自然悖论与经济生态的误区
在过去十年里，我一直在致力于解决一个深刻的悖论。简单来说，如果你随机与一百个人交谈，问他们对大自然的感受，你会得到极其积极的回答，比如“大自然令人振奋”、“大自然是最美好的事物”。这种喜爱体现在方方面面，人们将自然景象设为桌面或手机壁纸。然而，尽管每个人都对自然怀有积极情感，但作为一个集体文明，我们却在以行星规模摧毁自然。这就是悖论所在：一个热爱自然的个体群体，如何创造出一个对自然进行行星级别攻击的工业化经济体？我认为，这源于我们无意识中采纳了一个“破碎的心智模型”。
这个被我们意外采纳的心智模型，将经济的增长与生态的健康对立起来。它认为，要想获得经济上的成功，就必须牺牲生态环境；反之，如果想保护生态，就必须牺牲经济利益。这就像一个跷跷板，根据我们对经济或生态的重视程度，天平会向一端倾斜。虽然这是一种可以采取的心理立场，但它在物理现实中并不完全准确。我是一名受过严格物理学训练的人，我思考的是物理上的真实。
Original English
So over the last decade, I've been working on resolving a profound paradox. And, simply stated, it's that if you go and talk with any person, actually -- you could go grab 100 random people off the street -- and you were to go ask them how do they feel about nature, you're going to end up with extremely positive answers, ranging from, you know, "nature's inspiring," "nature is the most beautiful thing that exists." And you'll see it everywhere. They'll put it as the backdrop of their desktop and their phones, like, every little thing, absolutely. Every single person you ask, you're going to get, like, a pretty positive response. And even though, if you ask all these individuals how they feel about nature, they're really positive about it, somehow, as a collective civilization, we've come together and we are destroying nature at a planetary scale. And therein lies the paradox. How did this happen? How did a group of individuals that all love nature somehow create a civilization, create an industrial economy that is out there, effectively planetary level assault on nature? Well, I think it actually stems from a broken mental model that we've kind of unconsciously adopted. And I'd like to start dissolving that right now. Now, you can see the mental model with the X below it, and that is the one that I would like to say that we've accidentally adopted. And that's a mental model that whenever you get economic wins, they are traded off against the ecology. And if you really care about the ecology and want to make that healthier, unfortunately, you're going to have to trade off economic wins, right? It's one or the other, kind of a balancing act. And depending on how much we care about the economy, or how much we care about the ecology at any given time, the pendulum swings one way, the scale swings one way, or it swings the other. And I'm going to tell you that, sure, that is a psychological position that you can take, but it's actually not one that is particularly physically true.经济的本质：生态系统的内在组成
我作为物理学家的训练让我关注物理上的真实。事实上，关于经济更真实的情况是：经济并非与生态对立，而是生态系统的一个子集。这或许对一些人来说是新想法，但我可以很快证明。事实上，你甚至可以通过自己身上的衣物或周围的事物来证明。
当我们审视经济所生产的一切时，会发现它们要么是“开采”（mined）而来，要么是“生长”（grown）而来，这意味着它们直接来源于自然，没有任何抽象。你可能穿着棉质衣物，这是生长的；你坐的椅子下面可能有金属，这是开采的。经济中的一切，就是开采或生长，仅此而已。有些人可能会想：“我们不是正走向数字经济、虚拟经济吗？”其实不然。每一行代码的运行都依赖于一个被开采或生长出来的“基底”（substrate）；你使用过的每一个服务，都在利用被开采或生长出来的服务器架构。当我强调整个经济体是“开采或生长”时，我是字面意思。真的没有任何东西不直接来自自然。因此，当你损害生态时，你实际上是在为经济制造问题。而这正是我们目前正在经历的。如果继续用“跷跷板”式的思维来看待，我们将无法找到真正解决这些问题的正确途径。
Original English
I'm formally trained as a physicist. I do think about what is physically true, and what is actually much more true about the economy is the economy is not versus ecology. The economy is a subset of the ecology. And maybe this is a new idea to you guys, but I can prove it to you very quickly. Actually, you could prove it to yourself, even just with the clothes on your body or the things immediately around you. Because if you think about and look at everything the economy has produced, everything in the economy is either mined or grown, which means it comes directly from nature, no abstractions. You guys might be wearing some cotton, obviously grown. You'll be sitting on a chair that's got some metal underneath -- that was mined. Everything in the economy is mined or grown, full stop. And some of you guys might be thinking, "Aren't we moving to a digital economy, a virtual economy?" Well, not really. Like, every line of code that is ever run runs on a substrate that was mined or grown. Every single service you've ever used is using server architectures that are mined or grown. When I say the entire economy is mined or grown, I mean it literally. There's literally nothing that doesn't come directly from nature. And to the extent that you damage the ecology, you actually start to create problems for the economy. And this is what we're experiencing right now. And if you think about it in this balancing-act-type way, you're going to miss the right way to actually fix these problems.资源掠夺的规模与工业模式的革新
