Audio: The true story of how 96 critically endangered sea turtle hatchlings survived New York City

first_img Popular in the CommunitySponsoredSponsoredOrangutan found tortured and decapitated prompts Indonesia probeEMGIES17 Jan, 2018We will never know the full extent of what this poor Orangutan went through before he died, the same must be done to this evil perpetrator(s) they don’t deserve the air that they breathe this has truly upset me and I wonder for the future for these wonderful creatures. So called ‘Mankind’ has a lot to answer for we are the only ones ruining this world I prefer animals to humans any day of the week.What makes community ecotourism succeed? In Madagascar, location, location, locationScissors1dOther countries should also learn and try to incorporateWhy you should care about the current wave of mass extinctions (commentary)Processor1 DecAfter all, there is no infinite anything in the whole galaxy!Infinite stupidity, right here on earth.The wildlife trade threatens people and animals alike (commentary)Anchor3dUnfortunately I feel The Chinese have no compassion for any living animal. They are a cruel country that as we knowneatbeverything that moves and do not humanily kill these poor animals and insects. They have no health and safety on their markets and they then contract these diseases. Maybe its karma maybe they should look at the way they live and stop using animals for all there so called remedies. DisgustingConservationists welcome China’s wildlife trade banThobolo27 JanChina has consistently been the worlds worst, “ Face of Evil “ in regards our planets flora and fauna survival. In some ways, this is nature trying to fight back. This ban is great, but the rest of the world just cannot allow it to be temporary, because history has demonstrated that once this coronavirus passes, they will in all likelihood, simply revert to been the planets worst Ecco Terrorists. Let’s simply not allow this to happen! How and why they have been able to degrade this planets iconic species, rape the planets rivers, oceans and forests, with apparent impunity, is just mind boggling! Please no more.Probing rural poachers in Africa: Why do they poach?Carrot3dOne day I feel like animals will be more scarce, and I agree with one of my friends, they said that poaching will take over the world, but I also hope notUpset about Amazon fires last year? Focus on deforestation this year (commentary)Bullhorn4dLies and more leisSponsoredSponsoredCoke is again the biggest culprit behind plastic waste in the PhilippinesGrapes7 NovOnce again the article blames companies for the actions of individuals. It is individuals that buy these products, it is individuals that dispose of them improperly. If we want to change it, we have to change, not just create bad guys to blame.Brazilian response to Bolsonaro policies and Amazon fires growsCar4 SepThank you for this excellent report. I feel overwhelmed by the ecocidal intent of the Bolsonaro government in the name of ‘developing’ their ‘God-given’ resources.U.S. allocates first of $30M in grants for forest conservation in SumatraPlanet4dcarrot hella thick ;)Melting Arctic sea ice may be altering winds, weather at equator: studyleftylarry30 JanThe Arctic sea ice seems to be recovering this winter as per the last 10-12 years, good news.Malaysia has the world’s highest deforestation rate, reveals Google forest mapBone27 Sep, 2018Who you’re trying to fool with selective data revelation?You can’t hide the truth if you show historical deforestation for all countries, especially in Europe from 1800s to this day. WorldBank has a good wholesome data on this.Mass tree planting along India’s Cauvery River has scientists worriedSurendra Nekkanti23 JanHi Mongabay. Good effort trying to be objective in this article. I would like to give a constructive feedback which could help in clearing things up.1. It is mentioned that planting trees in village common lands will have negative affects socially and ecologically. There is no need to even have to agree or disagree with it, because, you also mentioned the fact that Cauvery Calling aims to plant trees only in the private lands of the farmers. So, plantation in the common lands doesn’t come into the picture.2.I don’t see that the ecologists are totally against this project, but just they they have some concerns, mainly in terms of what species of trees will be planted. And because there was no direct communication between the ecologists and Isha Foundation, it was not possible for them to address the concerns. As you seem to have spoken with an Isha spokesperson, if you could connect the concerned parties, it would be great, because I see that the ecologists are genuinely interested in making sure things are done the right way.May we all come together and make things happen.Rare Amazon bush dogs caught on camera in BoliviaCarrot1 Feba very good iniciative to be fallowed by the ranchers all overSponsored This past summer, beachgoers in New York City spotted a nesting Kemp’s Ridley sea turtle on West Beach, which is on National Park Service land. Luckily, two of those beachgoers had the presence of mind to call the Riverhead Foundation for Marine Research and Preservation’s 24-hour hotline to report the nesting turtle — which very likely saved the lives of 96 Kemp’s Ridley sea turtle hatchlings.Though the critically endangered species is known to forage in the waters off New York, this was the farthest north a Kemp’s Ridley has ever been known to nest — usually they nest in northern Mexico, with some additional nesting sites in Texas.In consultation with the US Fish & Wildlife Service, National Park Service staff put an exclosure around the nest to protect it from humans and predators, then started making plans to monitor the nest and protect the hatchlings once they arrived and began their trek out to the ocean. But it soon became apparent that unusually high tides were swamping the nest, which could have meant disaster for the developing sea turtle embryos — so the difficult decision was made to excavate the nest and incubate the eggs in a secure facility, which ended up being a National Park Service office closet.We speak with the conservationists and government scientists who discovered and cared for the nest and its occupants in this episode of the Mongabay Newscast, including Patti Rafferty of the National Park Service, Steve Sinkevich of the US Fish & Wildlife Service, and Maxine Montello of the Riverhead Foundation for Marine Research and Preservation. (If you happen to be in New York and spot a sea turtle or any other marine animal in need of help, you can call the Riverhead Foundation’s 24-hour stranding hotline at +1 (631) 369-9829.)Together, these guests help tell this incredible conservation success story, and answer questions such as whether or not it’s a bad sign that a Kemp’s Ridley came all the way to NYC to nest in the first place.Here’s this episode’s top news:New research quantifies ecosystem services provided by Amazon rainforestMega-dam costs outweigh benefits, global building spree should end: expertsFirst wild Sumatran rhino in Borneo captured for breeding campaignWould you like to hear about the Mongabay team’s long list of snake bites, or learn which huge mammal chased our Program Manager up a tree? Have you ever wondered about the origins of Mongabay, and how we got that name? We now offer Insider Content that gives members exclusive access to behind-the-scenes reporting and stories from our team. For a small monthly donation, you’ll get answers to questions like these and support our work in a new way. Visit mongabay.com/insider to learn more and join the growing community of Mongabay readers on the inside track.If you enjoy the Mongabay Newscast, we ask that you please consider becoming a monthly sponsor via our Patreon page, at patreon.com/mongabay. Just a dollar per month will really help us offset the production costs and hosting fees, so if you’re a fan of our audio reports from nature’s frontline, please support the Mongabay Newscast at patreon.com/mongabay.You can subscribe to the Mongabay Newscast on Android, the Google Podcasts app, Apple Podcasts, Stitcher, TuneIn, RSS, Castbox, Pocket Casts, and via Spotify. Or listen to all our episodes via the Mongabay website here on the podcast homepage.A Kemp’s Ridley sea turtle hatchling on West Beach in New York City, making its way to the Atlantic Ocean. Photo Credit: US National Park Service.Follow Mike Gaworecki on Twitter: @mikeg2001FEEDBACK: Use this form to send a message to the author of this post. If you want to post a public comment, you can do that at the bottom of the page. Article published by Mike Gaworecki On this episode, the true story of how 96 critically endangered sea turtles survived a New York City beach — with a little help from some dedicated conservationists.This past summer, beachgoers in New York City spotted a nesting Kemp’s Ridley sea turtle on West Beach, which is on National Park Service land.Luckily, two of those beachgoers had the presence of mind to call the Riverhead Foundation for Marine Research and Preservation’s 24-hour hotline to report the nesting turtle — which very likely saved the lives of 96 Kemp’s Ridley sea turtle hatchlings. On this episode, the true story of how 96 critically endangered sea turtles survived a New York City beach — with a little help from some dedicated conservationists and scientists.Listen here: Amazon Rainforest, Amphibians, Animals, Climate Change and Dams, Conservation, Critically Endangered Species, Dams, Ecosystem Services, Endangered Species, Endangered Species Act, Environment, Forests, Happy-upbeat Environmental, Herps, Hydroelectric Power, Interviews, Mammals, Marine Animals, Oceans, Podcast, Rhinos, Saving Species From Extinction, Sea Turtles, Sumatran Rhino, Tropical Forests, Turtles, Wildlife Conservation last_img read more

