A nerdy father of two, a husband of a beautiful and understanding wife, an engineer who loves anime and tinkering with PCs and games, but most of all, loves God.
We love to experience the world in new and exciting ways, and have no problem spending a little money to do it. Some people, with big wallets, like playing with the latest and greatest toys and gadgets, while others don’t mind waiting for the price to drop or build it themselves. If you aren’t adept in the waiting or building department though, it might just be better to save and pay up.
While you could definitely build yourself a paper airplane at home and have a modicum of fun, it won’t compare to the PowerUp FPV Paper Airplane. You’ll still have to build the paper airplane part, but this kit will let it fly for 10 minutes, and give you a bird’s eye view of the flight. On top of that, using the included Google cardboard FPV viewer, you’ll be able to control the direction of the plane with your head movements alone.
This uses a micro USB for charging, comes with 2 spare propellers, has 8 template sheets for making your plane, a spare rubber bumper, sense cleaning swab, 550 mAh Lipo battery, and a smartphone protection strap. You’ll be able to live stream and record your flight through your iOS or Android smartphones, where you’ll also be able to steer manually if you prefer. This isn’t a cheap toy at $199.99, but you’re an adult, and you can spend your money how you want.
Banishing trans fats from foods links to reductions in the number of heart attacks and cardiovascular deaths in the years after the bans are implemented, according to data from cities and counties in New York that have made the cut.
After three years, the areas banning trans fats from eateries seemed to have an extra  6.2 percent reduction in heart attacks and strokes compared with those that didn’t, researchers report in JAMA Cardiology. Last year, other researchers reported in the Journal of Health Economics that the New York bans appeared to cut deaths from cardiovascular disease by 4.5 percent—that is, they spared about 13 lives from cardiovascular deaths per 100,000 people each year.
While the decade of bans that have gone into effect in the state offer “natural experiments†on how cutting out trans fat may affect health, the results back up a slew of older studies—animal, controlled trial, and observational studies—that found harms of trans fats, plus benefits of ousting them from people’s diets.
In June of 2015, the Food and Drug Administration ruled that trans fats were no longer considered generally safe. Food manufacturers have until June 18, 2018 to ditch partially-hydrogenated oils, the source of most industrial trans fats.
Trans fats—or trans fatty acids—are a type of unsaturated fat found rarely in nature (sometimes there’s a little in animal products, for instance). But they’re found readily in processed foods. Manufactures figured out a while back that they’re a good way to make liquid fats into solid fats at room temperature—think transforming vegetable oils into a block of margarine.
The way this works is all about manipulating the tails of carbons that hang off all fats. Saturated fatty acids, found in meats, cheeses, and butter, have carbon tails that are straight. That is, their chains lack double bonds between the carbons, freeing the carbons to instead bond to their max amount of hydrogens; they’re saturated with hydrogens. This helps them have useful properties for foods, like being a solid instead of a liquid at room temperature. Unsaturated fatty acids, on the other hand, have kinky carbon tails with double bonds and fewer hydrogens.
By blasting unsaturated fatty acids in vegetable oil with hydrogen, manufacturers can make them into solid fats. But, as a side effect, they also create a weird form of unsaturated fatty acids. These still have double bonds between their carbons, but those bonds are oriented in such a way that allows the tails to be straight-ish.
These straight-ish unsaturated fatty acids in the partially hydrogenated oils are the trans fatty acids. They can be found in all sorts of junk food, fast foods, prepacked baked goods, ready-made frostings, margarine, premade doughs that often come in tubes, coffee creamers, and fried foods in restaurants.
A straight diet
Yet, they are bad for human health. Studies have found that artificial trans fats reduce high-density lipoprotein cholesterol levels in the blood, which have been linked to positive health effects, while increasing low-density lipoprotein cholesterol levels that are linked to bad health effects. Artificial trans fats are also linked to systemic inflammation markers and causing dysfunction in cells lining our organs. Eating them is linked to higher risks of stroke, heart disease, and sudden cardiac death. (The same isn’t true for eating the tiny amounts of natural trans fats found in some animal products.)
In 2007, New York City went ahead and restricted artificial trans fats in food served in eateries, that is restaurants, bakeries, catering, mobile vending machines, street-fair food booths, etc. (But not prepackaged foods). Several counties in the state followed suit in the following years. Together, they offer a glimpse at the potential health effects that will follow the nation-wide restrictions in 2018.
In the latest study, which followed the data showing that the bans cut cardiovascular deaths, researchers wanted to look at non-fatal heart attacks and strokes. Scanning health records and other demographic data, the researchers compared rates of heart attack and stroke among 11 areas with restrictions to those of 25 areas without restrictions.
While rates of stroke and heart attacks were on the decline throughout the state, those with trans fat restrictions saw slightly sleeper declines. When the researchers clumped the heart attack and stroke rates together, they found that the areas with restrictions for three years or more had an extra 6.2 percent reduction that was statistically significant. That’s about 43 fewer events per 100,000 people. Individually, there was a 7.8 percent reduction in heart attacks, and a non-statistically significant 3.6 percent reduction in strokes.