现在，让我们具体看看我们开采和生长的规模。这张图表展示了过去50年来我们每年从地球上进行的资源提取量。目前，每年提取量已超过900亿吨，人均约11.5吨。如果你觉得这个数字对你个人来说似乎不多，那么我告诉你，这是亚洲的平均水平，欧洲大约是亚洲的两倍，而美国则是三倍。与此同时，全球仍有20亿人每天的生活费不足5美元，他们的资源消耗量远低于此——正是这种巨大的差异使得整体数据得以平衡。
然而，在如此大规模的开采和生长过程中，并将其用于驱动经济的方方面面——因为如我所说，经济中的一切都源于开采或生长——我们却一直沿用着非常陈旧的工业理念和流程。我们今天大部分的种植方式是在大约50年前发明的；我们开采、提炼金属等方式，大多是在100到150年前发明的。这些技术并未得到近期更新。随着机器人和**人工智能（AI）**工具的出现，我认为现在是时候提出新的问题：我们能否以不同的方式进行开采和生长，从而开始尊重“经济是生态系统子集”这一理念？
这正是我所处的领域。我的整个职业生涯都建立在新发明、机器人技术、人工智能和先进算法之上。我曾参与过从Microsoft Office（抱歉了大家）到网络搜索，再到自动驾驶汽车等各种项目。鉴于此，我或许拥有一个独特的背景，能够审视这些问题，并探索我们是否能以不同的方式应对它们。今天我将分享一些例子。
这些例子涵盖了三个主要转变——请记住，一切都源于开采或生长——它们由这三张图片代表。左侧是我们开采的材料。我们需要找到更多生态友好的开采方式，最大限度地提取矿石，从而在开采过程中对地球和流域的干扰最小。此外，比更生态地开采和减少开采量更优的是完全不进行开采。通过高度精湛的机械或化学回收技术实现闭环，我们可以让工业原料的更大比例来自闭环材料，而非原生提取的材料。
第二个主要转变与我们的生长方式有关。目前许多生长方式都非常不可持续，它们正在损害土壤功能。全球的农田都在一点点地消耗着表层土壤。图中这位是Gabe Brown，他是一位朋友，教会了我关于再生农业的许多知识。我从他那里学到，如果你投资于土壤功能，每年都能让生长变得更容易、更便宜、利润更高，同时还能再生土壤功能，为生物多样性提供更多服务，甚至修复土壤的水文功能。
最后，我们需要考虑大规模修复。我们已经经历了数百年工业革命，许多景观已被严重退化。如果我们认真致力于更新生态系统以支持未来充满活力的经济，那么我们就需要更好的大规模修复工具。
Original English
Now, let's talk about exactly how much we are mining and growing. This graph actually kind of tracks 50 years of how much extraction we've been taking every single year from the planet. At this point in history, it's over 90 billion tonnes per year. It comes out to about 11.5 tonnes per person per year. And if you guys feel like you don't do that much, well, I'll shock you to say that 11.5 is the average in Asia, but Europe is about two x that, and America is three x that. Yay. There's two billion people that live on less than five dollars a day doing substantially less than that -- that's why it all balances out. But this is exactly how much we're mining and exactly how much we're growing. Now, in the process of mining and growing this much, and using it to power everything in the economy -- because, like I said, literally everything in the economy is mined or grown -- we've been using really old industrial ideas and industrial processes. Most of how we're growing today was invented about 50 years ago. Most of the ways that we've been mining, refining metals, all that sort of thing, were invented about 100 years ago, 150 years ago. These are not technologies that we've updated recently. And with the arrival of new robotic and AI tools, I think it's the right time to go ask new questions about whether we could be mining and growing differently, in a way that starts to honor this idea that the economy is a subset of the ecology. Now, this is where it overlaps into my world, because my entire career has been built off of doing new inventions and robotics, artificial intelligence, advanced algorithms. And I've shipped everything from Microsoft Office -- sorry about that -- (Laughter) you know, to web search -- I think that one was fine -- to self-driving cars. So I've worked on relatively sophisticated things, and given that, I have an interesting background, perhaps, to be able to go look at these problems and see if we can take a different swing at them. And I'm going to share a number of examples with you today. Now, these examples fall into three major shifts -- remember, everything's mining or growing -- and they're represented by these three images here. So on the left-hand side, we have a bunch of mined materials, and what we need to be doing is we need to figure out more and more ecological ways to be able to go mine materials and get the most of the ores that we extract so we do the least disturbance of earth and watersheds in the process of mining. In addition, what is even better than mining more ecologically and mining less is to not mine at all. And to the extent