Rescue of soccer team in Thailand Cave

In July 2018, the world held its breath as an international team of cave divers endeavored to rescue 12 boys and their soccer coach stranded deep in a flooded cave in Thailand. Follow the harrowing operation and discover the scientific ingenuity that made the rescue possible. Hear how rescuers explored every option—from pumping water, to drilling a new exit, to ultimately cave diving with the children through the treacherous, flooded passages. (Premiered November 14, 2018)

Follow the harrowing operation to rescue 12 boys stranded in a flooded cave in Thailand.

Thai Cave Rescue

NOVA Wonders Can We Build a Brain? Hero

Artificially intelligent machines are taking over. They’re influencing our everyday lives in profound and often invisible ways. They can read handwriting, interpret emotions, play games, and even act as personal assistants. They are in our phones, our cars, our doctors’ offices, our banks, our web searches…the list goes on and is rapidly growing ever longer. But how does today’s A.I. actually work—and is it truly intelligent? And for that matter, what is intelligence? The world’s brightest computer programmers are trying to build brighter machines by reverse-engineering the brain and by inventing completely new kinds of computers, with exponentially greater speed and processing power. NOVA Wonders looks at how far we’ve come and where machines are headed as their software becomes ever more…cerebral. How close are we from a world in which computers take over—from diagnosing cancer to driving our cars to targeting weapons? If we place more and more of our lives under the control of these artificial brains, what are we putting at risk?

How does today’s artificial intelligence actually work—and is it truly intelligent?

Can We Build a Brain?

Pxhere.jpg

A two-year analysis of nearly a quarter of a million Twitter users reveals how Americans’ social lives are taking a toll on their sleep schedules.

Scientists Track “Social Jet Lag” Sleeping Habits With Twitter Data

topic-tech-engineering.jpg

Tech & Engineering

Koch Foundation

Draper

<div class="rich-text">Imagine having intense pelvic pain and a constant urge to pee. If you have a urinary tract infection (UTI), these symptoms can hit fast and with force.But a urine culture, the standard lab test to diagnose a UTI, can take up to three days to yield results. For patients desperate for relief, doctors often prescribe antibiotics before getting the lab results back, and in doing so, may prescribe the wrong drug.A new smartphone-based test called smaRT-LAMP, developed by researchers at UC Santa Barbara and Stanford University, could potentially cut out the need for a urine culture altogether. The test uses an app called Bacticount to analyze fluorescent light reflected from treated urine samples. In a study published in the journal EBioMedicine in September, the researchers reported that the app accurately identified and quantified pathogens in urine in just one hour—a far cry from the one to three days it can take for bacteria to grow in a urine culture.</div>
 

 
 <div class="related"></div>
 

 
 <div class="rich-text">“With a smartphone, we now potentially have a diagnostic tool that has no geographic or economic limitations, or certainly far fewer,” says Lynn Fitzgibbons, an infectious disease specialist who consulted on the project.Urinary tract infections are one of the most common bacterial infections seen in primary care in the United States. In 2011, the CDC reported that, based on its 2007 survey of all outpatient visits to doctor&#x27;s offices and hospitals, UTIs accounted for more than 8 million visits. Over 20 percent of those visits were to hospital emergency rooms.Currently, the only rapid urine test available to doctors is urinalysis, which can indicate whether there is an infection but does not identify specific bacteria. Urinalysis also frequently shows false positive results, leading doctors to prescribe antibiotics to patients who don’t need them while they wait for more specific results from a urine culture.“It’s a pretty big issue,” says Huma Farid, a gynecologist at Brigham and Women’s hospital. “If it’s a weekend, [the urine culture results] may not come back until Monday. By that point, a patient is three to four days into their antibiotic treatment.”Taking the wrong antibiotic is probably not going to kill you. But if there are any bacteria in your body that are resistant to the drug, the antibiotic wipes out their competition, allowing the <a href="https://www.pbs.org/wgbh/nova/article/beyond-antibiotics/">resistant bacteria</a> to thrive. These fortified bacteria can then pass on their resistance to other species of bacteria. This means that if you truly need that antibiotic in the future, it may not work. The CDC <a href="https://www.cdc.gov/drugresistance/biggest_threats.html">estimates</a> that approximately 23,000 people die from antibiotic-resistant infections each year.This problem is particularly acute in low-resource settings where patients can’t afford lab tests, or where clinics don’t have access to advanced diagnostic facilities to begin with. The other risk in these settings is detecting a UTI too late, when it can cause a life-threatening condition like sepsis.This is where the smaRT-LAMP system comes in. RT-LAMP is an acronym for an established method that has <a href="https://www.ncbi.nlm.nih.gov/pubmed/19396514">been demonstrated to</a> provide fast, accurate, and inexpensive diagnosis of infectious diseases like the Zika virus in research settings. The study authors are taking RT-LAMP a step further by applying it to urinary tract infections for the first time, and pairing it with an app that will help less-experienced clinicians decipher the results.With Zika, you just need to know whether or not the pathogen is present, explains Laura Lamb, a microbiologist at Beaumont Health in Michigan who has worked on Zika detection using RT-LAMP. When it comes to UTI infections, however, the concentration of pathogens is important.&quot;[The team is] looking at low, medium, high concentration. So that can be somewhat subjective,” she says. “And that&#x27;s where I think it&#x27;s nice that you have the smartphone paired with the [RT-LAMP] standard curve—to kind of evaluate where exactly you are on that low, medium to high concentration scale.”</div>
 

