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How You Hold Your Phone Reveals Whether You're Left- or Right-Brained

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In 1861, Pierre Paul Broca, a French surgeon, had a patient he referred to as “Tan.” After an accident, the patient had become aphasiac, meaning he could not speak. The only word he could utter was "tan" (think Hodor in Game of Thrones). Broca hypothesized that an area on the left side of the brain controlled speech, and when it sustained damage, people couldn’t speak (now called the Broca’s area).

This early discovery led to the theory that different hemispheres of the brain, left and right, controlled various functions, such as speech or logic. And this evolved into the idea that the dominant side of the brain affected personality characteristics—left-brain people are thought to be more analytic, objective, and logical, while right-brain people are believed to be more creative and insightful. Now, researchers have discovered that they can determine whether someone is a left- or right-brain person by looking at how they use their cell phones. 

Researchers at the Henry Ford Medical Center at Detroit wondered why people held their cell phones on one particular ear, and suspected that being left- or right-brained influenced it—between 70 and 95 percent of the population is right-handed and, of these people, 96 percent are left-hemisphere dominant.

The researchers asked 717 subjects to fill out an online survey, which determined their hemispheric dominance (left or right) and how they used their cell phone. Of the subjects, 90 percent were right handed and 9 percent were left handed. The researchers found that 68 percent of the righties held their phones to their right ear and 25 percent to the left, while 7 percent couldn’t commit to a side. Seventy-two percent of southpaws held their phones to the left side, 23 to the right side, and 5 percent had no preference. The average cell phone usage amounted to about 540 minutes per month over the past nine years.

The researchers note that there is a 73 percent association between hand dominance and the side people hold their cell phones—so how we use our cell phones allows people to predict whether someone is right- or left-brain dominant. While understanding this connection could lead to better cell phone design, the researchers believe it will help them better understand the relationship between mobile phones and brain tumors and help them improve brain imaging techniques.

"Our findings have several implications, especially for mapping the language center of the brain," says Michael Seidman, director of the division of otologic and neurotologic surgery at Henry Ford and one of the authors of the paper.

"By establishing a correlation between cerebral dominance and sidedness of cell phone use, it may be possible to develop a less-invasive, lower-cost option to establish the side of the brain where speech and language occurs.”      

The paper appears in JAMA Otolaryngology—Head & Neck Surgery.

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Google's AI Can Make Its Own AI Now
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Artificial intelligence is advanced enough to do some pretty complicated things: read lips, mimic sounds, analyze photographs of food, and even design beer. Unfortunately, even people who have plenty of coding knowledge might not know how to create the kind of algorithm that can perform these tasks. Google wants to bring the ability to harness artificial intelligence to more people, though, and according to WIRED, it's doing that by teaching machine-learning software to make more machine-learning software.

The project is called AutoML, and it's designed to come up with better machine-learning software than humans can. As algorithms become more important in scientific research, healthcare, and other fields outside the direct scope of robotics and math, the number of people who could benefit from using AI has outstripped the number of people who actually know how to set up a useful machine-learning program. Though computers can do a lot, according to Google, human experts are still needed to do things like preprocess the data, set parameters, and analyze the results. These are tasks that even developers may not have experience in.

The idea behind AutoML is that people who aren't hyper-specialists in the machine-learning field will be able to use AutoML to create their own machine-learning algorithms, without having to do as much legwork. It can also limit the amount of menial labor developers have to do, since the software can do the work of training the resulting neural networks, which often involves a lot of trial and error, as WIRED writes.

Aside from giving robots the ability to turn around and make new robots—somewhere, a novelist is plotting out a dystopian sci-fi story around that idea—it could make machine learning more accessible for people who don't work at Google, too. Companies and academic researchers are already trying to deploy AI to calculate calories based on food photos, find the best way to teach kids, and identify health risks in medical patients. Making it easier to create sophisticated machine-learning programs could lead to even more uses.

[h/t WIRED]

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European Space Agency Releases First High-Res Land Cover Map of Africa
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Land Cover CCI, ESA

This isn’t just any image of Africa. It represents the first of its kind: a high-resolution map of the different types of land cover that are found on the continent, released by The European Space Agency, as Travel + Leisure reports.

Land cover maps depict the different physical materials that cover the Earth, whether that material is vegetation, wetlands, concrete, or sand. They can be used to track the growth of cities, assess flooding, keep tabs on environmental issues like deforestation or desertification, and more.

The newly released land cover map of Africa shows the continent at an extremely detailed resolution. Each pixel represents just 65.6 feet (20 meters) on the ground. It’s designed to help researchers model the extent of climate change across Africa, study biodiversity and natural resources, and see how land use is changing, among other applications.

Developed as part of the Climate Change Initiative (CCI) Land Cover project, the space agency gathered a full year’s worth of data from its Sentinel-2A satellite to create the map. In total, the image is made from 90 terabytes of data—180,000 images—taken between December 2015 and December 2016.

The map is so large and detailed that the space agency created its own online viewer for it. You can dive further into the image here.

And keep watch: A better map might be close at hand. In March, the ESA launched the Sentinal-2B satellite, which it says will make a global map at a 32.8 feet-per-pixel (10 meters) resolution possible.

[h/t Travel + Leisure]

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