that we're able to go close the loop through really skillful mechanical or chemical recycling, we can have a larger and larger proportion of the feedstock for industry move over to closed-loop materials, as opposed to virginally extracted materials. The second major shift has to do with the way that we grow. A lot of the way that we've been growing currently is very unsustainable. It basically is damaging soil function. And little by little, we've been kind of wearing down topsoil in agricultural lands all across the world. And Gabe Brown is pictured here. He's a friend who has taught me a huge amount about regenerative agriculture, and I've really learned from him that if you invest in soil function, you can actually make it easier to grow, cheaper to grow, a higher margin to grow every single year, and do so in a way that is regenerating soil function, giving more services to biodiversity, and even healing the hydrological function of those soils. And lastly, we need to be thinking about large-scale repair, because we've been at the Industrial Revolution for a couple hundred years now, and there's a lot of landscapes that we've heavily degraded. And if we are serious about the task of renewing the ecology in order to support a vibrant economy going forward, then we're going to need better tools for large-scale repair. So let's jump into all three of these.科技赋能：循环经济与生态农业的复兴
让我们深入探讨这三个方面。首先是资源管理与回收。图中展示的是一家我曾有幸合作的公司，它们是迈向闭环世界的绝佳范例。这里是北美最大的锂离子电池回收厂之一的内部景象。这是一种先进的化学回收技术，能够处理所有废旧电池材料。大多数锂电池寿命约为10年，之后便无法在汽车或消费电子设备中使用。此时，回收这些材料就变得至关重要。该公司的回收工艺比其他任何接近的工艺便宜约两倍，并且能将材料恢复到完全的原始质量，甚至优于直接从地里开采的材料。
如果我们能熟练地实现这些闭环，那么汽车电池就不会凭空消失。它们是相对较大的物体，可以进行处理，通过逆向物流供应链将它们收集起来。无论是利用机器人和AI进行先进的机械回收，还是像这里展示的先进化学回收，我们都可以借助新技术，巧妙地实现闭环，使工业原料中来自消费后或工业后废弃物流的比例越来越高，而非来自原生开采。
接着转向再生生长领域。我们正处于一个引人注目的历史时刻，因为一场再生农业的复兴正在发生。世界各地的农民们正在发现农林复合、间作套种、免耕农业等实践的好处，这些方法有助于建立健康的土壤功能和土壤微生物群落。左侧展示的是一项新技术，它利用机器学习来消除歧义。这是一种新型的拉曼光谱技术，能以一种非常精妙的方式“倾听”土壤。它能够测量土壤中的各种关键化合物，让土壤“告诉”我们：“嘿，接下来你应该做这几件事来让我更健康。”土壤不再是一个需要解读的黑箱，农民现在可以直接与土壤建立联系，并以更精细的方式进行管理，以实现土壤健康、减少投入并提高年利润。
此外，人工智能和机器学习在农业领域还有其他重要应用。图中展示的是玉米的培育过程。这是一个持续了数百年的土著项目。最初，玉米只是一种不可食用的草。通过几代人的选择性育种，他们培育出了各种变种，最终形成了如今能提供大量卡路里的玉米，养活了全球大部分人口。这是我非常感激的一项土著活动。我们今天吃的绝大多数食物都经过了选择性育种，以达到我们所体验到的巨大、健康和营养的程度。
然而，利用人工智能和机器学习，我们与一家公司合作，能够快速加速这一过程，而且并非通过基因改造。他们能够获取现有商业作物以及大量未广泛流通的本地品种的序列信息，分析不同基因的功能，然后精确绘制出杂交路径，以获得期望的性状。例如，我们有了适应性甘蔗，它大大减少了为达到所需产量而进行的砍伐森林；还有耐热番茄，能在更热、更干燥的条件下生长，这在未来50年气候变化导致农田不稳定的时期尤为重要。此外，还有抗旱棉花，它只需极少的水（十分之一），并且农药和化肥的投入也大大减少。所有这些都对地球有益，也对我们在不稳定且不断变化的环境中获得可持续的食物和材料的未来至关重要。
Original English
So what's pictured here is a company I have the privilege to work with. And they are a great example of moving closer to that closed-loop world. So what you’re seeing here is actually an image from inside the largest lithium NMC battery recycling plant in North America. And this is an advanced form of chemical recycling that is able to go bring all of these used battery materials, because most lithium batteries kind of have a 10-year life; they don't go too many years beyond that. And after that's the case, you know, you can't use it in the car, or you can't use it in the consumer electronics device anymore. You want to be able to recover those materials. The process that they do here is about two times cheaper than the next closest process, and is able to return the material to complete virgin quality. It's better than the stuff that you would have been able to mine out of the ground in the first place. And if we get really skillful about closing these loops, what's great is the car battery doesn't just evaporate and disappear. It's a relatively large object that you can go handle, and you can do a reverse logistics supply chain and pull these things together. And whether it's robotics and AI to do advanced mechanical recycling -- or, in this case, advanced chemical recycling -- then there are really skillful ways, with our new technologies, to be able to go close the loop and make it so that a higher and higher fraction comes from a post-consumer or post-industrial waste stream, as opposed to from the