Tech
Topographies that talk
Published
3 years agoon
By
Terry Power
Perron’s research group at MIT discovered that a competition between two erosional mechanisms—the gradual movement of soil down slopes and the carving of valleys by rivers as they flow through a landscape over eons—creates these identifiable patterns. In a 2012 paper in Nature, they described the “erosional mechanics” at work, presenting a mathematical model that predicts both the pattern of river branches and their size—down to the smallest tributaries—based on a landscape’s climate and the strength of the rock or soil the waters are cutting into.
That willingness to get to the bottom of big questions, applying tools from multiple disciplines to deduce the history of Earth’s landscapes and predict how they might respond to further environmental changes, earned Perron a 2021 MacArthur Foundation fellowship, better known as a “genius grant.”
Many researchers see Perron as “the leading architect of a renaissance in geomorphology, transitioning the field from emphasis on qualitative descriptions toward physics-based modeling,” says Robert van der Hilst, Schlumberger Professor of Earth and Planetary Sciences and head of EAPS. And that renaissance is much needed. “Some of the most common patterns in landscape evolution, and the underlying processes that control them, have long remained stubbornly enigmatic,” he says.
That’s because Earth’s dynamic and complex landscapes are not static masses but shifting environments that emerge as multiple forces, both natural and human-directed, at work upon the planet’s varied surfaces.
To articulate the complexities of Earth’s physical processes and figure out how they shape the landscape, Perron and his team analyze a lot of data—from field observations, remote sensing instruments, high-resolution topographic surveys, and space missions. Then they use that data to develop and refine sophisticated quantitative models and computer simulations of landscape evolution. Perron also embraces interdisciplinary collaborations to add new detail and nuance to his research.
“If you have to piece together what happened to the Earth—or even to another planet—or try to forecast what might happen in the future,” he says, “it is to your advantage to draw on as many different kinds of evidence as you can.”
Understanding landscapes past is essential to navigating our precarious present, as climate change imperils both natural and man-made environments.
He has also found that a close analysis of Earth’s landscapes can deepen our understanding of the new worlds opened up by interplanetary exploration. The cold deserts of Mars and the methane atmosphere of Saturn’s moon Titan may be distant from our own world, but Perron and his team can discern dynamics similar to those on Earth, as well as key differences. Knowing the mechanics of our own planet’s rivers, for instance, suggests that the essential role of plate tectonics in shaping Earth’s landscapes had not been replicated on Mars or Titan.
Understanding landscapes past is essential to navigating our precarious present as climate change imperils both natural and man-made environments. It also gives us tools to model and perhaps even shape our uncertain future as well.
“Many of the landscapes we study have formed over thousands or millions of years,” Perron says. “Our work to measure how climate shapes landscapes helps give us an idea of what to expect as we continue to change Earth’s climate.
“Looking into the past, even over pretty long periods into the past,” he continues, “is really important and relevant to what’s happening now and to what might happen in the near future—even over human time scales.”
A river runs through it
Perron’s work explores landscapes from the Amazon to Mars, but his journey began in New England. Even as a child in rural Vermont, he was curious about the whodunits glimpsed in the visible landscape.
“One of the first times I remember thinking about science and the landscape in the same context,” he says, “was learning that the mountains that I could see when I was on my way to school had been underneath hundreds of meters of ice at some point.”
Perron was left with a “sense that there was this enormous part of Earth’s history that wasn’t directly accessible to us,” he says. “You’re going to have to piece it together from whatever nature left around for you.”
He delved into Earth and planetary sciences and archaeology as an undergrad at Harvard University, spent a year at the US Geological Survey, and got a PhD in Earth and planetary sciences at the University of California, Berkeley in 2006. After postdoctoral studies at Harvard, Perron joined the faculty at MIT, where he’s been piecing together Earth’s history since 2009.
“We’re mostly interested in how rivers, mountains, and other landforms change over time,” says Perron of his research group’s focus. “That is the common thread—especially rivers—that runs through most, if not all, of our research.”
Perron’s team has uncovered how bedrock rivers shape landscapes—and how the landscape’s evolution can reshape the networks rivers create. These rivers that flow over beds of rock, he says, drive topographical changes by creating steep-sloped valleys where surface material must fall downward: “Think about these networks of rivers, these spindly, branching treelike networks, as carving down into the rock and just dragging the rest of the surface along with them.”
