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Category: robotics/AI – Page 2,322

New Neural Network Could Solve The Three-Body Problem 100 Million Times Faster

The three-body problem, one of the most notoriously complex calculations in physics, may have met its match in artificial intelligence: a new neural network promises to find solutions up to 100 million times faster than existing techniques.

First formulated by Sir Isaac Newton, the three-body problem involves calculating the movement of three gravitationally interacting bodies – such as the Earth, the Moon, and the Sun, for example – given their initial positions and velocities.

It might sound simple at first, but the ensuing chaotic movement has stumped mathematicians and physicists for hundreds of years, to the extent that all but the most dedicated humans have tried to avoid thinking about it as much as possible.

Chameleon’s tongue strike inspires fast-acting robots

Chameleons, salamanders and many toads use stored elastic energy to launch their sticky tongues at unsuspecting insects located up to one-and-a-half body lengths away, catching them within a tenth of a second.

Ramses Martinez, an assistant professor in Purdue’s School of Industrial Engineering and in the Weldon School of Biomedical Engineering in Purdue University’s College of Engineering and other Purdue researchers at the FlexiLab have developed a new class of entirely and actuators capable of re-creating bioinspired high-powered and high-speed motions using stored elastic energy. These robots are fabricated using stretchable polymers similar to rubber bands, with internal pneumatic channels that expand upon pressurization.

The elastic energy of these robots is stored by stretching their body in one or multiple directions during the fabrication process following nature-inspired principles. Similar to the chameleon’s tongue strike, a pre-stressed pneumatic soft robot is capable of expanding five times its own length, catch a live fly beetle and retrieve it in just 120 milliseconds.

Elon Musk’s Neuralink unveils device to connect your brain to a smartphone

Neuralink seeks to build a brain-machine interface that would connect human brains with computers. No tests have been performed in humans, but the company hopes to obtain FDA approval and begin human trials in 2020. Musk said the technology essentially provides humans the option of “merging with AI.”

Dr. Bill Andrews Presentation & Tour of Sierra Sciences on October 11TH, 2019

Excellent lecture. Darwin’s turtle, sharks and clams 500 years old, talking about Liz Parrish at an hour and 8. And then a tour.

My mission is to drastically improve your life by helping you break bad habits, build and keep new healthy habits to make you the best version of yourself. I read the books and do all the research and share my findings with you!

This video is DR. BILL ANDREWS PRESENTATION & TOUR OF SIERRA SCIENCES ON OCTOBER 11TH, 2019. Brent Nally recorded, edited and produced this video. My apologies for the poor audio and camera work in the first few minutes. Infinite gratitude to Bill for opening up Sierra Sciences to us. Here’s a link to purchase IsaGenesis. You have to sign up first: https://getstarted.isagenix.com/VF234XXQV001

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This Electric Toothbrush Uses AI Because Nothing is Sacred Anymore

These days, it seems like every brand is trying to leverage machine learning to imbue their products with special powers — and, most importantly, make an extra buck in the process.

But does your next electric toothbrush really need a dose of AI? Oral-B’s says its new $220 electric toothbrush, called “Oral-B GENIUS X with Artificial Intelligence,” will leverage data from sensors inside the brush head and Bluetooth to deliver AI-derived brushing tips through an app. The future is now, huh?

What’S Next For Spectrum Sharing? IEEE Spectrum

“You’ve graduated from the school of spectral hard knocks,” Paul Tilghman, a U.S. Defense Advanced Research Projects Agency (DARPA) program manager, told the teams competing in the agency’s Spectrum Collaboration Challenge (SC2) finale on 23 October. The three-year competition had just concluded, and the top three teams were being called on stage as a song that sounded vaguely like “Pomp and Circumstance” played overhead.

“Hard knocks” wasn’t an exaggeration—the 10 teams that made it to the finale, as well as others who were eliminated in earlier rounds of the competition—had been tasked with proving something that hadn’t been demonstrated before. Their challenge was to see if AI-managed radio systems could work together to share wireless spectrum more effectively than static, pre-allocated bands. They had spent years battling it out in match-ups in a specially-built RF emulator DARPA built for the competition, Colosseum.

By the end of the finale, the top teams had demonstrated their systems could transmit more data over less spectrum than existing standards like LTE, and shown an impressive ability to reuse spectrum over multiple radios. In some finale match-ups, the radio systems of five teams were transmitting over 200 or 300 percent more data than is currently possible with today’s rigid spectrum band allocations. And that’s important, given that we’re facing a looming wireless spectrum crunch.

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