Lifeboat Foundation

After just over 10 and a half days in space, Peregrine Mission One, which was hosted by the private space company, Astrobotic Technology, burned up in the Earth’s atmosphere over the South Pacific Ocean on January 18, 2024, at approximately 4:04 pm EST (1:04 pm EST). This concluded what is being deemed as a mostly successful mission for the first commercial mission for NASA’s Commercial Lunar Payload Services (CLPS) program, although the spacecraft was unable to land on the lunar surface due to a fuel leak that occurred about seven hours after launch on January 8, 2024. Despite this, Peregrine was able to test several of its onboard instruments during the short mission, which will provide valuable data for future missions to the Moon, specifically for NASA’s Artemis program.

Had Peregrine landed on the Moon, it would have marked the first time a US-built spacecraft would have landed on the lunar surface since NASA’s Apollo 17 in 1972. Despite this, four of the five instruments on Peregrine successfully collected data during the 10-day mission: Linear Energy Transfer Spectrometer (LETS), Near-Infrared Volatile Spectrometer System (NIRVSS), Neutron Spectrometer System (NSS), Peregrine Ion-Trap Mass Spectrometer (PITMS), with the fifth instrument, NASA’s Laser Retroreflector Array (LRA), designed to only be used on the lunar surface.

“Astrobotic’s Peregrine mission provided an invaluable opportunity to test our science and instruments in space, optimizing our process for collecting data and providing a benchmark for future missions,” said Dr. Nicola Fox, who is the associate administrator for NASA’s Science Mission Directorate at NASA Headquarters. “The data collected in flight sets the stage for understanding how some of our instruments may behave in the harsh environment of space when some of the duplicates fly on future CLPS flights.”

At a new restaurant, Miso Robotics’ Flippy robot prepares french fries, while its BurgerBot makes burgers from a wagyu blend, grinding the meat for each burger only after an order is placed.⁠

The AI robots making burgers and fries at CaliExpress could help the restaurant industry address its persistent labor shortage.

Researchers have doubted how useful the AI protein-structure tool will be in discovering medicines — now they are learning how to deploy it effectively.

Photonic integrated circuits have grown as potential hardware for neural networks and quantum computing, yet the tuning speed and large power consumption limited the application. Here, authors introduce the memresonator, a memristor heterogeneously integrated with a microring resonator, as a non-volatile silicon photonic phase shifter to address these limitations.

Biomedical engineers at Duke University have developed a new method to improve the effectiveness of machine learning models. By pairing two machine learning models, one to gather data and one to analyze it, researchers can circumvent limitations of the technology without sacrificing accuracy.

This new technique could make it easier for researchers to use machine learning algorithms to identify and characterize molecules for use in potential new therapeutics or other materials.

The research is published in the journal Artificial Intelligence in the Life Sciences.

Researchers have created a novel technology utilizing meta-optical devices for thermal imaging. This method offers more detailed information about the objects being imaged, potentially expanding thermal imaging applications in autonomous navigation, security, thermography, medical imaging, and remote sensing.

“Our method overcomes the challenges of traditional spectral thermal imagers, which are often bulky and delicate due to their reliance on large filter wheels or interferometers,” said research team leader Zubin Jacob from Purdue University. “We combined meta-optical devices and cutting-edge computational imaging algorithms to create a system that is both compact and robust while also having a large field of view.”

In Optica, Optica Publishing Group’s journal for high-impact research, the authors describe their new spectro-polarimetric decomposition system, which uses a stack of spinning metasurfaces to break down thermal light into its spectral and polarimetric components. This allows the imaging system to capture the spectral and polarization details of thermal radiation in addition to the intensity information that is acquired with traditional thermal imaging.

Scientific investigations on the ISS’s latest resupply mission include advancements in 3D metal printing, semiconductor manufacturing, reentry thermal protection, robotic surgery, and cartilage tissue regeneration. These studies aim to enhance space mission sustainability and have significant implications for Earth-based technologies and health care.

Tests of a 3D metal printer, semiconductor manufacturing, and thermal protection systems for reentry to Earth’s atmosphere are among the scientific investigations that NASA and international partners are launching to the International Space Station on Northrop Grumman’s 20th commercial resupply services mission. The company’s Cygnus cargo spacecraft is scheduled to launch on a SpaceX Falcon 9 rocket from Cape Canaveral Space Force Station in Florida by late January.

Continue reading “NASA Sending Surgical Robot and 3D Metal Printer to Space Station” »

The Glaze/Nightshade team, for its part, denies it is seeking destructive ends, writing: Nightshade’s goal is not to break models, but to increase the cost of training on unlicensed data, such that licensing images from their creators becomes a viable alternative.

In other words, the creators are seeking to make it so that AI model developers must pay artists to train on data from them that is uncorrupted.

How did we get here? It all comes down to how AI image generators have been trained: by scraping data from across the web, including scraping original artworks posted by artists who had no prior express knowledge nor decision-making power about this practice, and say the resulting AI models trained on their works threatens their livelihood by competing with them.

Radar altimeters are the sole indicators of altitude above a terrain. Spectrally adjacent 5G cellular bands pose significant risks of jamming altimeters and impacting flight landing and takeoff. As wireless technology expands in frequency coverage and utilizes spatial multiplexing, similar detrimental radio-frequency (RF) interference becomes a pressing issue.

To address this interference, RF front ends with exceptionally low latency are crucial for industries like transportation, health care, and the military, where the timeliness of transmitted messages is critical. Future generations of wireless technologies will impose even more stringent latency requirements on RF front-ends due to increased data rate, carrier frequency, and user count.

Additionally, challenges arise from the physical movement of transceivers, resulting in time-variant mixing ratios between interference and signal-of-interest (SOI). This necessitates real-time adaptability in mobile wireless receivers to handle fluctuating interference, particularly when it carries safety-to-life critical information for navigation and autonomous driving, such as aircraft and ground vehicles.