ground. Now, moving over into the regenerative growing side, we're actually at a really compelling point in history, because there’s a mini renaissance in regenerative agriculture that's happening right now, with different farmers around the world discovering the benefits of agroforestry, intercropping, you know, no-till agriculture and a lot of other practices that really help to establish healthy soil function and a healthy soil microbiome. And what you're actually seeing on the left-hand side is a new technology, which also uses machine learning to be able to go disambiguate. It's a new form of Raman spectroscopy that allows folks to be able to listen to the soils in this really skillful way. So effectively, they're able to go and measure all these compelling compounds from the soil, so they're able to have the soil speak to them in ways that the soil can basically tell them, “Hey, here’s the next couple of things that you should do to make me healthier.” Instead of it kind of being a black box that needs to get interpreted, now farmers can have a direct relationship with their soils and be really skillful in the management toward greater and greater health, fewer inputs and higher margins every single year. Moving on into other ways that artificial intelligence and machine learning might be really useful for agriculture, what you see pictured here is the development of corn or maize. And it was an Indigenous project that happened over the course of hundreds of years. And they started with, basically, an inedible bit of grass, because corn is actually a type of grass. And by selective breeding over generations and generations, they went through lots of different varieties, until we got to the "lots of calories per grow cycle" version of corn that is now feeding a huge percentage of the calories around the world. Now, this was an Indigenous activity that happened over hundreds of years, and I'm really thankful for it, because most of the foods that we eat today were selectively bred to be as large and healthy and nutritious as we experience them. But using artificial intelligence and machine learning, we've been working with a company that has been able to rapidly speed up this process, and not through genetic modification. What they do is they're able to take the sequence information from all the existing commercial crops, plus a bunch of native varietals that are not in current circulation, and work out what the different gene functions do, and then map out exactly the crossbreeding pathway in order to go get the desired traits. So what we have here is adaptive sugar cane, which dramatically reduces the amount of deforestation required to get to the yield level that you want. You also have heat-resistant tomatoes that are able to grow in way hotter, way drier conditions, which is really important, because we're going to go through at least a 50-year period where we're going to be destabilizing a lot of the farmlands of the Earth as the climate destabilizes, whether that's hotter or colder, wetter, drier. It's all going to happen, and being able to have seed stock that is ready for that challenge is really powerful. And lastly, a cotton that basically is drought-tolerant as well, requires a fraction of the water, one-tenth the water, and much less pesticide and fertilizer input. And all of these things are fantastic for the planet, but they're also fantastic for the future of us having viable food and materials in a destabilized, growing environment.机器人军团：主动式生态修复的未来
最后，我们来谈谈可扩展的生态修复。图中是Chloris Geospatial公司的图像，描绘的是亚马逊盆地。这家公司在传感器融合方面做了深入研究，整合了多个卫星数据源，并结合了十多年在丛林中进行的逐米地面验证数据。这使得他们能够精确验证卫星遥感信号所关联的陆地生物量。基于此，他们能够对地球地表生物量进行最准确的历史和当前评估，数据可追溯至21世纪初，覆盖超过20年的信息。这让我们能够清晰地看到哪些景观正在受到损害，哪些正在恢复，以及修复或碳项目的发展情况。
这项技术虽然先进，但它在某种程度上是“被动”的。它本身并不能恢复森林或草原，只是帮助人们监测变化。但如果我们能更进一步，挑战工业机器与自然之间原有的破坏性联系，将其转变为一种有意识的主动修复关系，那会是怎样的景象？接下来的两个例子将展示这一点。
这是一个短视频，你能听到细微的“滴答”声，每一次滴答都代表一颗红树林种子被种下。这些滴答声的速度大约是一架无人机每分钟种植100棵红树林。视频中可以看到种植过程。两个月后，超过90%的种子成功发芽。14个月后，景观已完全建立，超过85%的红树林得以成活。这项技术及其潜力是惊人的。仅四人一天就能种植超过80公顷土地，相当于种植12万棵红树林，并成功建立超过10万棵。当技术达到机器人规模时，人类的行动和意图——如果我们有良好的意图——就能以指数级方式放大，彻底重塑我们的景观。
