How AI Is Unearthing Hidden Scientific Knowledge | Sara Beery | TED

Imagine you're a doctor and you're trying to save the life of a patient, but you can only see a fifth of their body. How are you going to prescribe medicine? do surgery? See, this is exactly the situation we're in with nature across the planet. We need to act now to protect ecosystems under threat, but there's so much we don't know about life on Earth. 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. 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. Now let me tell you why this is so important. Scientists estimate there are 10 million species sharing the planet with us. But we have only ever observed two million of those. That means eight million species, 80 percent of the diversity of life on Earth, remains unknown. And we need to know much more than just a species exists to be able to protect it. 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. So, for an example, 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 able to adapt to other food sources? What about predators further up the food chain that eat birds? 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. Unfortunately, species are under threat from every direction. Habitats are shrinking, temperatures are rising, food and water sources are disappearing. Natural disasters like fire are causing large-scale death and displacement. And invasive species are moving in and outcompeting native species for resources. As a result, extinction rates are now 100 to 1,000 times higher than what we would expect based on past data. 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 Tapanuli orangutan. We discovered this orangutan in 2017. It's one of only three species of orangutan on Earth, and it was critically endangered before we even knew it existed. 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. Let's talk about just one of these databases, which is a platform called iNaturalist. 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. So let's look at just one of these images. In iNaturalist, this was labeled Grant’s zebra. And it's clearly evidence that Grant's zebra were sighted in this place and time. But it shows us so much more than that. There are three Grant's zebra in this image. We can identify each of them to the individual level based on their unique stripe pattern. 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 wildebeest. And if we look closely, we can even see an oxpecker, a bird that eats ticks and helps reduce the spread of disease. 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. Take this much knowledge in one image and multiply it by 300 million images in iNaturalist, and then add in our other ecological databases. Millions of bioacoustic recordings on xeno-canto, tens of millions of camera-trap images and wildlife insights, thousands of hours of deep-sea footage in FathomNet. We're sitting on an ecological goldmine and the problem is accessing the knowledge efficiently. 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 40 years to look through all the images in iNaturalist alone. And this is where AI is transformative. It can just help us look through all the data quickly. So an ecologist 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. 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. Scientific discovery really begins with scientific curiosity, with asking questions about the world and how it works. Things like, how far can a Grant's zebra migrate? What plants grow back after a forest fire? 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? 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. 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. 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. 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 ChatGPT. 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. And then they took that data that they discovered quickly and they analyzed differences in species' diets between summer and winter. 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. This entire process, question-to-answer, took them about three hours. Another team spent 1,560 hours manually curating the data to do a similar study. And when you compare the results from Inquire to that study, you see an almost perfect match. I think this is so exciting, right? It means that we can start quickly getting access to all of this hidden knowledge. 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. 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. 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 bioacoustic recordings, for aerial video, for satellite data, for GPS trajectories coming from animal collars, any ecological data type you can think of. 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. And I can imagine a future where we have systems that help scientists quickly discover hidden connections between them all. 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. 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. We stand at a unique point in history. We have both an unprecedented biodiversity crisis, but we also have unprecedented tools to address it. 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. 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 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 iNaturalist. 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. And together with scientific AI tools in our toolbox, we can help by building the complete picture of life on Earth. Thank you. (Cheers and applause)