These Al devices protect nature in real time - Juan M. Lavista Ferres

You're listening to ted talks daily where we bring you new ideas to spark your curiosity every day. I'm your host Elise Hu. The work of conservationists across the globe is vital, but also painstakingly slow, too slow compared to the rate of climate change. In this talk AI visionary Juan M. Lavista Ferres, who leads Microsoft's AI for goodlab, introduces a new technology that is transforming how conservationists work and shares how it can dramatically increase our ability to care for this planet's vital ecosystems.

您正在收听的是每日TED演讲，我们每天都会为您带来激发好奇心的新想法。我是主持人Elise Hu。全球各地自然保护主义者的工作至关重要，但进展却异常缓慢，与气候变化的速度相比实在太慢了。在本次演讲中，人工智能领域的远见卓识者、微软人工智能公益实验室（AI for goodlab）负责人Juan M. Lavista Ferres将介绍一项正在改变自然保护主义者工作方式的新技术，并分享这项技术如何能够显著提升我们保护地球重要生态系统的能力。

Let me introduce you to Andres Rojas. Every couple of weeks, Andres hikes deep in the Colombian rainforest, passing through mud and swamps of mosquitoes, not for adventure, definitely not for fun,l. But to do his job, he needed to replace batteries and change memory cards of camera traps and bio acoustic devices. This is the critical infrastructure of conservation science today. People like Andres are heroes and thanks to their effort, they have saved species from the brink of extinction.

让我把你介绍给安德烈斯·罗哈斯。每隔几周，安德烈斯就会在哥伦比亚雨林深处徒步旅行。穿过泥泞和蚊子的沼泽。不是为了冒险，绝对不是为了好玩。但为了完成他的工作，他需要更换电池，更换相机陷阱和生物声学设备的存储卡。这是当今保护科学的关键基础设施。像安德烈斯这样的人是英雄，由于他们的努力，他们将物种从灭绝的边缘拯救了出来。

There are two hundred thousand conservationists in the world, and all of them share one thing in common. To do their job, they need data.

全世界有二十万名自然保护主义者，他们都有一个共同点：为了做好自己的工作，他们需要数据。

But we live in an interesting world where we have refrigerators that can text you if you're running out of milk. Conservationists still need to hike for days just to see if an animal passed by. Conservation today is heroic, is needed, but is painfully slow.

但我们生活在一个有趣的世界里，如果你的牛奶用完了，我们的冰箱可以给你发短信。环保主义者仍然需要徒步数天，看看是否有动物经过。今天的保护是英勇的，是必要的，但速度非常缓慢。

Last year I was proudly presenting at a biodiversity conference some of our latest air models, but it was in fact a very humbling moment.

去年，我在一次生物多样性会议上自豪地展示了我们的一些最新空气模型，但这实际上是一个非常令人谦卑的时刻。

Because when presenting to them, I realized that even though they were using our models, once we understood the hazard that they needed to go through, from installing these devices to collecting the data to eventually having time to analyze it, I realized that our solutions were not making such big fuss difference. I realized that in order for us to make a difference, we need to completely reinvent how data works in biodiversity.

因为在向他们展示的过程中，我意识到，尽管他们使用了我们的模型，但一旦我们了解了他们需要经历的种种困难——从安装设备到收集数据，再到最终抽出时间进行分析——我意识到我们的解决方案并没有产生多大的影响。我意识到，为了真正有所作为，我们需要彻底革新生物多样性数据的工作方式。

This is why we develop Sparrow. Sparrow stands for Solar power acoustic remote recording observation watch. Sparrow is a small network of devices that acts as a hub in the middle of nature, connecting to camera traps, acoustic devices, sensors, processing the information using Solar power, processing the information on the edge using a low voltage GPU, sending the results back using a low orbit satellite. With Sparrow, you install it once, you no longer need to hike to collect data. You can connect online and see the data real time.

这就是我们开发 Sparrow 的原因。Sparrow 的全称是 Solar Power Acoustic Remote Recording Observation Watch（太阳能声学远程记录观测系统）。Sparrow 是一个小型设备网络，它如同一个位于自然环境中的枢纽，连接着相机陷阱、声学设备和传感器，利用太阳能处理信息，并通过低压 GPU 在边缘端进行处理，最终通过低轨道卫星将结果传回地球。有了 Sparrow，您只需安装一次，无需再徒步跋涉去收集数据。您可以在线连接并实时查看数据。

One of my biggest lessons in life is the realization that we as humans are addicted to complexity. We like complex projects and we like complex things. This is the reason we put a person on the moon before we add wheels to your luggage.