 
 <div class="ad"></div>
 

 
 <div class="rich-text">To analyze a urine sample using smaRT-LAMP, a healthcare provider would prepare the urine with a treatment that will generate a fluorescent signal if bacteria are present. After heating the sample on a hot plate, they would cover it with a cardboard box lined with LED lights and record the reaction through a hole in the top with a smartphone. The Bacticount app analyzes the video to identify and count the pathogens. The app compares the reaction to the standard curve that Lamb mentioned, which is a map of the fluorescent light reflected for several known concentrations of pathogens, to approximate the number of pathogens in the test sample.In the study, the researchers found that the app could identify several different pathogens in bacteria-spiked mouse urine. They also tested urine from 10 human patients who showed symptoms of sepsis. Bacticount accurately detected three different strains of bacteria and matched the culture results from the hospital’s microbiology lab in all 10 cases.“This study was a head-to-head comparison with hospital diagnostics,” Michael Mahan, director of the study, said in an email. “The next step is to determine whether it works in local clinics that cannot afford to ‘ID’ the pathogen.”The low-cost lab kit resembles a DIY project: All you need is a hot plate, a cardboard box, a strip of LED lights, and a tray to hold urine samples. The authors assert that the whole kit can be assembled for under $100, plus the cost of a smartphone.But Jeong-yeol Yoon, a professor of biomedical engineering at the University of Arizona, who developed a test for Zika virus using RT-LAMP, is skeptical of the pricetag. “I think they are actually underestimating the labor,” Yoon says. In his experience, the technology requires a trained technician operating in a sterile environment to avoid contamination of the samples.Lamb says that it just requires taking the right precautions: “They&#x27;re going to have to test it at different sites and so if [contamination] is a concern, it will show up then.”She’s optimistic that RT-LAMP has a future in clinics, and says that there are lots of potential applications for it. “The nice thing about this technology is that it’s really fast,” Lamb says. “Diseases where you have to make a decision really quickly about treatment, where you can’t wait—you can do this in one hour. That’s a huge advantage.”</div>
 

 
 <div class="newsletter"></div>

A new app could help diagnose urinary tract infections by analyzing fluorescent light reflected by urine samples.

Could a Smartphone App Diagnose Your UTI Faster than the Lab?

<div class="rich-text">When Yanira was a teenager, she didn’t feel like she could ask her parents about sex. So like most kids her age, she would try looking up answers to her questions online—only to get stuck looking up the medical terms some sites used, or trying to sort out whether the source was legitimate or not.Now Yanira is 20 years old, and her 15-year-old sister is turning to her with questions. But Yanira doesn’t have to Google anything anymore: She sends her sister to a chatbot named <a href="http://laylasgotyou.com/">Layla</a>, an AI-powered tool built by a public health nonprofit for the girls of Syracuse, New York.“It was a lot better than I could have thought of off the top of my head,&quot; she says of the information provided by Layla.Yanira knew about the site because she had been part of a group of young women to give feedback on the chatbot. She liked the way it responded to questions, and thought it made answers clear. She also liked that it provided links to where to get real-world services in her area.</div>
 

 
 <div class="related"></div>
 

 
 <div class="rich-text">“When I was 15, I didn’t know much about birth control, much less where to get it,&quot; Yanira says.Teen pregnancy is a real problem in Syracuse. For girls between ages 15 and 17, the rate is 21.8 per 1,000, compared to 18.8 in the U.S. as a whole. And for black and Hispanic teenagers, the rates are much higher: 65.6 and 53.3 per 1,000, respectively. A study found that 80 percent of those pregnancies were unplanned.That same research, conducted by Public Good Projects (PGP), the public health nonprofit behind the chatbot, found that a major cause behind the high pregnancy rates was these young women’s limited understanding of sex.“What does a condom look like? They couldn’t tell you,&quot; says Joe Smyser, the CEO of PGP.Finding useful, accurate information can be hard for teens. Random internet searches run the risk of finding incorrect information, while reputable sources can be written in dry language that doesn’t reflect the lived experience of teenage girls. Going to parents isn’t always an option, either.But Layla is available 24/7, and no question is off-limits. As the opening prompt says, Layla wants to be considered “your new best friend with all the deets about those uncomfortable topics like birth control, sex, and STDs.”PGP enlisted a group of young women from Syracuse to help them design the chatbot, which uses IBM’s Watson technology—specifically its natural language processing abilities—to make the chatbot nimbler at answering questions phrased in a variety of ways. Although cutting edge AI powers the site, everything from the resources it recommends, to the language it uses, to its aesthetic look and feel were designed by local teenagers who need it most.“We didn’t want this to come off like a healthcare department,” says Tiffany Lloyd, Layla’s community manager. “It was created by brown girls, for brown girls.”
 <figure class="richtext-image full-width"><img alt="LaylaUsers.jpeg" class="richtext-image full-width" height="533" width="800"
 src="/wgbh/nova/media/images/Unknown_MbIH13c.width-800.jpg"/><figcaption>Jason Wagner, 16, and Deziah Thomas, 15, are two youth influencers that helped to promote Layla, a chatbot designed to help young people of color get reliable information about sexual health, including where to find additional resources and services. Image credit: Tiffany Lloyd, Layla<div class="credit"></div></figcaption>
 </figure>
 <h4>AI for the Globe</h4>The field of public health has the ambitious goal of preventing the spread of diseases and extending life across human populations that span everything from small communities to entire continents. It’s a daunting challenge with new problems constantly cropping up, often with insufficient resources to tackle them. Those challenges have led public health workers to look to AI to find novel ways to solve global health issues like tuberculosis and teenage pregnancy.One of the earliest and best-known attempts to use big data for public health—done before AI research really took off—was <a href="https://en.wikipedia.org/wiki/Google_Flu_Trends">predicting the spread of flu from Google searches</a>. While Google has ended its internally run research, the company has added new AI capabilities and still passes data along so that places like <a href="https://www.sciencedaily.com/releases/2019/01/190111143744.htm">Boston Children&#x27;s Hospital can continue the work</a>. By going through electronic health records, flu-related Google searches, and historical flu activity in a given location, the AI-powered tool has demonstrated the ability to spot flu outbreaks two weeks before the Centers for Disease Control (CDC) can.This approach is not entirely new. Disease surveillance systems now routinely track people researching symptoms in Google searches, as well as more traditional, expensive-to-gather clinical data. But more sophisticated data crunching algorithms exist for marketers: Layla developer PGP, for example, used a tool originally designed to monitor “brand health,” a mix of various metrics companies look at to see how their company is perceived by the public, and repurposed it for tracking opioid use and mental health trends.“No one in academia understands Twitter the way marketers do,” Smyser says.With data from a wide range of traditional and social media sources, including closed captioning from local television, newspapers, Facebook, Twitter, Instagram, and YouTube, PGP is generating new insights into opioid use. But it’s only possible with natural language processing, a subset of AI that parses written or spoken word—like the Layla chatbot does with IBM’s Watson. In this instance, the AI learns to find stories and tweets about opioids, even if slang is used, without having to be taught every different way the drug use might be referenced.Right now, the system picks up about 55,000 references to opioids a day. These might include everything from a tweet from <a href="https://twitter.com/realdonaldtrump/status/1091127643720859649?lang=en">President Trump boasting of a fentanyl bust</a> to Facebook posts of dealers hawking a product. With such a large stream of data, experts are able to conduct a lot of previously out-of-reach analysis. Smyser is sharing what PGP is learning with presentations to agents from the Drug Enforcement Agency (DEA) and community groups, but there are limits to how much small, community-based nonprofits like his can help.“There is no red phone for me to pick up and tell CDC they need to be aware of this,” Smyser says.</div>
 