Libya lift African Nations Championship title

first_img2 February 2014 Libya were crowned the 2014 African Nations Championship winners at Cape Town Stadium on Saturday evening, edging Ghana 4-3 in a penalty shootout after the teams had shared a goalless draw. It was a first ever African title for the Mediterranean Knights, who had lost to the Black Stars on penalties in the final of the Africa Cup of Nations way back in 1982. President of the Confederation of African Football Issa Hayatou and Fifa President Sepp Blatter were joined by a crowd of about 17 000, who were treated to a tightly contested match which either side could have won in regulation time had their finishing been better.Close Ghana’s Seidou Bansey went close with a header barely a minute into the game, forcing Mohamed Abdaula, in the Libyan goal, into a save. Three minutes later, however, Elmutasem Abuschnaf was presented with a wonderful opportunity to give the Mediterranean Knights the lead after a mistake in the Ghanaian defence allowed him through on goal. Black Stars’ goalie Stephen Adams reacted superbly to rob the striker of the ball to keep the teams level. Most of the action took place in the middle of the field as both teams struggled to break free of the opposition’s gritty defence and the pattern was repeated in the second half. With 10 minutes left in regulation time, Ahmed Almaghasi looked as if he would finally break the deadlock, but Samuel Ainooson, with a fantastic effort, managed to block a goal-bound shot with his body.Extra time In extra time Libya again came close to finding a goal, but Abuschnaf spurned a fine opportunity by heading over the bar from close range after Elmehdi Elhouni had picked him out with a beautiful cross. Abdul Mohammed went oh so close to netting for Ghana, but his shot angled just wide of the far post of the Libyan goal. Libya’s Abdelsalam Omar then had time to pick his spot from about eight metres out, but Adams was up to the challenge and pulled off a fine save from the young striker. The momentum swung Ghana’s way as the clock ticked down, but they couldn’t find a way through the Libyan defence and the game went to penalties, as both semi- finals had done.Shootout Abdaula excelled, saving the Black Stars’ first two spot kicks from Michael Akuffo and Ainooson, to hand the Libyans the advantage, but Ghana’s Adams responded with two saves of his own, from Elgadi and Omar. When Ahmed El Tribi netted for Libya, Tijani Joshua had to score for Ghana, and when he missed wide of the goal the Mediterranean Knights had claimed victory. Third place Nigeria claimed third place with a 1-0 win over Zimbabwe in the curtain-raiser. The Warriors had to cope with the loss of Masimba Mambare to a straight red card after only 17 minutes, but it took the Super Eagles until five minutes from time to score the winner, which came from substitute Christian Obiozor, who beat George Chigova with a header.Awards There was some consolation for hosts South Africa, who had been ousted in the group stages, when striker Bernard Parker won the Golden Boot after scoring four goals in Bafana Bafana’s three matches. Nigeria’s Ejike Uzoenyi was named the Player of the Tournament.last_img read more