我与这家公司合作了大约十年，深受启发。他们不仅修复了红树林，还在四大洲修复了20种不同的陆地生态系统——包括旱地、内陆、山区和近岸等。这让我思考：我们能否在水下也做到这一点？我将展示我创立并担任首任电气工程师的项目——Reefgen机器人。它本质上是一种种植无人机，能够将活体珊瑚种植回珊瑚礁，并将活体海草种植回海草床，也可以以种子的形式进行种植。
这款机器人一天就能种植1万粒海草种子，覆盖一英亩水下区域。我们还确保这款机器人足够经济实惠，以便大规模生产。因为当我向人们谈论我要建造的水下机器人来修复生态系统时，他们说：“你应该预算两百万美元。如果花得少，可能做不出什么有意思的东西。”我当时的想法是大约5000美元，虽然还没完全达到，但现在成本约为1万美元，这在宏观上远低于200万美元。
其核心理念是，这项技术应该对所有拥有近岸生态系统需要修复的社区开放，无论是珊瑚还是海草。从资本支出（CapEx）的角度来看，这种技术也应该是可扩展的。例如，一位亿万富翁可以花费5000万美元购买一万台这样的机器人，这就能在各种海洋生态修复方面实现有意义的规模。
这里展示的是一个“桩”，它不是种子种植执行器，而是幼苗种植执行器。因为种植海草有两种方式：一种是播种，另一种是将海草幼苗种植下去。它们通过根茎（rhizomes）横向生长，然后从侧生的根茎上长出新的草。这个桩就是用来固定海草幼苗的，然后通过管道输送到料斗。目前的设计，一个机器人一天大约能种植半英亩幼苗。我们的下一代版本将能实现每台机器人每天种植一到一英亩半的面积。
Original English
So let's jump into all three of these. So what's pictured here is a company I have the privilege to work with. And they are a great example of moving closer to that closed-loop world. So what you’re seeing here is actually an image from inside the largest lithium NMC battery recycling plant in North America. And this is an advanced form of chemical recycling that is able to go bring all of these used battery materials, because most lithium batteries kind of have a 10-year life; they don't go too many years beyond that. And after that's the case, you know, you can't use it in the car, or you can't use it in the consumer electronics device anymore. You want to be able to recover those materials. The process that they do here is about two times cheaper than the next closest process, and is able to return the material to complete virgin quality. It's better than the stuff that you would have been able to mine out of the ground in the first place. And if we get really skillful about closing these loops, what's great is the car battery doesn't just evaporate and disappear. It's a relatively large object that you can go handle, and you can do a reverse logistics supply chain and pull these things together. And whether it's robotics and AI to do advanced mechanical recycling -- or, in this case, advanced chemical recycling -- then there are really skillful ways, with our new technologies, to be able to go close the loop and make it so that a higher and higher fraction comes from a post-consumer or post-industrial waste stream, as opposed to from the ground. Now, moving over into the regenerative growing side, we're actually at a really compelling point in history, because there’s a mini renaissance in regenerative agriculture that's happening right now, with different farmers around the world discovering the benefits of agroforestry, intercropping, you know, no-till agriculture and a lot of other practices that really help to establish healthy soil function and a healthy soil microbiome. And what you're actually seeing on the left-hand side is a new technology, which also uses machine learning to be able to go disambiguate. It's a new form of Raman spectroscopy that allows folks to be able to listen to the soils in this really skillful way. So effectively, they're able to go and measure all these compelling compounds from the soil, so they're able to have the soil speak to them in ways that the soil can basically tell them, “Hey, here’s the next couple of things that you should do to make me healthier.” Instead of it kind of being a black box that needs to get interpreted, now farmers can have a direct relationship with their soils and be really skillful in the management toward greater and greater health, fewer inputs and higher margins every single year. Moving on into other ways that artificial intelligence and machine learning might be really useful for agriculture, what you see pictured here is the development of corn or maize. And it was an Indigenous project that happened over the course of hundreds of years. And they started with, basically, an inedible bit of grass, because corn is actually a type of grass. And by selective breeding over generations and generations, they went through lots of different varieties, until we got to the "lots of calories per