Lifeboat Foundation

As the universe evolves, scientists expect large cosmic structures to grow at a certain rate: dense regions such as galaxy clusters would grow denser, while the void of space would grow emptier.

But University of Michigan researchers have discovered that the rate at which these large structures grow is slower than predicted by Einstein’s Theory of General Relativity.

They also showed that as dark energy accelerates the universe’s global expansion, the suppression of the cosmic structure growth that the researchers see in their data is even more prominent than what the theory predicts. Their results are published in Physical Review Letters.

Similarly, allowing the MyoLegs to flail around for a while in a seemingly aimless fashion gave them better performance with locomotion tasks, as the researchers described in another paper presented at the recent Robotics Science and Systems meeting. Vittorio Caggiano, a Meta researcher on the project who has a background in both AI and neuroscience, says that scientists in the fields of neuroscience and biomechanics are learning from the MyoSuite work. “This fundamental knowledge [of how motor control works] is very generalizable to other systems,” he says. “Once they understand the fundamental mechanics, then they can apply those principles to other areas.”

This year, MyoChallenge 2023 (which will also culminate at the NeurIPS meeting in December) requires teams to use the MyoArm to pick up, manipulate, and accurately place common household objects and to use the MyoLegs to either pursue or evade an opponent in a game of tag.

Emo Todorov, an associate professor of computer science and engineering at the University of Washington, has worked on similar biomechanical models as part of the popular Mujoco physics simulator. (Todorov was not involved with the current Meta research but did oversee Kumar’s doctoral work some years back.) He says that MyoSuite’s focus on learning general representations means that control strategies can be useful for “a whole family of tasks.” He notes that their generalized control strategies are analogous to the neuroscience principle of muscle synergies, in which the nervous system activates groups of muscles at once to build up to larger gestures, thus reducing the computational burden of movement. “MyoSuite is able to construct such representations from first principles,” Todorov says.

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We’ll soon be capable of building self-replicating robots. This will not only change humanity’s future but reshape the galaxy as we know it.

Continue reading “Self-Replicating Robots and Galactic Domination” »

Experts at Berkeley Lab finished winding more than 2000 kilometers of superconducting wire into cables for new magnets that will help upgrade the Large Hadron Collider and the search for new physics.

Scientists from the University of Sydney and Fudan University have found human brain signals traveling across the outer layer of neural tissue that naturally arrange themselves to resemble swirling spirals.

Published in the journal Nature Human Behaviour, the study suggests that these widespread spiral patterns, seen during both rest and cognitive activity, play a role in organizing brain function and cognitive processes.

Senior author Associate Professor Pulin Gong, from the School of Physics in the Faculty of Science, said the discovery could have the potential to advance powerful computing machines inspired by the intricate workings of the human brain.

Active matter is any collection of materials or systems composed of individual units that can move on their own, thanks to self-propulsion or autonomous motion. They can be of any size—think clouds of bacteria in a petri dish, or schools of fish.

Roman Grigoriev is mostly interested in the emergent behaviors in active matter systems made up of units on a molecular scale—tiny systems that convert stored energy into directed motion, consuming energy as they move and exert mechanical force.

“Active matter systems have garnered significant attention in physics, biology, and materials science due to their unique properties and potential applications,” Grigoriev, a professor in the School of Physics at Georgia Tech, explains.

After scientists created oxygen-28 in the lab, it almost immediately degraded, baffling physicists around the world.

In what can only bode poorly for our species’ survival during the inevitable robot uprisings, an AI system has once again outperformed the people who trained it. This time, researchers at the University of Zurich in partnership with Intel, pitted their “Swift” AI piloting system against a trio of world champion drone racers — none of whom could best its top time.

Swift is the culmination of years of AI and machine learning research by the University of Zurich. In 2021, the team set an earlier iteration of the flight control algorithm that used a series of external cameras to validate its position in space in real-time, against amateur human pilots, all of whom were easily overmatched in every lap of every race during the test. That result was a milestone in its own right as, previously, self-guided drones relied on simplified physics models to continually calculate their optimum trajectory, which severely lowered their top speed.

This week’s result is another milestone, not just because the AI bested people whose job is to fly drones fast, but because it did so without the cumbersome external camera arrays= of its predecessor. The Swift system “reacts in real time to the data collected by an onboard camera, like the one used by human racers,” an UZH Zurich release reads. It uses an integrated inertial measurement unit to track acceleration and speed while an onboard neural network localizes its position in space using data from the front-facing cameras. All of that data is fed into a central control unit — itself a deep neural network — which crunches through the numbers and devises a shortest/fastest path around the track.

Aleksandarnakovski / iStock.

The Tokyo Institute of Technology scientists in Japan created the O28 isotope with eight protons and 20 neutrons. Yosuke Kondo led the study.