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.
Its arms and hands, though, look pretty spindly, and the company has flagged its intention that this robot will act as a rehab therapy assistant, with grab handles at its waist to help people stand up out of wheelchairs and beds. So there’s every chance that’s where these loads will be carried.
Be-easy / Canva.
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.
What is quantum squeezing?
Posted in internet, quantum physics
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
Running could be for everyone even at Olympic levels with biocomputing and crispr.
Citation: (2004) Gene Targeting Turns Mice into Long-Distance Runners. PLoS Biol 2(10): e322. https://doi.org/10.1371/journal.pbio.
Copyright: © 2004 Public Library of Science. This is an open-access distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Have you ever noticed that long-distance runners and sprinters seem specially engineered for their sports? One’s built for distance, the other speed. The ability to generate quick bursts of power or sustain long periods of exertion depends primarily on your muscle fiber type ratio (muscle cells are called fibers), which depends on your genes. To this extent, elite athletes are born, not made. No matter how hard you train or how many performance-enhancing drugs you take, if you’re not blessed with the muscle composition of a sprinter, you’re not going to break the 100-meter world record in your lifetime. (In case you’d like to try, that’s 9.78 seconds for a man and 10.49 seconds for a woman.)
Driving the technology forward are advances in materials science and AI, plus patient benefits like shorter recovery times and less pain.
In the ever-evolving landscape of artificial intelligence and natural language processing, one phenomenon has taken center stage like never before is the large language model. These creations, infused with billions of parameters and fuelled by vast data repositories, are transforming the way we interact with machines. As this field has begun to take over the Software engineering industry, understanding the technology and diving deep into its nuances becomes paramount.
In this blog, we will do a deep dive into the pieces and components inside a Large Language Model.