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From the Cellar Vault: Animal Spies

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Last week, police in Iran apprehended a group of 14 squirrels on, get this, suspicion of spying. Apparently the rodents had been outfitted with eavesdropping equipment and were picked up on a tip from foreign intelligence services. Now, it's possible that this is all a stunt to show how tough Iranians are on crime, but if the squirrels really are enemy operative, they would join the historic ranks of cloak-and-dagger animals. Here's a look at some other non-human spies, illustrated with help from our friends at the Image of the Day archive at The Cellar.

Pigeons are noted for their delivery skills and the appeal was not lost on troops. They were used since the 19th Century to transmit messages in the field until the walkie-talkie took that over. In World War II, British officials even awarded 32 pigeons the Dickin Medal for animal valor. Plans were even in the works to use pigeons to drop bacteria on enemy troops, though they presumably never got off the ground. Here we see a war pigeon in it's natural environment- hunkered down in the trenches.

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During World War II, the US military would use dolphins to aid in naval missions. The creatures used sonar to detect underwater mines, carried explosives to enemy boats and retrieved objects from the sea floor. Even though the practice of using them has gone out of style, dolphins were recently used to clear away mines at ports in Iraq. Here the dolphins seemed poised for a grand-scale attack- those surfers better watch out!
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No human weapons inspectors were able to find the WMD's in Iraq, but that didn't stop American troops from using chickens to find chemical weapons in Kuwait. Operation Kuwait Field Chicken (KFC for short. Seriously.) employed 43 chickens to act like canaries and show signs of chemical infestation. The army warns that the oil-masked air in Kuwait can make some chemicals hard to detect, so using chickens as a warning system is the best low-tech method of keeping troops safe from dangerous fumes. Here's a chicken already incognito for his spying trip.

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Finally, Indian officials picked up a number of hawks in early 2000 carrying high-tech equipment and promptly apprehended them for being Pakistani spies. It was later revealed that the hawks were only being used for sport and had infiltrated India because "birds don't understand international borders," as the handler so astutely put it. The technology attached to the hawks wasn't surveillance equipment to steal military plans, but tracking equipment so they didn't get lost. Still, if that story has you thinking that hawks must all be good and tame, just look at this picture of one with its talons bared to remind you how dangerous they really are.

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9 Exhilarating Close-Up Photos of Sharks
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Dive into the world of Shark, a new book by award-winning photographer Brian Skerry.

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AI Algorithm Tells You the Ingredients in Your Meal Based on a Picture
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Your food photography habit could soon be good for more than just updating your Instagram. As Gizmodo reports, a new AI algorithm is trained to analyze food photos and match them with a list of ingredients and recipes.

The tool was developed by researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL). To build it, they compiled information from sites like All Recipes and Food.com into a database dubbed Recipe1M, according to their paper. With more than a million annotated recipes at its disposal, a neural network then sifted through each one, learning about which ingredients are associated with which types of images along the way.

The result is Pic2Recipe, an algorithm that can deduce key details about a food item just by looking at its picture. Show it a picture of a cookie, for example, and it will tell you it likely contains sugar, butter, eggs, and flour. It will also recommend recipes for something similar pulled from the Recipe1M database.

Pic2Recipe is still a work in progress. While it has had success with simple recipes, more complicated items—like smoothies or sushi rolls, for example—seem to confuse the system. Overall, it suggests recipes with an accuracy rate of about 65 percent.

Researchers see their creation being used as a recipe search engine or as a tool for situations where nutritional information is lacking. “If you know what ingredients went into a dish but not the amount, you can take a photo, enter the ingredients, and run the model to find a similar recipe with known quantities, and then use that information to approximate your own meal,” lead author Nick Hynes told MIT News.

Before taking the project any further, the team plans to present its work at the Computer Vision and Pattern Recognition Conference in Honolulu later this month.

[h/t Gizmodo]

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