Lifeboat Foundation

The technology uses a centrifuge and high-spin rates to accelerate a payload to Mach 5 before a strap-on rocket fires to get to orbit.

The wave likely scoured the surface of the weakly magnetic planet.

Previously, scientists were unsure if Mercury’s magnetic field was strong enough to induce geomagnetic storms. However, research published in two papers in the journals Nature Communications and Science China Technological Sciences in February has proved that the magnetic field is, indeed, strong enough. The first paper showed that Mercury has a ring current, a doughnut-shaped stream of charged particles flowing around a field line between the planet’s poles, and the second paper pointed to this ring current being capable of triggering geomagnetic storms.

“The processes are quite similar to here on Earth,” Hui Zhang, a co-author of both studies and a space physics professor at the University of Alaska Fairbanks Geophysical Institute, said in a statement. “The main differences are the size of the planet and Mercury has a weak magnetic field and virtually no atmosphere.”

Continue reading “The sun has blasted Mercury with a plasma wave” »

It was cute⁠—even though LaMDA went on to make a few errors. The AI language model that powers it is still in development, Pichai explained. And Google says it has no plans yet to use LaMDA in its products. Even so, the company is using it to explore new ways to interact with computers—and new ways to search for information. “LaMDA already understands quite a lot about Pluto and millions of other topics,” he said.

The vision of a know-it-all AI that dishes out relevant and accurate information in easy-to-understand bite-size chunks is shaping the way tech companies are approaching the future of search. And with the rise of voice assistants like Siri and Alexa, language models are becoming a go-to technology for finding stuff out in general.

The JWST has been gradually cooling down ever since its successful, but the telescope took a major step forward on that front when it its massive 70-foot sunshield at the start of the year. That component allowed JWST’s systems, including its critical Mid-Infrared Instrument (MIRI), to drop to a temperature of approximately minus 298 degrees Fahrenheit (or about minus 183 degrees Celsius).

Getting the JWST to its final operating temperature required NASA and the European Space Agency to activate the telescope’s electric “cryocooler.” That in itself involved passing a technical hurdle dubbed the “pinch point,” or the stage at which the James Webb’s instruments went from minus 433 degrees Fahrenheit to minus 448 Fahrenheit.

“The MIRI cooler team has poured a lot of hard work into developing the procedure for the pinch point,” said Analyn Schneider, MIRI project manager for NASA’s Jet Propulsion Laboratory. “The team was both excited and nervous going into the critical activity. In the end, it was a textbook execution of the procedure, and the cooler performance is even better than expected.”

After launching late last year, NASA’s revolutionary James Webb Space Telescope is finally getting ready to fixate its numerous golden mirrors on distant targets.

Intriguingly, though, one of its 13 early targets isn’t so distant at all — at least in the grand scheme of things. It’ll be looking at Jupiter, the iconic gas giant in our own star system. Of course, we already know quite a bit about the planet already— so why investigate it using the JWST if it can have a closer look at far more distant objects?

“We’ve been there with several spacecraft and have observed the planet with Hubble and many ground-based telescopes at wavelengths across the electromagnetic spectrum (from the UV to meters wavelengths),” Berkeley astronomer Imke de Pater, leader of the Jupiter observation team, told Digital Trends, “so we’ve learned a tremendous amount about Jupiter itself, its atmosphere, interior, and about its moons and rings.”

More than 13 months after the Perseverance rover landed on Mars (on 18 February 2021), the rover’s cameras have finally spotted some of the parts of the Mars 2020 landing system that got the rover safely to the ground.

The parachute and backshell were imaged by Perseverance’s MastCam-Z, seen off in the distance, just south of the rover’s current location. The image was taken on Sol 404, or 6 April 2022 on Earth.

Continue reading “After 404 Days on Mars, Perseverance Has Finally Spotted Its Parachute” »

Ars Technica had the opportunity to tour NASA’s Jet Propulsion Laboratory in California this week, suiting up for a clean-room sneak peek at the Psyche spacecraft now nearing completion. This ambitious mission, named after the eponymous asteroid it will explore, is due to launch in August on a Falcon Heavy rocket. Scientists are hopeful that learning more about this unusual asteroid will advance our understanding of planet formation and the earliest days of our Solar System.

Discovered in March 1,852 by the Italian astronomer Annibale de Gasparis, 16 Psyche is an M-type asteroid (meaning it has high metallic content) orbiting the Sun in the main asteroid belt, with an unusual potato-like shape. The longstanding preferred hypothesis is that Psyche is the exposed metallic core of a protoplanet (planetesimal) from the earliest days of our Solar System, with the crust and mantle stripped away by a collision (or multiple collisions) with other objects. In recent years, scientists concluded that the mass and density estimates aren’t consistent with an entirely metallic remnant core. Rather, it’s more likely a complex mix of metals and silicates.

Alternatively, the asteroid might once have been a parent body for a particular class of stony-iron meteorites, one that broke up and re-accreted into a mix of metal and silicate. Or perhaps it’s an object like 1 Ceres, a dwarf planet in the asteroid belt between the orbits of Mars and Jupiter—except 16 Psyche may have experienced a period of iron volcanism while cooling, leaving highly enriched metals in those volcanic centers.

In 2020, OpenAI introduced GPT-3 and, a year later, DALL.E, a 12 billion parameter model, built on GPT-3. DALL.E was trained to generate images from text descriptions, and the latest release, DALL.E 2, generates even more realistic and accurate images with 4x better resolution. The model takes natural language captions and uses a dataset of text-image pairings to create realistic images. Additionally, it can take an image and create different variations inspired by original images.

DALL.E leverages the ‘diffusion’ process to learn the relationship between images and text descriptions. In diffusion, it starts with a pattern of random dots and tracks it towards an image when it recognises aspects of it. Diffusion models have emerged as a promising generative modelling framework and push the state-of-the-art image and video generation tasks. The guidance technique is leveraged in diffusion to improve sample fidelity for images and photorealism. DALL.E is made up of two major parts: a discrete autoencoder that accurately represents images in compressed latent space and a transformer that learns the correlations between language and this discrete image representation. Evaluators were asked to compare 1,000 image generations from each model, and DALL·E 2 was preferred over DALL·E 1 for its caption matching and photorealism.

DALL-E is currently only a research project, and is not available in OpenAI’s API.

It’s 85 miles wide, which is larger than the US state of Rhode Island. And it’s coming this way.