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Satellites to Monitor UN Forest Protection Goals

VTT.deforestation.homepageClimate change negotiators agreed Sunday to monitor deforestation and to pay developing countries for keeping carbon trapped in forests. To measure just how much forest those countries are conserving, the United Nations Collaborative Program on Reducing Emissions from Deforestation and Forest Degradation in Developing Countries (REDD+, to its friends) will rely on a complex system of satellite measurements and field checks. The agreement is a victory for advocates in the research and conservation communities. Yet they face a lot of work implementing the program.

Many countries and agencies already have experience conducting their own long-term monitoring, but the programs often differ in their goals and methods. That makes their data hard to compare. For example Brazil spent US $1.4 billion on a satellite system a decade ago for monitoring the Amazon, but some researchers accused it of being more of a drug-smuggling interdiction tool than a forestry tool. And the U.S. National Oceanic and Atmospheric Administration (NOAA ) has been monitoring wildfire-driven deforestation since the 1980s, but that is not suited to monitoring illegal logging because the two forms of deforestation occur on different timescales. Earlier this year, Nature published a commentary by researchers and satellite builders calling for a single international standard for forest-monitoring data.

A Finnish-led team has been working on a more technical forestry problem: how to combine the various bands of data satellites can collect. Optical data, such as that provided by the Landsat system, are common, but do not penetrate the clouds that often cover tropical forests. The team found that they could boost their ability to estimate forest cover and degradation by including radar data, they reported earlier this year in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. The project, called ReCover, collected satellite data in multiple bands from forests at five study sites and compared satellite-based interpretations to measurements of forest cover and quality on the ground. Their first-pass analyses achieved from 75 to 91 percent accuracy in forest classification, depending on the method, but combining methods should help them improve.

First published on IEEE Spectrum’s Tech Talk blog: [html] [pdf]

IBM Nairobi Lab’s First Offering is a Traffic-Dodging Mobile App

Debates about how best to avoid Nairobi traffic can take nearly as long as a drive across town. The city has three dozen traffic cameras downtown, but that’s not enough information for a city of over three million people. Traffic costs the city US $600 000 a day, by one estimate. IBM’s Nairobi lab, in beta since a year ago, tackled traffic early on and today launched a mobile application to help drivers avoid traffic.

Read the rest of this post at IEEE Spectrum’s Tech Talk blog: [html] [pdf]

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Human Brain Project Needs Artificial Brains to Understand Real Ones

mail_image_preview-1-1383232402792If neuroscientist Henry Markram had a dollar for every neuron he wants to map, he still wouldn’t have enough money.

As it happens, the Swiss Federal Institute of Technology in Lausanne (EPFL) researcher has a billion euros, or $1.38 billion, from the European Union to spend over the next ten years, but the normal means of determining a neuron’s activity can cost $1 million and take a year. By the time he got through the 3000-odd pathways shown in the photograph of a pinhead-sized slice of brain behind him in a conference room last month, he’d be flat broke, decades older, and he’d still have to map countless more pinheads’ worth of neurons to understand the brain.

As Markram has been telling everyone since he got the €1 billion nod to lead the Human Brain Project, the way researchers study the brain needs to change. His approach—and it’s not the only one—stands on an emerging type of computing that he and others claim will let machines learn more like humans do. They could then offer generalizations from what’s known about a handful of neural pathways and find shortcuts to understanding the rest of the brain, he argues. The concept will rely as much on predictions of neural behavior as on experimental observations.

Yet such predictions will have to come from people until they can better train their computers to do it. So-called cognitive computing, which relies on recognizing elements of a familiar thing in new settings, is difficult to achieve through the kind of raw calculation to which most supercomputers are suited. It’s not like winning at chess or even “Jeopardy!”, two tasks IBM machines have mastered. But IBM researchers are already turning Watson, the supercomputer that beat “Jeopardy!”, into a recipe-remixing machine, and they are sure to program it for other tasks that require massive data sifting and some level of semantic analysis.

That’s the direction Markram expects computing to go for biologists, who need their computers to think more like people do. Human intelligence seems to rely on the art of the analogy, as Douglas Hofstadter writes in his new book on artificial intelligence, which James Somers explores at length in The Atlantic this month. That’s why CAPTCHAS have been so hard to defeat: the letters are easy for a computer to learn but difficult to recognize out of context. Yet we can quickly hypothesize what’s important enough about a letter to recognize it when it is distorted.

Markram is counting on those computing capabilities to improve over the course of the project, he says. He’s also counting on being able to persuade his colleagues that such computer-generated hypotheses about neural behavior will be good enough to start making higher-level hypotheses about the brain’s emergent structures. The computer they will use, an updated version of the Blue Brain project’s Swiss-owned IBM Blue Gene, will use a hybrid memory approach, which is handy for keeping massive datasets close to the processors, but it does not yet offer any special artificial intelligence.

In the meantime Markram is focusing his efforts on another kind of computing: cultural. A major element of the Human Brain Project is the ability to unite researchers around common problems and share questions, findings, and interpretations: “we’re basically layering social networking on top of neuroscience,” Markram says.

But he will have a lot of persuading to do. One colleague told The Observer; “whatever your take is on Big Neuro, do not expect them to make good on all their promises to find causes, let alone cures, for any of the big neuro diseases they list in 10 years, and as for new computing technologies? They are pulling your leg.”

To his credit, Markram did not oversell cures to diseases during a conversation with journalists earlier this month shortly after the project’s formal launch. He also gave a realistic reply to a question about whether cognitive findings from the brain project could change the way supercomputing is done: the short version is “not yet.”

Instead, the remarkable thing about the Human Brain Project may not be its computing power so much as its convening power. The social networking layer, Markram says, “is designed for tens of thousands of scientists to be able to collaboratively work on unifying all the knowledge that we have about the brain.”

First published in IEEE Spectrum’s Tech Talk blog: [html] [pdf]

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Data for the 31st Century

11NBigDataMemorymaster-1382128425994Computer scientist Peter Kazansky at the University of Southampton, in England, has some words for the ages. He and a group of collaborators wrote them in quartz crystal using new optical techniques that could preserve the text for millennia. The message, which consisted of the abstract of the paper announcing the work, is stored as two types of alterations in the way quartz glass refracts light. The combination of the two allows for data-storage densities as high as 360 terabytes per disc, or more than 7000 times today’s 50-gigabyte double-layer Blu-ray capacity.

There’s always a catch, though. Reading the message requires an electron microscope, and the process may never provide faster access to stored data than existing technology can. This and similar over-the-horizon memory research may someday improve big-data storage, but such systems aren’t an easy fit with today’s data-storage needs, experts say. Improved density and durability are both helpful, but readability and the capacity to rewrite data in a different format might be more important.

Read the rest of this news story in this month’s issue of IEEE Spectrum: [html] [pdf] and see my related blog post.