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One of the most striking patterns in the aftermath of many urban fires is how much unburned green vegetation remains amid the wreckage of burned neighborhoods. In some cases, a row of shrubs may be all that separates a surviving house from one that burned just a few feet away. As scientists who study how vegetation ignites and burns, we recognize that well-maintained plants and trees can actually help protect homes from wind-blown embers and slow the spread of fire in some cases. So, we are concerned about new wildfire protection regulations being developed by the state of California that would prohibit almost all plants and other combustible material within 5 feet of homes, an area known as Zone 0. Photos before and after the 2025 Palisades Fire show thick green vegetation between two closely spaced homes. The arrow shows the direction of the fires spread. [Image: Max Moritz; CAL FIRE Damage Inspection photos, CC BY] Wildfire safety guidelines have long encouraged homeowners to avoid having flammable materials next to their homes. But the states plan for an ember-resistant zone, being expedited under an executive order from Gov. Gavin Newsom, goes further by also prohibiting grass, shrubs, and many trees in that area. If that prohibition remains in the final regulation, its likely to be met with public resistance. Getting these rules right also matters beyond California, because regulations that originate in California often ripple outward to other fire-prone regions. Lessons from the devastation Research into how vegetation can reduce fire risk is a relatively new area of study. However, the findings from plant flammability studies and examination of patterns of where vegetation and homes survive large urban fires highlight its importance. When surviving plants do appear scorched after these fires, it is often on the side of the plant facing a nearby structure that burned. That suggests that wind-blown embers ignited houses first: The houses were then the fuel as the fire spread through the neighborhood. We saw this repeatedly in the Los Angeles area after wildfires destroyed thousands of homes in January 2025. The pattern suggests a need to focus on the many factors that can influence home losses. Shrubs in Zone 0 of a home did not ignite during the Eaton Fire, despite the home burning. [Photo: Max Moritz] Several guides are available that explain steps homeowners can take to help protect houses, particularly from wind-blown embers, known as home hardening. For example, installing rain gutter covers to keep dead leaves from accumulating, avoiding flammable siding, and ensuring that vents have screens to prevent embers from getting into the attic or crawl space can lower the risk of the home catching fire. However, guidance related to landscaping plants varies greatly and can even be incorrect. For example, some fire-safe plant lists contain species that are drought tolerant but not necessarily fire resistant. What matters more for keeping plants from becoming fuel for fires is how well theyre maintained and whether theyre properly watered. How a plant bursts into flames When living plant material is heated by a nearby energy source, such as a fire, the moisture inside it must be driven off before it can ignite. That evaporation cools the surrounding area and lowers the plants flammability. In many cases, high moisture can actually keep a plant from igniting. Weve seen this in some of our experimental work and in other studies that test the flammability of ornamental landscaping. With enough heat, dried leaves and stems can break down and volatilize into gases. And, at that point, a nearby spark or flame can ignite these gases and set the plant on fire. Plant flammability testing shows how quickly twigs, grasses, plants, and leaves will burn at different moisture levels. The images on the right are from an experiment at the University of Californias South Coast Research and Extension Center to test flammability of a living but overly dry plant. [Image: Max Moritz (left); Luca Carmignani (right)] Even when the plant does burn, however, its moisture content can limit other aspects of flammability, such as how hot it burns. Up to the point that they actually burn, green, well-maintained plants can slow the spread of a fire by serving as heat sinks, absorbing energy and even blocking embers. This apparent protective role has been observed in both Australia and California studies of home losses. How often vegetation buffers homes from igniting during urban conflagrations is still unclear, but this capability has implications for regulations. Californias “Zone 0” regulations The Zone 0 regulations Californias State Board of Forestry is developing are part of broader efforts to reduce fire risk around homes and communities. They would apply in regions considered at high risk of wildfires or defended by Cal Fire, the states firefighting agency. Many of the latest Zone 0 recommendations, such as prohibiting mulch and attached fences made of materials that can burn, stem from large-scale tests conducted by the National Institute of Standards and Technology and the Insurance Institute for Business and Home Safety. These features can be systematically analyzed. But vegetation is far harder to model. The states proposed Zone 0 regulations oversimplify complex conditions in real neighborhoods and go beyond what is currently known from scientific research regarding plant flammability. A mature, well-pruned shrub or tree with a high crown may pose little risk of burning and can even reduce exposure to fires by blocking wind and heat and intercepting embers. Aspen trees, for example, have been recommended to reduce fire risk near structures or other high-value assets. In contrast, dry, unmanaged plants under windows or near fences may ignite rapidly and make it more likely that the house itself will catch fire. As California and other states develop new wildfire regulations, they need to recognize the protective role that well-managed plants can play, along with many other benefits of urban vegetation. We believe the California proposals current emphasis on highly prescriptive vegetation removal, instead of on maintenance, is overly simplistic. Without complementary requirements for hardening the homes themselves, widespread clearing of landscaping immediately around homes could do little to reduce risk and have unintended consequences. Max Moritz is a wildfire specialist at the University of California Cooperative Extension and an adjunct professor at the Bren School at the University of California, Santa Barbara. Luca Carmignani is an assistant professor of engineering at San Diego State University. This article is republished from The Conversation under a Creative Commons license. Read the original article.