His research has expanded our understanding of the formation and substance of the so-called “critical zone”—a thin layer a few feet below Earth’s surface where rocks break down to form soil.
To study how the complex variables of climate shape the landscape, his group is exploring such things as how extreme rainfall might affect the location, frequency, and severity of landslides, or how wave climate (wave intensity averaged over a year) affects the rate of coastal retreat or erosion. By looking at the varying wave climates on the Hawaiian islands, Perron and colleagues have measured how much faster a coast with larger waves erodes than a coast with smaller waves does.
Nature itself has conducted vast and useful long-term experiments that can shed light on landscape evolution. “Complicated systems have so many different factors that can change and influence them,” he says. “We like to try to identify natural experiments. And that can include natural experiments in climate, where we try to find landscapes where nature has controlled for a number of key factors and changed one that is especially important, and we can study that.” For example, again looking at Hawaii, the side of the islands exposed to the trade winds is rainier and wetter, allowing researchers to gauge how rainfall influences river erosion.
Lunar landscapes and lost lakes
Perron’s work on the role of Earth’s rivers has also laid a foundation for his research on planetary landscapes. The same tools that allow us to read Earth’s history backward through its landscapes also help us understand more about the fate of now-vanished lakes and rivers on Mars, or how the methane rivers and lakes of Saturn’s moon Titan work.
“The uptick in planetary exploration starting around 25 years ago has had a huge impact on my work,” he says. “I realized as a graduate student that many of the things I was learning as an Earth scientist could be applied to [other] planets.” His decision to expand his research beyond Earth was inspired in part by the global digital topographic map of Mars developed by MIT’s vice president for research Maria Zuber in the late 1990s, as well as the discovery of Titan’s active rivers.
“Seeing Earthlike landforms on other planets is also a great opportunity to analyze experiments that nature has done for us.”
“It’s incredible how much we humans have learned about the solar system in such a short time,” he says. He also realized that studying other planets could inform his research on Earth. “Seeing Earthlike landforms on other planets is also a great opportunity to analyze experiments that nature has done for us,” he says.
Obviously, scientists cannot do fieldwork on Mars or Titan. (“For now,” quips Perron.) “If we wanted to know how much water and sediment a river on Earth carries, we’d measure the size of the sediment on the riverbed and survey a cross-section of the river channel,” he says. “If we want to know how fast a mountain range is rising up or eroding away on Earth, we go collect samples there and bring them back to the lab. We can’t do any of those things on Mars or Titan. So we have to get creative.”
Perron and his team blend information from planetary missions with their work on Earth’s surfaces to paint a picture of faraway landscapes. He says, “We know it rains methane on Titan, but we can’t see it happening—so my group estimated how hard it rains based on measurements of river networks in pictures from the Cassini-Huygens spacecraft mission. We also came up with a way to calculate how much water an ancient Martian river carried, or how much methane a modern Titan river carries, using only dimensions we can measure from orbit.”
But Perron concedes that a lack of field samples means some questions must remain open for now: “How long did it take for Titan’s landscapes to form? Have they been active for billions of years? We don’t know yet.”
Getting granular
Perron sees collaboration with researchers in other disciplines as essential to answering foundational questions and expanding the boundaries of his research.
“We’ve worked with colleagues at MIT who study everything from the carbon cycle to the physics of granular materials,” he says. “We’ve worked with colleagues at other institutions who study the genetics of fish, or the archeology of the ancient Amazon.”
One important example is his work with mechanical engineering professor Ken Kamrin, who specializes in the mechanics and physics of granular materials, to study the movement of gravel and sand in rivers (known as sediment transport) that leads to erosion.
“People have been studying how rivers move sediment for many years,” says Perron. “Ken has a fresh take on rivers and has opened my eyes to things that have been overlooked by people in my own field.”
Their collaboration focuses particularly on how the size and shape of sediment grains affect sediment transport. “This is an interesting problem from the geophysical standpoint,” Kamrin says, “because more-sensitive modeling tools will lead to better predictions of bed erosion.”
