Age of AI) Ep.7 Saving the World One Algorithm at a Time

 

• Kurt Vonnegut once said, “Science is magic that works,” and nowhere is this more evident than in the field of artificial intelligence (AI). Today, AI is not just about building smart machines; it’s about saving our planet. One of the critical areas where AI is making a difference is in the fight against illegal poaching. By using sophisticated algorithms, AI systems can analyze data from drones and sensors in wildlife reserves, helping rangers predict and prevent poaching activities. This technology is crucial in protecting endangered species and maintaining biodiversity, ensuring that our world’s natural heritage is preserved for future generations.
• Another groundbreaking application of AI is in the development of plant-based foods. With the world’s population growing rapidly, traditional farming methods are no longer sustainable. AI algorithms are helping to create nutritious, plant-based alternatives that can feed more people with fewer resources. These innovations are essential in reducing our reliance on animal agriculture, a significant contributor to environmental degradation. Additionally, AI is playing a pivotal role in predicting natural disasters. By analyzing vast amounts of environmental data, AI systems can forecast catastrophic events like earthquakes and storms, enabling timely evacuations and saving countless lives. In these ways, AI is not just a tool for technological advancement, but a beacon of hope in preserving our planet and its inhabitants.

Poached

• One of the promises of A.I. is that it’ll enable us to use machine learning for prediction and conservation, anything from protecting wildlife to anticipating earthquakes. Seeing the future may not prevent disasters, but I think we can all agree we need an equipment upgrade.
• There are about 250,000 parks in the world and about 80,000 of them are under threat.
• Poachers kill roughly 35,000 African elephants every year. A single pound of ivory tusk can sell for $1,500, and tusks can weigh 250 pounds each.
• We’ve come all the way over here to Africa to stop poachers before they kill by using artificial intelligence.
• Elephants are about a decade away from extinction, but it’s not just about protecting one kind of animal
• or keystone species. There are about one million other species which are in danger of being wiped out. Animals affect vegetation, biodiversity shapes ecosystems, all of which, in turn, impact people. It’s all connected. So, it’s not a stretch to say that protecting African elephants protects humanity and our future.
• To keep tabs on the larger bulls aerial teams tranquilize them, fit them with GPS collars, and track them via satellite
• It also allows us to look at that historical data and say, these are corridors. These are conflict areas. These are areas where elephants are at risk. In the hopes of identifying poachers, rangers have installed trap cameras throughout the park. A traditional camera trap system takes a photo of anything that moves which then results in all kinds of false positives. The man hours of having to look through thousands of photos… maybe one got a poacher, and it was three days ago. hat’s too late for those animals. They’re already dead. Clearly, traditional camera systems are not enough.
• If we are able to get those poachers before they’ve killed the wildlife, red-handed with their snares as they’re coming in, we’re really now getting ahead of the game. Every one of these, if we hadn’t removed it, costs an animal its life
• What I do is talk to people and organizations that have a mission, and I connect them with the technology that they need to see their missions through.
• Intel partnered with Resolve, an N.G.O. that uses technical innovation to solve some of the planet’s more pressing environmental problems.
• They began developing an A.I.-powered anti-poaching device they call “TrailGuard.” TrailGuard at its core is a motion-capture camera designed to help prevent poaching
• So what we have done is input this very powerful computer chip called a Vision Processing Unit. All the pictures that the camera trap is taking will go through this VPU chip, and it’ll figure out if there is a person present or not… Okay. And only send you the pictures there where there’s a person. So it’s gonna reduce, like, 95% of that noise that you’re getting
• It has an A.I. algorithm in it which looks at every single picture, and sees if this is a picture that the park rangers are interested in, and right now, they really just want to know if there is a person going past. [Downey] The algorithm is fed thousands of images of both humans and animals. It analyzes body shapes, facial geometry, movement, and other features until it learns how to distinguish one from the other, from any angle, in any light. [Bethke] It takes a picture whenever any motion happens in front of it, but the A.I. is going to send only images of people going past.
• Image recognition is looking at an image and understanding what’s in it. It’s the perfect example of something that we take for granted. Human beings can just, you know, look at an image and understand what’s there without even thinking about it, but it’s actually an incredibly hard problem. Amazingly, we actually have computers now who, at least within certain parameters, can do this as well as human beings can.
• The beauty of this system is, here in the Mara, the amount of time it takes from when the poacher walks in front of the camera to when it says, “That’s a human,” and sends it to the headquarters. Under two minutes. We would be able to then deploy a ranger team to that site, and hopefully make an arrest before they’ve even had a chance to come in and kill those, those animals.

