You're listening to ted talks daily where we bring you new ideas to spark your curiosity every day. I'm your host, elise Hugh. What if we could map every living species on earth and use that knowledge to protect the planet? In her talk ecologist and AI researchers Sarah beery shares how she and her team at MIT are building tools that let scientists ask questions directly to vast ecological databases. Unlocking hidden insights from millions of images and recordings, she says AI can become a powerful ally in understanding and saving the natural world.

你每天都在听ted演讲，我们每天都会给你带来新的想法，激发你的好奇心。我是你的主持人，elise Hugh。如果我们能绘制地球上所有生物的地图，并利用这些知识来保护地球，那会怎么样？生态学家和人工智能研究人员Sarah beery在她的演讲中分享了她和她在麻省理工学院的团队如何构建工具，让科学家直接向庞大的生态数据库提问。她说，通过从数百万张图像和记录中解锁隐藏的见解，人工智能可以成为理解和拯救自然世界的强大盟友。

Imagine you're a doctor and you're trying to save the life of a patient that you can only see a fifth of their body.

想象一下，你是一名医生，你正试图挽救一名只能看到身体五分之一的病人的生命。

How are you going to prescribe medicine? How are you going to do surgery?

你打算怎么开药？你打算怎么做手术？

See, this is exactly the situation we're in with nature across the planet.

看，这正是我们在地球上与大自然相处的情况。

We need to act now to protect ecosystems under threat, but there's so much we don't know about life on earth.

我们现在需要采取行动保护受到威胁的生态系统，但我们对地球上的生命知之甚少。

I'm an AI researcher and an ecologist, and as a professor at MIT, I lead a research group that develops methods to help us learn more about the natural world. And I see a future where AI can help exponentially increase our ecological knowledge across species and ecosystems.

我是一名人工智能研究员和生态学家，作为麻省理工学院的教授，我领导着一个研究小组，该小组开发了帮助我们更多地了解自然界的方法。我看到了一个未来，人工智能可以帮助我们成倍地增加跨物种和生态系统的生态知识。

But to get there, we need to change how we use AI in ecology. We need methods that are flexible, methods that are interactive, methods that scientists can use to discover knowledge hidden in our data.

但要实现这一目标，我们需要改变在生态学中使用人工智能的方式。我们需要灵活的方法，互动的方法，科学家可以用来发现隐藏在我们数据中的知识的方法。

Now, let me tell you why this is so important.

现在，让我告诉你为什么这如此重要。

Scientists estimate there are10 million species sharing the planet with us.

科学家估计，有1000万种物种与我们共享地球。

But we have only ever observed 2 million. Of those, that means 8 million species, 80% of the diversity of life on earth remains unknown.

但我们只观察到200万。其中，这意味着800万种物种——地球上80%的生命多样性仍然未知。

And we need to know much more than just a species exists to be able to protect it.

为了保护它，我们需要知道的不仅仅是一个物种的存在。

Where does it live? What does it eat? Does it migrate? How far, This deeper knowledge about species takes far more than a single observation, but it's necessary to understand what puts species at risk.

它住在哪里？它吃什么？它会迁移吗？对物种的深入了解远远不止一次观察，但有必要了解是什么使物种面临风险。

So for an example.

举个例子。

What if insect populations crash across North America? We know this is currently happening. What does that mean for birds that eat insects? Which birds are going to be most at risk and which are going to be able to adapt to other food sources? What about predators further up the food chain that eat birds?

如果北美各地的昆虫种群崩溃怎么办？我们知道目前正在发生这种情况。这对吃昆虫的鸟类意味着什么？哪些鸟类面临的风险最大，哪些鸟类能够适应其他食物来源？那么，食物链上游以鸟类为食的捕食者呢？

Everything is interconnected, and a threat to one species or a group of species can ripple outward and trigger the complete collapse of an ecosystem as we know it.

一切都是相互关联的，对一个物种或一组物种的威胁可能会向外蔓延，引发我们所知道的生态系统的完全崩溃。

Unfortunately, species are under threat from every direction.

不幸的是，物种正受到来自各个方向的威胁。

Habitats are shrinking.

栖息地正在缩小。

Temperatures are rising.

气温正在上升。

Food and water sources are disappearing.

食物和水源正在消失。

Natural disasters like fire are causing large scale death and displacement.

火灾等自然灾害正在造成大规模的死亡和流离失所。

An invasive species are moving in and out competing native species for resources.

入侵物种不断迁徙，与本地物种争夺资源。

As a result, extinction rates are now 100 to 1000 times higher than what we would expect based on past data.

因此，灭绝率现在比我们根据过去的数据所预期的高出100到1000倍。

Scientists, policymakers and community members worldwide are racing to understand what is causing this, what are the factors that are most contributing to this loss, and what actions we can take to stop it. But unfortunately, it can feel like we're discovering species just in time to write their obituaries. Take the top newly orangutan.