“Granular media is a notoriously difficult material to model,” he adds, but this research may even “lead to breakthroughs in modeling granular flow that apply beyond the riverbed context.”
Perron also sees a bigger picture. “Ken’s approach to understanding the behavior of individual sediment grains, and then developing ways to scale that up to entire landforms, is inspiring,” he says. “One colleague recently summarized [the collaboration] as a ‘gift that keeps on giving.’ We just keep discovering new aspects of granular dynamics in landscapes to explore together.”
Studying landscapes’ effects on biodiversity is another interdisciplinary area attracting graduate students and postdocs to Perron’s team. Instead of asking the usual questions about how species are lost, they look at why some landscapes retain or enhance biodiversity.
That biodiversity is under threat adds urgency to this research. “If you can understand what influences biodiversity and why some parts of Earth’s surface are much more diverse than others,” Perron says, “then hopefully we can do a better job of conserving it.”
Other collaborations help when traditional geomorphological research hits a dead end. “Landscapes that form through erosion are hard to trace through time, because the erosion destroys the evidence of what they looked like in the past,” he says.
Perron is intrigued by the work of Greg Fournier, an EAPS colleague who studies the genetic record of life on Earth. “Organisms that live in a landscape—and whose evolution depends on the landscape’s topography—might have preserved a more persistent record of a landscape’s past,” Perron says. Their DNA can serve as what he calls a molecular clock, giving geologists a new way to measure time.
“If you can take advantage of this genetic data, in addition to geological data, you have really expanded your arsenal … to figure out what happened to the landscape—and how those two things might have influenced each other,” says Perron.
Landscapes and life
Maya Stokes, PhD ’21, tackled one aspect of the biodiversity question at Perron’s urging. “When I was deciding on where to go for graduate school,” Stokes recalls, “I was looking for research that involved mountains and rivers. But when he suggested we throw fish into the mix, I couldn’t resist.”
Stokes’s dissertation examines how changes to river landscapes influence the evolution and distribution of aquatic organisms. Perron “pushed me to simultaneously consider big, generalizable scientific questions while also making sure I was focused on unraveling specific mechanisms and problems,” she says.
Now a postdoctoral fellow in ecology and evolutionary biology at Yale University, Stokes is using geomorphic research methods to collect and analyze DNA sequence data so she can piece together the intertwined histories of the fish and rivers of the Appalachian Mountains.
“Scientists have long suggested that Earth processes have fundamentally altered the evolution of life,” says Stokes, “but with the advent of next-generation DNA sequencing techniques and ever-improving methods for piecing together the physical history of landscapes, we are now poised to understand the exact mechanisms that govern such links.”
Perron plans to use his $625,000 MacArthur grant to collaborate with other researchers without having to wait for federal grant funding.
The award won’t alter his course, however. “I really do think that the answers to some of the big questions about Earth and the solar system are recorded in landscapes,” he says.
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My senior spring in high school, I decided to defer my MIT enrollment by a year. I had always planned to take a gap year, but after receiving the silver tube in the mail and seeing all my college-bound friends plan out their classes and dorm decor, I got cold feet. Every time I mentioned my plans, I was met with questions like “But what about school?” and “MIT is cool with this?”
Yeah. MIT totally is. Postponing your MIT start date is as simple as clicking a checkbox.
Now, having finished my first year of classes, I’m really grateful that I stuck with my decision to delay MIT, as I realized that having a full year of unstructured time is a gift. I could let my creative juices run. Pick up hobbies for fun. Do cool things like work at an AI startup and teach myself how to create latte art. My favorite part of the year, however, was backpacking across Europe. I traveled through Austria, Slovakia, Russia, Spain, France, the UK, Greece, Italy, Germany, Poland, Romania, and Hungary.
Moreover, despite my fear that I’d be losing a valuable year, traveling turned out to be the most productive thing I could have done with my time. I got to explore different cultures, meet new people from all over the world, and gain unique perspectives that I couldn’t have gotten otherwise. My travels throughout Europe allowed me to leave my comfort zone and expand my understanding of the greater human experience.
“In Iceland there’s less focus on hustle culture, and this relaxed approach to work-life balance ends up fostering creativity. This was a wild revelation to a bunch of MIT students.”