 
 <div class="ad"></div>
 

 
 <div class="rich-text"><h4>Old Methods, New AI Insights</h4>Other public health AI projects don’t rely on internet data. There are nearly 2 million homeless young people between 13 and 24 in the United States, so researchers out of the University of Southern California’s Center for AI in Society (CAIS) developed software called <a href="https://www.cais.usc.edu/projects/predictive-models-of-vulnerability-and-housing-prioritization/">TAY Triage Tool</a> to combat the problem. It uses information from a short questionnaire to predict which young people are at risk of long-term homelessness, allowing decision-makers to divert precious resources to those most in need.A similar tool called <a href="http://teamcore.usc.edu/people/amulya/healer/index.html">HEALER</a> figures out who in a group of homeless youth is the best person to train about HIV prevention. The traditional approach is to bring in and train the most popular person. But HEALER actually discovers different members of the group with more widespread connections than the most popular individuals, who typically hang out with smaller groups.“We are trying to train gatekeepers to watch over others in their social networks,” says Milind Tambe, founding co-director of CAIS and an engineering professor at USC, who worked on both projects.Sometimes, AI algorithms just confirm best practices and the cost-effectiveness of solutions, always a priority for cash-strapped public health programs. There are over two million individuals with tuberculosis in India, and treating it presents a major challenge: Multi-drug resistant (MDR) tuberculosis is widespread and hard to diagnose at scale.“Finding these strains of tuberculosis is really important,” says Sze-chuan Suen, a professor of engineering at USC.<a href="http://bit.ly/2OmYA1b">GeneXpert</a> is a test that can detect drug-resistant bacteria—but it does at a high cost. Suen did a study on this expensive screening and concluded, despite GeneXpert being costlier in the short term than other methods, it should be used at initial diagnosis for all patients. The benefits of identifying patients with drug-resistant strains of TB should save money—and lives—in the long run.<h4>Part of a Balanced Global Diet</h4>Compared to industries like healthcare or education, public health has not had a massive influx of AI projects. In part, that’s because much of the progress in AI has been around image recognition, which can be most readily applied to individual medical diagnoses. For example, Stanford researchers reported in 2017 that an artificial neural network they built could diagnose dangerous melanomas from photographs as accurately as experienced dermatologists, which could expand access to care in areas with fewer specialists.</div>
 

 
 

<div class='cove-video'>
 <iframe allowfullscreen="" class="inline-cove-player" frameborder="0" height="100%" id="partnerPlayer" marginheight="0" marginwidth="0" scrolling="no" src="//player.pbs.org/partnerplayer/mBsCawbZnOV78ixQxlxyIg==/?topbar=false&amp;end=0&amp;endscreen=true&amp;start=0&amp;autoplay=false" width="100%">
</iframe>
</div>


 

 
 <div class="rich-text">Another factor limiting AI applications is the small budgets and profits to be made in public health, which lowers the likely return on investment in new technologies. And many public health projects go through ethics boards that determine the rules around use of public data and research protocols. If anything, these processes “can be cautious to a fault” and delay approval, according to Henry Kautz, a computer science professor at the University of Rochester.That doesn’t mean AI won’t be utilized in public health in the near future in a significant way. The problems are simply too big, and AI’s potential too enticing, not to try.“There are problems out there waiting for us,” Tambe says.Another challenge in applying AI to public health is the field’s focus on whole populations, not individuals. Unlike other industries that create tools that individuals can put into use—such as <a href="https://www.pbs.org/wgbh/nova/article/does-ai-hold-key-to-new-green-revolution-in-agriculture/">apps for recognizing farm pests</a> or <a href="https://pbs.org/wgbh/nova/article/ai-technology-is-disrupting-the-traditional-classroom/">personalized learning tools</a>—public health demands solutions for broad populations with an inherently wider set of needs. This often means that data and algorithms can only take us so far before humans must get involved.Take foodborne illnesses: AI algorithms can sift quickly through large datasets to spot problems, but that’s only half the battle—government inspectors must then ground truth the findings with in-person inspections.For the chatbot Layla, that means providing phone numbers to real people for further help for search queries that require follow up, like where to get STD testing or services for sexual assault victims. Lloyd, Layla’s community manager, says that more outreach work needs to be done to understand and address issues that are too nuanced for current AI.“Make no mistake, the chatbot is not the end-all,” Lloyd says.Lloyd continues to connect with the young women Layla is meant to serve in focus groups, and makes sure the word gets out with social media campaigns and posters at places like hair salons.“They need an ongoing conversation,” Lloyd says.Still, for underserved populations like teenage girls in Syracuse, AI like Layla has its place and has room to grow. Yanira had suggested other capabilities for the site, like a period tracker. In the meantime, Yanira says she would recommend it to friends and even her two other little sisters.“It is kind of like another big sister,” she says.</div>
 

