]Does it change how we view ourselves[2271.08] [2271.08][S02]and our place in the world?[2272.35] [2272.35][S01] I think yeah.[2272.83] [2272.83][S01]I think it absolutely has that potential.[2274.782] [2274.782][S01]Most of the work we're doing at the moment, as I mentioned,[2277.24] [2277.24][S01]is filling known information gaps[2279.54] [2279.54][S01]in known processes, et cetera.[2281.46] [2281.46][S01]But sometimes you think that the real change[2284.07] [2284.07][S01]can come, in the long run, from almost these moments[2287.82] [2287.82][S01]of awakening where people almost overnight can change[2291.3] [2291.3][S01]their relationship with nature.[2292.81] [2292.81][S01]And I think we've seen at least two examples in the past anyway.[2295.477] [2295.477][S01]One was the first picture of the Earth on the moon.[2297.91] [2297.91][S01]And I guess historians can debate this,[2300.22] [2300.22][S01]but they often trace some of the more recent increases[2302.82] [2302.82][S01]in the modern conservation movement to that picture[2306.077] [2306.077][S01]when people looked at the Earth for the first time[2308.16] [2308.16][S01]and realized there was just one in this dark universe[2310.48] [2310.48][S01]and that all of us shared it together, et cetera.[2313.57] [2313.57][S01]Another one is actually whale song,[2315.13] [2315.13][S01]and this is just listening to whales, even if we[2317.688] [2317.688][S01]can't tell what they're saying.[2318.98] [2318.98][S01]That really changed the way people thought about whales.[2321.313] [2321.313][S01]So the idea of being able to do that for more species[2323.608] [2323.608][S01]with things like Perch but then being[2325.15] [2325.15][S01]able to decode that to say what they're actually saying[2327.2] [2327.2][S01]and maybe one day even have some kind of conversation.[2329.45] [2329.45][S01]And if AI can help to empower that,[2330.925] [2330.925][S01]that might, in the long run, be the most powerful role of AI.[2333.467] [2333.467][S02] Well, OK, we've covered a lot of ground[2335.592] [2335.592][S02]in this episode.[2336.8] [2336.8][S02]So maybe I'll finish by asking about the future.[2340.01] [2340.01][S02]How will AI change the kinds of questions[2342.76] [2342.76][S02]that ecologists can ask?[2344.473] [2344.473][S01] So I think up to now,[2345.89] [2345.89][S01]as we've discussed, amazing progress in our ability[2348.43] [2348.43][S01]to monitor and extract data from the field[2350.62] [2350.62][S01]and from the literature.[2351.77] [2351.77][S01]Amazing progress, I think, in mapping and geo.[2354.7] [2354.7][S01]It's only going to get better.[2356.27] [2356.27][S01]I think the couple of the future directions[2358.24] [2358.24][S01]to explore-- one is, what if we really could confidently predict[2362.32] [2362.32][S01]the future of ecosystems?[2364.55] [2364.55][S01]All the plants, all the insects, everything, the soil,[2368.42] [2368.42][S01]the soil organisms, the fungi under a range[2370.57] [2370.57][S01]of different scenarios.[2371.87] [2371.87][S01]If I do this, this is what the ecosystem[2373.61] [2373.61][S01]will look like in 10 years.[2374.735] [2374.735][S01]If I do this, this will happen.[2376.14] [2376.14][S01]And also in a way that took into account future climate change,[2379.36] [2379.36][S01]et cetera.[2379.86] [2379.86][S01]So these future-proof, robust, conditional predictions[2383.72] [2383.72][S01]of the future of ecosystems down at the fine grain.[2386.14] [2386.14][S01]If we could do that, of course, that[2387.64] [2387.64][S01]could unlock an entirely different sort of relationship[2390.35] [2390.35][S01]between us and nature.[2391.91] [2391.91][S01]We could use that simulation ability[2393.502] [2393.502][S01]to then be able to find all the optimal ways that we should[2395.96] [2395.96][S01]behave, identify the key trade-offs-- which species[2398.48] [2398.48][S01]to plan, build new kinds of regenerative agriculture systems[2402.17] [2402.17][S01]and agroforestry with mixed species.[2404.313] [2404.313][S01]We've also got rewilding, and we're also[2405.98] [2405.98][S01]bringing the soil carbon back, and it's all[2407.45] [2407.45][S01]robust to climate change.[2408.51] [2408.51][S01]So if we could do that, then every person[2410.42] [2410.42][S01]that's affecting nature or taking a decision that they[2412.67] [2412.67][S01]think affects nature would be fully[2414.128] [2414.128][S01]informed as to the future consequences of those actions.[2417.5] [2417.5][S01]And that feels, to me, like an amazing potential.[2421.04] [2421.04][S01]Of course, it would depend on values as well,[2423.06] [2423.06][S01]but I would hope that, on average, most people[2424.977] [2424.977][S01]would use that ability to do good for nature.[2427.647] [2427.647][S02] Amazing.[2428.48] [2428.48][S02]Drew, thank you so much for joining me.[2430.105] [2430.105][S01] Oh, thanks for the invite.[2431.73] [2431.73][S01]It's been great, really.[2432.83] [2432.83][S02] I think it can be tempting to imagine that ecology[2435.47] [2435.47][S02]is a bit of a niche application for AI,[2437.64] [2437.64][S02]a nice, worthy project that sits behind all[2440.39] [2440.39][S02]of the big, flashy advances like video generation and drug[2443.57] [2443.57][S02]discovery.[2444.54] [2444.54][S02]But in reality, this is just a few steps behind the others.[2448.995] [2448.995][S02]They're still at the data gathering and data assimilation[2451.37] [2451.37][S02]phase of the process, but the roadmap here[2454.49] [2454.49][S02]is just as ambitious.[2456.63] [2456.63][S02]And in the long run, I think there is real potential[2459.23] [2459.23][S02]here not to just use AI to conserve what we have already[2463.38] [2463.38][S02]but to completely change our relationship[2465.71] [2465.71][S02]with the natural world itself.[2467.705] [2467.705][S02]You have been listening to "Google DeepMind--[2469.58] [2469.58][S02]The Podcast" with me, Professor Hannah Fry.[2471.87] [2471.87][S02]If you enjoyed this episode, then[2473.63] [2473.63][S02]do subscribe to our YouTube channel[2475.49] [2475.49][S02]or leave a review on your favorite podcast platform.[2478.14] [2478.14][S02]And of course, we have plenty more episodes[2480.98] [2480.98][S02]on a whole range of topics to come, so do check those out.[2484.38] [2484.38][S02]See you next time.[2485.3] [2485.3][MUSIC PLAYING][2488.35]