Half a century ago, the pioneers of chaos theory discovered that the “butterfly effect” makes long-term prediction impossible. Even the smallest perturbation to a complex system (like the weather, the economy or just about anything else) can touch off a concatenation of events that leads to a dramatically divergent future. Unable to pin down the state of these systems precisely enough to predict how they’ll play out, we live under a veil of uncertainty.

Quanta Magazine


Original story reprinted with permission from Quanta Magazine, an editorially independent publication of the Simons Foundation whose mission is to enhance public understanding of science by covering research developments and trends in mathematics and the physical and life sciences.

But now the robots are here to help.

In a series of results reported in the journals Physical Review Letters and Chaos, scientists have used machine learning—the same computational technique behind recent successes in artificial intelligence—to predict the future evolution of chaotic systems out to stunningly distant horizons. The approach is being lauded by outside experts as groundbreaking and likely to find wide application.

“I find it really amazing how far into the future they predict” a system’s chaotic evolution, said Herbert Jaeger, a professor of computational science at Jacobs University in Bremen, Germany.

The findings come from veteran chaos theorist Edward Ott and four collaborators at the University of Maryland. They employed a machine-learning algorithm called reservoir computing to “learn” the dynamics of an archetypal chaotic system called the Kuramoto-Sivashinsky equation. The evolving solution to this equation behaves like a flame front, flickering as it advances through a combustible medium. The equation also describes drift waves in plasmas and other phenomena, and serves as “a test bed for studying turbulence and spatiotemporal chaos,” said Jaideep Pathak, Ott’s graduate student and the lead author of the new papers.

After training itself on data from the past evolution of the Kuramoto-Sivashinsky equation, the researchers’ reservoir computer could then closely predict how the flamelike system would continue to evolve out to eight “Lyapunov times” into the future, eight times further ahead than previous methods allowed, loosely speaking. The Lyapunov time represents how long it takes for two almost-identical states of a chaotic system to exponentially diverge. As such, it typically sets the horizon of predictability.

“This is really very good,” Holger Kantz, a chaos theorist at the Max Planck Institute for the Physics of Complex Systems in Dresden, Germany, said of the eight-Lyapunov-time prediction. “The machine-learning technique is almost as good as knowing the truth, so to say.”

The algorithm knows nothing about the Kuramoto-Sivashinsky equation itself; it only sees data recorded about the evolving solution to the equation. This makes the machine-learning approach powerful; in many cases, the equations describing a chaotic system aren’t known, crippling dynamicists’ efforts to model and predict them. Ott and company’s results suggest you don’t need the equations—only data. “This paper suggests that one day we might be able perhaps to predict weather by machine-learning algorithms and not by sophisticated models of the atmosphere,” Kantz said.

Besides weather forecasting, experts say the machine-learning technique could help with monitoring cardiac arrhythmias for signs of impending heart attacks and monitoring neuronal firing patterns in the brain for signs of neuron spikes. More speculatively, it might also help with predicting rogue waves, which endanger ships, and possibly even earthquakes.

Ott particularly hopes the new tools will prove useful for giving advance warning of solar storms, like the one that erupted across 35,000 miles of the sun’s surface in 1859. That magnetic outburst created aurora borealis visible all around the Earth and blew out some telegraph systems, while generating enough voltage to allow other lines to operate with their power switched off. If such a solar storm lashed the planet unexpectedly today, experts say it would severely damage Earth’s electronic infrastructure. “If you knew the storm was coming, you could just turn off the power and turn it back on later,” Ott said.

He, Pathak and their colleagues Brian Hunt, Michelle Girvan and Zhixin Lu (who is now at the University of Pennsylvania) achieved their results by synthesizing existing tools. Six or seven years ago, when the powerful algorithm known as “deep learning” was starting to master AI tasks like image and speech recognition, they started reading up on machine learning and thinking of clever ways to apply it to chaos. They learned of a handful of promising results predating the deep-learning revolution. Most importantly, in the early 2000s, Jaeger and fellow German chaos theorist Harald Haas made use of a network of randomly connected artificial neurons—which form the “reservoir” in reservoir computing—to learn the dynamics of three chaotically coevolving variables. After training on the three series of numbers, the network could predict the future values of the three variables out to an impressively distant horizon. However, when there were more than a few interacting variables, the computations became impossibly unwieldy. Ott and his colleagues needed a more efficient scheme to make reservoir computing relevant for large chaotic systems, which have huge numbers of interrelated variables. Every position along the front of an advancing flame, for example, has velocity components in three spatial directions to keep track of.

It took years to strike upon the straightforward solution. “What we exploited was the locality of the interactions” in spatially extended chaotic systems, Pathak said. Locality means variables in one place are influenced by variables at nearby places but not by places far away. “By using that,” Pathak explained, “we can essentially break up the problem into chunks.” That is, you can parallelize the problem, using one reservoir of neurons to learn about one patch of a system, another reservoir to learn about the next patch, and so on, with slight overlaps of neighboring domains to account for their interactions.

Parallelization allows the reservoir computing approach to handle chaotic systems of almost any size, as long as proportionate computer resources are dedicated to the task.

Ott explained reservoir computing as a three-step procedure. Say you want to use it to predict the evolution of a spreading fire. First, you measure the height of the flame at five different points along the flame front, continuing to measure the height at these points on the front as the flickering flame advances over a period of time. You feed these data-streams in to randomly chosen artificial neurons in the reservoir. The input data triggers the neurons to fire, triggering connected neurons in turn and sending a cascade of signals throughout the network.

The second step is to make the neural network learn the dynamics of the evolving flame front from the input data. To do this, as you feed data in, you also monitor the signal strengths of several randomly chosen neurons in the reservoir. Weighting and combining these signals in five different ways produces five numbers as outputs. The goal is to adjust the weights of the various signals that go into calculating the outputs until those outputs consistently match the next set of inputs—the five new heights measured a moment later along the flame front. “What you want is that the output should be the input at a slightly later time,” Ott explained.

To learn the correct weights, the algorithm simply compares each set of outputs, or predicted flame heights at each of the five points, to the next set of inputs, or actual flame heights, increasing or decreasing the weights of the various signals each time in whichever way would have made their combinations give the correct values for the five outputs. From one time-step to the next, as the weights are tuned, the predictions gradually improve, until the algorithm is consistently able to predict the flame’s state one time-step later.

“In the third step, you actually do the prediction,” Ott said. The reservoir, having learned the system’s dynamics, can reveal how it will evolve. The network essentially asks itself what will happen. Outputs are fed back in as the new inputs, whose outputs are fed back in as inputs, and so on, making a projection of how the heights at the five positions on the flame front will evolve. Other reservoirs working in parallel predict the evolution of height elsewhere in the flame.

In a plot in their PRL paper, which appeared in January, the researchers show that their predicted flamelike solution to the Kuramoto-Sivashinsky equation exactly matches the true solution out to eight Lyapunov times before chaos finally wins, and the actual and predicted states of the system diverge.