When I became a full-time student last fall, I realized that StartLabs, the premier undergraduate entrepreneurship club on campus, gives MIT undergrads a similar opportunity to expand their horizons and experience new things. I immediately signed up. At StartLabs, we host fireside chats and ideathons throughout the year. But our flagship event is our annual TechTrek over spring break. In previous years, StartLabs has gone on TechTrek trips to Germany, Switzerland, and Israel. On these fully funded trips, StartLabs members have visited and collaborated with industry leaders, incubators, startups, and academic institutions. They take these treks both to connect with the global startup sphere and to build closer relationships within the club itself.
Most important, however, the process of organizing the TechTrek is itself an expedited introduction to entrepreneurship. The trip is entirely planned by StartLabs members; we figure out travel logistics, find sponsors, and then discover ways to optimize our funding.
In organizing this year’s trip to Iceland, we had to learn how to delegate roles to all the planners and how to maintain morale when making this trip a reality seemed to be an impossible task. We woke up extra early to take 6 a.m. calls with Icelandic founders and sponsors. We came up with options for different levels of sponsorship, used pattern recognition to deduce the email addresses of hundreds of potential contacts at organizations we wanted to visit, and all got scrappy with utilizing our LinkedIn connections.
And as any good entrepreneur must, we had to learn how to be lean and maximize our resources. To stretch our food budget, we planned all our incubator and company visits around lunchtime in hopes of getting fed, played human Tetris as we fit 16 people into a six-person Airbnb, and emailed grocery stores to get their nearly expired foods for a discount. We even made a deal with the local bus company to give us free tickets in exchange for a story post on our Instagram account.
Tech
The Download: spying keyboard software, and why boring AI is best
Published
1 year agoon
22 August 2023By
Terry Power
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
How ubiquitous keyboard software puts hundreds of millions of Chinese users at risk
For millions of Chinese people, the first software they download onto devices is always the same: a keyboard app. Yet few of them are aware that it may make everything they type vulnerable to spying eyes.
QWERTY keyboards are inefficient as many Chinese characters share the same latinized spelling. As a result, many switch to smart, localized keyboard apps to save time and frustration. Today, over 800 million Chinese people use third-party keyboard apps on their PCs, laptops, and mobile phones.
But a recent report by the Citizen Lab, a University of Toronto–affiliated research group, revealed that Sogou, one of the most popular Chinese keyboard apps, had a massive security loophole. Read the full story.
—Zeyi Yang
Why we should all be rooting for boring AI
Earlier this month, the US Department of Defense announced it is setting up a Generative AI Task Force, aimed at “analyzing and integrating” AI tools such as large language models across the department. It hopes they could improve intelligence and operational planning.
But those might not be the right use cases, writes our senior AI reporter Melissa Heikkila. Generative AI tools, such as language models, are glitchy and unpredictable, and they make things up. They also have massive security vulnerabilities, privacy problems, and deeply ingrained biases.
Applying these technologies in high-stakes settings could lead to deadly accidents where it’s unclear who or what should be held responsible, or even why the problem occurred. The DoD’s best bet is to apply generative AI to more mundane things like Excel, email, or word processing. Read the full story.
This story is from The Algorithm, Melissa’s weekly newsletter giving you the inside track on all things AI. Sign up to receive it in your inbox every Monday.
The ice cores that will let us look 1.5 million years into the past
To better understand the role atmospheric carbon dioxide plays in Earth’s climate cycles, scientists have long turned to ice cores drilled in Antarctica, where snow layers accumulate and compact over hundreds of thousands of years, trapping samples of ancient air in a lattice of bubbles that serve as tiny time capsules.
By analyzing those cores, scientists can connect greenhouse-gas concentrations with temperatures going back 800,000 years. Now, a new European-led initiative hopes to eventually retrieve the oldest core yet, dating back 1.5 million years. But that impressive feat is still only the first step. Once they’ve done that, they’ll have to figure out how they’re going to extract the air from the ice. Read the full story.
—Christian Elliott
This story is from the latest edition of our print magazine, set to go live tomorrow. Subscribe today for as low as $8/month to ensure you receive full access to the new Ethics issue and in-depth stories on experimental drugs, AI assisted warfare, microfinance, and more.