 
 <div class="newsletter"></div>

Scarce funding, ethical concerns, and other problems have limited the use of artificial intelligence to tackle public health issues. That’s starting to change.

With ‘Problems Out There Waiting for Us,’ Public Health Practitioners Turn to AI for Help

Artificially intelligent face swap videos, known as deepfakes, are more sophisticated and accessible than ever.
Deepfakes are an increasingly popular editing technique that uses artificial intelligence to alter faces and voicesin videos. Part of the name comes from the AI used to create them, deep learning. Similar technology has been used in Hollywood for years, but with AI getting better and more accessible, today they’re all over the internet.
Deepfake Videos Are Getting Terrifyingly RealPublished: April 5, 2019Onscreen: All of these videos...are fake. An increasingly popular editing technique uses artificial intelligence to alter faces and voices. The end product is called a &quot;deep fake.&quot;Deepfake Obama: You see, I would never say these things … at least not in a public address. But someone else would. Someone, like Jordan Peele.Onscreen: Part of the name comes from the AI used to create them...deep learning. And they&#x27;re all over the internet.Collin Frend: It’s a lot more research than you would think that goes into making a goofy video like that. I didn’t do any after-touching, that was just using the technology available from the machine learning side.It’s sort of learning how to re-create a person’s face from looking at thousands of images over and over and over again.Onscreen: Some from of this technology has been used in Hollywood for years, but the AI is getting better and more accessible.Hany Farid: You don’t have to know what Github is, you don’t have to know how to program in Python — none of that matters. You pay somebody 20 bucks and they’ll create the fake for you. That’s a bit of a game changer.Onscreen: But deep fakes of average people could be really damaging.Danielle Citron: For the everyday person who a deepfake sex video emerges in a Google search of your name, for that person it can be almost impossible to debunk.Onscreen: Some worry about the impact a convincing deep fake might have if it&#x27;s strategically timed...like on the eve of the 2020 election.Farid: While I don’t think that’s likely, I don’t think it’s out of the question. And that’s enough to keep me up at night. You don’t have to fool tens of millions of people. The last national election here in the US was 80,000 votes in eight states. The margins are very very thin.Tim Hwang: I don’t think that a deep fake will be a major cause of disruption during the campaign. Think about a well-placed rumor before an IPO. I’m not sure how much machine learning is adding to this. I find it very hard to believe that there is something uniquely devastating about video.Onscreen: The state department is working with experts to identify and combat malicious deep fakes.Farid: Right now we are developing techniques to protect world leaders from deepfakes we are particularly worried about how the Donald Trump, the Theresa May’s of the world, how their likeness will be used to disrupt elections, incite violence by creating fake content. There is a weaponization of the technology and I am simply advocating that we as technologists starting thinking, not after the fact, but before the fact about how we can start putting some safeguards in place.

Artificially intelligent face swap videos, known as deepfakes, are more sophisticated and accessible than ever.

Deepfake Videos Are Getting Terrifyingly Real

<div class="rich-text">Plastic has infiltrated oceans, soils, and entire ecosystems around the globe—in part because 40 percent of plastics are currently produced for single-use packaging only. Yesterday, New York State lawmakers signed a budget <a href="https://www.nytimes.com/2019/03/28/nyregion/plastic-bag-ban-.html">instituting a statewide ban</a> on most types of single-use plastic bags, a decision that may reflect increased awareness of the effects non-biodegradable products like plastic have on the natural world.“These bags have blighted our environment and clogged our waterways,” said New York State Governor Andrew Cuomo in a statement released Thursday afternoon.</div>
 