grow cycle" version of corn that is now feeding a huge percentage of the calories around the world. Now, this was an Indigenous activity that happened over hundreds of years, and I'm really thankful for it, because most of the foods that we eat today were selectively bred to be as large and healthy and nutritious as we experience them. But using artificial intelligence and machine learning, we've been working with a company that has been able to rapidly speed up this process, and not through genetic modification. What they do is they're able to take the sequence information from all the existing commercial crops, plus a bunch of native varietals that are not in current circulation, and work out what the different gene functions do, and then map out exactly the crossbreeding pathway in order to go get the desired traits. So what we have here is adaptive sugar cane, which dramatically reduces the amount of deforestation required to get to the yield level that you want. You also have heat-resistant tomatoes that are able to grow in way hotter, way drier conditions, which is really important, because we're going to go through at least a 50-year period where we're going to be destabilizing a lot of the farmlands of the Earth as the climate destabilizes, whether that's hotter or colder, wetter, drier. It's all going to happen, and being able to have seed stock that is ready for that challenge is really powerful. And lastly, a cotton that basically is drought-tolerant as well, requires a fraction of the water, one-tenth the water, and much less pesticide and fertilizer input. And all of these things are fantastic for the planet, but they're also fantastic for the future of us having viable food and materials in a destabilized, growing environment. Lastly, let's get on to scalable restoration. And what you're seeing here is an image from the company Chloris Geospatial. And it is, of course, of the Amazon basin. But what's really compelling about this company is that they've done really deep work on sensor fusion across a bunch of satellite feeds, and they also paired that with over a decade in the jungles, meter by meter, doing ground-truthing data, to be able to go really verify how much terrestrial biomass is associated with signals that can be detected from satellites, via remote sensing. And given this, they've been able to make the most accurate, both historical and current assessment of aboveground biomass on planet Earth. And the data stretches all the way back to the beginning of the 21st century, so over 20 years of data on that front. And that really allows us to see which landscapes we're hurting, which landscapes are recovering, and if people are developing restoration projects or carbon projects, this is a fantastic way to go stay on top of how those are going. Now, this is great technology and also uses really advanced algorithms and allows the things that I've been talking about. But in some ways, it's a little bit passive. This doesn't restore the forest itself. This doesn't restore the grassland itself. This just helps people monitor the changing of that. But what if -- going back to this triangle for a moment -- we were to get more ambitious and we were to say, "Let's challenge this linkage between the industrial machines that run our economy and nature, and instead of it having to be an accidental relationship of damage, what would it look like if it was an intentional relationship of active repair?" And I'm going to show you that right now with my last two examples. This one's a short video. You're going to hear these little ticks, and every one of those ticks is a mangrove seed being planted. The pace of these ticks is basically planting about 100 mangroves per minute from one drone. Video: (Frequent ticks over music) Tom Chi: And that's what it looks like when we start planting. And then, two months later, you see, actually, we have over 90 percent that get to germination. And 14 months later, you can see the landscape is fully established, over 85 percent full establishing