Drones are just one of several new technologies that are rising to the challenge of more frequent wildfires.
Robots have been a game-changer. Inspired by Paris, the Los Angeles Fire Department (LAFD) was the first in the U.S. to deploy a firefighting robot in 2021, the Thermite Robotics System 3 – RS3, for short.
RS3 is a 3,500-pound turbine on a crawler—the size of a Smart car—with a 36.8 horsepower engine that can go for 20 hours without refueling. It can plow through hazardous terrain, move cars from its path, and pull an 8,000-pound object from a fire.
All that while spurting 2,500 gallons of water per minute with a rear exhaust fan clearing the smoke. At a recent trade show, RS3 was billed as equivalent to 10 firefighters. The Los Angeles Times referred to it as “a droid on steroids.”
Robots such as the Thermite RS3 can plow through hazardous terrain and pull an 8,000-pound object from a fire.
Los Angeles Fire Department
The advantage of the robot is obvious. Operated remotely from a distance, it greatly reduces an emergency responder’s exposure to danger, says Wade White, assistant chief of the LAFD. The robot can be sent into airplane fires, nuclear reactors, hazardous areas with carcinogens (think East Palestine, Ohio), or buildings where a roof collapse is imminent.
Advances for firefighters are taking many other forms as well. Fibers have been developed that make the firefighter’s coat lighter and more protective from carcinogens. New wearable devices track firefighters’ biometrics in real time so commanders can monitor their heat stress and exertion levels. A sensor patch is in development which takes readings every four seconds to detect dangerous gases such as methane and carbon dioxide. A sonic fire extinguisher is being explored that uses low frequency soundwaves to remove oxygen from air molecules without unhealthy chemical compounds.
The demand for this technology is only increasing, especially with the recent rise in wildfires. In 2021, fires were responsible for 3,800 deaths and 14,700 injuries of civilians in this country. Last year, 68,988 wildfires burned down 7.6 million acres. Whether the next generation of firefighting can address these new challenges could depend on special cameras, robots of the aerial variety, AI and smart systems.
Fighting fire with cameras
Another key innovation for firefighters is a thermal imaging camera (TIC) that improves visibility through smoke. “At a fire, you might not see your hand in front of your face,” says White. “Using the TIC screen, you can find the door to get out safely or see a victim in the corner.” Since these cameras were introduced in the 1990s, the price has come down enough (from $10,000 or more to about $700) that every LAFD firefighter on duty has been carrying one since 2019, says White.
TICs are about the size of a cell phone. The camera can sense movement and body heat so it is ideal as a search tool for people trapped in buildings. If a firefighter has not moved in 30 seconds, the motion detector picks that up, too, and broadcasts a distress signal and directional information to others.
To enable firefighters to operate the camera hands-free, the newest TICs can attach inside a helmet. The firefighter sees the images inside their mask.
TICs also can be mounted on drones to get a bird’s-eye, 360 degree view of a disaster or scout for hot spots through the smoke. In addition, the camera can take photos to aid arson investigations or help determine the cause of a fire.
More help From above
Firefighters prefer the term “unmanned aerial systems” (UAS) to drones to differentiate them from military use.
A UAS carrying a camera can provide aerial scene monitoring and topography maps to help fire captains deploy resources more efficiently. At night, floodlights from the drone can illuminate the landscape for firefighters. They can drop off payloads of blankets, parachutes, life preservers or radio devices for stranded people to communicate, too. And like the robot, the UAS reduces risks for ground crews and helicopter pilots by limiting their contact with toxic fumes, hazardous chemicals, and explosive materials.
“The nice thing about drones is that they perform multiple missions at once,” says Sean Triplett, team lead of fire and aviation management, tools and technology at the Forest Service.
Experts predict we’ll see swarms of drones dropping water and fire retardant on burning buildings and forests in the near future.
The UAS is especially helpful during wildfires because it can track fires, get ahead of wind currents and warn firefighters of wind shifts in real time. The U.S. Forest Service also uses long endurance, solar-powered drones that can fly for up to 30 days at a time to detect early signs of wildfire. Wildfires are no longer seasonal in California – they are a year-long threat, notes Thanh Nguyen, fire captain at the Orange County Fire Authority.
In March, Nguyen’s crew deployed a drone to scope out a huge landslide following torrential rains in San Clemente, CA. Emergency responders used photos and videos from the drone to survey the evacuated area, enabling them to stay clear of ground on the hillside that was still sliding.