The usual approach to predicting a chaotic system is to measure its conditions at one moment as accurately as possible, use these data to calibrate a physical model, and then evolve the model forward. As a ballpark estimate, you’d have to measure a typical system’s initial conditions 100,000,000 times more accurately to predict its future evolution eight times further ahead.

That’s why machine learning is “a very useful and powerful approach,” said Ulrich Parlitz of the Max Planck Institute for Dynamics and Self-Organization in Göttingen, Germany, who, like Jaeger, also applied machine learning to low-dimensional chaotic systems in the early 2000s. “I think it’s not only working in the example they present but is universal in some sense and can be applied to many processes and systems.” In a paper soon to be published in Chaos, Parlitz and a collaborator applied reservoir computing to predict the dynamics of “excitable media,” such as cardiac tissue. Parlitz suspects that deep learning, while being more complicated and computationally intensive than reservoir computing, will also work well for tackling chaos, as will other machine-learning algorithms. Recently, researchers at the Massachusetts Institute of Technology and ETH Zurich achieved similar results as the Maryland team using a “long short-term memory” neural network, which has recurrent loops that enable it to store temporary information for a long time.

Since the work in their PRL paper, Ott, Pathak, Girvan, Lu and other collaborators have come closer to a practical implementation of their prediction technique. In new research accepted for publication in Chaos, they showed that improved predictions of chaotic systems like the Kuramoto-Sivashinsky equation become possible by hybridizing the data-driven, machine-learning approach and traditional model-based prediction. Ott sees this as a more likely avenue for improving weather prediction and similar efforts, since we don’t always have complete high-resolution data or perfect physical models. “What we should do is use the good knowledge that we have where we have it,” he said, “and if we have ignorance we should use the machine learning to fill in the gaps where the ignorance resides.” The reservoir’s predictions can essentially calibrate the models; in the case of the Kuramoto-Sivashinsky equation, accurate predictions are extended out to 12 Lyapunov times.

The duration of a Lyapunov time varies for different systems, from milliseconds to millions of years. (It’s a few days in the case of the weather.) The shorter it is, the touchier or more prone to the butterfly effect a system is, with similar states departing more rapidly for disparate futures. Chaotic systems are everywhere in nature, going haywire more or less quickly. Yet strangely, chaos itself is hard to pin down. “It’s a term that most people in dynamical systems use, but they kind of hold their noses while using it,” said Amie Wilkinson, a professor of mathematics at the University of Chicago. “You feel a bit cheesy for saying something is chaotic,” she said, because it grabs people’s attention while having no agreed-upon mathematical definition or necessary and sufficient conditions. “There is no easy concept,” Kantz agreed. In some cases, tuning a single parameter of a system can make it go from chaotic to stable or vice versa.

Wilkinson and Kantz both define chaos in terms of stretching and folding, much like the repeated stretching and folding of dough in the making of puff pastries. Each patch of dough stretches horizontally under the rolling pin, separating exponentially quickly in two spatial directions. Then the dough is folded and flattened, compressing nearby patches in the vertical direction. The weather, wildfires, the stormy surface of the sun and all other chaotic systems act just this way, Kantz said. “In order to have this exponential divergence of trajectories you need this stretching, and in order not to run away to infinity you need some folding,” where folding comes from nonlinear relationships between variables in the systems.

The stretching and compressing in the different dimensions correspond to a system’s positive and negative “Lyapunov exponents,” respectively. In another recent paper in Chaos, the Maryland team reported that their reservoir computer could successfully learn the values of these characterizing exponents from data about a system’s evolution. Exactly why reservoir computing is so good at learning the dynamics of chaotic systems is not yet well understood, beyond the idea that the computer tunes its own formulas in response to data until the formulas replicate the system’s dynamics. The technique works so well, in fact, that Ott and some of the other Maryland researchers now intend to use chaos theory as a way to better understand the internal machinations of neural networks.

Original story reprinted with permission from Quanta Magazine, an editorially independent publication of the Simons Foundation whose mission is to enhance public understanding of science by covering research developments and trends in mathematics and the physical and life sciences.

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The robot arm hovers over a pile of products before it makes its move, snagging a toothbrush with its suction cup. It holds the product up, waits for the red flash of a barcode scanner, then turns and drops the toothbrush in a cubby hole. Next the arm suction-cups a box of Goldfish crackers, turns, and files it, too.

At a startup called Kindred in San Francisco, technicians are teaching robots how to precisely manipulate objects like these. Why? Because somebody's got one hell of an online shopping habit. The idea is to get robots so good at picking and placing products that they make human workers look like sloths on sedatives, thus supercharging order fulfillment centers. And how these researchers are trying to do it has big implications for robots beyond the warehouse.

If you want to teach a robot to pick up an object, you could do it the classical way and program it with line after line of code. Or like Kindred says its system works, you can use more modern approaches in artificial intelligence: reinforcement learning and imitation learning.

According to Kindred, its robots start with the former. With reinforcement learning, the robots practice manipulating products on their own with trial and error. When they do something right, they “score,” hence the reinforcement. “The goal is to maximize the score over time,” says George Babu, cofounder of Kindred. “When you do something correctly, then you explore actions similar to the one that gave you a correct response.”

Reinforcement learning has its limitations, though. For one, it’s slow. In a purely digital environment, a simulator could rapidly try and fail, over and over and over—but with a robot in the real world, that iteration is constrained by the laws of the physical universe.

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And two, Kindred’s robots can only teach themselves so much; there are simply too many scenarios that play out in the real world. So a human operator steps in to initiate the second of Kindred’s approaches: so-called imitation learning, looking through the robot’s eyes and guiding its arms. “Some of our algorithms are imitating where the human picked the object,” says Babu, “some of our algorithms are imitating how the human is moving through space to get the objects.”

This builds on what the robot learned through reinforcement, showing it what constitutes a good or bad grip. Essentially, it fills in knowledge gaps by creating lessons that the robot couldn’t practice on its own. Thus a robot learns to more precisely manipulate products like boxes of drugs and toothbrushes.

Which will be essential in an ecommerce environment (Gap is currently testing Kindred’s system), where a robot may encounter objects that are hard or soft or floppy or fragile. And with a human in the loop, the robot will have a tutor to guide it remotely if it comes across something novel. “If something changes, our algorithms say, Wait, I don't recognize this object. I don't feel confident doing this,” says Babu. “We quickly kick in the human to help the robot do the task and then we can learn from that and we can improve our algorithms.”

The power to easily teach robots will make for highly adaptable machines far beyond an order fulfillment center. “Long term, it'll likely mean you don't necessarily think of robots just doing one specific thing, like buying a robot for X or Y or Z,” says UC Berkeley roboticist Pieter Abbeel, whose own startup Embodied Intelligence is using VR controls to teach robots skills. “But you buy a robot that can help you with anything, assuming you can give a few demonstrations.”

Sure, the education of the robots has just begun—even boxes of allergy medicine still give them pause. But soon enough they’ll be running laps around us, all thanks to the gold old human touch.