How AI detectives are cracking open the black box of deep learning

first_img GRAPHIC: G. GRULLÓN/SCIENCE By Paul VoosenJul. 6, 2017 , 2:00 PM Jason Yosinski sits in a small glass box at Uber’s San Francisco, California, headquarters, pondering the mind of an artificial intelligence. An Uber research scientist, Yosinski is performing a kind of brain surgery on the AI running on his laptop. Like many of the AIs that will soon be powering so much of modern life, including self-driving Uber cars, Yosinski’s program is a deep neural network, with an architecture loosely inspired by the brain. And like the brain, the program is hard to understand from the outside: It’s a black box. This particular AI has been trained, using a vast sum of labeled images, to recognize objects as random as zebras, fire trucks, and seat belts. Could it recognize Yosinski and the reporter hovering in front of the webcam? Yosinski zooms in on one of the AI’s individual computational nodes—the neurons, so to speak—to see what is prompting its response. Two ghostly white ovals pop up and float on the screen. This neuron, it seems, has learned to detect the outlines of faces. “This responds to your face and my face,” he says. “It responds to different size faces, different color faces.”No one trained this network to identify faces. Humans weren’t labeled in its training images. Yet learn faces it did, perhaps as a way to recognize the things that tend to accompany them, such as ties and cowboy hats. The network is too complex for humans to comprehend its exact decisions. Yosinski’s probe had illuminated one small part of it, but overall, it remained opaque. “We build amazing models,” he says. “But we don’t quite understand them. And every year, this gap is going to get a bit larger.”Sign up for our daily newsletterGet more great content like this delivered right to you!Country *AfghanistanAland IslandsAlbaniaAlgeriaAndorraAngolaAnguillaAntarcticaAntigua and BarbudaArgentinaArmeniaArubaAustraliaAustriaAzerbaijanBahamasBahrainBangladeshBarbadosBelarusBelgiumBelizeBeninBermudaBhutanBolivia, Plurinational State ofBonaire, Sint Eustatius and SabaBosnia and HerzegovinaBotswanaBouvet IslandBrazilBritish Indian Ocean TerritoryBrunei DarussalamBulgariaBurkina FasoBurundiCambodiaCameroonCanadaCape VerdeCayman IslandsCentral African RepublicChadChileChinaChristmas IslandCocos (Keeling) IslandsColombiaComorosCongoCongo, The Democratic Republic of theCook IslandsCosta RicaCote D’IvoireCroatiaCubaCuraçaoCyprusCzech RepublicDenmarkDjiboutiDominicaDominican RepublicEcuadorEgyptEl SalvadorEquatorial GuineaEritreaEstoniaEthiopiaFalkland Islands (Malvinas)Faroe IslandsFijiFinlandFranceFrench GuianaFrench PolynesiaFrench Southern TerritoriesGabonGambiaGeorgiaGermanyGhanaGibraltarGreeceGreenlandGrenadaGuadeloupeGuatemalaGuernseyGuineaGuinea-BissauGuyanaHaitiHeard Island and Mcdonald IslandsHoly See (Vatican City State)HondurasHong KongHungaryIcelandIndiaIndonesiaIran, Islamic Republic ofIraqIrelandIsle of ManIsraelItalyJamaicaJapanJerseyJordanKazakhstanKenyaKiribatiKorea, Democratic People’s Republic ofKorea, Republic ofKuwaitKyrgyzstanLao People’s Democratic RepublicLatviaLebanonLesothoLiberiaLibyan Arab JamahiriyaLiechtensteinLithuaniaLuxembourgMacaoMacedonia, The Former Yugoslav Republic ofMadagascarMalawiMalaysiaMaldivesMaliMaltaMartiniqueMauritaniaMauritiusMayotteMexicoMoldova, Republic ofMonacoMongoliaMontenegroMontserratMoroccoMozambiqueMyanmarNamibiaNauruNepalNetherlandsNew CaledoniaNew ZealandNicaraguaNigerNigeriaNiueNorfolk IslandNorwayOmanPakistanPalestinianPanamaPapua New GuineaParaguayPeruPhilippinesPitcairnPolandPortugalQatarReunionRomaniaRussian FederationRWANDASaint Barthélemy Saint Helena, Ascension and Tristan da CunhaSaint Kitts and NevisSaint LuciaSaint Martin (French part)Saint Pierre and MiquelonSaint Vincent and the GrenadinesSamoaSan MarinoSao Tome and PrincipeSaudi ArabiaSenegalSerbiaSeychellesSierra LeoneSingaporeSint Maarten (Dutch part)SlovakiaSloveniaSolomon IslandsSomaliaSouth AfricaSouth Georgia and the South Sandwich IslandsSouth SudanSpainSri LankaSudanSurinameSvalbard and Jan MayenSwazilandSwedenSwitzerlandSyrian Arab RepublicTaiwanTajikistanTanzania, United Republic ofThailandTimor-LesteTogoTokelauTongaTrinidad and TobagoTunisiaTurkeyTurkmenistanTurks and Caicos IslandsTuvaluUgandaUkraineUnited Arab EmiratesUnited KingdomUnited StatesUruguayUzbekistanVanuatuVenezuela, Bolivarian Republic ofVietnamVirgin Islands, BritishWallis and FutunaWestern SaharaYemenZambiaZimbabweI also wish to receive emails from AAAS/Science and Science advertisers, including information on products, services and special offers which may include but are not limited to news, careers information & upcoming events.Required fields are included by an asterisk(*)Each month, it seems, deep neural networks, or deep learning, as the field is also called, spread to another scientific discipline. They can predict the best way to synthesize organic molecules. They can detect genes related to autism risk. They are even changing how science itself is conducted. The AIs often succeed in what they do. But they have left scientists, whose very enterprise is founded on explanation, with a nagging question: Why, model, why?That interpretability problem, as it’s known, is galvanizing a new generation of researchers in both industry and academia. Just as the microscope revealed the cell, these researchers are crafting tools that will allow insight into the how neural networks make decisions. Some tools probe the AI without penetrating it; some are alternative algorithms that can compete with neural nets, but with more transparency; and some use still more deep learning to get inside the black box. Taken together, they add up to a new discipline. Yosinski calls it “AI neuroscience.” Mark Riedl, Georgia Institute of Technology Like many AI coders, Mark Riedl, director of the Entertainment Intelligence Lab at the Georgia Institute of Technology in Atlanta, turns to 1980s video games to test his creations. One of his favorites is Frogger, in which the player navigates the eponymous amphibian through lanes of car traffic to an awaiting pond. Training a neural network to play expert Frogger is easy enough, but explaining what the AI is doing is even harder than usual.Instead of probing that network, Riedl asked human subjects to play the game and to describe their tactics aloud in real time. Riedl recorded those comments alongside the frog’s context in the game’s code: “Oh, there’s a car coming for me; I need to jump forward.” Armed with those two languages—the players’ and the code—Riedl trained a second neural net to translate between the two, from code to English. He