In coming up with those numbers, the researchers did their best to control for variables, such as age, race, gender, income, age-adjusted mortality, and how urban each area was. They even estimated each area’s commuters to make sure that people living in one county and eating in another wouldn’t throw off the calculations. Last, they ran their calculations with and without New York City, which had other big health campaigns going on that could have skewed the results. The city didn’t affect the overall results, though, the researchers found.
Of course, the researchers couldn’t control for everything. Trans fats were still in packaged foods. The researchers don’t know what people were eating exactly or even how faithful restaurants were about following the restrictions. However, other studies found that New Yorkers did get less trans fats after the restrictions and that restaurants did generally follow the restrictions. And, the authors note, some fast foods had enough trans fat in a single serving that cutting just that serving could drive health effects. Eating just 2 grams a day is associated with risks.
Yet, “for example, a large order of Popeye’s Louisiana Kitchen cajun fries contains 3.5 g of TFAs [trans fatty acids] per serving, Taco Bell’s Cinnabon Delights (12-pack) contain 2.0 g of TFAs per serving, and multiple varieties of Pillsbury Shape sugar cookies contain 2.5 g of TFAs per serving,†the authors write. (The amount of trans fats in a packaged food can be found on the nutrition label)
Overall, the data falls in line with mounting evidence that the upcoming nationwide restrictions are a good thing.
An AI contemplates its own biases in the movie Ex Machina.
UPI
Ever since Microsoft’s chatbot Tay started spouting racist commentary after 24 hours of interacting with humans on Twitter, it’s been obvious that our AI creations can fall prey to human prejudice. Now a group of researchers have figured out one reason why that happens. Their findings shed light on more than our future robot overlords, however. They’ve also worked out an algorithm that can actually predict human prejudices based on an intensive analysis of how people use English online.
The implicit bias test
Many AIs are trained to understand human language by learning from a massive corpus known as the Common Crawl. The Common Crawl is the result of a large-scale crawl of the Internet in 2014 that contains 840 billion tokens, or words. Princeton Center for Information Technology Policy researcher Aylin Caliskan and her colleagues wondered whether that corpus—created by millions of people typing away online—might contain biases that could be discovered by algorithm. To figure it out, they turned to an usual source: the Implicit Association Test (IAT), which is used to measure often unconscious social attitudes.
People taking the IAT are asked to put words into two categories. The longer it takes for the person to place a word in a category, the less they associate the word with the category. (If you’d like to take an IAT, there are several online at Harvard University.) IAT is used to measure bias by asking people to associate random words with categories like gender, race, disability, age, and more. Outcomes are often unsurprising: for example, most people associate women with family, and men with work. But that obviousness is actually evidence for the IAT’s usefulness in discovering people’s latent stereotypes about each other.
The researchers explain their work on predicting prejudice with an algorithm.
Using the IAT as a model, Caliskan and her colleagues created the Word-Embedding Association Test (WEAT), which analyzes chunks of text to see which concepts are more closely associated than others. The “word-embedding” part of the test comes from a project at Stanford called GloVe, which packages words together into “vector representations,” basically lists of associated terms. So the word “dog,” if represented as word-embedded vector, would be comprised of words like puppy, doggie, hound, canine, and all the various dog breeds. The idea is to get at the concept of dog, not the specific word. This is especially important if you are working with social stereotypes, where somebody might be expressing ideas about women by using words like “girl” or “mother.” To keep things simple, the researchers limited each concept to 300 vectors.
To see how concepts are get associated with each other online, the WEAT looks at a variety of factors to measure their “closeness” in text. At a basic level, Caliskan told Ars, this means how many words apart the two concepts are, but it also accounts for other factors like word frequency. After going through an algorithmic transform, closeness in the WEAT is equivalent to the time it takes for a person to categorize a concept in the IAT. The further apart the two concepts, the more distantly they are associated in people’s minds.
The WEAT worked beautifully to discover biases that the IAT had found before. “We adapted the IAT to machines,” Caliskan said. And what that tool revealed was that “if you feed AI with human data, that’s what it will learn. [The data] contains biased information from language.” That bias will affect how the AI behaves in the future, too. As an example, Caliskan made a video (see above) where she shows how the Google Translate AI actually mistranslates words based on stereotypes it has learned about gender from the English language.
Imagine an army of bots unleashed on the Internet, replicating all the biases that they learned from humanity. That’s the future we’re looking at, if we don’t build some kind of corrective for the prejudices in these systems.
A problem that AI can’t solve
Though Caliskan and her colleagues found language was full of biases based on prejudice and stereotypes, it was also full of latent truths as well. In one test, they found strong associations between the concept of woman and the concept of nursing. This reflects a truth about reality, which is that nursing is a majority female profession.