全世界的科学家、政策制定者和社区成员都在争先恐后地了解造成这一现象的原因，是什么因素导致了这一损失，以及我们可以采取什么行动来阻止它。但不幸的是，这可能会让人觉得我们正在及时发现物种，为它们写讣告。以猩猩为例。

We discovered this orangutan in2017. It's one of only three species of orangutan on earth, and it was critically endangered before we even knew it existed.

我们在2017年发现了这只猩猩。它是地球上仅有的三种猩猩之一，在我们知道它存在之前，它就已经濒临灭绝。

Traditional forms of data collection are just too slow to keep up with our current crisis, and this is where I finally have some good news because we are sitting on vast databases of ecological knowledge and we have barely scratched the surface.

传统形式的数据收集速度太慢，无法跟上我们当前的危机，这就是我终于有一些好消息的地方，因为我们坐拥庞大的生态知识数据库，我们还没有触及表面。

Let's talk about just one of these databases, which is a platform called I naturalist.

让我们来谈谈其中一个数据库，它是一个名为I naturalist的平台。

300 million images have been uploaded to this platform by passionate volunteers. In every single image, the community has identified a species and that level of species occurrence data has already been transformative for science. But there is a hidden treasure trove of knowledge that remains in the pixels. In I naturalist, this was labeled grant zebra.

热情的志愿者已将3亿张图片上传到这个平台。在每一张图片中，该社区都确定了一个物种，而物种出现数据的水平已经对科学产生了变革性的影响。但像素中仍有一个隐藏的知识宝库。在I naturalist，这被标记为“格兰特斑马”。

And it’s clearly evidence that grant zebra were cited in this place and time.

这清楚地证明了格兰特斑马在这个地方和时间被引用。

But it shows us so much more than that. There are three grant zebra in this image. We can identify each of them to the individual level based on their unique stripe pattern.

但它向我们展示的远不止这些。这张照片中有三只斑马。我们可以根据它们独特的条纹图案在个人层面上识别它们中的每一个。

By identifying individuals, we can do things like monitoring how species move across the planet, looking at social networks of species growth, health, even estimating the full population size. These zebra are also coexisting with a herd of will to beast. We can even see an oxpecker, a bird that eats ticks and helps reduce the spread of disease.

通过识别个体，我们可以做一些事情，比如监测物种如何在地球上移动，观察物种生长、健康的社交网络，甚至估计整个种群规模。这些斑马也与一群对野兽的意志共存。我们甚至可以看到一只啄牛鸟，这种鸟吃蜱虫，有助于减少疾病的传播。

We could look at the background of the image and identify the type and coverage of vegetation. We can estimate biomass, use that to learn about locally stored carbon. We can look at what the animals are eating in the image and build a stronger knowledge of a local food chain.

我们可以查看图像的背景，确定植被的类型和覆盖范围。我们可以估算生物量，用它来了解当地储存的碳。我们可以看看图像中的动物在吃什么，从而对当地的食物链有更深入的了解。

Take this much knowledge in one image and multiply it by 300 million images in I naturalist, and then add in our other ecological databases. Millions of bioacoustic recordings in xeno-canto, tens of millions of camera trap images and wildlife insights, thousands of hours of deep sea footage in fathom net. We were sitting on an ecological goldmine.

将这些知识放在一张图像中，并将其乘以I naturalist的3亿张图像，再加上我们的其他生态数据库。Xeno-Canto 中数百万条生物声学录音，数千万张相机陷阱拍摄的图像和野生动物信息，以及 FathomNet 中数千小时的深海影像。我们正坐拥一座生态金矿。

And the problem is accessing the knowledge efficiently.

问题在于如何高效地获取知识。

So say you want to look through all this data, assuming it takes you about a second to look at every image, you would need to work full time for forty years to look through all the images in I naturalist alone.

所以，假设你想浏览所有这些数据，假设你需要大约一秒钟的时间来浏览每一张图像，你需要全职工作四十年，才能浏览I naturalist所有图像。

And this is where AI is transformative. It can just help us look through all the data quickly.

这就是人工智能变革的地方。它可以帮助我们快速浏览所有数据。

So an ecologists today say they're interested in bird diets and they want to find examples of birds eating insects in the database. What they can do is they can train an AI model to help them. So to do this, they collect hundreds or even thousands of examples to teach the model what to look for. Now, once they've trained this model, it's an incredible tool. It can very, very quickly find new examples of birds eating insects in the database.

