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Science Channel

The Science Behind the Nearly Escape-Proof Rooms in 'Race to Escape'

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Science Channel

Inspiration can strike at the oddest times. For producer and psychologist Riaz Patel, that time was during the blackout in New York City in 2003. “It was this bizarre situation where you were with people you didn’t know trying to accomplish certain things and figure out solutions to everyday problems,” he tells mental_floss. “That’s where I thought, ‘There’s something about working with people you don’t know, in situations you’ve never been in, that could be a really interesting basis of some sort of a show.’” A couple of years of rumination later, and the idea developed into his latest series, Race to Escape. The game show, which premieres tomorrow on the Science Channel and is hosted by Jimmy Pardo, pits two teams of strangers against each other—and a clock—in an attempt to escape a room by working together to find clues and solve puzzles. Each week, there are two new teams, locked in two new rooms, with five bolts separating them from a grand prize of $25,000. The game, Patel says, had to be challenging, but winnable—“because if it hadn’t been winnable, then the audience would feel it and they would just turn the channel.”

The designs of the rooms run the gauntlet from a study to an auto mechanic’s shop. Picking the environments happened during a “very long brainstorming day,” Patel says, and each one had to fit very specific criteria. They couldn’t be places that were “so foreign that someone wouldn’t know where to start,” Patel says. “Putting them in sort of a weird crypt that’s set in Mesopotamia would be very, very hard, because they’d go on like, ‘We don’t even know where we are.’” So they stuck with places that would be familiar to people, among them a barber shop, a neighborhood bar, a study, and a Chinese restaurant. The rooms also had to be tactile and big enough to fit multiple people and give them space to move around. (One idea that didn’t make the cut for this very reason? An elevator.)

Next up: creating the challenges. Like the environments, the challenges had to meet certain guidelines. First, Patel and the show’s team wanted them to be in line with the theme of the room. “They’re all very, very organically connected to the environment,” he says. “A challenge that you find in the neighborhood bar would be different than a challenge you would find in the auto garage.” The challenges needed to be big enough so the audience could see what was happening, and doable in the amount of time allotted. They also needed to be equal parts brainy and physical. “We’d call them MacGyver challenges,” Patel says. “They’d have to physically do things as opposed to just sitting and figuring things out just in their head. That’s not good TV.”

The hardest part of designing the challenges, Patel says, was “keeping the contestants on a course so they couldn’t jump from clue one to clue four.” A tough thing when some clues were hidden in plain sight: “We would be so nervous: What if they happened to look under this rug? Then they’d see something that they’re not supposed to see yet. So it really has to be very well designed—they’re only given as much information as they need to solve that challenge.”

When the challenges were done, the art department created another layer that producers called the “red herring path”: Things that made sense for the environment but weren’t necessarily connected to the puzzles. “That’s something that we really went back and forth on,” Patel says. “How to streamline those rooms so that they feel like real environments, but still don’t have too much that it would be distracting and hard to move forward—that was a balance we had to find.” 

Once the teams are locked inside, there’s no communication between the producers and the contestants, so each room, and the puzzles it contained, didn’t just have to be carefully designed—they also had to be thoroughly tested. Individual challenges were tested eight to 10 times, then assigned to certain rooms, at which point, the room “was tested five times from start to finish just to make sure that we didn’t have any issues,” Patel says. “We’d have a target, and then we would see if the testers would veer off course and make adjustments,” which included details as small as the size and type of font used for the clues. They never had to throw out a challenge, Patel says, just adjust the amount of information given: “We had to troubleshoot a million things before we could actually lock that door.” 

All told, Patel says, “hundreds and hundreds of hours went into every room.” Each room was built in four days, tested, and then filmed in for 60 minutes. That night, the crew would strip the room and start over. “I’ve never done a show where I had to throw everything out after an episode and start from scratch,” Patel says. “That was a challenge.” 

The show isn’t just a game: There’s a heavy dose of science, too. As the contestants are trying to solve the puzzles, Pardo is offering scientific explanations for their behavior. “I didn’t want people to look at it and just assume that human behavior is random,” Patel says. “There are certain stresses and factors that are going into their behavior—in certain rooms, the heat would even go up. In retrospect, we could certainly look at the influences on the contestants and explain to the audience this is what’s happening—this is what they’re feeling physiologically, this is what’s preventing them from seeing the solution to a problem.”

For Patel, whose first job was at a mental institution and who graduated with a triple major from the University of Pennsylvania (where he also won a medal from The National Psychology Honor Society), the most fascinating part of the game show was, perhaps, the behavior of the contestants once they were locked in the rooms. “You cannot predict human behavior. You cannot,” he says. “I think there’s a real difference between who you project you are and who you really are. You have no history with these people, and no history with this room. What comes out of you organically is a bit different than your normal day-to-day. People who say ‘I'm a diehard leader,’ they get into the room, and suddenly they are terrified and they are a follower. Or someone who says ‘I'm amazing at puzzles,’ and in that moment they cannot figure out the simplest things. I feel like this game really shows you authentically because you have no time to prep. All you can do is react—and I love that.”

Race to Escape premieres July 25 at 10/9c on the Science Channel.

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Google's AI Can Make Its Own AI Now
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iStock

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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Land Cover CCI, ESA
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Afternoon Map
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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