我人生中最大的教训之一是认识到我们作为人类沉迷于复杂性。我们喜欢复杂的项目，也喜欢复杂的事物。这就是为什么我们在给你的行李加轮子之前先把人送上月球的原因。

Don't get me wrong. If you want to impress people, your solutions can’t be complex. If you want to have an impact in the world, if you want people to use your solutions, your solutions need to be simple. Building simple solutions is hard, but it's certainly worth the effort. This is why our most important principle designing Sparrow is to keep it simple, simple to develop, simple to deploy, simple to assemble.

别误会我的意思。如果你想给人留下深刻印象，你的解决方案就不能太复杂。但如果你想对世界产生影响，如果你想让人们使用你的解决方案，你的解决方案就必须简单易用。构建简单的解决方案很难，但绝对值得付出努力。这就是为什么我们在设计 Sparrow 时最重要的原则就是保持简单——易于开发、易于部署、易于组装。

Sparrow is open source. Anyone from conservation scientists to researchers to park rangers can use it and improve upon it. You don't buy a Sparrow. You buy off the shelf component and you assemble it together. If you have the ability to assemble your own Ikea furniture, and I know that's not for everybody, you're ready to assemble a Sparrow.

Sparrow是开源的。从自然保护科学家到研究人员再到公园管理员，任何人都可以使用它并对其进行改进。Sparrow不是买来的，而是购买现成的组件，然后自行组装。如果你有能力组装宜家家具（我知道这并非人人都能做到），那么你就可以组装Sparrow了。

Even if simplest power is actually quite powerful. Camera traps is a technology that was created four decades ago. They have a sensor, and all any time that they see movement, they take a picture. Some of that movement is caused by animals. The majority of that movement is caused by wind or something else that move. This is a big hassle for conservation is because in order for them to get just a few pictures of the species they care, they need to review thousands of pictures costing them hundreds of hours of their time.

即使是最简单的电力，实际上也相当强大。相机陷阱这项技术诞生于四十年前。它们内置传感器，一旦检测到任何移动，就会自动拍照。部分移动是由动物引起的，而大部分移动则是由风或其他因素造成的。这对自然保护来说是个巨大的难题，因为为了获得他们关注物种的几张照片，他们需要筛选成千上万张照片，耗费数百小时的时间。

Sparrows solves this problem. With sparrow, we have AI models that can automatically classify and identify the animals in them. But Sparrow goes further. Sparrow not only can find a giraffe. Sparrow can find that giraffe. Animals like giraffe have a unique pattern, and that unique pattern doesn't change over time. You can use these to re-identify. It's like a fingerprint. You can use to re-identify that particular giraffe. Animal reunification is critical for conservation because it allows them to understand things like survival or even measure population.

Sparrow 解决了这个问题。Sparrow 拥有人工智能模型，可以自动对动物进行分类和识别。但 Sparrow 的功能远不止于此。Sparrow 不仅能找到长颈鹿，还能找到特定的长颈鹿。像长颈鹿这样的动物拥有独特的图案，而且这种图案不会随时间改变。你可以利用这些图案进行重新识别，就像指纹一样。你可以利用这些图案重新识别特定的长颈鹿。动物重聚对于保护工作至关重要，因为它能帮助人们了解生存状况，甚至统计种群数量。

Sparrow can automatically do this and thanks to our collaboration with the y nature institute, we have this model running in Sparrow today. Well, a picture might be worth a thousand words. If we only focus on pictures, we might be missing the forest for the trees. But if you listen, the story is different.

Sparrow 可以自动完成这项工作，而且得益于我们与自然研究所的合作，我们现在已经在 Sparrow 中运行了这个模型。俗话说，一图胜千言。如果我们只关注图片，可能会只见树木不见森林。但如果你仔细聆听，就会发现事情并非如此简单。

Sparrow has the ability to isolate and classify sounds. Here for example, there's a frog. That's a cicada, That's a macaw. Thanks to Sparrow, through sound, we can measure the true health of a forest. Identifying an animal from a picture is not difficult. Identifying through sound requires very deep expertise. People like Paul lakesera, from foundation universe, Colombia, has this expertise. In every expedition she collected 600 hours worth of sounds. And then she listened to every one of these hours.

