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Why Do Seahorses Have Square Tails?      

Scientists use a 3D printer and a hammer to find out

by Mary Beth Griggs

We’ve got a lot to learn from a seahorse’s tail. Unlike other animals, these fishes’ tails are square, not round–a fundamental difference in shape that scientists believe could lead to new developments in medicine, robotics, and even defense.

In a paper published today in Science, researchers found that the difference in shape actually made a huge difference in how resilient the seahorse’s tail is.

In order to figure out why the seahorse tail is square while so many other animal’s tails are round (rats, lizards, monkeys, cats, etc.), scientists printed out 3D replicas of the square tails and similarly sized round tails.

Then they smashed them with a hammer…

(read more: Popular Science)

photograph: shellac/Flickr; illustration: Michael Porter, Clemson Univ.

“The story gained some morbid attention earlier today when a Financial Times employment reporter named Sarah O’Connor tweeted the story, not realizing the connection between her name and character who has a similar name (Sarah Connor) in the Terminator series. Her tweet was retweeted more than 3,500 times and she received an influx of messages making jokes about the news.”

http://arstechnica.com/business/2015/07/man-killed-by-a-factory-robot-in-germany

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AADRL Spyropoulos Design Lab

Visual portfolio reel of various projects related to self-assembly robotics which could apply to architecture - a good primer on current ideas in the field:

Research from the AADRL Spyropoulos Design Lab exploring an architecture that is self-aware, self-structured and self-assembles. The research explores high population of mobility agents that evolve an architecture that moves beyond the fixed and finite towards a behavioural model of interactive human and machine ecologies.

You can find out more about the AA DRL program at their website here

USA Builds Giant Piloted Robot, Challenges Japan to a Robot Duel

If the challenge is accepted, MegaBots may take “America’s first fully-functional giant piloted robot” called the MegaBot Mark II into the battle.

The 15-foot-tall mech is equipped with guns that can fire three-pound paint cannonballs at more than 120 miles per hour. The current bipedal mech gets around on caterpillar treads; however, the team is a designing full-scale walking robot.+

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#WeAreWakanda

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The robot that learns from its mistakes

UC Berkeley researchers have developed algorithms that enable robots to learn motor tasks through trial and error using a process that more closely approximates the way humans learn. This marks a major milestone in the field of artificial intelligence.

Motor learning is much more challenging than passive recognition of images and sounds.

The researchers demonstrated their technique, a type of reinforcement learning, by having a robot complete various motor tasks without pre-programmed details about its surroundings.

“Most robotic applications are in controlled environments where objects are in predictable positions,” said UC Berkeley faculty member Trevor Darrell, who is one of the leads on the project.

“The challenge of putting robots into real-life settings, like homes or offices, is that those environments are constantly changing. The robot must be able to perceive and adapt to its surroundings,” said Darrell.

Conventional, but impractical approaches to helping a robot make its way through a 3D world include pre-programming it to handle the vast range of possible scenarios or creating simulated environments within which the robot operates.

Instead, the researchers turned to a new branch of artificial intelligence known as deep learning, which is loosely inspired by the neural circuitry of the human brain when it perceives and interacts with the world.

Deep learning helps the robot recognize patterns and categories among the sensory data it is receiving.

Learn more about how deep learning works in the robot