Deploying Cameras

• The reality is that poaching incursions are not random.
• In fact, poachers actually try to follow trails, because they want to get in and out as fast as they can.
• There’s about ten major access routes that account for 80% of all the poacher traffic.
• [ranger Wilson] I think this is where the elephants normally come.
• Night is also more difficult for image recognition. Details and features are harder to see, so for this test, the A.I. will have to rely on a narrow data set.
• Catching poachers has really been a sort of cat-and-mouse behavior, if you like. The TrailGuard, really, is giving us this new edge. We will get the poachers now before they’ve even had a chance to come in and kill the wildlife.

Avoiding Mass Extinction

• This is a global problem. There are many more bad guys out there than we have the good guys defending these parks. Right now, we can help tilt that balance back to the good guys to save the world’s large mammals and prevent the sixth mass extinction.
• The mass extinction is when most of the Earth’s animals and plants die out, wiping out biodiversity.
• Now, we’re arguably in the midst of a sixth mass extinction. Poachers are part of the problem, but so is climate change and what we eat. -[cows mooing] All of us. Can we rewire our destructive behavior?
• Animal-based protein food has been our main source of nutrition for the last, you know, thousand years. I actually grew up eating steak, but the way we’re farming it… [cows mooing] it’s not the right way. The food industry has become the common denominator to every major environmental ill known to humankind…deforestation, water scarcity, world hunger!
• Eating meat causes climate change? Sure. Cows and other animals emit methane, a harmful greenhouse gas. One third of the world’s farmable land is used to feed livestock. Look at it this way, eating one burger has about the same environmental impact as driving a gas car for ten miles. So, what do we do?
• There is a new way, a better way of making food, and that’s plant-based. Save the planet by reducing meat consumption.
• Their challenge is not creating a plant-based alternative to popular animal proteins. Plenty of companies already do that. Using an A.I. algorithm they call “Giuseppe,” they’re trying to make people think they’re eating steak, or eggs, or milk, when they’re actually… not.
• A technology that is able to tell us how to reproduce an animal-based food just by using plants. [Muchnick] The algorithm is able to understand that there are clear connections between the molecular components in food and the human perception of taste and texture.
• The magic behind Giuseppe is all about chemistry. The A.I. looks at the molecular makeup of foods, like, say, milk. It then creates a list of ingredients from its most basic building blocks. Finally, using machine learning and a massive database, Giuseppe then recombines select elements from plant-based foods to recreate the taste, and texture of the original.
• Humans are good at reasoning about two ingredients or maybe three ingredients at a time, but after that, it becomes very difficult for us to think about it, but the machine can start thinking about five ingredients, ten ingredients, and how they all go together, and what the flavor profiles will be, and that’s really the great power of the machine.
• Here are a lot of similarities between plants and animals, because we share part of the chemical nature. All of them have DNA, all of them have RNA, all of them have proteins, lipids, and carbohydrates.
• An almond and a walnut share, like, 97% of the molecules are the same molecules. Just the 3% give the identity to your brain that the walnut is a walnut, and an almond is an almond, so we need to really identify the molecular features that give the identity to food to pick up the specific type of plants and the specific ingredient from plants to rebuild.
• We are building an ecosystem that really will see food in a unique way because Giuseppe suggests really… crazy ingredients sometimes.
• The first food they tried to recreate is one of Chile’s favorite condiments… mayonnaise. We identified that cabbage, in a specific environment, will release a molecule that is really similar to lactose, and to your brain, it’s kind of the same thing. We tasted the emulsion, and we said, “It tastes exactly like mayo…” but it’s red. The algorithm still didn’t understand that one of the characteristics that we value in the sensorial experience of a product is color.
• The success of Not Mayo led to the creation of Not Milk, Not Ice Cream, and Not Meat. Now, they’re working on Not Tuna. Generating a replacement for tuna is something that will move the needle in the world, so fish makes sense.
• If we don’t do things that are as good or better, things won’t change, and we won’t move the needle
• One of the things with large-scale behavior changes, it’s often not driven by nutrition or sustainability considerations, it’s driven by flavor. I think everyone has this experience of wanting to try the new chip flavor, and if we can have that same property in healthy and sustainable foods, that can be really powerful.
• The algorithm is not ready yet to understand the complexity of a muscle. How do we explain that to an algorithm?
• 92% of our consumers are non-vegetarian. They don’t care about sustainability. What they care about is eating tasty food.
• Barroso cut his teeth at El Bulli, in Spain, arguably the best restaurant in the world, and his own joint in Chile is top 50. He’s a culinary innovator, can taste flavors invisible to the average human tongue, and carries around his own personal spatula, gunslinger-style.

Plant Based Food

• We are going to eat everything in the same way that we prepare it in a normal recipe in the restaurant. We would put one liter of milk, we are going to put one liter of Not Milk. This is the ultimate challenge for us.
• At the end of the day, if NotCo’s gonna take off, their food just has to taste good for everybody, A.I. or no A.I.
• We’re so nervous because this is going to prove that plant-based food can actually replace animal-based food.
• My brain told me that I wasn’t eating meat, but my soul told me that it was meat.
• The dream of NotCo is that it uses A.I. to preserve our planet from the damage humans are doing but does it cut both ways? Can A.I. protect people from destructive forces of nature?