 
 <div class="newsletter"></div>
 

 
 <div class="rich-text">Traditional plastics are an environmental double whammy in that they have the potential to do harm at both the start and end of their lifecycles. Mainstream varieties like polyethylene terephthalate (PET), used to manufacture common products like plastic bottles, are derived from non-renewable fossil fuels like petroleum and natural gas. Moreover, synthetic plastics are designed to be durable and can take up to hundreds of years to decompose.All stages of plastic’s life cycle, then, are fair game in scientists’ attempts to minimize damage.“We’re seeing this momentum right now in Western societies to change—on a fundamental level—how we&#x27;re using plastics,” says Martin Wagner, an ecotoxicologist at Norwegian University of Science and Technology.<h2>Engineering alternatives</h2>Some experts are looking to nature itself for inspiration. Take, for example, a spider’s silk web. Melik Demirel, a materials science engineer at Penn State University, has used proteins from spider silk to develop natural fibers that could replace synthetic plastic fibers like nylon and polyester. Demirel says spider silk proteins are obvious candidates because spider silk is known for its remarkable strength.But recently, Demirel has found new bio-inspiration. He’s working with the rings of powerful “teeth” that line squid tentacles’ suction cups. Used to grasp their prey, squid ring teeth proteins share properties with spider silk proteins—so Demirel produced those proteins in the lab to develop a strong, degradable new fiber. If his team can scale up the material’s production, it might be able to replace the plastic coating on many synthetic textiles. That’s important because, <a href="https://www.vox.com/the-goods/2018/9/19/17800654/clothes-plastic-pollution-polyester-washing-machine">when put in the washing machine</a>, modern plastics-based clothing can release microplastics into water systems.
 <figure class="richtext-image full-width"><img alt="Loligo_vulgaris.jpg" class="richtext-image full-width" height="600" width="800"
 src="/wgbh/nova/media/images/Loligo_vulgaris.width-800.jpg"/><figcaption>Some scientists are using proteins from the teeth of squid tentacles to develop a strong, degradable new fiber. | Photo credit: Hans Hillewaert / Wikimedia Commons (CC BY-SA 4.0)<div class="credit"></div></figcaption>
 </figure>
 Meanwhile, Geoffrey Coates, a chemical biologist at Cornell University, and his team are exploring ways to create plastic out of carbon dioxide. The gas appealed to Coates as a starting material because it’s commercially available and because industries that release carbon dioxide into the atmosphere could potentially capture the gas and use it to make plastic.“You&#x27;d have to make a lot of plastic to make a dent in the amount of carbon dioxide that we add to the atmosphere,” Coates says. “But there&#x27;s not going to be one solution to carbon dioxide capture. So anything you can do is certainly a good thing.”The most commercially successful bioplastic thus far—a biodegradable plastic made from renewable resources—is made with corn. Called polylactic acid (PLA), it’s not yet strong enough to be practical and remains energy-intensive to produce due to its reliance on agriculture. But researchers like Marc Hillmyer, director of the Center for Sustainable Polymers at the University of Minnesota, haven’t given up hope, and continue to search for the key to making PLA tougher.<h2>Some plastics are here to stay</h2>Unlike single-use plastic bags, some plastic items are unlikely to be banned or replaced anytime soon. Many are even beneficial to ecosystems and human health. Margaret Sobkowicz Kline, a plastics engineer at the University of Massachusetts, Lowell, points to the biomedical and food security sectors, which rely on plastic packaging for sanitation and protection purposes. “I think it&#x27;s important the public realizes we&#x27;re not going to completely get away from plastics altogether,” Sobkowicz Kline says. “And I don&#x27;t think we would want to.”</div>
 

 
 <div class="related"></div>
 

 
 <div class="rich-text">Researchers are also looking for ways to better reuse and recycle these materials. Current plastic recycling often results in what is called “downcycling” because the second-generation product is of a lower quality. Researchers like Hillmyer and Richard Gross, a synthetic chemist at Rensselaer Polytechnic Institute, are exploring ways to break down used plastic into its original, high-value starting material for optimal reuse. They are also testing ways to make different plastics more compatible so they require less sorting and can be recycled <a href="https://to.pbs.org/2U4xbre">more efficiently</a>.“We want to have the materials that we know and love,” Gross says. “But not have things that pollute our planet. So we&#x27;re really interested in the end of life.”<h2>The road ahead</h2>With these innovations on the horizon, it might seem like better materials could hit the market any day now. But it remains challenging to develop materials that are strong, useful, and biodegradable. One barrier is cost: These new products are currently more expensive to produce—especially at the scales needed to make a dent in the plastics industry.</div>
 

 
 <div class="ad"></div>
 

 
 <div class="rich-text">“There has to be a desire or a push to take on the expenses that are required to move these new products up the chain,” Gross says.In 2015, around <a href="https://www.weforum.org/agenda/2018/08/the-world-of-plastics-in-numbers">322 million tons of plastic</a> were produced worldwide. Shifting gears in an industry this massive is no small task. “Plastics are so entangled with our society,” Wagner says. “They are connected to all aspects of our lives and all aspects of the environment.”</div>

As issues with mainstream plastic get more public attention, scientists turn to animals to innovate improvements and potential replacements.

Spiders and Squid Could Help Solve Our Global Plastic Problem

<div class="rich-text">Global communications technology has streamlined the power of suggestion.Behind every <a href="https://projects.propublica.org/facebook-ads/">Facebook ad</a>, <a href="https://www.cnn.com/2019/03/22/tech/twitter-algorithm-political-rhetoric/index.html">Twitter feed</a>, and <a href="https://datasociety.net/output/alternative-influence/">YouTube recommendation</a> is an algorithm that’s designed to keep users using: It tracks preferences through clicks and hovers, then spits out a steady stream of content that’s in line with your tastes. The longer you spend on the platform, the better the algorithm is doing—and the deeper down various rabbit holes you go.Some of these forays are harmless; many end in nothing more than an innocuous kitten video or a list of things that will (and <a href="https://www.pbs.org/wgbh/nova/article/how-does-microwaving-grapes-create-plumes-plasma/?no-cache">definitely won’t</a>) survive your microwave.But others can have more dire consequences. Guided by suggested hyperlinks and <a href="https://www.npr.org/sections/thetwo-way/2017/01/10/508363607/what-happened-when-dylann-roof-asked-google-for-information-about-race">auto-fills in search bars</a>, web surfers are being nudged in the direction of political or <a href="https://www.theguardian.com/media/2019/feb/01/facebook-youtube-anti-vaccination-misinformation-social-media">unscientific propaganda</a>, abusive content, and <a href="https://www.theguardian.com/us-news/2016/dec/05/gunman-detained-at-comet-pizza-restaurant-was-self-investigating-fake-news-reports">conspiracy theories</a>—the same types of ideas that appear to have driven the perpetrators of several mass shootings. That includes the alleged Christchurch gunman, whose white supremacist and Neo-Nazi fascist views appear to have motivated the slaughter of <a href="https://www.nytimes.com/2019/03/14/world/asia/new-zealand-shooting-updates-christchurch.html?module=inline">at least 50 people earlier this month</a>.</div>
 