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Earlier this year, a robot completed a half-marathon in Beijing in just under 2 hours and 40 minutes. Thats slower than the human winner, who clocked in at just over an hourbut its still a remarkable feat. Many recreational runners would be proud of that time. The robot kept its pace for more than 13 miles (21 kilometers). But it didnt do so on a single charge. Along the way, the robot had to stop and have its batteries swapped three times. That detail, while easy to overlook, speaks volumes about a deeper challenge in robotics: energy. Modern robots can move with incredible agility, mimicking animal locomotion and executing complex tasks with mechanical precision. In many ways, they rival biology in coordination and efficiency. But when it comes to endurance, robots still fall short. They dont tire from exertionthey simply run out of power. As a robotics researcher focused on energy systems, I study this challenge closely. How can researchers give robots the staying power of living creaturesand why are we still so far from that goal? Though most robotics research into the energy problem has focused on better batteries, there is another possibility: Build robots that eat. Robots move well but run out of steam Modern robots are remarkably good at moving. Thanks to decades of research in biomechanics, motor control, and actuation, machines such as Boston Dynamics Spot and Atlas can walk, run, and climb with an agility that once seemed out of reach. In some cases, their motors are even more efficient than animal muscles. But endurance is another matter. Spot, for example, can operate for just 90 minutes on a full charge. After that, it needs nearly an hour to recharge. These runtimes are a far cry from the eight- to 12-hour shifts expected of human workersor the multiday endurance of sled dogs. The issue isnt how robots moveits how they store energy. Most mobile robots today use lithium-ion batteries, the same type found in smartphones and electric cars. These batteries are reliable and widely available, but their performance improves at a slow pace: Each year new lithium-ion batteries are about 7% better than the previous generation. At that rate, it would take a full decade to merely double a robots runtime. Animals store energy in fat, which is extraordinarily energy dense: nearly 9 kilowatt-hours per kilogram. Thats about 68 kWh total in a sled dog, similar to the energy in a fully charged Tesla Model 3. Lithium-ion batteries, by contrast, store just a fraction of that, about 0.25 kilowatt-hours per kilogram. Even with highly efficient motors, a robot like Spot would need a battery dozens of times more powerful than todays to match the endurance of a sled dog. And recharging isnt always an option. In disaster zones or remote fields, or on long-duration missions, a wall outlet or a spare battery might be nowhere in sight. In some cases, robot designers can add more batteries. But more batteries mean more weight, which increases the energy required to move. In highly mobile robots, theres a careful balance between payload, performance, and endurance. For Spot, for example, the battery already makes up 16% of its weight. Some robots have used solar panels, and in theory these could extend runtime, especially for low-power tasks or in bright, sunny environments. But in practice, solar power delivers very little power relative to what mobile robots need to walk, run, or fly at practical speeds. Thats why energy harvesting like solar panels remains a niche solution today, better suited for stationary or ultra-low-power robots. Why it matters These arent just technical limitations. They define what robots can do. A rescue robot with a 45-minute battery might not last long enough to complete a search. A farm robot that pauses to recharge every hour