The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 How AI got dragged into the culture wars
Fears about ‘woke’ AI fundamentally misunderstand how it works. Yet they’re gaining traction. (The Guardian)
+ Why it’s impossible to build an unbiased AI language model. (MIT Technology Review)
2 Researchers are racing to understand a new coronavirus variant
It’s unlikely to be cause for concern, but it shows this virus still has plenty of tricks up its sleeve. (Nature)
+ Covid hasn’t entirely gone away—here’s where we stand. (MIT Technology Review)
+ Why we can’t afford to stop monitoring it. (Ars Technica)
3 How Hilary became such a monster storm
Much of it is down to unusually hot sea surface temperatures. (Wired $)
+ The era of simultaneous climate disasters is here to stay. (Axios)
+ People are donning cooling vests so they can work through the heat. (Wired $)
4 Brain privacy is set to become important
Scientists are getting better at decoding our brain data. It’s surely only a matter of time before others want a peek. (The Atlantic $)
+ How your brain data could be used against you. (MIT Technology Review)
5 How Nvidia built such a big competitive advantage in AI chips
Today it accounts for 70% of all AI chip sales—and an even greater share for training generative models. (NYT $)
+ The chips it’s selling to China are less effective due to US export controls. (Ars Technica)
+ These simple design rules could turn the chip industry on its head. (MIT Technology Review)
6 Inside the complex world of dissociative identity disorder on TikTok
Reducing stigma is great, but doctors fear people are self-diagnosing or even imitating the disorder. (The Verge)
7 What TikTok might have to give up to keep operating in the US
This shows just how hollow the authorities’ purported data-collection concerns really are. (Forbes)
8 Soldiers in Ukraine are playing World of Tanks on their phones
It’s eerily similar to the war they are themselves fighting, but they say it helps them to dissociate from the horror. (NYT $)
9 Conspiracy theorists are sharing mad ideas on what causes wildfires
But it’s all just a convoluted way to try to avoid having to tackle climate change. (Slate $)
10 Christie’s accidentally leaked the location of tons of valuable art
Seemingly thanks to the metadata that often automatically attaches to smartphone photos. (WP $)
Quote of the day
“Is it going to take people dying for something to move forward?”
—An anonymous air traffic controller warns that staffing shortages in their industry, plus other factors, are starting to threaten passenger safety, the New York Times reports.
The big story
Inside effective altruism, where the far future counts a lot more than the present
October 2022
Since its birth in the late 2000s, effective altruism has aimed to answer the question “How can those with means have the most impact on the world in a quantifiable way?”—and supplied methods for calculating the answer.
It’s no surprise that effective altruisms’ ideas have long faced criticism for reflecting white Western saviorism, alongside an avoidance of structural problems in favor of abstract math. And as believers pour even greater amounts of money into the movement’s increasingly sci-fi ideals, such charges are only intensifying. Read the full story.
—Rebecca Ackermann
We can still have nice things
A place for comfort, fun and distraction in these weird times. (Got any ideas? Drop me a line or tweet ’em at me.)
+ Watch Andrew Scott’s electrifying reading of the 1965 commencement address ‘Choose One of Five’ by Edith Sampson.
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+ Cannot deal with this utterly ludicrous wooden vehicle.
+ Learn about a weird and wonderful new instrument called a harpejji.
Tech
Why we should all be rooting for boring AI
Published
1 year agoon
22 August 2023By
Terry Power
This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.
I’m back from a wholesome week off picking blueberries in a forest. So this story we published last week about the messy ethics of AI in warfare is just the antidote, bringing my blood pressure right back up again.
Arthur Holland Michel does a great job looking at the complicated and nuanced ethical questions around warfare and the military’s increasing use of artificial-intelligence tools. There are myriad ways AI could fail catastrophically or be abused in conflict situations, and there don’t seem to be any real rules constraining it yet. Holland Michel’s story illustrates how little there is to hold people accountable when things go wrong.
Last year I wrote about how the war in Ukraine kick-started a new boom in business for defense AI startups. The latest hype cycle has only added to that, as companies—and now the military too—race to embed generative AI in products and services.