of the mangroves that were planted. Now, the scale of this technology and the scale that it's capable of is incredible. Just four people are able to go plant over 80 hectares of land, representing 120,000 mangroves being planted, and over 100,000 being established in one day. When you get to robotic scale on things, all of a sudden, then human action, human intention -- and if we have good intentions, we can really multiply that in ways that can completely rewrite our landscapes. And I've worked with this company for about a decade at this point, and I got really inspired by them, you know, because they have not just restored mangroves, but they've restored 20 different terrestrial ecosystems on four different continents -- dry land, inland, mountainous, you know, nearshore, all these sorts of things. And I got inspired, like, "Could we also do this below the water?" And I'm going to show you something that I founded and was the original electrical engineer for. And this is this is the Reefgen robot, which is basically its own kind of planting drone. Oh, it's already going to do some planting things, that's fun. And this robot line is the first in the world to plant live corals back into a coral reef. It's the first in the world to plant live seagrasses back into seagrass meadows. And it can also plant them in seed form as well. And this robot, you know, has been able to plant 10,000 seagrass seeds in a single day, which covers an entire underwater acre with one robot in one day. And the other thing that we did is we wanted to make sure that this robot was affordable enough that we could make a bunch of them, right? Because when I went around and I talked to people about the underwater robot that I was going to go build to restore these ecosystems, they're like, "You should budget, like, two million dollars for the robot. If you spent less, it's probably not going to do anything interesting." And I remember thinking more like 5,000 dollars, and we’re not quite there, but this is more like 10,000 dollars. And in the grand scheme of things, it's way, way less than two million. And the whole point is you want this to be an accessible technology to all the communities that have nearshore ecosystems to restore, whether they be coral, whether they be seagrasses. You want something like this to be also scalable from the CapEx perspective, right? Like, a single billionaire could spend 50 million dollars and have a fleet of 10,000 of these, and that is actually meaningful scale in terms of ocean restoration of all different types. I'll show you a little bit more here. So right here is a stake, because this is actually not a seed-planting end effector. That's a seedling-planting end effector, because there's actually two ways to plant seagrasses. You can plant it from seed, but then, there's other types of seagrasses that want to be planted as a sapling. And they want to grow rhizomatically, so they send out these little rhizomes laterally, and then the grasses grow up from the lateral rhizomes that are heading out. And this is basically a stake that we put the seagrass seedlings into, and then that feeds in through a tube into a hopper, and basically, bit by bit, this current layout is able to go and plant about half an acre of seedlings per day with just one robot. And our next version of it is going to be able to do an acre to an acre and a half in a day, per robot.人机协作：构建可持续的共生未来
因此，我们正朝着这样一个方向迈进：通过深入挖掘并以不同的方式理解心智模型，我们不再将经济与生态视为对立，而是开始利用当前经济中的最佳工具——机器人和人工智能——并有意识地用它们来支持生态系统，从而为未来构建一个健康的地球和一个健康的经济。
Original English
So we are really kind of moving into this space where, by really digging into that mental model in a different way, instead of economy versus ecology, we start taking the best tools that we're using in the current economy, and robotics and AI, and intentionally using them to support ecology so that we're able to go build both a healthy planet and a healthy economy for the future. Thanks so much. (Cheers and applause) Thank you.📌 文中提及的人物和组织
公司/组织: Chloris Geospatial, Reefgen