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A new study reveals a process for tattooing gold nanopatterns onto single cells. Could this pave the way for electrode arrays, antennae, and circuits on living cells and tissues?

A metallic-looking rock that smashed through the roof of a residential home in New Jersey’s Hopewell Township earlier this week is indeed a meteorite — a rare one about 4.6 billion years old, scientists confirmed on Thursday (May 11).

“It was obvious right away from looking at it that it was a meteorite in a class called stony chondrite,” Nathan Magee, chair of the physics department at The College of New Jersey (TCNJ), whose office was contacted by the Hopewell Township police soon after the rock was found on Monday (May 8), told Space.com.

The James Webb Telescope detected a POSSIBLE sign of LIFE on a far away exoplanet, K2-18b. Here’s the real-talk on what’s happening, and why scientists are h…

As Tesla’s Optimus robot shows off new capabilities in pick ‘n’ place sorting and one-legged yoga balancing, Singaporean company Fourier Intelligence has released new video showing the production process for its super-strong GR-1 humanoid.

Fourier claims the GR-1 can carry up to an extraordinary 50 kg (110 lb) of weight, thanks to a particularly beefy pair of robo-buttocks in the form of two 300-Nm (221-lb-ft) hip actuators.

Continue reading “Fourier and Tesla show off impressive humanoid robot progress” »

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OpenAI reported that the company will begin rolling out the new voice and image capabilities in ChatGPT over the next two weeks. The features are devised to be user-friendly, enabling individuals to engage in voice conversations and visually demonstrate the subjects of concern to ChatGPT.

Scientists at the National Institute of Standards and Technology (NIST) with colleagues elsewhere have employed neutron imaging and a reconstruction algorithm to reveal for the first time the 3D shapes and dynamics of very small tornado-like atomic magnetic arrangements in bulk materials.

These collective atomic arrangements, called skyrmions—if fully characterized and understood—could be used to process and store information in a densely packed form that uses several orders of magnitude less energy than is typical now.

The conventional, semiconductor-based method of processing information in binary form (on or off, 0 or 1) employs electrical charge states that must be constantly refreshed by current which encounters resistance as it passes through transistors and connectors. That’s the main reason that computers get hot.

How many times have you shown up to a video meeting with people at work only to find you have terrible internet that day? Maybe the others on the call are cutting in and out, or maybe your own signal is being corrupted on their screen. Regardless, many remote workers have found a simple solution—turn down the video quality and focus on audio.

In a very general sense, this is the same technique that researchers leverage when using quantum squeezing to improve the performance of their sensors. Mark Kasevich, a professor of physics and applied physics at Stanford University and a member of Q-NEXT, uses quantum squeezing in his work developing quantum sensors.

Q-NEXT is a U.S. Department of Energy (DOE) National Quantum Information Science Research Center led by DOE’s Argonne National Laboratory. Center researchers use quantum squeezing to make better measurements of quantum systems.

In the future, quantum computers may be able to solve problems that are far too complex for today’s most powerful supercomputers. To realize this promise, quantum versions of error correction codes must be able to account for computational errors faster than they occur.

However, today’s quantum computers are not yet robust enough to realize such error correction at commercially relevant scales.

On the way to overcoming this roadblock, MIT researchers demonstrated a novel superconducting qubit architecture that can perform operations between qubits—the building blocks of a quantum computer—with much greater accuracy than scientists have previously been able to achieve.

A team of computer scientists at UC Riverside has developed a new method to detect manipulated facial expressions in deep fake videos. The method could detect these expressions with up to 99% accuracy, making it more accurate than the current state-of-the-art methods.

The new research paper titled “Detection and Localization of Facial Expression Manipulations” was presented at the 2022 Winter Conference on Applications of Computer Vision.

Detecting Any Facial Manipulation