Earlier this month, the US Department of Defense announced it is setting up a Generative AI Task Force, aimed at “analyzing and integrating” AI tools such as large language models across the department.
The department sees tons of potential to “improve intelligence, operational planning, and administrative and business processes.”
But Holland Michel’s story highlights why the first two use cases might be a bad idea. Generative AI tools, such as language models, are glitchy and unpredictable, and they make things up. They also have massive security vulnerabilities, privacy problems, and deeply ingrained biases.
Applying these technologies in high-stakes settings could lead to deadly accidents where it’s unclear who or what should be held responsible, or even why the problem occurred. Everyone agrees that humans should make the final call, but that is made harder by technology that acts unpredictably, especially in fast-moving conflict situations.
Some worry that the people lowest on the hierarchy will pay the highest price when things go wrong: “In the event of an accident—regardless of whether the human was wrong, the computer was wrong, or they were wrong together—the person who made the ‘decision’ will absorb the blame and protect everyone else along the chain of command from the full impact of accountability,” Holland Michel writes.
The only ones who seem likely to face no consequences when AI fails in war are the companies supplying the technology.
It helps companies when the rules the US has set to govern AI in warfare are mere recommendations, not laws. That makes it really hard to hold anyone accountable. Even the AI Act, the EU’s sweeping upcoming regulation for high-risk AI systems, exempts military uses, which arguably are the highest-risk applications of them all.
While everyone is looking for exciting new uses for generative AI, I personally can’t wait for it to become boring.
Amid early signs that people are starting to lose interest in the technology, companies might find that these sorts of tools are better suited for mundane, low-risk applications than solving humanity’s biggest problems.
Applying AI in, for example, productivity software such as Excel, email, or word processing might not be the sexiest idea, but compared to warfare it’s a relatively low-stakes application, and simple enough to have the potential to actually work as advertised. It could help us do the tedious bits of our jobs faster and better.
Boring AI is unlikely to break as easily and, most important, won’t kill anyone. Hopefully, soon we’ll forget we’re interacting with AI at all. (It wasn’t that long ago when machine translation was an exciting new thing in AI. Now most people don’t even think about its role in powering Google Translate.)
That’s why I’m more confident that organizations like the DoD will find success applying generative AI in administrative and business processes.
Boring AI is not morally complex. It’s not magic. But it works.
Deeper Learning
AI isn’t great at decoding human emotions. So why are regulators targeting the tech?
Amid all the chatter about ChatGPT, artificial general intelligence, and the prospect of robots taking people’s jobs, regulators in the EU and the US have been ramping up warnings against AI and emotion recognition. Emotion recognition is the attempt to identify a person’s feelings or state of mind using AI analysis of video, facial images, or audio recordings.
But why is this a top concern? Western regulators are particularly concerned about China’s use of the technology, and its potential to enable social control. And there’s also evidence that it simply does not work properly. Tate Ryan-Mosley dissected the thorny questions around the technology in last week’s edition of The Technocrat, our weekly newsletter on tech policy.
Bits and Bytes
Meta is preparing to launch free code-generating software
A version of its new LLaMA 2 language model that is able to generate programming code will pose a stiff challenge to similar proprietary code-generating programs from rivals such as OpenAI, Microsoft, and Google. The open-source program is called Code Llama, and its launch is imminent, according to The Information. (The Information)
OpenAI is testing GPT-4 for content moderation
Using the language model to moderate online content could really help alleviate the mental toll content moderation takes on humans. OpenAI says it’s seen some promising first results, although the tech does not outperform highly trained humans. A lot of big, open questions remain, such as whether the tool can be attuned to different cultures and pick up context and nuance. (OpenAI)
Google is working on an AI assistant that offers life advice
The generative AI tools could function as a life coach, offering up ideas, planning instructions, and tutoring tips. (The New York Times)
Two tech luminaries have quit their jobs to build AI systems inspired by bees
Sakana, a new AI research lab, draws inspiration from the animal kingdom. Founded by two prominent industry researchers and former Googlers, the company plans to make multiple smaller AI models that work together, the idea being that a “swarm” of programs could be as powerful as a single large AI model. (Bloomberg)