 
 <div class="ad"></div>
 

 
 <div class="rich-text">It would be an oversimplification to point to any single video, article, or blog and say it caused a real-world hate crime. But social media, news sites, and online forums have given an indisputably powerful platform to ideas that can drive extreme <a href="https://www.theguardian.com/us-news/2018/mar/06/texas-church-shooting-conspiracy-theorists-arrested">violence</a>. Through the work of recommendation algorithms, macabre musings no longer lie only in the darkest, dankest corners of the internet, but instead bask in the limelight of our feeds and queues.These cryptic lines of code certainly play a role in perpetuating hate and misinformation. But are the algorithms truly to blame?“These things aren’t malicious, and they’re not out of control,” says <a href="https://fas.yale.edu/book/new-ladder-faculty-2018-19/social-science/elisa-celis">Elisa Celis</a>, a data scientist studying fairness and diversity in artificial intelligence at Yale University. “But it’s also important to acknowledge that these algorithms are small pieces of machinery that affect billions of people.”<h3>The slippery slope of polarization</h3>To be clear: We do have plenty of reason to be wary of these algorithms. One of the biggest reasons? <a href="https://www.theguardian.com/technology/2018/feb/02/how-youtubes-algorithm-distorts-truth">Only a limited subset of people are privy to what’s actually in them</a>. Companies like YouTube and Facebook have kept pretty mum about the exact contents of their code, Celis explains, but most of these algorithms seem to revolve around one central tenet: maximizing user engagement—and, ultimately, revenue.“These companies are just trying to optimize their metrics,” she says. Clicks and engagement are, after all, what ultimately translate into revenue and success for these entities. “If the platform doesn’t show you things you’re interested in, you won’t come back. So they build their algorithms to learn what your preferences are.”</div>
 

 
 

<div class='cove-video'>
 <iframe allowfullscreen="" class="inline-cove-player" frameborder="0" height="100%" id="partnerPlayer" marginheight="0" marginwidth="0" scrolling="no" src="//player.pbs.org/partnerplayer/zOUHwL8RaeXNzWup5pMK-w==/?topbar=false&amp;end=0&amp;endscreen=true&amp;start=0&amp;autoplay=false" width="100%">
</iframe>
</div>


 

 
 <div class="rich-text">To your garden variety recommendation algorithm, your first dive into a website might present something of a tabula rasa. With nothing to guide its suggestions, a platform will spit out a little of everything: sports, politics, science, kittens. Whatever you do with that content will inform the algorithm’s next move. Over time, as it stockpiles behaviors ranging from the types of articles you share to the amount of time you spend watching a video, the algorithm will start to refine its recommendations.The goal, of course, is to keep you coming back—to keep turning profits. That means filtering out anything you might deem a waste of time until the algorithm has effectively personalized your experience of a platform, showing you, in theory, only the things you’d want to see (it’s worth mentioning that these codes don’t have the capacity to craft a distinct model for each and every user; people are probably getting binned into pools based on preferences and demographics).At first pass, this sounds like a great system. It’s certainly a successful one, as evidenced by the billions of people around the world currently enjoying content online. The problem is, it doesn’t take much for personalization to shapeshift into polarization.This can be a tough distinction to make. Polarization is arguably an extreme form of personalization on topics we’re nervous about, Celis says. Obsessive dives into birdwatching and presidential primaries might plumb the same depths; the potential consequences just don’t carry the same weight.But these algorithms aren’t drawing those kinds of distinctions. They certainly don’t have a conscience that tells them when they’ve gone too far. Their top priority is that of their parent companies: to showcase the most engaging content—even if that content happens to be disturbing, wrathful, or factually incorrect.</div>
 

