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Category: robotics/AI – Page 2,651

Meet the Man With a Thought-Controlled Robotic Arm

Johnny Matheny is the first person to attach a mind-controlled prosthetic limb directly to his skeleton. After losing his arm to cancer in 2008, Johnny signed up for a number of experimental surgeries to prepare himself to use a DARPA-funded prosthetic prototype. The Modular Prosthetic Limb, developed by the Johns Hopkins Applied Physics Laboratory, allows Johnny to regain almost complete range of motion through the Bluetooth-controlled arm. (Video by Drew Beebe, Brandon Lisy) (Source: Bloomberg)

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This fully autonomous drone taxi is your traffic-jam dream come true

Yup, you read that headline right. A Chinese UAV company named Ehang just unveiled the world’s first autonomous flying taxi.

The plainly-named 184 drone is essentially a giant quadcopter designed to carry a single passenger — and it needs no pilot. Inside the cockpit, there are absolutely zero controls. No joystick, no steering wheel, no buttons, switches, or control panels — just a seat and a small tablet stand.

To fly it, the user simply hops in the cockpit, fires up the accompanying mobile app, and chooses a destination. From that point onward, you’re just along for the ride. The drone takes care of all the piloting and navigation autonomously — so you supposedly don’t need a pilot’s license to use it.

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Nvidia announces a ‘supercomputer’ GPU and deep-learning platform for self-driving cars

Nvidia took pretty much everyone by surprise when it announced it was getting into self-driving cars; it’s just not what you expect from a company that’s made its name off selling graphics cards for gamers.

At this year’s CES, it’s taking the focus on autonomous cars even further.

The company today announced the Nvidia Drive PX2. According to CEO Jen-Hsun Huang, it’s basically a supercomputer for your car. Hardware-wise, it’s made up of 12 CPU cores and four GPUs, all liquid-cooled. That amounts to about 8 teraflops of processing power, is as powerful as 6 Titan X graphics cards, and compares to ‘about 150 MacBook Pros’ for self-driving applications.

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Computer model matches humans at predicting how objects move

We humans take for granted our remarkable ability to predict things that happen around us. For example, consider Rube Goldberg machines: One of the reasons we enjoy them is because we can watch a chain-reaction of objects fall, roll, slide and collide, and anticipate what happens next.

But how do we do it? How do we effortlessly absorb enough information from the world to be able to react to our surroundings in real-time? And, as a computer scientist might then wonder, is this something that we can teach machines?

That last question has recently been partially answered by researchers at MIT’s Computer Science and Artificial Intelligence Lab (CSAIL), who have developed a computational model that is just as accurate as humans at predicting how objects move.

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Keloid — A Short Film

In a not too distant future, societies of all countries come to rely on an intricate network of artificial intelligence devices designed to bring efficacy to man’s life. Yet, man continues to devour himself in useless wars. A strong political hierarchy now divides all powers into three factions, and A. I. devices rapidly gain ground as efficiency becomes a priority.

As social revolts grow worse everyday, authorities seek ways to control their citizens. They decide to carry out a series of tests that will determine not only whether some crucial powers can be transferred to non human entities, but also whether man is ready to yield those powers.

The world has become a cell for all man and women, who withstand and endure their lives, rather than living them. Machines might have found a solution.

From now on, you are set free…

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Keloid is not about robots, it is about men. Big Lazy Robot created this spec film — more than two years in the making — with the purpose of giving free reign to the creative aspects of production that many times are so much missed during profit work. And yes, we enjoyed the ride!

Continue reading “Keloid — A Short Film” | >

Deep Learning in Action | How to learn an algorithm

Deep Learning in Action | A talk by Juergen Schmidhuber, PhD at the Deep Learning in Action talk series in October 2015. He is professor in computer science at the Dalle Molle Institute for Artificial Intelligence Research, part of the University of Applied Sciences and Arts of Southern Switzerland.

Juergen Schmidhuber, PhD | I review 3 decades of our research on both gradient based and more general problem solvers that search the space of algorithms running on general purpose computers with internal memory.

Architectures include traditional computers, Turing machines, recurrent neural networks, fast weight networks, stack machines, and others. Some of our algorithm searchers are based on algorithmic information theory and are optimal in asymptotic or other senses.

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