Improvements in drone batteries are enabling them to fly for longer with heavier payloads. Experts predict we’ll see swarms of drones dropping water and fire retardant on burning buildings and forests in the near future.
AI to the rescue
The biggest peril for a firefighter is often what they don’t see coming. Flashovers are a leading cause of firefighter deaths, for example. They occur when flammable materials in an enclosed area ignite almost instantaneously. Or dangerous backdrafts can happen when a firefighter opens a window or door; the air rushing in can ignite a fire without warning.
The Fire Fighting Technology Group at the National Institute of Standards and Technology (NIST) is developing tools and systems to predict these potentially lethal events with computer models and artificial intelligence.
Partnering with other institutions, NIST researchers developed the Flashover Prediction Neural Network (FlashNet) after looking at common house layouts and running sets of scenarios through a machine-learning model. In the lab, FlashNet was able to predict a flashover 30 seconds before it happened with 92.1% success. When ready for release, the technology will be bundled with sensors that are already installed in buildings, says Anthony Putorti, leader of the NIST group.
The NIST team also examined data from hundreds of backdrafts as a basis for a machine-learning model to predict them. In testing chambers the model predicted them correctly 70.8% of the time; accuracy increased to 82.4% when measures of backdrafts were taken in more positions at different heights in the chambers. Developers are working on how to integrate the AI into a small handheld device that can probe the air of a room through cracks around a door or through a created opening, Putorti says. This way, the air can be analyzed with the device to alert firefighters of any significant backdraft risk.
Early wildfire detection technologies based on AI are in the works, too. The Forest Service predicts the acreage burned each year during wildfires will more than triple in the next 80 years. By gathering information on historic fires, weather patterns, and topography, says White, AI can help firefighters manage wildfires before they grow out of control and create effective evacuation plans based on population data and fire patterns.
The future is connectivity
We are in our infancy with “smart firefighting,” says Casey Grant, executive director emeritus of the Fire Protection Research Foundation. Grant foresees a new era of cyber-physical systems for firefighters—a massive integration of wireless networks, advanced sensors, 3D simulations, and cloud services. To enhance teamwork, the system will connect all branches of emergency responders—fire, emergency medical services, law enforcement.
FirstNet (First Responder Network Authority) now provides a nationwide high-speed broadband network with 5G capabilities for first responders through a terrestrial cell network. Battling wildfires, however, the Forest Service needed an alternative because they don’t always have access to a power source. In 2022, they contracted with Aerostar for a high altitude balloon (60,000 feet up) that can extend cell phone power and LTE. “It puts a bubble of connectivity over the fire to hook in the internet,” Triplett explains.
A high altitude balloon, 60,000 feet high, can extend cell phone power and LTE, putting a "bubble" of internet connectivity over fires.
Courtesy of USDA Forest Service
Advances in harvesting, processing and delivering data will improve safety and decision-making for firefighters, Grant sums up. Smart systems may eventually calculate fire flow paths and make recommendations about the best ways to navigate specific fire conditions. NIST’s plan to combine FlashNet with sensors is one example.
The biggest challenge is developing firefighting technology that can work across multiple channels—federal, state, local and tribal systems as well as for fire, police and other emergency services— in any location, says Triplett. “When there’s a wildfire, there are no political boundaries,” he says. “All hands are on deck.”
Artificial Intelligence is already helping to grow some of the food we eat.
The chef-farmers choose from among 45 types of herb and leafy-greens seeds, plop them into grow trays, and a few weeks later they pick and serve. While success is predicated on at least a small amount of these humans’ care, the systems are autonomously surveilled round-the-clock from Babylon’s base of operations. And artificial intelligence is helping to run the show.
Babylon piloted the use of specialized cameras that take pictures in different spectrums to gather some less-obvious visual data about plants’ wellbeing and alert people if something seems off.
Imagine consistently perfect greens and tomatoes and strawberries, grown hyper-locally, using less water, without chemicals or environmental contaminants. This is the hefty promise of controlled environment agriculture (CEA) — basically, indoor farms that can be hydroponic, aeroponic (plant roots are suspended and fed through misting), or aquaponic (where fish play a role in fertilizing vegetables). But whether they grow 4,160 leafy-green servings per year, like one Babylon farm, or millions of servings, like some of the large, centralized facilities starting to supply supermarkets across the U.S., they seek to minimize failure as much as possible.