 
 <div class="related"></div>
 

 
 <div class="rich-text">In other words, what’s good for a social media giant isn’t always in line with what’s good for an individual, says <a href="https://www.math.upenn.edu/~hads/">Hadi Elzayn</a>, a computer scientist studying algorithmic learning theory at the University of Pennsylvania.Elzayn compares the process to craving junk food. Asked point blank, most people would say they want to eat a balanced diet. But broccoli doesn’t give us the same high that sugar, fat, and salt do; that’s why most of us will still reach for cookies after waxing eloquent about cutting calories. Every time the broccoli goes untouched, the platform learns that it’s a safe bet to just fill your plate with cookies instead, rather than wasting time and space with more diverse options—your future stomachache notwithstanding. These algorithms aren’t tabulating your long-term health goals. They’re catering to your immediate gratification.“This principle is often called ‘revealed preference’ in economics,” Elzayn says. “If you want to maximize engagement, or make money, you should follow what someone does, not what they say they’ll do.”From here, it’s not a far leap to start to sway someone’s opinion, says <a href="http://www.cs.yale.edu/homes/vishnoi/Home.html">Nisheeth Vishnoi</a>, a computer scientist specializing in the intersection of artificial intelligence, ethics, and society at Yale University. Over time, the stuff that initially sated inherent cravings might no longer do the trick. Algorithms learn to lure us in with sweeter, fattier, saltier foods—or more radical content—whatever continues to elicit that primal response.Preferences also become reinforced by familiarity, Vishnoi says. As a person gathers more information on a topic, that knowledge can start to bolster a sense of camaraderie or empathy, he says. Viewpoints get closer, and the sense of connection grows.“Hate is a relative thing,” he says. “If you see it from a hundred kilometers, it’s different than when you’re right in the middle of it…this may be one reason people are becoming more extreme without even realizing it.”<h3>Behind the curtain</h3>All this paints a pretty grim picture. But the algorithms themselves aren’t the problem—not exactly.“People tend to anthropomorphize these algorithms, but they’re not something sentient,” says <a href="https://merbroussard.github.io/about/">Meredith Broussard</a>, a data journalist who specializes in the <a href="https://www.amazon.com/Artificial-Unintelligence-Computers-Misunderstand-World/dp/0262038005">ethics of artificial intelligence</a>. “These are all processes happening inside a dumb box.”After all, these algorithms can only steer users in the direction of content that’s already hosted on their sites—most of which simply aren’t set up to reliably present unbiased, factual news. “There’s a huge disconnect between what people think these platforms are and what they actually are,” says <a href="https://safiyaunoble.com/">Safiya Noble</a>, a communications expert who studies <a href="https://www.amazon.com/gp/product/1479837245/ref=dbs_a_def_rwt_bibl_vppi_i0">how digital media platforms affect society</a> at the University of Southern California. “People are accustomed to thinking these platforms are reliable, trustworthy news sources. What they really are is large-scale advertising platforms.”
 <figure class="richtext-image full-width"><img alt="iStock-926131762.jpg" class="richtext-image full-width" height="534" width="800"
 src="/wgbh/nova/media/images/iStock-926131762.width-800.jpg"/><figcaption>Recently, Facebook has come under fire over privacy concerns and its mishandling of fake news and extremist content. Image Credit: amesy, iStock<div class="credit"></div></figcaption>
 </figure>
 Virality doesn’t really come about organically. Paid-for advertisements, boosting options, and more have rejiggered the landscape of content visibility on social media websites. “This flies in the face of what many people think—that these are free speech zones, that everything has an equal chance of being seen and known,” Noble says. “That’s just not true.”Unfortunately, visibility and truth don’t always go hand in hand.Amid growing concerns about misinformation, <a href="https://www.theguardian.com/media/2019/feb/01/facebook-youtube-anti-vaccination-misinformation-social-media">both Facebook and YouTube</a> have vowed to curtail the spread of false and provocative messages on their platforms, The Guardian reported last month. But with content streaming onto to platforms at unprecedented rates, there’s simply no real capacity to monitor every individual post, never mind how it’s weighted or ranked in the feeds of billions of users. Additionally, these algorithms are far from foolproof: It’s become pretty trivial to engineer fraudulent clicks and views to inflate the amount of screen time an idea receives.And the barriers to entry are far lower than those to removal. Policing, flagging, and removing the overwhelming deluge of extremist and radical content is about as fun as it sounds. The people who hold these jobs are “looking at the worst kinds of filth every day,” Broussard says.With so many loopholes, it’s no wonder “garbage floats to the top,” Broussard says.<h3>Taking back the reins</h3>There’s no silver bullet for the issues at hand. But that doesn’t mean we’re out of options.One potential avenue forward might involve placing limits on algorithms, Celis says. Current iterations don’t have a clear cap on the amount of personalization—or polarization—that feeds can offer.If a user’s political leanings tend leftward, for instance, that person’s feed might eventually be dominated entirely by liberal content. Even if that’s in line with the individual’s interests, this presents a pretty skewed version of reality. Algorithms of the future, however, might be able to maintain more diversity in what users see by occasionally slipping a few more moderate articles into the queue.Celis, Vishnoi, and their colleagues at Yale are currently <a href="https://dl.acm.org/citation.cfm?id=3287601">putting these ideas to work</a>. Admittedly, it’s not a perfect system: There’s no guarantee that users will take the bait. But it all boils down to perspective. The simple act of showing that the other viewpoint still exists—and might just have some valid points to make—could go far.Before such algorithms hit the market, though, society will need to weigh in on how they can be best put into action (if at all). The most effective way to present “alternatives” is also still up for debate, Vishnoi says. Presented in the wrong way, or in too high a proportion, they could easily backfire and end up reinforcing already extreme views.All this means that addressing our algorithm woes will require more tweaking a few lines of code. Social inequality isn’t a problem that will be solved with an app—and algorithms don’t operate in a vacuum. The best solutions will also require rethinking the larger environment in which these technologies operate. As such, curbing the promotion of extremist ideologies will require partnering computational perspectives with diverse views from the fields of sociology, psychology, public policy, and more.Many of the most important discussions are yet to come, but Broussard, Noble, and Vishnoi are among those that agree that the companies that host these algorithms must continue to be held accountable. Drugs and other new technologies that carry potential risks must undergo substantial vetting and safety testing before they’re allowed to hit the market, Noble says, but so far, the same hasn’t applied to algorithms—even though they affect billions around the world. “We expect that companies shouldn’t be allowed to pollute the air and water in ways that might hurt us,” she says. “We should also expect a high-quality media environment not polluted with disinformation, lies, propaganda. We need for democracy to work. Those are fair things for people to expect and require policymakers to start talking about.”</div>
 

 
 

<div class='cove-video'>
 <iframe allowfullscreen="" class="inline-cove-player" frameborder="0" height="100%" id="partnerPlayer" marginheight="0" marginwidth="0" scrolling="no" src="//player.pbs.org/partnerplayer/usIAM8FQ6DrE7jEOZSXV5g==/?topbar=false&amp;end=0&amp;endscreen=true&amp;start=0&amp;autoplay=false" width="100%">
</iframe>
</div>


 