Sparrow 能够分离和分类声音。例如，这里是青蛙，那是蝉，那是金刚鹦鹉。多亏了 Sparrow，我们才能通过声音来衡量森林的真实健康状况。从图片上识别动物并不难，但通过声音识别则需要非常深厚的专业知识。像来自哥伦比亚 Foundation Universe 的 Paula Lakesera 这样的人就拥有这种专业知识。每次探险，她都会收集 600 小时的声音，然后仔细聆听每一个小时的录音。

This is like binge watching the whole complete 8 seasons of game of thrones 10 times just to get a few samples of the animal she cares. Sparrow can help people like Paula.

这就像为了从中找出她所关心的动物的几个例子，就把《权力的游戏》全部80集看了十遍一样。斯帕罗能帮助像宝拉这样的人。

Paula can train Sparrow to focus on a particular animal or a particular call so she can save hundreds of hours of her time so she can focus on what she does best, having a better understand and helping protect the animals she loves.

Paula 可以训练 Sparrow 专注于特定的动物或特定的叫声，这样她就可以节省数百小时的时间，从而专注于她最擅长的事情，更好地了解和帮助保护她所爱的动物。

Because Sparrow is connected online, Sparrow can actually send alerts. Wildfires are a major global threat, costing lives, billions in infrastructure and the complete destruction of some of the most important biodiversity ecosystems. In a wildfire, every minute counts. Detect it early and you can stop it with a shovel. But if you wait, you will need wood dozers, air tankers and sometimes a miracle. Sparrow has the ability to do well in detection of fire and send alerts to authorities.

由于 Sparrow 能够联网，因此它可以发送警报。野火是全球面临的重大威胁，造成人员伤亡、数十亿美元的基础设施损失，并彻底摧毁一些最重要的生物多样性生态系统。在野火肆虐时，每一分钟都至关重要。及早发现火情，你甚至可以用铲子扑灭它。但如果拖延，你将需要推土机、空中灭火飞机，有时甚至需要奇迹。Sparrow 能够有效地探测火灾并向有关部门发送警报。

With Sparrow, we're not only collecting data, we can act on that data and that data can help save lives.

借助 Sparrow，我们不仅可以收集数据，还可以根据这些数据采取行动，而这些数据可以帮助拯救生命。

By the end of 2025, we will have sparrow running in all continents. Sparrow will change the way biodiversity data works. Today conservation moves at the speed of data. And when a conservation is installed a device to the time that it takes for that data to eventually get analyzed today, it takes months, sometimes a year. With Sparrow, we want to move from months to days. For some species, this difference, this data can be the difference between survival and extinction.

到2025年底，Sparrow系统将覆盖所有大洲。Sparrow将彻底改变生物多样性数据的运作方式。如今，保护工作的进展速度取决于数据的处理速度。而从安装保护设备到最终分析这些数据，通常需要数月甚至一年的时间。我们希望借助Sparrow系统，将分析时间从数月缩短到数天。对于某些物种而言，这短短的几天时间，可能意味着它们的生死存亡。

I dedicate this talk to the conservation it’s still there, who have dedicated and even sacrificed their lives to help protect biodiversity on this planet. They might not wear capes, but make absolutely no mistake, they are superheroes. Yet they need our help. Our job, our responsibility, and our commitment today is that we will provide them with the best tools we can, so they have a fighting chance. Thank you.

我谨以此演讲献给那些仍在为保护地球生物多样性而奉献甚至牺牲生命的环保人士。他们或许没有披风，但请不要误解，他们是真正的超级英雄，然而他们也需要我们的帮助。我们今天的工作、责任和承诺是，尽我们所能为他们提供最好的工具，让他们拥有战胜困难的机会。谢谢。

That was Juan M. Lavista Ferres at the TED countdown summit, Nairobi in Kenya in 2025.

这是胡安·M·拉维斯塔·费雷斯在 2025 年于肯尼亚内罗毕举行的 TED 倒计时峰会上的讲话。