Babylon’s soilless hydroponic growing system
Courtesy Babylon Micro-Farms
Here, AI is starting to play a pivotal role. CEA growers use it to help “make sense of what’s happening” to the plants in their care, says Scott Lowman, vice president of applied research at the Institute for Advanced Learning and Research (IALR) in Virginia, a state that’s investing heavily in CEA companies. And although these companies say they’re not aiming for a future with zero human employees, AI is certainly poised to take a lot of human farming intervention out of the equation — for better and worse.
Most of these companies are compiling their own data sets to identify anything that might block the success of their systems. Babylon had already integrated sensor data into its farms to measure heat and humidity, the nutrient content of water, and the amount of light plants receive. Last year, they got a National Science Foundation grant that allowed them to pilot the use of specialized cameras that take pictures in different spectrums to gather some less-obvious visual data about plants’ wellbeing and alert people if something seems off. “Will this plant be healthy tomorrow? Are there things…that the human eye can't see that the plant starts expressing?” says Amandeep Ratte, the company’s head of data science. “If our system can say, Hey, this plant is unhealthy, we can reach out to [users] preemptively about what they’re doing wrong, or is there a disease at the farm?” Ratte says. The earlier the better, to avoid crop failures.
Natural light accounts for 70 percent of Greenswell Growers’ energy use on a sunny day.
Courtesy Greenswell Growers
IALR’s Lowman says that other CEA companies are developing their AI systems to account for the different crops they grow — lettuces come in all shapes and sizes, after all, and each has different growing needs than, for example, tomatoes. The ways they run their operations differs also. Babylon is unusual in its decentralized structure. But centralized growing systems with one main location have variabilities, too. AeroFarms, which recently declared bankruptcy but will continue to run its 140,000-square foot vertical operation in Danville, Virginia, is entirely enclosed and reliant on the intense violet glow of grow lights to produce microgreens.
Different companies have different data needs. What data is essential to AeroFarms isn’t quite the same as for Greenswell Growers located in Goochland County, Virginia. Raising four kinds of lettuce in a 77,000-square-foot automated hydroponic greenhouse, the vagaries of naturally available light, which accounts for 70 percent of Greenswell’s energy use on a sunny day, affect operations. Their tech needs to account for “outside weather impacts,” says president Carl Gupton. “What adjustments do we have to make inside of the greenhouse to offset what's going on outside environmentally, to give that plant optimal conditions? When it's 85 percent humidity outside, the system needs to do X, Y and Z to get the conditions that we want inside.”
AI will help identify diseases, as well as when a plant is thirsty or overly hydrated, when it needs more or less calcium, phosphorous, nitrogen.
Nevertheless, every CEA system has the same core needs — consistent yield of high quality crops to keep up year-round supply to customers. Additionally, “Everybody’s got the same set of problems,” Gupton says. Pests may come into a facility with seeds. A disease called pythium, one of the most common in CEA, can damage plant roots. “Then you have root disease pressures that can also come internally — a change in [growing] substrate can change the way the plant performs,” Gupton says.
AI will help identify diseases, as well as when a plant is thirsty or overly hydrated, when it needs more or less calcium, phosphorous, nitrogen. So, while companies amass their own hyper-specific data sets, Lowman foresees a time within the next decade “when there will be some type of [open-source] database that has the most common types of plant stress identified” that growers will be able to tap into. Such databases will “create a community and move the science forward,” says Lowman.
In fact, IALR is working on assembling images for just such a database now. On so-called “smart tables” inside an Institute lab, a team is growing greens and subjects them to various stressors. Then, they’re administering treatments while taking images of every plant every 15 minutes, says Lowman. Some experiments generate 80,000 images; the challenge lies in analyzing and annotating the vast trove of them, marking each one to reflect outcome—for example increasing the phosphate delivery and the plant’s response to it. Eventually, they’ll be fed into AI systems to help them learn.
For all the enthusiasm surrounding this technology, it’s not without downsides. Training just one AI system can emit over 250,000 pounds of carbon dioxide, according to MIT Technology Review. AI could also be used “to enhance environmental benefit for CEA and optimize [its] energy consumption,” says Rozita Dara, a computer science professor at the University of Guelph in Canada, specializing in AI and data governance, “but we first need to collect data to measure [it].”
The chef-farmers can choose from 45 types of herb and leafy-greens seeds.
