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Will This New Development in CGI Skin Overcome the Uncanny Valley?

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Computer graphics populate the levels of your favorite video games and help turn actors into superheroes. Bad CGI can derail a project and distract the audience, but when done skillfully, it can seamlessly fill gaps and enhance the overall viewing experience. A recent breakthrough in creating CGI skin by researchers at the USC Institute for Creative Technologies and Imperial College London is changing the game, and will most certainly raise the bar for what can be considered “realistic.”

By developing a “10-micron resolution scanning technique” to capture very subtle skin microstructure deformations, the researchers were able to translate the tiniest movements in the skin and pores into usable data. The data was then used to manipulate the CGI character’s artificial flesh, resulting in rendered skin that stretches and compresses in ways that are more nuanced and realistic than ever before. The next step would be to study and use this technique to mimic various emotions, as well as differences in expression across age, race, and gender.

This is a major development, and one that computer graphics designers have been building towards for decades. Back in 1992, the makers of the film Death Becomes Her used CGI skin software (paired with silicone and animatronics) in various scenes and took home the Academy Award for Best Achievement in Visual Effects. Fast forward to 1997, and Pixar was pushing the envelope with CGI textures and skin with Geri’s Game, a short that also snagged an Academy Award. 

As the years progressed, the software seemed to peak at a point where CGI skin looked real, but not photo-real (this scene in The Matrix Reloaded is a good example). Attempts to make actors seem younger or to help them achieve inhuman feats with “digital cosmetic enhancements” was impressive, but still unnatural and a little weird. This CGI sits firmly in what is often called the “uncanny valley,” a step just shy of photorealism that evokes a negative emotional response. 

Is it possible that this study could be the key to escaping that uncanny valley? Check out the video below, which explains the study and its findings, and head to the project website to read the technical paper in full.

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11-Year-Old Creates a Better Way to Test for Lead in Water
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In the wake of the water crisis in Flint, Michigan, a Colorado middle schooler has invented a better way to test lead levels in water, as The Cut reports.

Gitanjali Rao, an 11-year-old seventh grader in Lone Tree, Colorado just won the 2017 Discovery Education 3M Young Scientist Challenge, taking home $25,000 for the water-quality testing device she invented, called Tethys.

Rao was inspired to create the device after watching Flint's water crisis unfold over the last few years. In 2014, after the city of Flint cut costs by switching water sources used for its tap water and failed to treat it properly, lead levels in the city's water skyrocketed. By 2015, researchers testing the water found that 40 percent of homes in the city had elevated lead levels in their water, and recommended the state declare Flint's water unsafe for drinking or cooking. In December of that year, the city declared a state of emergency. Researchers have found that the lead-poisoned water resulted in a "horrifyingly large" impact on fetal death rates as well as leading to a Legionnaires' disease outbreak that killed 12 people.

A close-up of the Tethys device

Rao's parents are engineers, and she watched them as they tried to test the lead in their own house, experiencing firsthand how complicated it could be. She spotted news of a cutting-edge technology for detecting hazardous substances on MIT's engineering department website (which she checks regularly just to see "if there's anything new," as ABC News reports) then set to work creating Tethys. The device works with carbon nanotube sensors to detect lead levels faster than other current techniques, sending the results to a smartphone app.

As one of 10 finalists for the Young Scientist Challenge, Rao spent the summer working with a 3M scientist to refine her device, then presented the prototype to a panel of judges from 3M and schools across the country.

The contamination crisis in Flint is still ongoing, and Rao's invention could have a significant impact. In March 2017, Flint officials cautioned that it could be as long as two more years until the city's tap water will be safe enough to drink without filtering. The state of Michigan now plans to replace water pipes leading to 18,000 households by 2020. Until then, residents using water filters could use a device like Tethys to make sure the water they're drinking is safe. Rao plans to put most of the $25,000 prize money back into her project with the hopes of making the device commercially available.

[h/t The Cut]

All images by Andy King, courtesy of the Discovery Education 3M Young Scientist Challenge.

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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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