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You enter the University of Colorado Boulder's newest research laboratory through the side entrance. The door—which is heavy and white, with a black, jug-style handle—slides open from right to left. Crammed inside are a plain wooden dresser, two chairs, and a small desk, above which someone has taped a mediocre landscape-print (mountains, trees, clouds, etc.). A kaleidoscopic purple tapestry hangs from the far wall. The ceiling slings so low that it forces some visitors to duck, and the flooring is made of wood. Well, wood laminate.

The modest setup occupies just a few dozen square feet of space—a tight but necessary fit, given that CU Boulder's newest research laboratory is located not in a building on the university's campus, but the back of a Ram ProMaster cargo van.

The lab is mobile because it has to be. Researchers at CU Boulder’s Change Lab built it to study marijuana’s effects on human test subjects. But even in states like Colorado, where recreational marijuana has been legal since 2014, federal law prohibits scientists from experimenting with anything but government-grown pot.

And Uncle Sam’s weed is weak.

Cultivated by the University of Mississippi with funding from the National Institute on Drug Abuse, federally sanctioned cannabis is less potent and less chemically diverse than the range of cannabis products available for purchase at dispensaries. According to findings published in the journal Nature Scientific Reports earlier this year, the weed that researchers use in clinical cannabis studies is very different from the weed people actually use.

CU Boulder's mobile lab (aka the CannaVan, aka the Mystery Machine) lets researchers drive around that problem. "The idea is: If we can’t bring real-world cannabis into the lab, let’s bring the lab to the people," says neurobiologist Cinnamon Bidwell, a coauthor on the aforementioned Nature study and head of the CannaVan research team.

It works like this: CannaVan researchers first meet with test subjects on CU Boulder campus, where they assign study participants specific commercial cannabis products with known potency and chemical makeups (including edibles and concentrates). Once the test subjects leave, they purchase their assigned cannabis from a local dispensary. Later, CannaVan researchers drive to the subjects' homes. Participants enter the van sober, and researchers perform blood draws and establish test subjects' baseline mental and physical states. Then they go back into their homes; eat, smoke, vape, or dab their product as they please; and return to the van, where researchers draw the subjects' blood again, perform interviews, and evaluate things like memory and motor control.

Bidwell's team is currently using the van to investigate the potential risks of high-potency cannabis concentrates, like dabs, and the potential benefits of cannabis use among medical patients with anxiety and chronic pain. The researchers use the lab to evaluate the drugs' acute effects, track usage and quality of life, monitor symptoms, and investigate how patients titrate their doses. "Basically, we're looking at whether people can have pain relief without walking around feeling stoned all the time," Bidwell says.

Crucially, all of this happens without any CU researchers buying, touching, or even seeing commercial cannabis themselves. "As Colorado citizens, we can purchase and use these products. But as researchers, we can't legally bring them into our lab and directly test their effects, or directly analyze them," Bidwell says. The CannaVan studies are less precise than those her team could perform in a traditional lab (where they'd have greater influence over things like dosage, timing, and chemical makeup), but more controlled than a pure observational study. Plus, these studies are actually legal. “We’ve worked very closely with CU Boulder administration, our legal team, research compliance officers—the list goes on—to see that everything is above board,” Bidwell says.

The upshot: Randomized controlled trials these are not, but these first observational investigations from CU Boulder's CannaVan are liable to be some of the most relevant behavioral and therapeutic studies on cannabis in 2018, and—it seems likely—several years to come.

That's because weak government weed isn't the only thing holding back medical marijuana research. Even as California, Nevada, Massachusetts, and Maine this year join the list of states where recreational weed is legal, in a country where 93 percent of voters support some form of legal pot, cannabis retains its designation under federal law as a Schedule I narcotic. That's a classification on par with heroin and ecstasy, and one that seems unlikely to change in the current political climate.

Attorney General Jeff Sessions' aversion to medical marijuana has been well documented. In April, he directed a Justice Department task force to review and recommend changes to the Cole Memo, which, since 2013, has enabled states to implement their own medical marijuana laws with minimal intervention by the US government. A month later, Sessions asked Congress to undo the protections afforded by the Rohrbacher-Blumenauer amendment, which also shields state-legal medical marijuana programs from federal interference.

"He hasn't yet, but if Sessions prevails at rolling these protections back, everything becomes harder for everybody, and that scares me" says geneticist Reggie Gaudino, chief science officer of marijuana analytics company Steep Hill. "I think it would have a chilling effect on the entire field—sales, medical research, genetic studies, chemical analyses. All of it."

And experts agree a chilling effect is the opposite of what cannabis research needs. "There needs to be an enormous amount of work done not just on the compounds present in various cannabis products, but on the best ways to characterize exposure to those compounds," says Harvard pediatrician and public health researcher Marie McCormick. Earlier this year, she chaired a review by the National Academies of Science, Medicine and Engineering of existing marijuana research—the most thorough evaluation of its kind to date. The report found strong evidence for marijuana's therapeutic potential, but gaping holes in foundational research that could guide its medical and recreational use. "It's not terribly sexy work. It's slow and methodological. But it's critical to understanding the effects of cannabis exposure, its potential risks, and its potential remedies," McCormick says. That's not all going to happen in 2018, she adds, "but developing a solid research agenda would go a long way toward moving things forward, and a big thing that would help would be the removal of marijuana's Schedule I status."

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In Colorado, for example, rescheduling marijuana could embolden CU Boulder's legal team to allow locally grown, non-NIDA weed on campus. This summer, state lawmakers passed House Bill 1367, a law which, when it goes into effect in July of 2018, will allow licensed Colorado cultivators and researchers to grow and study marijuana for clinical investigations. "But it’s still up to the university to say whether they’ll go with state or federal laws," Bidwell says. CU Boulder researchers receive hundreds of millions of dollars in federal funding every year; adhering to local laws over federal ones could put some of that money at risk. "We don't know how the university will come on that," Bidwell says. "But the institution is, understandably, pretty risk averse, and we have no sense of a timeline on when they might decide."

In the meantime, Bidwell and her team will continue cruising Colorado in the CannaVan, conducting observational studies of real-world pot usage. And if you're in the Boulder area, the researchers are looking for study participants. Just … do be sure any vans you climb into are university-affiliated. Look for the CU-Boulder insignia, the chintzy purple tapestry, and the fake wood floors.

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It will start with a flash of light brighter than any words of any human language can describe. When the bomb hits, its thermal radiation, released in just 300 hundred-millionths of a second, will heat up the air over K Street to about 18 million degrees Fahrenheit. It will be so bright that it will bleach out the photochemicals in the retinas of anyone looking at it, causing people as far away as Bethesda and Andrews Air Force Base to go instantly, if temporarily, blind. In a second, thousands of car accidents will pile up on every road and highway in a 15-mile radius around the city, making many impassable.

That’s what scientists know for sure about what would happen if Washington, DC, were hit by a nuke. But few know what the people—those who don’t die in the blast or the immediate fallout—will do. Will they riot? Flee? Panic? Chris Barrett, though, he knows.