因此，今天的一位生态学家表示，他们对鸟类饮食感兴趣，并希望在数据库中找到鸟类吃昆虫的例子。他们能做的就是训练一个人工智能模型来帮助他们。因此，为了做到这一点，他们收集了数百甚至数千个例子来教模型寻找什么。现在，一旦他们训练了这个模型，它就是一个令人难以置信的工具。它可以非常非常迅速地在数据库中找到鸟类吃昆虫的新例子。

But this process of collecting hundreds or thousands of examples every time we want to look for something new, it's still too slow. So let's reframe the question.

但是，每次我们想寻找新的东西时，收集数百或数千个例子的过程仍然太慢了。那么，让我们重新定义这个问题。

Scientific discovery really begins with scientific curiosity, with asking questions about the world and how it works. Things like how far can a grant zebra migrate?

科学发现真正始于对科学的好奇心，即对世界及其运作方式的提问。比如斑马可以迁徙多远？

What plants grow back after a forest fire?

什么植物在森林大火后会重新生长？

Do birds eat insects during the winter? Wouldn't it be great if instead we could just directly ask questions to our databases and get answers back?

鸟类在冬天吃昆虫吗？如果我们可以直接向数据库提问并得到答案，那岂不是很棒？

This is what my team at MIT has been working towards, and we've developed a system that we call inquire that helps ecologists find answers in the data without collecting any examples to teach an AI model or needing to write any lines of code.

这就是我在麻省理工学院的团队一直在努力的方向，我们开发了一个我们称之为inquire的系统，可以帮助生态学家在数据中找到答案，而无需收集任何示例来教授人工智能模型或编写任何代码行。

Now, under the hood, what we're doing is we're developing AI models that can learn and understand similarities between images and scientific language. And this is what allows us to just ask.

现在，在幕后，我们正在开发可以学习和理解图像和科学语言之间相似性的人工智能模型。这就是我们可以问的。

So how does inquire work? Well, first, an ecologist designs an experiment by taking a scientific question and breaking it down into a series of search terms that they can use to discover data that they'll analyze downstream. So one of those terms might be bird eating insect. Now what happens is inquire takes that search and it directly compares it to all 300 million images within seconds.

那么查询是如何工作的呢？首先，生态学家设计一个实验，将一个科学问题分解为一系列搜索词，他们可以使用这些搜索词来发现他们将在下游分析的数据。所以其中一个术语可能是食鸟昆虫。现在发生的是inquire进行搜索，并在几秒钟内直接将其与所有3亿张图像进行比较。

It's engineered to do this both quickly and efficiently, which is important because it means the system is truly interactive, but it also requires far less computational power than a generative AI approach like chat GPT.

它的设计目的是快速有效地做到这一点，这很重要，因为这意味着系统是真正的交互式的，但它也需要比像chat GPT这样的生成式人工智能方法少得多的计算能力。

Now, once all of these images are sorted based on their relevance to the query, it's really easy for a scientist to just focus their attention on the data that's most likely to be relevant to them and quickly verify the true matches. Now you have human verified examples of data that you can directly export and analyze. One of our collaborators used this system and they found thousands of examples of birds eating insects, but also seeds, fruit, nuts, carrion, nectar plants.

现在，一旦所有这些图像都根据它们与查询的相关性进行了排序，科学家就很容易将注意力集中在最有可能与它们相关的数据上，并快速验证真实的匹配。现在，您有了经过人工验证的数据示例，可以直接导出和分析。我们的一位合作者使用了这个系统，他们发现了数千只鸟类吃昆虫的例子，还有种子、水果、坚果、腐肉、花蜜植物。

And then they took that data that they discovered quickly, and they analyzed differences in species diets between summer and winter.

然后，他们迅速发现了这些数据，并分析了夏季和冬季物种饮食的差异。

Now what they found was that, yeah, some birds do eat insects in the winter, American robins actually do, but far less than they do in the summer. And some species like American tree sparrow that are incredibly dependent on insects as a food source in the summer, don't eat them at all in the winter.

现在他们发现，是的，有些鸟类在冬天确实会吃昆虫，美国知更鸟确实会吃，但比夏天少得多。一些物种，如美国树麻雀，在夏天非常依赖昆虫作为食物来源，在冬天根本不吃昆虫。

This entire process, questioned to answer, took them about three hours.

整个过程，问到答案，花了他们大约三个小时。

Another team spent 1560 hours manually curating the data to do a similar study, and when you compare the results from enquirer to that study, you see an almost perfect match. I think this is so exciting, right? it means that that we can start quickly getting access to all of this hidden knowledge.

另一个团队花了1560个小时手动整理数据来进行类似的研究，当你将inquirer的结果与该研究进行比较时，你会看到几乎完美的匹配。我认为这太令人兴奋了，这意味着我们可以快速开始获取所有这些隐藏的知识。

And really, I've been so inspired by the creativity of the scientists using the system, all of the flexible ways that people have explored, many, many different questions. Things like looking at how forests regenerate after fire, or discovering differences in species mortality between urban and rural areas, or looking at how flowering events are changing in relation to a changing climate.