cant harvest crops in time. Even in warehouses or hospitals, short runtimes add complexity and cost. If robots are to play meaningful roles in society assisting the elderly, exploring hazardous environments, and working alongside humans, they need the endurance to stay active for hours, not minutes. New battery chemistries such as lithium-sulfur and metal-air offer a more promising path forward. These systems have much higher theoretical energy densities than todays lithium-ion cells. Some approach levels seen in animal fat. When paired with actuators that efficiently convert electrical energy from the battery to mechanical work, they could enable robots to match or even exceed the endurance of animals with low body fat. But even these next-generation batteries have limitations. Many are difficult to recharge, degrade over time, or face engineering hurdles in real-world systems. Fast charging can help reduce downtime. Some emerging batteries can recharge in minutes rather than hours. But there are trade-offs. Fast charging strains battery life, increases heat, and often requires heavy, high-power charging infrastructure. Even with improvements, a fast-charging robot still needs to stop frequently. In environments without access to grid power, this doesnt solve the core problem of limited onboard energy. Thats why researchers are exploring alternatives such as refueling robots with metal or chemical fuelsmuch like animals eatto bypass the limits of electrical charging altogether. An alternative: Robotic metabolism In nature, animals dont recharge; they eat. Food is converted into energy through digestion, circulation, and respiration. Fat stores that energy, blood moves it, and muscles use it. Future robots could follow a similar blueprint with synthetic metabolisms. Some researchers are building systems that let robots digest metal or chemical fuels and breathe oxygen. For example, synthetic stomach-like chemical reactors could convert high-energy materials such as aluminum into electricity. This builds on the many advances in robot autonomy, where robots can sense objects in a room and navigate to pick them up, but here they would be picking up energy sources. Other researchers are developing fluid-based energy systems that circulate like blood. One early example, a robotic fish, tripled its energy density by using a multifunctional fluid instead of a standard lithium-ion battery. That single design shift delivered the equivalent of 16 years of battery improvements, not through newchemistry but through a more bioinspired approach. These systems could allow robots to operate for much longer stretches of time, drawing energy from materials that store far more energy than todays batteries. In animals, the energy system does more than just provide energy. Blood helps regulate temperature, deliver hormones, fight infections, and repair wounds. Synthetic metabolisms could do the same. Future robots might manage heat using circulating fluids or might heal themselves using stored or digested materials. Instead of a central battery pack, energy could be stored throughout the body in limbs, joints and soft, tissue-like components. This approach could lead to machines that arent just longer-lasting but are more adaptable, resilient, and lifelike. The bottom line Todays robots can leap and sprint like animals, but they cant go the distance. Their bodies are fast and their minds are improving, but their energy systems havent caught up. If robots are going to work alongside humans in meaningful ways, well need to give them more than intelligence and agility. Well need to give them endurance. James Pikul is an associate professor of mechanical engineering at the University of Wisconsin-Madison. This article is republished from The Conversation under a Creative Commons license. Read the original article.