When the computer scientist began his career at Los Alamos National Laboratory, the birthplace of the atomic bomb, the Cold War was trudging into its fifth decade. It was 1987, still four years before the collapse of the Soviet Union. Researchers had made projections of the blast radius and fallout blooms that would result from a 10-kiloton bomb landing in the nation’s capital, but they mostly calculated the immediate death toll. They weren’t used for much in the way of planning for rescue and recovery, because back then, the most likely scenario was mutually assured destruction.

But in the decades since, the world has changed. Nuclear threats come not from world powers but from rogue nation states and terrorist organizations. The US now has a $40 billion missile interception system; total annihilation is not presupposed.

The science of prediction has changed a lot, too. Now, researchers like Barrett, who directs the Biocomplexity Institute of Virginia Tech, have access to an unprecedented level of data from more than 40 different sources, including smartphones, satellites, remote sensors, and census surveys. They can use it to model synthetic populations of the whole city of DC—and make these unfortunate, imaginary people experience a hypothetical blast over and over again.

That knowledge isn’t simply theoretical: The Department of Defense is using Barrett’s simulations—projecting the behavior of survivors in the 36 hours post-disaster—to form emergency response strategies they hope will make the best of the worst possible situation.

You can think of Barrett’s system as a series of virtualized representation layers. On the bottom is a series of datasets that describe the physical landscape of DC—buildings, roads, the electrical grid, water lines, hospital systems. On top of that is dynamic data, like how traffic flows around the city, surges in electrical usage, and telecommunications bandwidth. Then there’s the synthetic human population. The makeup of these e-peeps is determined by census information, mobility surveys, tourism statistics, social media networks, and smartphone data, which is calibrated down to a single city block.

So say you’re a parent in a two-person working household with two kids under the age of 10 living on the corner of First and Adams Streets. The synthetic family that lives at that address inside the simulation may not travel to the actual office or school or daycare buildings that your family visits every day, but somewhere on your block a family of four will do something similar at similar times of day. “They’re not you, they’re not me, they’re people in aggregate,” Barrett says. “But it’s just like the block you live in; same family structures, same activity structures, everything.”

Fusing together the 40-plus databases to get this single snapshot requires tremendous computing power. Blowing it all up with a hypothetical nuclear bomb and watching things unfold for 36 hours takes exponentially more. When Barrett’s group at Virginia Tech simulated what would happen if the populations exhibited six different kinds of behaviors—like healthcare-seeking vs. shelter-seeking—it took more than a day to run and produced 250 terabytes of data. And that was taking advantage of the institute’s new 8,600-core cluster, recently donated by NASA. Last year, the US Threat Reduction Agency awarded them $27 million to speed up the pace of their analysis, so it could be run in something closer to real time.

The system takes advantage of existing destruction models, ones that have been well-characterized for decades. So simulating the first 10 or so minutes after impact doesn’t chew up much in the way of CPUs. By that time, successive waves of heat and radiation and compressed air and geomagnetic surge will have barreled through every building within five miles of 1600 Pennsylvania Avenue. These powerful pulses will have winked out the electrical grid, crippled computers, disabled phones, burned thread patterns into human flesh, imploded lungs, perforated eardrums, collapsed residences, and made shrapnel of every window in the greater metro area. Some 90,000 people will be dead; nearly everyone else will be injured. And the nuclear fallout will be just beginning.

That’s where Barrett’s simulations really start to get interesting. In addition to information about where they live and what they do, each synthetic Washingtonite is also assigned a number of characteristics following the initial blast—how healthy they are, how mobile, what time they made their last phone call, whether they can receive an emergency broadcast. And most important, what actions they’ll take.

These are based on historical studies of how humans behave in disasters. Even if people are told to shelter in place until help arrives, for example, they’ll usually only follow those orders if they can communicate with family members. They’re also more likely to go toward a disaster area than away from it—either to search for family members or help those in need. Barrett says he learned that most keenly in seeing how people responded in the hours after 9/11.

Inside the model, each artificial citizen can track family members’ health states; this knowledge is updated whenever they either successfully place a call or meet them in person. The simulation runs like an unfathomably gnarled decision tree. The model asks each agent a series of questions over and over as time moves forward: Is your household together? If so, go to the closest evacuation location. If not, call all household members. That gets paired with the likelihood that the avatar’s phone is working at that moment, that their family members are still alive, and that they haven’t accumulated so much radiation that they’re too sick to move. And on and on and on until the 36-hour clock runs out.

Then Barrett’s team can run experiments to see how different behaviors result in different mortality rates. The thing that leads to the worst outcomes? If people miss or disregard messages that tell them to delay their evacuation, they may be exposed to more of the fallout—the residual radioactive dust and ash that “falls out” of the atmosphere. About 25,000 more people die if everyone tries to be a hero, encountering lethal levels of radiation when they approach within a mile of ground zero.

Those scenarios give clues about how the government might minimize lethal behaviors and encourage other kinds. Like dropping in temporary cell phone communication networks or broadcasting them from drones. “If phones can work even marginally, then people are empowered with information to make better choices,” Barrett says. Then they'll be part of the solution rather than a problem to be managed. “Survivors can provide first-hand accounts of conditions on the ground—they can become human sensors.”

Not everyone is convinced that massive simulations are the best basis for formulating national policy. Lee Clarke, a sociologist at Rutgers who studies calamities, calls these sorts of preparedness plans "fantasy documents," designed to give the public a sense of comfort, but not much else. "They pretend that really catastrophic events can be controlled," he says, "when the truth of the matter is, we know that either we can't control it or there's no way to know."

Maybe not, but someone still has to try. For the next five years, Barrett’s team will be using its high-throughput modeling system to help the Defense Threat Reduction Agency grapple not just with nuclear bombs but with infectious disease epidemics and natural disasters too. That means they’re updating the system to respond in real time to whatever data they slot in. But when it comes to atomic attacks, they’re hoping to stick to planning.

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Do you like a planet that hasn’t yet melted? Do you like sushi? How about breathing? Then you’re secretly in love with plankton, tiny marine organisms that float around at the mercy of currents. They sequester carbon dioxide and provide two thirds of the oxygen in our atmosphere and sacrifice themselves as baby food for the young fish that eventually end up on your plate.

Yet science knows little about the complex dynamics of plankton on ocean-wide scales. So researchers are asking the machines for help, developing clever robots that use AI to examine and classify plankton, the pivotal organisms at the base of our oceanic food chain. That kind of work will be critical as Earth’s oceans continue to transform, potentially throwing ecosystems in chaos.

Take IBM’s ocean-going microscopes—which, conveniently, leverage the same technology sitting in your pocket right now. Two LEDs sit a few inches above the same kind of image sensor you'd find in a smartphone. When plankton pass over the sensor, they cast two shadows. “So by taking two pictures, one with each LED, you can get the 3-D position of all the plankton in a drop of water on the image sensor,” says Tom Zimmerman, a researcher at IBM.

