Lifeboat Foundation

AI has finally come full circle.

A new suite of algorithms by Google Brain can now design computer chips —those specifically tailored for running AI software —that vastly outperform those designed by human experts. And the system works in just a few hours, dramatically slashing the weeks-or months-long process that normally gums up digital innovation.

At the heart of these robotic chip designers is a type of machine learning called deep reinforcement learning. This family of algorithms, loosely based on the human brain’s workings, has triumphed over its biological neural inspirations in games such as Chess, Go, and nearly the entire Atari catalog.

Why do so many people get frustrated with their “high-tech” prostheses? Though sophisticated robotics allow for prosthetic joints that can do everything a human can and more, the way we control robotic machines right now doesn’t allow us to operate them as naturally as you would a biological hand. Most robotic prostheses are controlled via metal pads on the skin that indirectly measure muscle action and then make some assumptions to determine what the person wants to do. Whil… See More.

We plan to use MM to provide natural control over prosthetic limbs by leveraging the human body’s proprioception. When you wiggle one of your fingers, your brain senses muscle lengths, speeds, and forces, and it uses these to figure out the position of that finger. This is called body awareness, or proprioception. When someone receives an amputation, if their muscle connections are maintained with what is called the “AMI technique,” their brain still perceives muscle flexion as it relates to joint movement, as if their limb was still present. In other words, they are sensing movement of a phantom limb. To give an amputee intuitive control over a robotic prosthesis, we plan to directly measure the muscle lengths and speeds involved in this phantom limb experience and have the robot copy what the brain expects, so that the brain experiences awareness of the robot’s current state. We see this technique as an important next step in the embodiment of the prosthetic limb (the feeling that it is truly part of one’s body).

Notably, the tracking of magnetic beads is minimally invasive, not requiring wires to run through the skin boundary or electronics to be implanted inside the body, and these magnetic beads can be made safe to implant by coating them in a biocompatible material. In addition, for muscles that are close to the skin, MM can be performed with very high accuracy. We found that by increasing the number of compass sensors we used, we could track live muscle lengths close to the surface of the skin with better than millimeter accuracy, and we found that our measurements were consistent to within the width of a human hair (about 37 thousandths of a millimeter).

Continue reading “Improving prosthetic control using magnetomicrometry” »

How true Eric Klien?

Something to look forward to: Solid-state batteries are still nebulous outside of the lab. Still, automakers are scrambling to be the first in the race to build the first electric car to take advantage of the added energy density and better safety when compared to lithium-ion designs. To that end, they’re investing in companies like QuantumScape, Solid Power, and Sakuu to develop manufacturing techniques that either build on existing approaches or rely on new additive manufacturing technology.

Continue reading “Sakuu’s 3D printed solid state battery could be a boon for electric vehicles” »

Hi guys! I created a Graphing Calculator with just redstone.

I know I say this every time, but this is without a doubt the most challenging and complicated machine I’ve ever made. It required multiple components working together to solve equations and plot over 10,000 different point values.

SPECS.

Albert Einstein’s most famous equation, E = mc², used in the theory of general relativity, has been used to create matter from light, scientists have said in a new study.

Researchers from New York’s Brookhaven National Laboratory used the Department of Energy’s Relativistic Heavy Ion Collider (RHIC), ordinarily used for nuclear physics research, to speed up two gold ions that are positively charged, in a loop.

🥲👍

Val Kilmer marked the release of his acclaimed documentary “Val” (now streaming on Amazon Prime Video) in a milestone way: He recreated his old speaking voice by feeding hours of recorded audio of himself into an artificial intelligence algorithm. Kilmer lost the ability to speak after undergoing throat cancer treatment in 2014. Kilmer’s team recently joined forces with software company Sonantic and “Val” distributor Amazon to “create an emotional and lifelike model of his old speaking voice” (via The Wrap).

Continue reading “Val Kilmer Recreated His Speaking Voice Using Artificial Intelligence and Hours of Old Audio” »

Quick – define common sense

Despite being both universal and essential to how humans understand the world around them and learn, common sense has defied a single precise definition. G. K. Chesterton, an English philosopher and theologian, famously wrote at the turn of the 20th century that “common sense is a wild thing, savage, and beyond rules.” Modern definitions today agree that, at minimum, it is a natural, rather than formally taught, human ability that allows people to navigate daily life.

Continue reading “An AI expert explains why it’s hard to give computers something you take for granted: Common sense” »

An artificial neural network (AI) designed by an international team involving UCL can translate raw data from brain activity, paving the way for new discoveries and a closer integration between technology and the brain.

The new method could accelerate discoveries of how brain activities relate to behaviors.

The study published today in eLife, co-led by the Kavli Institute for Systems Neuroscience in Trondheim and the Max Planck Institute for Human Cognitive and Brain Sciences Leipzig and funded by Wellcome and the European Research Council, shows that a convolutional neural network, a specific type of deep learning algorithm, is able to decode many different behaviors and stimuli from a wide variety of brain regions in different species, including humans.

Artificial intelligence, machine learning, data science.