then wired that translation network into his original game-playing network, producing an overall AI that would say, as it waited in a lane, “I’m waiting for a hole to open up before I move.” The AI could even sound frustrated when pinned on the side of the screen, cursing and complaining, “Jeez, this is hard.”Riedl calls his approach “rationalization,” which he designed to help everyday users understand the robots that will soon be helping around the house and driving our cars. “If we can’t ask a question about why they do something and get a reasonable response back, people will just put it back on the shelf,” Riedl says. But those explanations, however soothing, prompt another question, he adds: “How wrong can the rationalizations be before people lose trust?” Marco Ribeiro, a graduate student at the University of Washington in Seattle, strives to understand the black box by using a class of AI neuroscience tools called counter-factual probes. The idea is to vary the inputs to the AI—be they text, images, or anything else—in clever ways to see which changes affect the output, and how. Take a neural network that, for example, ingests the words of movie reviews and flags those that are positive. Ribeiro’s program, called Local Interpretable Model-Agnostic Explanations (LIME), would take a review flagged as positive and create subtle variations by deleting or replacing words. Those variants would then be run through the black box to see whether it still considered them to be positive. On the basis of thousands of tests, LIME can identify the words—or parts of an image or molecular structure, or any other kind of data—most important in the AI’s original judgment. The tests might reveal that the word “horrible” was vital to a panning or that “Daniel Day Lewis” led to a positive review. But although LIME can diagnose those singular examples, that result says little about the network’s overall insight.New counterfactual methods like LIME seem to emerge each month. But Mukund Sundararajan, another computer scientist at Google, devised a probe that doesn’t require testing the network a thousand times over: a boon if you’re trying to understand many decisions, not just a few. Instead of varying the input randomly, Sundararajan and his team introduce a blank reference—a black image or a zeroed-out array in place of text—and transition it step-by-step toward the example being tested. Running each step through the network, they watch the jumps it makes in certainty, and from that trajectory they infer features important to a prediction.Sundararajan compares the process to picking out the key features that identify the glass-walled space he is sitting in—outfitted with the standard medley of mugs, tables, chairs, and computers—as a Google conference room. “I can give a zillion reasons.” But say you slowly dim the lights. “When the lights become very dim, only the biggest reasons stand out.” Those transitions from a blank reference allow Sundararajan to capture more of the network’s decisions than Ribeiro’s variations do. But deeper, unanswered questions are always there, Sundararajan says—a state of mind familiar to him as a parent. “I have a 4-year-old who continually reminds me of the infinite regress of ‘Why?’”The urgency comes not just from science. According to a directive from the European Union, companies deploying algorithms that substantially influence the public must by next year create “explanations” for their models’ internal logic. The Defense Advanced Research Projects Agency, the U.S. military’s blue-sky research arm, is pouring $70 million into a new program, called Explainable AI, for interpreting the deep learning that powers drones and intelligence-mining operations. The drive to open the black box of AI is also coming from Silicon Valley itself, says Maya Gupta, a machine-learning researcher at Google in Mountain View, California. When she joined Google in 2012 and asked AI engineers about their problems, accuracy wasn’t the only thing on their minds, she says. “I’m not sure what it’s doing,” they told her. “I’m not sure I can trust it.”Rich Caruana, a computer scientist at Microsoft Research in Redmond, Washington, knows that lack of trust firsthand. As a graduate student in the 1990s at Carnegie Mellon University in Pittsburgh, Pennsylvania, he joined a team trying to see whether machine learning could guide the treatment of pneumonia patients. In general, sending the hale and hearty home is best, so they can avoid picking up other infections in the hospital. But some patients, especially those with complicating factors such as asthma, should be admitted immediately. Caruana applied a neural network to a data set of symptoms and outcomes provided by 78 hospitals. It seemed to work well. But disturbingly, he saw that a simpler, transparent model trained on the same records suggested sending asthmatic patients home, indicating some flaw in the data. And he had no easy way of knowing whether his neural net had picked up the same bad lesson. “Fear of a neural net is completely justified,” he says. “What really terrifies me is what else did the neural net learn that’s equally wrong?”Today’s neural nets are far more powerful than those Caruana used as a graduate student, but their essence is the same. At one end sits a messy soup of data—say, millions of pictures of dogs. Those data are sucked into a network with a dozen or more computational layers, in which neuron-like connections “fire” in response to features of the input data. Each layer reacts to progressively more abstract features, allowing the final layer to distinguish, say, terrier from dachshund.At first the system will botch the job. But each result is compared with labeled pictures of dogs. In a process called backpropagation, the outcome is sent backward through the network, enabling it to reweight the triggers for each neuron. The process repeats millions of times until the network learns—somehow—to make fine distinctions among breeds. “Using modern horsepower and chutzpah, you can get these things to really sing,” Caruana says. Yet that mysterious and flexible power is precisely what makes them black boxes. A new breed of scientist, with brains of silicon Special package: AI in science Opening up the black box Loosely modeled after the brain, deep neural networks are spurring innovation across science. But the mechanics of the models are mysterious: They are black boxes. Scientists are now developing tools to get inside the mind of the machine. How AI detectives are cracking open the black box of deep learning First, Yosinski rejiggered the classifier to produce images instead of labeling them. Then, he and his colleagues fed