So you’ve got an image of some plankton, which could be one of two types: zooplankton are animals like fish larvae, and phytoplankton are marine algae. The old way of identifying them—there are over 4,000 species of phytoplankton alone—used to be to sort through it with the eyeballs of a human expert. But now researchers have artificial intelligence: IBM is working to integrate AI into the system to automatically quantify and identify the specks. The idea is to create a floating instrument that dangles hoses of different lengths so it can sample plankton concentrates at different depths. A network of these microscopes could then alert scientists to anomalies as they unfold in real time.

Take, for example, the misadventures of a zooplankton called a copepod. It eats algae, which can contain a toxin that gets it drunk. “Now, you think that would be fun for the copepods, but it isn't, because usually copepods dart around in random directions which helps them avoid being eaten by their predators,” says Zimmerman. “But when they get drunk they go straight and fast, which makes it really easy for them to get picked off by their predators.”

So the local copepod population starts to crash, and the algae population in turn explodes, the phytoplankton poisoning themselves with all their waste products. They die and release toxins that poison other organisms, and suck all the oxygen out of the water as they decay. Now you’ve got a whole lot of dead critters on your hands. “That's a case where watching the behavior [of plankton] would indicate that there's some imbalance,” says Zimmerman. “That's the kind of stuff we have to monitor.”

The system can at the moment track plankton concentrations. But it’s not just about quantifying the amount of plankton in a given area—it’s about decoding the balance between the zooplankton that eat phytoplankton, and how the organisms are behaving individually and as part of a group. IBM eventually wants to track things like drunken copepod movements in real time; it's still building a library of plankton, but hopes to have a system of devices in the wild within five years.

Scientists have to consider shape, too. A giant single-celled organism called a stentor, for example, is normally trumpet-shaped, but will ball up when exposed to too much sugar. “So behavior, shape, these are all things that with AI we can definitely track to understand if something is going wrong,” says Simone Bianco, a researcher at IBM.

IBM isn’t the first to enlist AI in the quest to better understand plankton. The excellently named FlowCytobot sticks to piers and sucks in water, which passes through a laser. Particles like plankton scatter this light, which triggers an imager.

The system judges the images based on some 250 features, like symmetry. “Then through manual classification, where the user creates an image training set of hundreds of images at a time, the neural net learns to identify those plankton without user input,” says Ivory Engstrom, director of special projects at McLane Research Laboratories, a scientific instrument company that makes the FlowCytobot.

The FlowCytobot alerts scientists, like these studying algae blooms in Texas, to events like the outbreak of toxin, but it’s tethered in one place. Over at the Monterey Bay Aquarium Research Institute, scientists are working on a more mobile platform for monitoring plankton: the Wave Glider. Think of it like a very expensive surfboard, loaded with solar-powered instruments.

MBARI researcher Thom Maughan is developing his own microscope that’ll allow the Wave Glider to sniff out plankton. This data will be made publicly available through MBARI’s Oceanographic Decision Support System. “When we show the Wave Glider in its position out there, you'll be able to hover your mouse over it and get some idea of the size distribution of the microorganisms that the microscope is seeing,” says Maughan. “Then you should be able to drill down and see what types of organisms are being identified.”

This kind of automation isn’t just about convenience—it’s about necessity. “It's getting to be a rare person that can identify the plankton,” says Maughan. “Those are the old-school traditional microbiologists. Apparently they're getting to be fewer and fewer of those folks who are really intimate with that plankton world.”

With the oceans undergoing rapid transformation, science can’t afford to lose this knowledge. Plankton are all too important, and still all too mysterious. Leave it to the machines, though, to help make sense of a confounding ocean kingdom.

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Lyft Delivers Carbon-Neutral Rides

March 20, 2019 | Story | No Comments

This story originally appeared on CityLab and is part of the Climate Desk collaboration.

Over the years, John Zimmer, the co-founder and president of Lyft, has often pointed to a class he took as an undergraduate as the source of his ideas about environmental sustainability—and by extension, Lyft’s goals to create greener transportation options.

The class at Cornell University was called “Green Cities.” The professor, Robert Young, opened the first lecture by describing how roads and transit systems built decades ago weren’t designed to sustain the rapid growth of urban populations today, Zimmer recalled. “If we don’t fix the infrastructure problem, we’re going to have a major economic and environmental problem,” Zimmer told a roundtable of reporters in Washington, DC, in late March.

Founded in 2012, Lyft is now an $11 billion ride-hailing company, second in the industry to Uber alone. Its concept of ride-hailing has long been founded on reducing the need for personal car ownership. But today, the company made perhaps its most meaningful move yet towards reducing carbon emissions: Lyft is promising to offset the carbon emissions of every ride around the world, making all rides “carbon neutral.” From now on, Zimmer and his co-founder Logan Green wrote in a Medium post, “your decision to ride with Lyft will support the fight against climate change.”

According to the post, Lyft’s total annual investment will amount to over a million metric tons of carbon, “equivalent to planting tens of millions of trees or taking hundreds of thousands of cars off the road,” which will make Lyft one of the largest voluntary purchasers of carbon offsets in the world. Scott Coriell, a Lyft communications officer, said the company does not have a specific estimate for the cost of the investment, but that it will be in the millions of dollars. According to a 2015 report by the NGO Ecosystem Marketplace, General Motors, Barclays bank, and PG&E were the top three voluntary buyers of offsets between 2012 and 2013, respectively scooping up 4.6 million, 2.1 million, and 1.4 million carbon offsets, which are measured in metric tons, during that period.

Carbon offsets have been the subject of some scrutiny and scandal; some companies that take money promising to plant trees and capture emissions have been exposed as worthless or scams. Coriell noted that Lyft will become carbon neutral by investing in offset projects that would not have happened without their backing. These projects will all be US-based and close to Lyft’s largest markets, Corriel said, and will include investments in a manufacturing emissions reductions project in Michigan, oil recycling in Ohio, and a wind energy farm in Oklahoma. These projects are verified under the American Carbon Registry, Climate Action Reserve, or Verified Carbon Standard—all rigorous third-party standard setters of legitimacy.

The announcement is not Lyft’s first gesture towards environmental sustainability. In 2017, it signed “We Are Still In,” joining hundreds of states, cities, and corporations (including Uber) in pledging to uphold the US carbon emissions reduction goals set forth by the Paris climate accord, after President Donald Trump announced plans to withdraw the country’s commitment. At the time, Lyft also outlined plans to make the majority of its fleet autonomous and electric by 2025. “Bringing more electric vehicles onto the platform in the future will help us reduce the needs for offsets,” Coriell wrote.

As part of its own efforts to reduce car ownership, Uber has recently pivoted to become a multi-modal mobility provider, building car- and bike-sharing services into its app. It has not announced any plans to offset its carbon emissions. An Uber representative declined to comment on Lyft’s announcement.