事实上，我受到了使用该系统的科学家们的创造力的启发，人们探索了所有灵活的方法，许多不同的问题。比如研究森林在火灾后是如何再生的，或者发现城市和农村地区物种死亡率的差异，或者研究开花事件如何随着气候变化而变化。

The possibilities are truly endless, and the fact that it's open ended means that any scientist can ask the questions they're interested in.

可能性真的是无穷无尽的，而且它是开放式的，这意味着任何科学家都可以提出他们感兴趣的问题。

Now, this is also just the beginning because we've shown that we can do this for images, but we can also imagine designing similar discovery driven systems for bioacoustics recordings, for aerial video, for satellite data, for GPS trajectories coming from animal call, any ecological data type you can think of.

现在，这也只是一个开始，因为我们已经证明我们可以对图像进行处理，但我们也可以想象设计类似的发现驱动系统，用于生物声学记录、航空视频、卫星数据、来自动物叫声的GPS轨迹，以及你能想到的任何生态数据类型。

And that brings up a whole new opportunity, because all of these types of data are innately interrelated. They're all looking at the same thing. They're capturing complementary but distinct perspectives of life on earth.

这带来了一个全新的机会，因为所有这些类型的数据都是天生相互关联的。他们都在看同一件事。它们捕捉到了地球上生命的互补但不同的视角。

And I can imagine a future where we have systems that help scientists quickly discover hidden connections between them all.

我可以想象一个未来，我们有系统可以帮助科学家快速发现它们之间隐藏的联系。

Now, of course, this alone cannot solve our global nature crisis. But what it does do is it helps us maximize the value of data that we've already collected. And that means that then, in turn, we can carefully understand what knowledge gaps remain and strategically use our resources to collect new data to fill those. Overall, this means we're reducing the time and the cost of driving information that supports conservation actions.

当然，仅靠这一点无法解决我们的全球自然危机。但它所做的是帮助我们最大限度地提高我们已经收集的数据的价值。这意味着，反过来，我们可以仔细了解仍然存在的知识差距，并战略性地利用我们的资源收集新的数据来填补这些差距。总的来说，这意味着我们正在减少推动支持保护行动的信息的时间和成本。

Things like understanding how to ensure that food and habitat resources are available to species when they need them most, when they're migrating through an area, when they're breeding or rearing young, or when they're recovering from natural disasters like fire.

比如了解如何确保物种在最需要食物和栖息地资源的时候，在它们迁徙通过一个地区时，在它们繁殖或养育幼崽时，或者在它们从火灾等自然灾害中恢复时，都能获得这些资源。

We stand at a unique point in history.

我们正处于历史上的一个独特时刻。

We have both an unprecedented biodiversity crisis, but we also have unprecedented tools to address it.

我们都面临着前所未有的生物多样性危机，但我们也有前所未有的工具来解决这个问题。

We have millions of people around the world eager to contribute to nature conservation and scientific discovery, and we have AI tools that enable scientists to find patterns in all of that data at scales impossible for humans alone.

全世界有数百万人渴望为自然保护和科学发现做出贡献，我们有人工智能工具，使科学家能够在所有这些数据中找到模式，其规模是人类无法独自完成的。

The future of conservation doesn't just lie in remote rainforests or deep ocean trenches. The future of conservation is hiding in our ecological databases, both the ones we have now but also the ones we have yet to collect. And that is where all of you come in, because everyone can contribute. Everyone can collect data and upload it to platforms like I naturalist.

保护的未来不仅仅在于偏远的热带雨林或深海海沟。保护的未来隐藏在我们的生态数据库中，既有我们现在拥有的数据库，也有我们尚未收集的数据库。这就是你们所有人发挥作用的地方，因为每个人都可以做出贡献。每个人都可以收集数据并将其上传到像I naturalist这样的平台。

Every photo uploaded, every sound recorded, every observation shared is a piece of the puzzle. We know that we need to act now to save nature under threat.

上传的每一张照片，录制的每一个声音，分享的每一次观察都是拼图的一部分。我们知道，我们现在需要采取行动，拯救受到威胁的自然。

And together with scientific AI tools in our toolbox, we can help by building the complete picture of life on earth.

结合我们工具箱中的科学人工智能工具，我们可以通过构建地球上生命的完整图景来提供帮助。

Thank you.

非常感谢。

That was Sarah beery at a TED countdown event in new York city in partnership with the Bezos earth fund in2025.

这是莎拉·比里和贝索斯地球基金于2025年在纽约市合作举办的TED倒计时活动。