Courtesy Babylon Micro-Farms
Any system connected to the Internet of Things is also vulnerable to hacking; if CEA grows to the point where “there are many of these similar farms, and you're depending on feeding a population based on those, it would be quite scary,” Dara says. And there are privacy concerns, too, in systems where imaging is happening constantly. It’s partly for this reason, says Babylon’s Ratte, that the company’s in-farm cameras all “face down into the trays, so the only thing [visible] is pictures of plants.”
Tweaks to improve AI for CEA are happening all the time. Greenswell made its first harvest in 2022 and now has annual data points they can use to start making more intelligent choices about how to feed, water, and supply light to plants, says Gupton. Ratte says he’s confident Babylon’s system can already “get our customers reliable harvests. But in terms of how far we have to go, it's a different problem,” he says. For example, if AI could detect whether the farm is mostly empty—meaning the farm’s user hasn’t planted a new crop of greens—it can alert Babylon to check “what's going on with engagement with this user?” Ratte says. “Do they need more training? Did the main person responsible for the farm quit?”
Lowman says more automation is coming, offering greater ability for systems to identify problems and mitigate them on the spot. “We still have to develop datasets that are specific, so you can have a very clear control plan, [because] artificial intelligence is only as smart as what we tell it, and in plant science, there's so much variation,” he says. He believes AI’s next level will be “looking at those first early days of plant growth: when the seed germinates, how fast it germinates, what it looks like when it germinates.” Imaging all that and pairing it with AI, “can be a really powerful tool, for sure.”
Researchers from the University of Cambridge have laid the foundations for growing synthetic embryos that could develop a beating heart, gut and brain.
The organoid revolution
In 1981, scientists discovered how to keep stem cells alive. This was a significant breakthrough, as stem cells have notoriously rigorous demands. Nevertheless, stem cells remained a relatively niche research area, mainly because scientists didn’t know how to convince the cells to turn into other cells.
Then, in 1987, scientists embedded isolated stem cells in a gelatinous protein mixture called Matrigel, which simulated the three-dimensional environment of animal tissue. The cells thrived, but they also did something remarkable: they created breast tissue capable of producing milk proteins. This was the first organoid — a clump of cells that behave and function like a real organ. The organoid revolution had begun, and it all started with a boob in Jello.
For the next 20 years, it was rare to find a scientist who identified as an “organoid researcher,” but there were many “stem cell researchers” who wanted to figure out how to turn stem cells into other cells. Eventually, they discovered the signals (called growth factors) that stem cells require to differentiate into other types of cells.
For a human embryo (and its organs) to develop successfully, there needs to be a “dialogue” between these three types of stem cells.
By the end of the 2000s, researchers began combining stem cells, Matrigel, and the newly characterized growth factors to create dozens of organoids, from liver organoids capable of producing the bile salts necessary for digesting fat to brain organoids with components that resemble eyes, the spinal cord, and arguably, the beginnings of sentience.
Synthetic embryos
Organoids possess an intrinsic flaw: they are organ-like. They share some characteristics with real organs, making them powerful tools for research. However, no one has found a way to create an organoid with all the characteristics and functions of a real organ. But Magdalena Żernicka-Goetz, a developmental biologist, might have set the foundation for that discovery.
Żernicka-Goetz hypothesized that organoids fail to develop into fully functional organs because organs develop as a collective. Organoid research often uses embryonic stem cells, which are the cells from which the developing organism is created. However, there are two other types of stem cells in an early embryo: stem cells that become the placenta and those that become the yolk sac (where the embryo grows and gets its nutrients in early development). For a human embryo (and its organs) to develop successfully, there needs to be a “dialogue” between these three types of stem cells. In other words, Żernicka-Goetz suspected the best way to grow a functional organoid was to produce a synthetic embryoid.
As described in the aforementioned Nature paper, Żernicka-Goetz and her team mimicked the embryonic environment by mixing these three types of stem cells from mice. Amazingly, the stem cells self-organized into structures and progressed through the successive developmental stages until they had beating hearts and the foundations of the brain.
“Our mouse embryo model not only develops a brain, but also a beating heart [and] all the components that go on to make up the body,” said Żernicka-Goetz. “It’s just unbelievable that we’ve got this far. This has been the dream of our community for years and major focus of our work for a decade and finally we’ve done it.”
If the methods developed by Żernicka-Goetz’s team are successful with human stem cells, scientists someday could use them to guide the development of synthetic organs for patients awaiting transplants. It also opens the door to studying how embryos develop during pregnancy.
This article originally appeared on Big Think, home of the brightest minds and biggest ideas of all time.