Lyft’s commitment to carbon-neutrality is especially meaningful, because one irony of the ride-hailing industry is that, so far, it’s likely creating more vehicle miles traveled, not less. Though some studies have suggested that ride-hailing users are more likely to give up personal car ownership, more and more research shows that the convenience and relatively low cost of on-demand rides are leading travelers to take trips and generate pollution that they wouldn’t have otherwise. (Plus, all of those deadheading drivers.) As these services lure passengers off of public transit systems, it has become hard to argue that there’s anything particularly environmentally friendly about hailing an Uber or Lyft. This announcement changes that.

Lyft is hardly a perfect citizen, planet-saving-wise. Alongside Uber, it lobbies state legislators to preempt local regulations, which may limit the ability of cities from organizing road space in the most environmentally efficient way possible. And from a sustainability perspective, it would probably be better for Lyft to go carbon-neutral and invest in bike-sharing, as Uber is doing. Even renewably powered electric cars have a sizeable carbon footprint. If customers take a Lyft instead of walking or biking because they think these options are all equally green, they’re wrong.

Still, over the past year, Lyft has made genuine efforts to grow into its image as the “woke” alternative to scandal-ridden Uber, to borrow Zimmer’s term. Donations to the ACLU and free rides to anti-gun rallies have bought it credibility among progressives. Going carbon neutral is probably its most significant step in that direction: It is a lasting delivery of one of the company’s most fundamental promises. That really matters, especially as car manufacturers dial back their Obama-era eco-friendly branding efforts and push to weaken environmental regulations. Lyft seems to have real faith in the notion that there’s a market value in socially conscious transportation—that riders will choose Lyft over other apps, or their own vehicles, because they know it’s a better choice.

“We’re aggressively pursuing a set of values because one, we think it’s the right thing to do and two, it’s good for business,” Zimmer said last month. “That’s what we’re out to prove.”

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The Thomas Fire spread through the hills above Ventura, in the northern greater Los Angeles megalopolis, with the speed of a hurricane. Driven by 50 mph Santa Ana winds—bone-dry katabatic air moving at freeway speeds out of the Mojave desert—the fire transformed overnight from a 5,000-acre burn in a charming chaparral-lined canyon to an inferno the size of Orlando, Florida, that only stopped spreading because it reached the Pacific. Tens of thousands of people evacuated their homes in Ventura; 150 buildings burned and thousands more along the hillside and into downtown are threatened.

That isn’t the only part of Southern California on fire. The hills above Valencia, where Interstate 5 drops down out of the hills into the city, are burning. Same for a hillside of the San Gabriel Mountains, overlooking the San Fernando Valley. And the same, too, near the Mount Wilson Observatory, and on a hillside overlooking Interstate 405—the flames in view of the Getty Center and destroying homes in the rich-people neighborhoods of Bel-Air and Holmby Hills.

And it’s all horribly normal.

Southern California’s transverse ranges—the mostly east-west mountains that slice up and define the greater Los Angeles region—were fire-prone long before there was a Los Angeles. They’re a broken fragment of tectonic plate, squeezed up out of the ground by the Pacific Plate on one side and the North American on the other, shaped into the San Gabriels, the Santa Monica Mountains, the San Bernardino Mountains. Even the Channel Islands off Ventura’s coast are the tippy-tops of a transverse range.

Santa Anas notwithstanding, the transverse ranges usually keep cool coastal air in and arid desert out. Famously, they’re part of why the great California writer Carey McWilliams called the region “an island on the land.” The hills provided hiding places for cowboy crooks, hiking for the naturalist John Muir, and passes both hidden and mapped for natives and explorers coming from the north and east.

With the growth and spread of Los Angeles, fire became even more part of Southern California life. “It’s almost textbook. It’s the end of the summer drought, there has not been a lot of rain this year, and we’ve got Santa Ana winds blowing,” says Alexandra Syphard, an ecologist at the Conservation Biology Institute. “Every single year, we have ideal conditions for the types of wildfires we’re experiencing. What we don’t have every single year is an ignition during a wind event. And we’ve had several.”

Alexandra Syphard, Conservation Biology Institute

Before humans, wildfires happened maybe once or twice a century, long enough for fire-adapted plant species like chapparal to build up a bank of seeds that could come back after a burn. Now, with fires more frequent, native plants can’t keep up. Exotic weeds take root. “A lot of Ventura County has burned way too frequently,” says Jon Keeley, a research ecologist with the US Geological Survey at the Sequoia and Kings Canyon Field Station. “We’ve lost a lot of our natural heritage.”

Fires don’t burn like this in Northern California. That’s one of the things that makes the island on the land an island. Most wildfires in the Sierra Nevadas and northern boreal forests are slower, smaller, and more easily put out, relative to the south. (The Napa and Sonoma fires this year were more like southern fires—wind-driven, outside the forests, and near or amid buildings.) Trees buffer the wind and burn less easily than undergrowth. Keeley says northern mountains and forests are “flammability-limited ecosystems,” where fires only get big if the climate allows it—higher temperatures and dryer conditions providing more fuel. Climate change makes fires there more frequent and more severe.

Southern California, on the other hand, is an “ignition-limited ecosystem.” It’s always a tinderbox. The canyons that cut through the transverse ranges align pretty well with the direction of the Santa Ana winds; they turn into funnels. “Whether or not you get a big fire event depends on whether humans ignite a fire,” he says.

And there are just a lot more humans in Southern California these days. In 1969 Ventura County’s population was 369,811. In 2016 it was 849,738—a faster gain than the state as a whole. In 1970 Los Angeles County had 7,032,000 people; in 2015 it was 9,827,000. “If you look historically at Southern California, the frequency of fire has risen along with population growth,” Keeley says. Though even that has a saturation point. The number of fires—though not necessarily their severity—started declining in the 1980s, maybe because of better fire fighting, and maybe because with more people and more buildings and roads and concrete, there’s less to burn.

As Syphard told me back at the beginning of this year’s fire season, “The problem is not fire. The problem is people in the wrong places.”

Like most fresh-faced young actors in Southern California, the idea of dense development is a relatively recent arrival. Most of the buildings on the island on the land are low, metastasizing in a stellate wave across the landscape, over the flats, up the canyons, and along the hillsides. In 1960 Santa Paula, where the Thomas Fire in Ventura started, was a little town where Santa Paula Canyon hit the Santa Clara River. Today it’s part of greater Ventura, stretching up the canyon, reaching past farms along the river toward Saticoy.

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So the canyons are perfect places for fires. They’re at the Wildland-Urban Interface, developed but not too developed. Wall-to-wall hardscape leaves nothing to burn; no buildings at all means no people to provide an ignition source. But the hills of Ventura or Bel-Air? Firestarty.

As the transverse ranges defined Southern California before Los Angeles and during its spasmodic growth, today it’s defined by freeways. The mountains shape the roads—I-5 coming over the Grapevine through Tejon Pass in the Tehachapis, the 101 skirting the north side of the Santa Monica Mountains, and the 405 tucking through them via the Sepulveda Pass. The freeways, names spoken as a number with a "the" in front, frame time and space in SoCal. For an Angeleno like me, reports of fires closing the 101, the 210, and the 405 are code for the end of the world. Forget Carey McWilliams; that’s some Nathaniel West stuff right there—the burning of Los Angeles from Day of the Locust, the apocalypse that Hollywood always promises.