 
 <div class="rich-text">It’s clear that the spread of misinformation was an unintended consequence of the deployment of algorithms to maximize engagement. Social media platforms—very understandably—followed the money trail. But a lack of foresight doesn’t absolve these companies of culpability, Noble says.Policies won’t change overnight. In the meantime, though, it’s worth remembering that we’re not powerless against these algorithms—nor are we beholden to ingest only the content they serve up. We all have agency over what we look at, or don’t look at, online. Being more deliberate about these behaviors and expanding the breadth of content we engage with could even send a message to an algorithm, Celis says. Or, at the very least, offer a fresh perspective on a familiar topic. (And for those who’d rather not feed the beast, there are ways to <a href="https://www.wired.com/story/facebook-privacy-apps-ads-friends-delete-account/">adjust your settings on social media platforms like Facebook</a> for maximum privacy.)Artificial intelligence has come a long way, but there’s still a lot it can’t do. Search and suggestion algorithms can certainly come in handy when you’re looking up a state capital or converting pounds to kilograms. But relying on these platforms for more complex queries—those that engage empathy, open-mindedness, and instinct—is an exercise in futility, Noble says.Learning and the acquisition of knowledge inevitably go beyond the limits of social media and search engines. People should acknowledge these limitations and accept that technology is not a panacea for ignorance, she says. Only then can humanity be emboldened to write itself back into the conversation.After all, these algorithms didn’t just invent themselves. At the end of the day, the next move is ours.</div>
 

 
 <div class="newsletter"></div>

Originally designed to drive revenue on social media platforms, recommendation algorithms are now making it easier to promote extreme content. Addressing this problem will require more than a technical fix.

Radical Ideas Spread Through Social Media. Are the Algorithms to Blame?

A system of small cameras can help park rangers identify possible poachers.
TrailGuard AI, developed by Intel and conservation nonprofit RESOLVE, is a system of small cameras that can help park rangers identify possible poachers quickly, without human image analysis.
This AI Can Help Park Rangers Identify PoachersPublished: March 21, 2019Onscreen: Artificially intelligent technology is hidden in these treesEric Dinerstein: I would have put it here. On this tree here? Yeah, OK I see it.Onscreen: This tiny camera can do more than record. It can analyze what it sees. Scientists hope that it will save the lives of endangered elephants and rhinos in Africa.Dinerstein: We’re able to detect humans that could be poachers using inference, doing the analysis on the chip itself in the camera, identify them as humans, and send those images to park rangers who can stop them before they kill.Onscreen: Large remote areas are difficult for law enforcement to monitor. Poachers take advantage of this. They kill 20,000 - 30,000 African elephants every year.Dinerstein: Time is running out. We’re losing elephants at a phenomenally scary rate. Most parks in Africa only have a very small staff or almost skeletal staff, but TrailGuard can be their eyeballs on the trail.Anna Bethke: These poachers are often coming in with a lot of weapons, so AK-47s, etc, so then, the rangers can see this before they go out into the field and know what is the type of response that is necessaryOnscreen: The camera is in sleep mode until it detects movement. Then it wakes up and takes several photos. It uses artificial intelligence (AI) to determine if any of those images contain a human.Bethke: It mimics the processes that are happening in the brain. We feed this deep learning network, this neural network, a bunch of sample data sets. So in this instance, we’re giving it a bunch of images of poachers out in the wild and saying, “These are were the poachers are.”Onscreen: Gradually, the camera’s AI learns to recognize humansBethke: It’s able to say, “Oh yeah, I see something that’s very tall, has two arms potentially, a face.” And is able to learn those features by itself and make that inference with a fairly high accuracy.Onscreen: When the camera detects a human, it sends an alert. From there, it’s up to the rangers to stop the poachers.Dinerstein: This is the first time that we’re using artificial intelligence on the chip itself to identify humans, or human-like objects that might be intruders into a national park. It’s no longer random patrolling. It’s no longer hit or miss. They are only going out to respond to credible evidence that there are poachers coming into the park.Onscreen: TrailGuard AI was created by the non-profit RESOLVE in collaboration with Intel. The cameras will be deployed in the Serengeti in April.

A system of small cameras can help park rangers identify possible poachers.

This AI Can Help Park Rangers Identify Poachers

Without it, you wouldn&#x27;t be able to watch this video.
The World Wide Web revolutionized the way we use the internet. Before it existed, the internet was mostly used by scientists and engineers to share information.
The World Wide Web Celebrates its 30th BirthdayPublished: March 20, 2019Onscreen: The World Wide Web is 30 years old. In 1989, engineer and computer scientist Tim Berners-Lee wrote a proposal to link and share information across different computers.Ben Segal: We all dreamt this could be done, but I must say, when I saw his proposal I didn’t think this was going to do it.Onscreen: Before the web, the internet was mostly text primarily used by scientists and researchers.Segal: In those days, all the computers were different. The data inside of them was all jumbled up in different ways. It seemed to be impossible to get everybody to agree enough to make this work. He was right, though. He was right. They did agree on a few simple things. It was a miracle.Onscreen: The web would open the internet up to everyone. It allowed anyone to access information online.Ayanna Howard: The internet, like connections, it existed but it was big businesses, government and things like that whereas the World Wide Web was basically making the internet accessible to the world.Onscreen: The first website went live in 1991, paving the way for everything from Amazon to cat videos.Maria Klawe: At its best, what I think it is is an opportunity to share just a vast array of ideas, information, stories, just everything and share it very broadly.Segal: We dreamt that we would connect the world and we did, but we connected the bad stuff as well. I regret that the dark internet is such a size. We’re hoping that some smart young people will get together and solve some of these problems. It’s very, very easy to destroy and it’s very, very hard to build, particularly when things are chaining so quickly.Howard: Now the World Wide Web has really equalized the playing field for access to knowledge. Anyone who has access can have a better life, can have better opportunities.Klawe: Happy birthday, World Wide Web! I’m so proud of you.Howard: Happy birthday, World Wide Web! Without you, I think our lives would be horrible.Segal: Happy birthday to the web. Happy birthday to Tim and the pioneers who believed that we could connect the world and we did.

Without it, you wouldn&#x27;t be able to watch this video.

Topic: Tech & Engineering

Hero Feature