it colored static and sent a signal back through it to request, for example, “more volcano.” Eventually, they assumed, the network would shape that noise into its idea of a volcano. And to an extent, it did: That volcano, to human eyes, just happened to look like a gray, featureless mass. The AI and people saw differently.Next, the team unleashed a generative adversarial network (GAN) on its images. Such AIs contain two neural networks. From a training set of images, the “generator” learns rules about imagemaking and can create synthetic images. A second “adversary” network tries to detect whether the resulting pictures are real or fake, prompting the generator to try again. That back-and-forth eventually results in crude images that contain features that humans can recognize.Yosinski and Anh Nguyen, his former intern, connected the GAN to layers inside their original classifier network. This time, when told to create “more volcano,” the GAN took the gray mush that the classifier learned and, with its own knowledge of picture structure, decoded it into a vast array of synthetic, realistic-looking volcanoes. Some dormant. Some erupting. Some at night. Some by day. And some, perhaps, with flaws—which would be clues to the classifier’s knowledge gaps.Their GAN can now be lashed to any network that uses images. Yosinski has already used it to identify problems in a network trained to write captions for random images. He reversed the network so that it can create synthetic images for any random caption input. After connecting it to the GAN, he found a startling omission. Prompted to imagine “a bird sitting on a branch,” the network—using instructions translated by the GAN—generated a bucolic facsimile of a tree and branch, but with no bird. Why? After feeding altered images into the original caption model, he realized that the caption writers who trained it never described trees and a branch without involving a bird. The AI had learned the wrong lessons about what makes a bird. “This hints at what will be an important direction in AI neuroscience,” Yosinski says. It was a start, a bit of a blank map shaded in.The day was winding down, but Yosinski’s work seemed to be just beginning. Another knock on the door. Yosinski and his AI were kicked out of another glass box conference room, back into Uber’s maze of cities, computers, and humans. He didn’t get lost this time. He wove his way past the food bar, around the plush couches, and through the exit to the elevators. It was an easy pattern. He’d learn them all soon. Gupta has a different tactic for coping with black boxes: She avoids them. Several years ago Gupta, who moonlights as a designer of intricate physical puzzles, began a project called GlassBox. Her goal is to tame neural networks by engineering predictability into them. Her guiding principle is monotonicity—a relationship between variables in which, all else being equal, increasing one variable directly increases another, as with the square footage of a house and its price. Gupta embeds those monotonic relationships in sprawling databases called interpolated lookup tables. In essence, they’re like the tables in the back of a high school trigonometry textbook where you’d look up the sine of 0.5. But rather than dozens of entries across one dimension, her tables have millions across multiple dimensions. She wires those tables into neural networks, effectively adding an extra, predictable layer of computation—baked-in knowledge that she says will ultimately make the network more controllable.Caruana, meanwhile, has kept his pneumonia lesson in mind. To develop a model that would match deep learning in accuracy but avoid its opacity, he turned to a community that hasn’t always gotten along with machine learning and its loosey-goosey ways: statisticians.In the 1980s, statisticians pioneered a technique called a generalized additive model (GAM). It built on linear regression, a way to find a linear trend in a set of data. But GAMs can also handle trickier relationships by finding multiple operations that together can massage data to fit on a regression line: squaring a set of numbers while taking the logarithm for another group of variables, for example. Caruana has supercharged the process, using machine learning to discover those operations—which can then be used as a powerful pattern-detecting model. “To our great surprise, on many problems, this is very accurate,” he says. And crucially, each operation’s influence on the underlying data is transparent.Caruana’s GAMs are not as good as AIs at handling certain types of messy data, such as images or sounds, on which some neural nets thrive. But for any data that would fit in the rows and columns of a spreadsheet, such as hospital records, the model can work well. For example, Caruana returned to his original pneumonia records. Reanalyzing them with one of his GAMs, he could see why the AI would have learned the wrong lesson from the admission data. Hospitals routinely put asthmatics with pneumonia in intensive care, improving their outcomes. Seeing only their rapid improvement, the AI would have recommended the patients be sent home. (It would have made the same optimistic error for pneumonia patients who also had chest pain and heart disease.)Caruana has started touting the GAM approach to California hospitals, including Children’s Hospital Los Angeles, where about a dozen doctors reviewed his model’s results. They spent much of that meeting discussing what it told them about pneumonia admissions, immediately understanding its decisions. “You don’t know much about health care,” one doctor said, “but your model really does.”Sometimes, you have to embrace the darkness. That’s the theory of researchers pursuing a third route toward interpretability. Instead of probing neural nets, or avoiding them, they say, the way to explain deep learning is simply to do more deep learning. If we can’t ask … why they do something and get a reasonable response back, people will just put it back on the shelf. AI is changing how we do science. Get a glimpse Researchers have created neural networks that, in addition to filling gaps left in photos, can identify flaws in an artificial intelligence. PHOTOS: ANH NGUYEN Back at Uber, Yosinski has been kicked out of his glass box. Uber’s meeting rooms, named after cities, are in high demand, and there is no surge pricing to thin the crowd. He’s out of Doha and off to find Montreal, Canada, unconscious pattern recognition processes guiding him through the office maze—until he gets lost. His image classifier also remains a maze, and, like Riedl, he has enlisted a second AI to help him understand the first one.last_img read more