It won’t be the end end, of course. Southern California zoning and development are flirting, for now at least, with density, accommodating more people, dealing with the state’s broad crisis in housing, and incidentally minimizing the size of the wildland interface. No one can unbuild what makes the place an island on the land, but better building on the island might help stop the next fires before they can start.

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Most mornings when I step out of my San Francisco apartment, I hear the waves, the seagulls, and occasionally kids yelling out the window across the street. But over the past few weeks, the murmur of Ocean Beach has been cut with a low mechanistic rumble. Walk a few blocks and pop your head over the sand dunes and you’ll find the culprits: orange-yellow tractors piling sand into dump trucks, which caravan three miles south and spit out the sand—50,000 cubic yards, or 75,000 tons, of it in total—back on the beach.

That sandy exodus is part of San Francisco’s campaign to fight severe erosion at the southern end of the beach that faces the Pacific Ocean. During a big storm, the bluffs can lose 25 to 40 feet—which might be fine, if the city’s wastewater infrastructure didn’t run right alongside the beach. Specifically, a 14-foot-wide pipe that ferries both stormwater and sewage. If the sea steals the Earth that supports it, the thing could well snap.

The problem at Ocean Beach will only get worse, because the sea has nothing to do but rise in this era of rapid climate change. So will San Francisco spend the rest of its days shoveling sand in a quixotic battle against inevitability? Far from it—it’s all part of a plan to adapt to inevitability, which could set precedent for how this and other coastal cities fight rising seas.

Climate change modeling is complicated: It takes burly supercomputers crunching a galaxy of variables to understand, say, how a warming arctic might be mucking with weather in the United States. But sea level rise? “It's the one area in climate change that's probably more understood than others,” says Anna Roche, project manager at the San Francisco Public Utilities Commission, which is overseeing the digging. “We actually have calculations for how much sea level rise we're anticipating, and you can start making decisions on actual numbers, versus more just pie-in-the-sky discussions.”

Which is not to say those decisions come easy at Ocean Beach. “There's the whole jurisdictional puzzle,” says Ben Grant, urban design policy director at the San Francisco Bay Area Planning and Urban Research Association. The National Parks Service runs the beach, the San Francisco Recreation and Park Department owns the road that runs along it, and it’s all under the regulatory purview of the California Coastal Commission. “There's just all these different components that need to be considered and balanced.”

What Grant and his colleagues helped craft is a plan that has all those stakeholders and the public. The idea is to replace that beachfront road’s two southbound lanes—that's the Great Highway extension—with a trail as early as this winter. This is known as managed retreat: triaging the infrastructure you’re willing to lose. “By backing up a few hundred feet, you lessen the erosive pressure on the beach and you get a more stable beach,” says Grant.

Meanwhile, engineers will continue to bring in outside sand. But now it’ll come from the Army Corps of Engineers’ regular dredging of shipping channels in the San Francisco Bay—perhaps 10 times the amount the city is currently trucking in from up the beach. This is known as sacrificial protection: dumping sand you know full well will wash away, but will in the interim act as a buffer.

“Now, after that, depending on how sea level rise occurs, we'll see,” says Grant. “We may end up having to make more difficult choices. In fact, I guarantee you we'll have to make more difficult choices all up and down the coast.”

One choice that’s definitely not on a table: doing nothing and letting the sea roll in unabated. “It would be upwards of $75 billion in just replacement costs,” says Diana Sokolove, principal planner at the San Francisco Planning Department, which helped craft the city’s Sea Level Rise Action Plan. “That doesn't even include lost tax revenue or the emotional costs of relocation or lost jobs.”

While it’s relatively easy to map where rising seas are going to inundate land (elevation, elevation, elevation), it’s harder to determine what problems those risings seas are going to cause. The northern coast of San Francisco is particularly low-lying, so its potential for unpredictable flooding—especially during storm surges—is high. And much of the San Francisco Bay Area is built on landfill that’s sinking as seas are rising, exposing some areas more rapidly than others.

“We don't want to be retreating too soon, we don't want to be building walls too soon, we don't want to be spending a ton of money when we don't know exactly what's going to happen,” says Sokolove.

What you do want is what they’re doing at Ocean Beach—keep stakeholders happy, keep the public happy, and figure out how to protect critical infrastructure. We know sea level rise will cause trouble, but it will also unfold over decades, giving engineers and city planners time to perfect what works, and abandon to the sea what doesn’t.

Hopefully the former for Ocean Beach. “This will get us out quite some distance—three, four, five decades,” says Grant. “And long before we get to the end of the lifespan of this set of interventions, we will have to be having another set of conversations based on what we learn.”

What begins with boys and girls playing in the sand with big machines, ideally ends with the salvation of Ocean Beach.

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Are superheroes real? Maybe. In this recently released video, a firefighter in Latvia catches a man falling past a window. Let me tell you something. I have a fairly reasonable understanding of physics and this catch looks close to being impossible—but it's real.

Here is the situation (as far as I can tell). A dude is hanging on a window (actually, the falling human is only rumored to be a male) and then he falls. The firefighters were setting up a proper way to catch him, but it wasn't ready. Of course the only solution is then to catch him as he falls. It seems the victim fell from one level above the firefighter. At least that's what I'm going to assume. Now for some questions and answers.

How fast was the human moving?

This is a classic physics problem (I hope my students are paying attention). An object (or human) starts from rest and then begins to fall under the influence of the gravitational force. If the gravitational force is the only significant interaction on the human then that person will fall with a constant acceleration of 9.8 m/s2. That means that for every second of free fall, the human's speed will increase by 9.8 m/s (hint: 9.8 m/s is fairly fast—about 22 mph).

If I knew the time the human was falling, I could easily determine the speed since it increases a set amount every second. However, I can only approximate the distance the person falls. Of course that is only a small stumbling block for physics. In fact there is a kinematic equation that gives the speed of an object with a constant acceleration after a certain distance (you can also easily derive this with the definition of average velocity and acceleration). But if the object starts from rest and moves a distance y, then the final speed will be:

Yes, the greater the fall, the greater the speed. In this case, I'm just going to guess the distance at about 3 meters (it's just a guess). That would put the speed of the faller (is that a real word) at about 7.7 m/s. Maybe it's a little bit shorter fall at 2 meters—that would give a window-level speed of 6.3 m/s. Either way, it's fast.

How hard would it be to catch this human?

It doesn't take a superhuman to fall but it might take superhuman strength to stop someone during a fall. The key here is the nature of forces. A net force on an object changes the motion of that object. In this case, there will be two forces acting on the falling human. First, there is the gravitational force pulling down. This force depends on the gravitational field (g = 9.8 Newtons per kilogram) and the mass of the human (which I don't actually know). The second force is that of the firefighter pushing up during the catch. The total force (sum of these two forces) must be in the upward direction so that the change in motion is also up. This means the human (during the catch) will be slowing down. That's what we want.

