Lifeboat Foundation: Safeguarding Humanity
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Category: habitats – Page 11

In 2021, NASA’s Perseverance rover landed in the Jezero Crater on Mars. For the next three years, this astrobiology mission collected soil and rock samples from the crater floor for eventual return to Earth. The analysis of these samples is expected to reveal much about Mars’ past and how it transitioned from being a warmer, wetter place to the frigid and desiccated place we know today. Unfortunately, budget cuts have placed the future of the proposed NASA-ESA Mars Sample Return (MSR) mission in doubt.

As a result, NASA recently announced that it was seeking proposals for more cost-effective and rapid methods of bringing the samples home. This will consist of three studies by NASA and the Johns Hopkins University Applied Physics Laboratory (JHUAPL).

In addition, NASA has selected seven commercial partners for firm-fixed-price contracts for up to $1.5 million to conduct their own 90-day studies. Once complete, NASA will consider which proposals to integrate into the MSR mission architecture.

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Europol is proposing solutions to avoid challenges posed by privacy-enhancing technologies in Home Routing that hinder law enforcement’s ability to intercept communications during criminal investigations.

The agency has previously highlighted in its Digital Challenges series that law enforcement problem of end-to-end encryption on communication platforms is a hurdle when it comes to collecting admissible evidence.

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Generative AI models have changed the way we create and consume content, particularly images and art. Diffusion models such as MidJourney and Stable Diffusion have been trained on large datasets of scraped images from online, many of which are copyrighted, private, or sensitive in subject matter. Many artists have discovered significant numbers of their art pieces in training data such as LAION-5B, without their knowledge, consent, credit or compensation.

To make it worse, many of these models are now used to copy individual artists, through a process called style mimicry. Home users can take art work from human artists, perform “fine-tuning” or LoRA on models like stable diffusion, and end up with a model that is capable of producing arbitrary images in the “style” of the target artist, when evoked with their name as a prompt. Popular independent artists find low quality facsimilies of their artwork online, often with their names still embedded in the metadata from model prompts.

Style mimicry produces a number of harmful outcomes that may not be obvious at first glance. For artists whose styles are intentionally copied, not only do they see loss in commissions and basic income, but low quality synthetic copies scattered online dilute their brand and reputation. Most importantly, artists associate their styles with their very identity. Seeing the artistic style they worked years to develop taken to create content without their consent or compensation is akin to identity theft. Finally, style mimicry and its impacts on successful artists have demoralized and disincentivized young aspiring artists. We have heard administrators at art schools and art teachers talking about plummeting student enrollment, and panicked parents concerned for the future of their aspiring artist children.

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The four volunteers who have been living and working inside NASA’s first simulated yearlong Mars habitat mission are set to exit their ground-based home on Saturday, July 6. NASA will provide live coverage of the crew’s exit from the habitat at NASA’s Johnson Space Center in Houston at 5 p.m. EDT.

NASA will stream the activity, which will include a short welcome ceremony, on NASA+, NASA Television, the NASA app, the agency’s website, and NASA Johnson’s X and Facebook accounts. Learn how to stream NASA TV through a variety of platforms, including social media.

The first Crew Health and Performance Exploration Analog (CHAPEA) mission began in the 3D printed habitat on June 25, 2023, with crew members Kelly Haston, Anca Selariu, Ross Brockwell, and Nathan Jones. For more than a year, the crew simulated Mars mission operations, including “Marswalks,” grew and harvested several vegetables to supplement their shelf-stable food, maintained their equipment and habitat, and operated under additional stressors a Mars crew will experience, including communication delays with Earth, resource limitations, and isolation.

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Four dedicated explorers—Jason Lee, Stephanie Navarro, Shareef Al Romaithi, and Piyumi Wijesekara—just returned from a 45-day simulated journey to Mars, testing the boundaries of human endurance and teamwork within NASA’s HERA (Human Exploration Research Analog) habitat at Johnson Space Center in Houston.

Their groundbreaking work on HERA’s Campaign 7 Mission 2 contributes to NASA’s efforts to study how future astronauts may react to isolation and confinement during deep-space journeys.

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The desert moss Syntrichia caninervis is a promising candidate for Mars colonization thanks to its extreme ability to tolerate harsh conditions lethal to most life forms. The moss is well known for its ability to tolerate drought conditions, but researchers report in the journal The Innovation that it can also survive freezing temperatures as low as −196°C, high levels of gamma radiation, and simulated Martian conditions involving these three stressors combined. In all cases, prior dehydration seemed to help the plants cope.

“Our study shows that the environmental resilience of S. caninervis is superior to that of some of highly stress-tolerant microorganisms and tardigrades,” write the researchers, who include ecologists Daoyuan Zhang and Yuanming Zhang and botanist Tingyun Kuang of the Chinese Academy of Sciences.

“S. caninervis is a promising candidate pioneer plant for colonizing extraterrestrial environments, laying the foundation for building biologically sustainable human habitats beyond Earth.”

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With generative AI models, researchers combined robotics data from different sources to help robots learn better. MIT researchers developed a technique to combine robotics training data across domains, modalities, and tasks using generative AI models. They create a combined strategy from several different datasets that enables a robot to learn to perform new tasks in unseen environments.

Let’s say you want to train a robot so it understands how to use tools and can then quickly learn to make repairs around your house with a hammer, wrench, and screwdriver. To do that, you would need an enormous amount of data demonstrating tool use.

Existing robotic datasets vary widely in modality — some include color images while others are composed of tactile imprints, for instance. Data could also be collected in different domains, like simulation or human demos. And each dataset may capture a unique task and environment.

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