Quick Takeaways from the Pew Social Media Report

first_imgYou can download the full report from the Pew website.So what does this mean for your nonprofit marketing plans?Know your audience.Take the time to define the audience you’re trying to reach and understand where they’re spending their time. If your goal is to activate Boomers, assess your Facebook outreach and create content that appeals to their sense of identity and need for transparency. If you’re looking to mobilize younger supporters, consider documenting your work and the impact of donors via Instagram photos.Resist the urge to be everywhere.The Pew researchers found that 52% of online adults use multiple social media sites, which is an increase from 2013. For most nonprofits, though, it’s probably not advisable or realistic to spread resources too thin across multiple outlets. Your best bet, especially if you’re still establishing your social media strategy, is to focus on regular quality engagement on one platform. Measure your results and keep an eye on relevant activity on other networks before expanding. Remember: your social efforts need to reinforce your marketing efforts in other channels.Be realistic about your goals for social. We know that donors are engaging with nonprofits and each other on social, but most online dollars are coming in through non-social. Focus on using social as a listening and engagement platform, rather than expecting Twitter or Facebook to become your organization’s magic money machine. Think of social as a tool for understanding what interests your supporters and use your outreach to develop relationships with them.Carefully measure your ROI.Although Facebook is the most widely used social media site with the most engaged users, keep in mind that it is becoming increasingly more difficult to break through the noise (and the Facebook algorithm) and fully reach your audience through the platform. On the Care2 blog, Allyson Kapin recently outlined why it’s getting harder to see a return from Facebook advertising.Even if you’re not paying for social media advertising, weigh the time and attention your staff spends on social media with the results you see and progress to your goals. To get the most out social, you do need to commit to posting quality content and spending time building your presence and the relationships that result.Is social media on your 2015 list of priorities? Share your thoughts below and let us know how you’re incorporating Facebook, Twitter, and others into your nonprofit marketing strategy. The folks at the Pew Research Center recently published updates to their Social Media Report. Here are a few highlights:Facebook still reigns supreme. It comes as no surprise that 71% of all online adults are on Facebook, which also sees 70% of users engaging with the site at least daily.More older adults adopting social networks. But they’re mostly on Facebook. 56% of all online adults 65 and older now use Facebook, which equals 31% of all seniors. That said, all networks featured in the report saw significant jumps in the number of 65+ users.Visual platforms continue to emerge as key networks, especially with younger users. Over half of young adults (ages 18-29) online use Instagram. Nearly half of all Instagram users use the site daily.last_img read more

How to Turn Donors into Fundraisers

first_imgWant to add new donors and more donations to your fundraising results this year?One of the best ways to expand your reach and attract new supporters is by tapping into the networks of your existing supporters with a peer-to-peer fundraising campaign. Here’s why: people are more likely to give when asked by a friend or family member, and thanks to the multiplier effect, these supporter-fundraisers will increase their lifetime value to your organization by giving and bringing new donations to your cause.So, how do you do it? How do you inspire donors to create personalized fundraising campaigns and raise money on your behalf? Here are 11 tips for turning donors into fundraisers.Make it easy.First and foremost, you must make setting up a peer fundraising page and asking friends to donate dead simple to do. The same rules apply for getting donors to give as they do for getting supporters to ask their networks to give to your cause. The easier it is to do, the more likely they will be to do it. Focus on removing any roadblocks for your supporters-turned-fundraisers.Offer portable outreach. Arm your supporters with pre-written emails and social media posts. Provide grab-and-go templates so your advocates can focus on reaching out to their friends.Be clear. Make sure you are clear on what you’re asking your supporters to do when you recruit them to be fundraisers. Make your instructions short and simple. If there are too many steps or complex requests, they’ll get confused and give up. Simplify their part of the process as much as possible, and if you can do some of the steps for them, even better.Be realistic. You want your goals to be exciting and motivating, but requests don’t feel do-able will just turn potential fundraisers off. Make your ask feel possible so your supporters can see they can succeed and make an impact for your work. If possible, share other fundraisers’ good results to illustrate that a successful campaign is achievable.Have the right tools. Having the right software in place makes these types of social fundraising campaigns a lot easier for you, and your fundraisers. Focus on tools that empower supporters, offer built-in sharing options, and make your fundraisers look good. Schedule a personalized tour of Network for Good’s peer-to-peer fundraising software and learn how you can easily create campaigns that will extend your reach and attract new donors.Make it relevant.Giving back is often very personal, for both donors and fundraisers alike. Reinforce this important tie to your work by making the idea of fundraising for your organization tailored to your supporters.Think about their connection with your cause. Some donors have an affinity for certain projects or programs, or they have a story that shares a unique perspective. When asking supporters to join as fundraisers, make sure you connect these preferences to the campaign you’d like them to help spread. If a donor has always supported your senior meal delivery program, tap them to start a fundraiser to help fund a new van to distribute even more meals.Personalize your request. Use the details you have in your donor database to personalize your invitation to participate. Yes, start with getting their name correct on the emails, but also include relevant details about their history with your organization and how this makes them the perfect fit for your fundraising team. A request that seems generic or worse, disconnected, won’t inspire donors to get involved.Make it about the impact.Everyone wants to know they’re making a difference, and your fundraisers are no exception. Get your advocates on board by illustrating the impact that their efforts will have.Show the big picture. Give prospective fundraisers a clear view of how their efforts will add to your bigger goal. What is the vision that your campaign will make a reality? Paint a picture of how your supporter-fundraisers will make a difference and include this in your recruitment communications.But also get specific. Now that you’ve set the vision, break down what each campaign, donor, and donation can do. This will help fundraisers and donors alike understand how they can achieve the goals you’ve set, one step at a time. Will $20 help feed a family for a day? Does a $2,000 fundraiser goal equal a new refrigerator for your food pantry? Let supporters know exactly how their gifts will be used so they can visualize their specific impact.Make it fun.Social fundraising campaigns can create a deeper connection with your supporters … and they’re fun! Don’t forget to use this fact when you recruit and motivate fundraisers for your projects.Leave room for personalization and creativity. Give your fundraisers ownership over their campaigns and allow them to customize their communications and fundraising pages with their photos, stories, and video. Not only does this make their efforts feel more personal, these individual touches will make donors more likely to give as it evokes their recognition and relationship with the fundraiser.Offer motivation. Keep your supporters going with updates on how the campaign is going and how their contributions are adding up. Check in with encouraging words and tips for making their outreach more effective. Don’t forget: a little competition among your fundraisers is healthy and can drive extra participation. Consider offering an incentive for the best campaigns or when fundraisers meet certain milestones.Create goals and deadlines. While you want your goals to be realistic (see above), you do want to set some targets and track milestones to help motivate your fundraisers and drive a sense of urgency. This helps your supporters stay engaged and can spur them on to encourage more donations.Network for Good’s peer fundraising software will help you do all of these things and more. You can create beautiful campaigns that inspire donors to fundraise on your behalf and motivate their networks to give to your organization.last_img read more