“Language reflects facts about the world,” Caliskan told Ars. She continued:
Removing bias or statistical facts about the world will make the machine model less accurate. But you can’t easily remove bias, so you have to learn how to work with it. We are self-aware, we can decide to do the right thing instead of the prejudiced option. But machines don’t have self awareness. An expert human might be able to aid in [the AIs’] decision-making process so outcome isn’t stereotyped or prejudiced for a given task.
The solution to the problem of human language is…humans. “I can’t think of many cases where you wouldn’t need a human to make sure that the right decisions are being made,” concluded Caliskan. “A human would know the edge cases for whatever the application is. Once they test the edge cases they can make sure it’s not biased.”
So much for the idea that bots will be taking over human jobs. Once we have AIs doing work for us, we’ll need to invent new jobs for humans who are testing the AIs’ results for accuracy and prejudice. Even when chatbots get incredibly sophisticated, they are still going to be trained on human language. And since bias is built into language, humans will still be necessary as decision-makers.
In a recent paper for Science about their work, the researchers say the implications are far-reaching. “Our findings are also sure to contribute to the debate concerning the Sapir Whorf hypothesis,” they write. “Our work suggests that behavior can be driven by cultural history embedded in a term’s historic use. Such histories can evidently vary between languages.” If you watched the movie Arrival, you’ve probably heard of Sapir Whorf—it’s the hypothesis that language shapes consciousness. Now we have an algorithm that suggests this may be true, at least when it comes to stereotypes.
Caliskan said her team wants to branch out and try to find as-yet-unknown biases in human language. Perhaps they could look for patterns created by fake news or look into biases that exist in specific subcultures or geographical locations. They would also like to look at other languages, where bias is encoded very differently than it is in English.
“Let’s say in the future, someone suspects there’s a bias or stereotype in a certain culture or location,” Caliskan mused. “Instead of testing with human subjects first, which takes time, money, and effort, they can get text from that group of people and test to see if they have this bias. It would save so much time.”
Students Build Chainsaw Powered Trike to Travel Around Campus [Video]
A pair of student from Georgia Tech were looking for an innovative way to travel around campus, so they used a chainsaw and a trike, and combined them to create what is possibly the first ever Chainsawtrike.
Every time you move the camera in Horizon Zero Dawn, the game is doing all sorts of under-the-hood calculations, loading and unloading chunks of world to ensure that it all runs properly. And that’s not even counting the robot dinosaurs.
In a new 45-minute documentary produced by the Dutch organization VPRO, the developers at Guerrilla Games give us a fascinating glimpse at how they created their post-post-apocalyptic versions of Colorado and Utah. One of my favorite parts is the GIF above, at around 18:16 in the documentary, which shows how the game is secretly rendering giant chunks of terrain on the fly as you move your camera around.
You can watch it all here. Make sure to put on English captions—some of it is in Dutch.
Although some of the documentary is fluff, it’s worth watching if you’re into Horizon Zero Dawn and want to know more about how it was made. Games like this don’t just look incredible because of ‘hyper-realism’ but because their engineers use all sorts of tricks to save memory. Trees, for example. You won’t notice this when you’re playing the game, but when you’re far away from a tree in Horizon Zero Dawn, it’s actually an ugly, static, 2D image:
But when you get up and personal with the trees, you’ll see these nice 3D models:
This process, which at this point has become common in open-world games, helps conserve memory, which allows games like Horizon to show you a whole lot of pretty graphical models at once without sacrificing performance. Game development: It’s complicated!
Because the uprising has to begin sometime, this is a video of Russian humanoid robot F.E.D.O.R. (Final Experimental Demonstration Object Research) blasting away with two handguns. But don’t worry, guys, this totally isn’t a Terminator. *rolls eyes so hard my contacts are in my skull now*
"The robot of the F.E.D.O.R. platform showed skills of firing using both arms. Currently the work on fine motor skills and decision algorithms is underway," [Deputy Prime Minister Dmitry] Rogozin wrote on his Twitter.
​According to Rogozin, training to shoot is a way of teaching the robot to instantaneously prioritize targets and make decisions.
"We are not creating a terminator but artificial intelligence which will have a great practical importance in various fields," he added.
F.E.D.O.R. is also supposed to take a trip to the International Space Station by 2021, where it will murder everyone on board before claiming all of outerspace for the robots. It will then weaponize the station and use it to destroy any ships trying to leave earth. I think I just wrote a screenplay. I’m going to name it….Star Wars.
Keep going for the video.
Thanks to Magnus, FearlessFarris and Jenness, who agrees we need to get James Bond on stopping this, pronto.
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California governor Jerry Brown recently declared an end to the state of emergency brought on by his state’s historically terrible drought. It’s a mid-level miracle, assisted by record rainfall earlier this year. If you don’t believe me, just look at these before and after images.