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Artificial intelligence began as a quest to simulate the human brain. Is it now in the process of transforming the human brains role in daily life? The Industrial Revolution diminished the need for manual labor. As someone who researches the application of AI in international business, I cant help but wonder whether it is spurring a cognitive revolution, obviating the need for certain cognitive processes as it reshapes how students, workers, and artists write, design, and decide. Graphic designers use AI to quickly create a slate of potential logos for their clients. Marketers test how AI-generated customer profiles will respond to ad campaigns. Software engineers deploy AI coding assistants. Students wield AI to draft essays in record timeand teachers use similar tools to provide feedback. The economic and cultural implications are profound. What happens to the writer who no longer struggles with the perfect phrase, or the designer who no longer sketches dozens of variations before finding the right one? Will they become increasingly dependent on these cognitive prosthetics, similar to how using GPS diminishes navigation skills? And how can human creativity and critical thinking be preserved in an age of algorithmic abundance? Echoes of the Industrial Revolution Weve been here before. The Industrial Revolution replaced artisanal craftsmanship with mechanized production, enabling goods to be replicated and manufactured on a mass scale. Shoes, cars, and crops could be produced efficiently and uniformly. But products also became more bland, predictable, and stripped of individuality. Craftsmanship retreated to the margins, as a luxury or a form of resistance. Today, theres a similar risk with the automation of thought. Generative AI tempts users to conflate speed with quality, productivity with originality. The danger is not that AI will fail us, but that people will accept the mediocrity of its outputs as the norm. When everything is fast, frictionless, and good enough, theres the risk of losing the depth, nuance, and intellectual richness that define exceptional human work. The rise of algorithmic mediocrity Despite the name, AI doesnt actually think. Tools such as ChatGPT, Claude, and Gemini process massive volumes of human-created content, often scraped from the internet without context or permission. Their outputs are statistical predictions of what word or pixel is likely to follow based on patterns in data theyve processed. They are, in essence, mirrors that reflect collective human creative output back to usersrearranged and recombined, but fundamentally derivative. And this, in many ways, is precisely why they work so well. Consider the countless emails people write, the slide decks that strategy consultants prepare, and the advertisements that suffuse social media feeds. Much of this content follows predictable patterns and established formulas. It has been there before, in one form or the other. Generative AI excels at producing competent-sounding contentlists, summaries, press releases, advertisementsthat bears the signs of human creation without that spark of ingenuity. It thrives in contexts where the demand for originality is low and when good enough is, well, good enough. When AI sparksand stiflescreativity Yet, even in a world of formulaic content, AI can be surprisingly helpful. In one set of experiments, researchers tasked people with completing various creative challenges. They found that those who used generative AI produced ideas that were, on average, more creative, outperforming participants who used web searches or no aids at all. In other words, AI can, in fact, elevate baseline creative performance. However, further analysis revealed a critical trade-off: Reliance on AI systems for brainstorming significantly reduced the diversity of ideas produced, which is a crucial element for creative breakthroughs. The systems tend to converge toward a predictable middle rather than exploring unconventional possibilities at the edges. I wasnt surprised by these findings. My students and I have found that the outputs of generative AI systems are most closely aligned with the values and worldviews of wealthy, English-speaking nations. This inherent bias quite naturally constrains the diversity of ideas these systems can generate. More troubling still, brief interactions with AI systems can subtly reshape how people approach problems and imagine solutions. One set of experiments tasked participants with making medical diagnoses with the help of AI. However, the researchers designed the experiment so that AI would give some participants flawed suggestions. Even after those participants stopped using the AI tool, they tended to unconsciously adopt those biases and make errors in their own decisions. What begins as a convenient shortcut risks becoming a self-reinforcing loop of diminishing originalitynot because these tools produce objectively poor content, but because they quietly narrow the bandwidth of human creativity itself. Navigating the cognitive revolution True creativity, innovation, and research are not just probabilistic recombinations of past data. They require conceptual leaps, cross-disciplinary thinking, and real-world experience. These are qualities AI cannot replicate. It cannot invent the future. It can only remix the past. What AI generates may satisfy a short-term need: a quick summary, a plausible design, a passable script. But it rarely transforms, and genuine originality risks being drowned in a sea of algorithmic sameness. The challenge, then, isnt just technological. Its cultural. How can the rreplaceable value of human creativity be preserved amid this flood of synthetic content? The historical parallel with industrialization offers both caution and hope. Mechanization displaced many workers but also gave rise to new forms of labor, education, and prosperity. Similarly, while AI systems may automate some cognitive tasks, they may also open up new intellectual frontiers by simulating intellectual abilities. In doing so, they may take on creative responsibilities, such as inventing novel processes or developing criteria to evaluate their own outputs. This transformation is only at its early stages. Each new generation of AI models will produce outputs that once seemed like the purview of science fiction. The responsibility lies with professionals, educators, and policymakers to shape this cognitive revolution with intention. Will it lead to intellectual flourishing or dependency? To a renaissance of human creativity or its gradual obsolescence? The answer, for now, is up in the air. Wolfgang Messner is a clinical professor of international business at the University of South Carolina. This article is republished from The Conversation under a Creative Commons license. Read the original article.
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