Because You’re Awesome

first_imgTo our customers, our partners, our readers:  thank you for doing the good that you do.Every day we’re in awe of you.We’re so grateful for the amazing things that are accomplished by the nonprofits we work with: feeding the hungry, healing the sick, sheltering the homeless, saving animals, preserving the environment, fighting for justice, nurturing the arts, and so much more. Each day you are making the world a much brighter and more hopeful place with your passion and creativity. We know it’s not always easy, and we appreciate your dedication. We know that your work matters in a very real way to your communities and the lives that you impact. And this is why we come to work each day: to make it easier for you to focus on helping those that you serve.From all of us here at Network for Good, thank you. We are grateful for you and we wish you a very Happy Thanksgiving.last_img read more

Maternal Health Careers: Jacaranda Health and UCSF Bixby Center for Global Health

first_img ShareEmailPrint To learn more, read: Posted on January 29, 2014August 10, 2016Click to share on Facebook (Opens in new window)Click to share on Twitter (Opens in new window)Click to share on LinkedIn (Opens in new window)Click to share on Reddit (Opens in new window)Click to email this to a friend (Opens in new window)Click to print (Opens in new window)Our colleagues at Jacaranda Health and the UCSF Bixby Center for Global Health are now accepting applications from qualified maternal health professionals.Jacaranda Health is seeking a Knowledge/Special Projects Manager to:Join the management team to help us capture what we are learning and work with other players in the global health landscape to share and expand our influence. This should be someone who can roll up their sleeves and help work with our operations team to build systems and innovations as well as communicate them. We seek someone who is the consummate consultant and communicator, who wants to apply those talents to shape global health. This position is based in Nairobi, Kenya.To learn more, visit the full job description at Jacaranda Health.UCSF is seeking a Global Maternal Health Research Coordinator to:Coordinate planning, implementation and evaluation efforts for the “Que Vivan Las Madres” project, a scale-up of a proven three-pronged intervention to prevent maternal and neonatal mortality in Guatemala. Work with Guatemalan and other international colleagues in the implementation and evaluation of PRONTO (Programa de Rescate Obstétrico Neonatal: El Tratamiento Óptimo y Oportuno): http://prontointernational.org, a core component of the intervention strategy. Coordinate collaborative research efforts between the UCSF Global Health Sciences and the Center for Epidemiologic Research in Sexual and Reproductive Health (CIESAR) in Guatemala. This position requires excellent project management, scientific writing and editing skills.  Written and spoken Spanish language fluency is essential.To learn more, visit the full job description at UCSF.Share this:last_img read more

Wilson Center to Host Policy Dialogue on Training Skilled Birth Attendants

first_img ShareEmailPrint To learn more, read: Posted on September 26, 2014November 2, 2016By: Katie Millar, Technical Writer, Women and Health Initiative, Harvard T.H. Chan School of Public HealthClick to share on Facebook (Opens in new window)Click to share on Twitter (Opens in new window)Click to share on LinkedIn (Opens in new window)Click to share on Reddit (Opens in new window)Click to email this to a friend (Opens in new window)Click to print (Opens in new window)Skilled birth attendants are one of the most effective tools to decreasing maternal and newborn mortality and morbidity. Although many women do not receive skilled care. One way to ensure women have this care is to ensure providers can provide it.This Tuesday, September 30th, The Wilson Center will hold the policy dialogue, Innovative Training of Birth Attendants, to discuss innovative, technology-drive training models in low-resource settings. Experts from UNFP, Jhpiego, and Dartmouth Medical School will gather with the Maternal Health Initiative at the Wilson Center to share their models and next steps for promoting healthy deliveries for moms around the world.Interested in attending in Washington, D.C.? RSVP here to attend in person or join us for the live webcast by visiting the event home page from 3:00 pm – 5:00 pm EDT this Tuesday.Share this:last_img read more