Protecting From Nature

• The laws of nature don’t change. Human beings change in response to A.I… but nature doesn’t. [rumbling] If you take earthquakes, if you can predict when earthquakes will happen… this can save a lot of lives.
• It’s been 319 years since the last big one here. Seismically, it’s the quietest zone in the world right now, so the possibility that it’s quiet because it’s locked and loaded and ready to go, it certainly has freaked a lot of people out.
• Right now, we can forecast that big subduction zones or major faults will have an earthquake at some point in the future, but that’s not what people want to know. They want to know is the earthquake coming imminently? In six hours? In one day? In 30 seconds?
• The Cascadia fault line more than 600 miles long from Vancouver Island to northern California, could cause the biggest natural disaster in North American history. It’s generally accepted that The Big One is coming, maybe within 50 years, but experts can’t be certain, or more specific.
• All 400 of our instruments are sending a constant stream of data back to our data center. [Paul Bodin] In each of those 400 sensors, there’s a lot of signals which aren’t earthquakes.
• [Tobin] So we’re generating this incredible volume of data. This is probably a truck going by. [Gibbons]
• Where the A.I. comes in would be filtering out what we call cultural noise. Trains, trucks, people. [Downey] Vibration info from all 400 sensors in the region is fed into a machine-learning algorithm, which is trained to differentiate earthquake tremors from, say, construction or buses. Using machine learning and a huge database of known sounds, the A.I. can quickly sort through the noise of the natural world.
• We’re working hard on an earthquake early warning system, and it’s called Shake Alert. The emergency management center here would be one of our key immediate users. If it’s a big earthquake, then within a second or two our computer algorithms determine what area it’s going to affect and then create a warning for that.
• But when a massive earthquake struck northern Japan in 2011. The residents had warning… between 20 to 90 seconds, depending on how far they were from the quake’s epicenter and yet, almost 16,000 lives were still lost. Is there any way A.I. could buy us even more warning time by predicting the next big one?
• Earthquake prediction is a really hard problem, because we don’t know where the earthquakes are gonna happen. Not really, right? We don’t know when they’re gonna happen, and we don’t really know how big that zone is that the fault reaches before it all of a sudden snaps.
• Chris and his team have come up with a way to create earthquakes in the lab. The goal today is to try to figure out if we can create laboratory earthquakes under a range of conditions, and use machine learning to predict that whole range.
• On a small scale, this test mimics what happens on a huge fault like the Cascadia. It’s basically two massive tectonic plates, one under the coastal northwest, and one under the Pacific. One slides under the other, and sometimes snags, causing friction and pressure. When it’s too much, they violently slip, causing an earthquake. So that’s one side, and then I basically repeat the same thing for the other side block. That’s what Chris and his team are trying to replicate with mini granite blocks in the lab.
• We’re listening to everything that happens in the faults, and we’re using machine learning to find the patterns in it. Is there something happening that we can use to say, “Ah, this is about to go big.”
• That yellow line is the measure of the shear stress on the fault. We’re watching it build, and the whole time it’s increasing, there are micro earthquakes that we’re listening to, and what we’ve realized is that we can use those micro earthquakes along with machine learning to predict the time of the next event. [Downey] These micro earthquakes that they’re listening to are too faint for the human ear, but not for machine learning. It uses faint signals to predict earthquakes we can hear and feel.
• Listening to the small events to teach us about when the big events are gonna occur. We’re predicting the time of the next earthquake, and we’re predicting the duration of it.
• It’s a game changer, using artificial intelligence, because now we can use machine learning to ask questions about why… why is that happening? Is there some geometric structure that’s building inside the fault zone, that is somehow seen by artificial intelligence?
• We’ve shown beyond a shadow of a doubt that A.I., machine learning, can predict laboratory earthquakes.
• The challenge is how do we scale that from what’s being measured in the laboratory to what we measure with seismic sensors over an area of hundreds or thousands of square kilometers.
• As artificial intelligence goes from the lab out into the world, the world is much more complicated, and so there’s a lot of factors that might not have been modeled. If we can transfer out, if we can generalize, that would be really, really amazing.
• If A.I. can predict calamity, could it go one step further, and prevent it?