I can estimate the human's mass, but what about that firefighter force? There are two basic ideas that deal with force and motion. First is the momentum principle. This is a relationship between force, momentum (product of mass and velocity) and time. The second is the work energy principle. This deals with forces, energy, and displacement. So it comes down to this. Do I want to estimate the time it takes to catch the human or do I want to estimate the distance over which the human was caught? I think I'll go with distance and the work-energy principle.

Here is your super short intro to the work-energy principle. First, let's look at work. Work is a way to add or take away energy from a system. The work depends on both the magnitude of the force and the direction the object is moving. Let's say that the human travels a distance d during this catch. In that case, the gravitational force will do positive work (since it is pulling in the same direction as the displacement) and the firefighter will do negative work (pushing up in the opposite direction as the motion).

But what about the energy? For this system (of just the falling human), there is only one type of energy—kinetic energy. The kinetic energy depends on both the mass and the speed of the faller. The idea is to have the total work done on the human decrease the kinetic energy to zero (so that the human stops). Now to put it all together, it looks like this (yes, I skip a bunch of details).

I already have the estimated speed (from above) so I just need the human mass and stopping distance. Let's say this is a human that isn't super big—maybe 50 kilograms. For the stopping distance, it looks like the firefighter grabs the falling human and moves about 1.5 meters before coming to a stop. With these values, the force the firefighter needs to exert on the human would be 1,478 Newtons. For you imperials, that is about 330 pounds. It's a large force, but not impossible. Still very impressive for just one hand.

Oh, and don't forget that if the firefighter pulls on the human with almost 1,500 Newtons, the person pulls on the firefighter with the same force in the opposite direction. This means that the hero has to hold onto the window sill in order to not get pulled out of the building and fall along with the victim. Yes, there does appear to be a harness on the firefighter but it doesn't look like it has tension. Still a superhero in my mind.

I have one final comment. Since I used the work-energy principle to estimate this force it seems like this is a good time to add an important note about energy. Remember—energy isn't a real thing. It's just something that we can calculate the can be conserved in many situations. There. I said it.

This past year, 2017, was the worst fire season in American history. Over 9.5 million acres burned across North America. Firefighting efforts cost $2 billion.

This past year, 2017, was the seventh-worst Atlantic hurricane season on record and the worst since 2005. There were six major storms. Early estimates put the costs at more than $180 billion.

As the preventable disease hepatitis A spread through homeless populations in California cities in 2017, 1 million Yemenis contracted cholera amid a famine. Diphtheria killed 21 Rohingya refugees in Bangladesh, on the run from a genocide.

Disaster, Pestilence, War, and Famine are riding as horsemen of a particular apocalypse. In 2016, the amount of carbon dioxide in Earth’s atmosphere reached 403 parts per million, higher than it has been since at least the last ice age. By the end of 2017, the United States was on track to have the most billion-dollar weather- and climate-related disasters since the government started counting in 1980. We did that.

Transnational corporations and the most powerful militaries on Earth are already building to prepare for higher sea levels and more extreme weather. The FIRE complex—finance, insurance, and real estate—knows exactly what 2017 cost them (natural and human-made disasters: $306 billion and 11,000 lives), and can calculate more of the same in 2018. They know that the radical alteration of Earth’s climate isn’t just something that’s going to happen in 100 years if we’re not careful, or in 50 years if we don’t change our economy and moonshot the crap out of science and technology. It’s here. Now. It happened. Look behind you.

Let me rephrase: Absent any changes, by 2050 Earth will be a couple degrees hotter overall. Sea levels will be a foot higher. Now, 2050 seems as impossibly far away to me as 2017 did when I was 12 years old. I live in the future! And I like a lot of it. I like the magic glass slab in my pocket and the gene therapy and the robots. I mention this because in 2050, my oldest child will be the same age I am today, and I have given him a broken world.

I don’t want that.

So 2017 taught a lesson, at last, that scientists and futurists have been screaming about. Humanity has to reduce the amount of carbon it’s pumping into the air. Radically. Or every year will be worse from here on out.

But 2017 also made plain the shape of the overall disaster. All those fires and floods and outbreaks are symptoms of the same problem, and it’s time to start dealing with that in a clear-eyed way. It’s also time to start building differently—to start making policies that understand that the American coastline is going to be redrawn by the sea, and that people can’t keep building single-family homes anywhere they can grade a flat pad. The wildland-urban interface can’t keep spreading at will. People can’t keep pumping fresh water out of aquifers without restoring them. Infrastructure for water and power has to be hardened against more frequent, more intense storms, backed up and reinforced so hundreds of thousands of people don’t go without electricity as they are in post-hurricane Puerto Rico.

In short: Change, but also adapt. Fire season in the West is now a permanent condition; don’t build buildings that burn so easily in places that burn every year. Hurricanes and storm surges are going to continue to walk up the Caribbean and onto the Gulf Coast, or maybe along the seaboard. Don’t put houses on top of the wetlands that absorb those storms. Don’t insure the people who do. Build ways for people to get around without cars. Create a power grid that pulls everything it can from renewable sources like wind and solar. Keep funding public health research, surveillance, and ways to deal with mosquito-borne diseases that thrive in a hotter world.

And the next time someone in a city planning meeting says that new housing shouldn’t get built in a residential area because it’s not in keeping with the sense of the community and might disrupt parking, tell them what that means: that they want young people to have lesser lives, that they don’t want poor people and people of color to have the same opportunities they did, and that they’d rather the planet’s environment get crushed by letting bad buildings spread to inhospitable places than increasing density in cities.

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This apocalypse doesn't hurt everyone. Some people benefit. It’s not a coincidence that the FIRE industries also donate the most money to federal political campaigns. Rich people living behind walls they think can’t be breached by any rising tide, literal or metaphoric, made this disaster. And then they gaslighted the vulnerable into distrusting anyone raising the alarm. The people who benefit have made it seem as if this dark timeline was all perfectly fine.

It isn’t. And that’s why it’ll change.

In 1957 Charles Fritz and Harry Williams, the research associate and technical director, respectively, of the National Academy of Sciences’ Disaster Studies Committee, wrote a paper that sparked the field of disaster sociology. Their findings were counterintuitive then, and somehow remain so. People in disasters, they said, don’t loot and riot. They help each other. “The net result of most disasters is a dramatic increase in social solidarity among the affected populace during the emergency and immediate post-emergency periods,” they wrote. “The sharing of a common threat to survival and the common suffering produced by the disaster tend to produce a breakdown of pre-existing social distinctions and a great outpouring of love, generosity, and altruism.”

In a disaster, we help each other. The trick is recognizing the disaster. Through that lens, fixing the problem and protecting one another against its consequences isn’t merely inarguable. It’s human nature. We’re all in this together.

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