Lauri Romanzi on Rethinking Maternal Morbidity Care in a Historical Context

first_imgPosted on July 28, 2015June 12, 2017By: Josh Feng, Intern, Environmental Change and Security Program, Wilson CenterClick to share on Facebook (Opens in new window)Click to share on Twitter (Opens in new window)Click to share on LinkedIn (Opens in new window)Click to share on Reddit (Opens in new window)Click to email this to a friend (Opens in new window)Click to print (Opens in new window) Audio Playerhttps://cdn1.sph.harvard.edu/wp-content/uploads/sites/2413/2015/07/ecsp-wwc_2015-07-24T08_24_57-07_00.mp300:0000:0000:00Use Up/Down Arrow keys to increase or decrease volume. In May 1855, Dr. James Marion Sims opened the first obstetric fistula hospital in New York City. Just 40 years later, it closed, reflecting a sharp decline in maternal morbidity rates in the United States and other Western countries. The Waldorf Astoria Hotel now stands on the site of the former hospital. “We know that we have eradicated obstetric fistula in high income countries; it happened at the turn of the 20th century,” says Dr. Lauri Romanzi, project director of Fistula Care Plus, in this week’s podcast.That timing is crucial, says Romanzi, because there is a narrative that argues certain social determinants must be changed to eradicate fistula in developing countries today, such as forced marriage, teen pregnancy, women’s education and suffrage, antenatal care, and gender-based violence. Yet at the turn of the 20th century in the United States and Europe, many of these “mandatory” determinants were far from modern progressive standards (teen pregnancy remains substantially higher in the United States than other industrialized nations).Speaking at a Wilson Center Maternal Health Initiative event, Romanzi says the turning point for fistula eradication in Western countries coincided with the advent of crude anesthetics, such as chloroform on cloth, which revolutionized surgical practices and made Caesarian sections more feasible for mothers. “Possibly that was a catalyst at that time, in those cultures,” she says. “We need to figure out what today’s catalyst is.”Beyond the “Truffle Hunt”Obstetric fistula is a childbirth injury caused by prolonged obstructed labor, often leading to incontinence, social stigmatization, infection, and even mental illness. Though fistula is almost entirely preventable and largely eradicated in high-income countries, it is still widespread in the developing world. Prevention and treatment is very simple says Romanzi, yet progress is moving slowly, leading some to question existing approaches.Romanzi notes that in countries where fistula is more common, Cesarean section rates hover around 5 percent, whereas the ideal rate to prevent maternal morbidities is about 15 percent. But increasing the Cesarean section rate without regard to quality of care may cause further complications such as iatrogenic fistula, which is a form of genital fistula unintentionally caused by a health care provider. Iatrogenic fistula is often much more complicated than obstetric cases and is more likely to damage the kidneys, says Romanzi.The “invisibility” of fistula and maternal morbidity care in general is often reflected in funding streams. A bigger budget for one West African hospital increased the number of deliveries the maternity ward could handle from 5,000 to 15,000 a year. Yet there was still only one operating theater, and poor quality of care caused many women to develop complications. “It’s an obstetric fistula factory,” says Romanzi. Patients are often funneled to a fistula clinic literally down the hill from the hospital to treat these maternal morbidities.“Fistula has gotten a lot of attention, and deservedly so,” she says. “But there are many other morbidities as well.” Romanzi proposes implementing an obstructed labor screening program that would utilize many existing resources to address the multiple needs that obstructed labor patients have, rather than simply focusing on the “truffle hunt” of targeting fistula.It’s important to look at the many factors that make eliminating maternal morbidities such a stubborn challenge in many places – patient to midwife ratios, midwifery education programs, waste management, water security, medical supply chains, and others, says Romanzi. She suggests focusing on localized, multi-sectoral, and self-sufficient systems that target disparities between the poor and wealthy to improve all areas of women’s health.“The goal is that every woman, every time, has access to a facility that is outfitted and staffed to meet a minimum standard of care, within which both the health outcomes of the baby and the mother are optimized,” she says, “and that that care is rendered in a humane, kind, and caring fashion.”Lauri Romanzi spoke at the Wilson Center on July 14.Friday Podcasts are also available for download on iTunes.Sources: Academy of Surgical Research, Center for Disease Control and Prevention, Fistula Care Plus, Fistula Foundation, Medscape, RH Reality Check, The World Bank, World Health Organization.This podcast and summary originally appeared on The New Security Beat, the blog of the Environmental Change and Security Program at the Wilson CenterShare this: ShareEmailPrint To learn more, read:last_img read more

Three policemen killed in Easter blasts posthumously promoted

first_imgColombo: Three policemen, who were killed when a suicide bomber blew himself up causing the concrete floor of a two-storey building to crash on them in the Sri Lankan capital, have been posthumously promoted. One Sub Inspector and two constables were killed during a raid at a house in Colombo north suburb of Orugodawatta at Dematagoda on Sunday. When they entered the house, a suicide bomber blew himself, killing all of them. “They have been posthumously promoted,” Police spokesman Ruwan Gunasekara said. The Sub-Police Inspector was posthumously promoted as an Inspector while the two constables have been promoted to the ranks of Sergeants, he said.last_img read more