Preventing Calamity

• Machine learning is being used by corporations and governments to help solve large-scale conflicts and catastrophes. One area that causes a lot of problems is food, or lack of it.
• Agriculture is just a critical component of national security and the health of the world. Food shortages sometimes lead to famine. Famine leads to political unrest. The way I see A.I. is it’s not a scary force to be feared, but something that’s going to help us address these big problems
• There’s a tremendous amount of satellite imagery out there. That’s one of the biggest datasets that humanity has collected. The most astonishing thing to me about satellites is that we have had excellent scientific information coming down since the ’70s, and yet it’s really hard to use that information.
• There are thousands of satellites photographing Earth, so Descartes built a supercomputer in the cloud that uses machine learning to analyze these images and make models from the information. They’re trying to predict when disease, disaster, or even war might strike.
• Artificial intelligence has progressed so much in the past decade, and now you can start to build models on top of this data instead of just having a bunch of pictures that are hard to deal with.
• So the first task, we built what we call a similarity search engine. The search engine uses object recognition, a type of A.I. that learns to identify and differentiate specific things within images. Instead of scanning for poachers in Africa, they’re looking for everything from solar panels to riverbeds. The computer is just seeing numbers. It does not see the image like we see it. It just sees raw numbers, so we teach it over time that’s what a river looks like. That’s a street. That’s a building, and the algorithm is able to, over time, if it sees enough examples of this, you know, it says, “Yeah, I get it.”
• So we started looking around for a problem that we could solve with this, and what we settled on was agriculture. Plants are really neat because they’re like these little factories, right? Just by watching how the light bounces off of these plants, you can tell a lot about the production of this factory.
• For innovators, it’s not totally unusual to find an answer, and then reverse-engineer a question. The question Mark asked his A.I. was about corn. How many cornfields are there, where, and what are their growing patterns and then comparing satellite images over time, can we predict how much corn the country would produce next year?
• We combined two things, so the satellite data will give you a sense of the health of the plants today. Then we looked at the weather data, which will give you a sense of how healthy the plants will be in a week or two.
• In 2017, the U.S. produced over two billion bushels of corn. Descartes’ estimate was within one percent. …and I think what really shocked the industry was this wasn’t a bunch of agronomists, these weren’t people who were experts in corn. We were a bunch of physicists just using the principles of physics and light. This really woke up the market that data can really change traditional forecasting methods.
• If you have lots of data, and you’ve got an algorithm that can analyze that data, it can learn things that no human being would be able to perceive, and it will be able to make predictions that no human being would ever have been able to make. [Downey] …but it’s not just corn. You can do this with almost anything that’s photographed from space. Water, forests, factories, roads.
• There’s a lot of places where the food supply is highly dependent on the amount of snow that falls in the winter.
• So if we know how much snow falls in the winter, the farmers can better plan their crops. Maybe they plant drought resistant crops. This should give you some indication of later in the summer when the snow is melting, how much rice that area can handle.

DARPA

• DARPA is the Defense Advanced Research Projects. They look at the hardest problems that are gonna affect the military of the United States.
• Arab Spring was touched off by, you know, wheat shortages that led to shortages of, of bread.
• The problem that they challenged us with is looking at food production in the Middle East and North Africa, and the goal is to understand where there might be food shortages.
• When you don’t have water, when you don’t have food, when you don’t have shelter, you fight, right? You will do anything in order to survive.
• Okay, so we’ve been looking at this current year growing season. We’re seeing a lot of healthy wheat fields in Syria this year. It looks like they’re, we’re about 20%, um, above last year’s production -in Syria.
• This is a wonderful leap forward in the ability to get, um, you know, objective measures of what’s really happening in agriculture in these troubled areas. [Johnson] It’s really important to know where there’s crop failure so we can send aid there before it gets really bad and turns into famine.
• The fact that we can look across the world, and find where famine might happen four months from now is… it’s mind-blowing. [Johnson] It’s pretty astonishing to me that you can look at the health of crops with satellites that are flying hundreds of miles above the Earth.
• The idea that corporations and governments have technology that predicts, and maybe prevents, large-scale human catastrophes, like war and famine, is mind-blowing.
• Computers are sorta like three-year olds right now, and we’re training them to be our helpers, to help us make better decisions, to help us be better humans. I believe deeply that science is going to help us save the planet and save ourselves.
• In the old days, people used to think disasters were caused by God or magic. Now we know better. If a sixth mass extinction happens, it’s probably gonna be on us.
• We have this technology that we can use to save life on Earth. A.I. might not prevent disasters, but new scientific tools, like machine learning, image recognition, and predictive modeling, might help us at least get ahead of them.
• Artificial intelligence, machine learning is gonna be a very, very powerful tool for making predictions at a precision that previously has been impossible.
• There’s been a lot of projects in sustainability that are deploying artificial intelligence to address problems that the world faces.
• If we can have a better insight into what the future looks like for food, then we can really save a lot of people.
• Conserving the planet or preserving our species doesn’t really have anything to do with magic, but it would be divine.

The insights and information presented in these articles are based on the YouTube Originals Series “The Age of AI.” All script and content rights belong to the creators and producers of the series. This series served as a primary reference in the development of these articles.