Lifeboat Foundation

All medical breakthroughs have to start somewhere, and Intellia Therapeutics is ready to show the world the first-in-human gene editing data that could be the start of a | Interim results are in for Intellia and partner Regeneron’s in vivo CRISPR/Cas9 genome editing candidate, NTLA-2001, in patients with transthyretin (ATTR) amyloidosis: and the numbers look good. This is the first time gene editing has been proven to work in humans, which “opens up a whole new area of therapies for patients that wasn’t there.”

Scientists have discovered new insights into how our brain stores episodic memories—a type of long-term, conscious memory of a previous experience—that could be critical to the development of new neuroprosthetic devices to help patients with memory problems, like Alzheimer’s disease and dementia.

The new study—led by the University of Glasgow, in collaboration with the University of Birmingham and University of Erlangen—used special electrodes, implanted directly into the brains of epilepsy patients requiring surgery, to allow scientists to observe the activity of individual neurons in the hippocampus region of the brain.

The hippocampus is a challenging area to study, due to its location deep within the brain, yet this area is critical for our memory, acting as the librarian to the memory library in our brain.

Researchers from the University of Jyväskylä were able to simplify the most popular technique of artificial intelligence, deep learning, using 18th-century mathematics. They also found that classical training algorithms that date back 50 years work better than the more recently popular techniques. Their simpler approach advances green IT and is easier to use and understand.

The recent success of artificial intelligence is significantly based on the use of one core technique: deep learning. Deep learning refers to artificial intelligence techniques where networks with a large number of data processing layers are trained using massive datasets and a substantial amount of computational resources.

Deep learning enables computers to perform complex tasks such as analyzing and generating images and music, playing digitized games and, most recently in connection with ChatGPT and other generative AI techniques, acting as a natural language conversational agent that provides high-quality summaries of existing knowledge.

Modular forms are one of the most beautiful and mysterious objects in mathematics. What are they?

It was already on the way out due to cost. AI just sped it all up.

In an AI world, job applicants’ actual skill sets will matter more than diplomas, LinkedIn says.

NASA will begin a new RS-25 test series Oct. 5, the final round of certification testing ahead of production of an updated set of the engines for the SLS (Space Launch System) rocket. The engines will help power future Artemis missions to the Moon and beyond.

A series of 12 tests stretching into 2024 is scheduled to occur on the Fred Haise Test Stand at NASA’s Stennis Space Center near Bay St. Louis, Mississippi. The tests are a key step for lead SLS engines contractor Aerojet Rocketdyne, an L3Harris Technologies company, to produce engines that will help power the SLS rocket, beginning with Artemis V.

“NASA and our industry partners continue to make steady progress toward restarting production of the RS-25 engines for the first time since the space shuttle era as we prepare for our more ambitious missions to deep space under Artemis with the SLS rocket,” said Johnny Heflin, liquid engines manager for SLS at NASA’s Marshall Space Flight Center in Huntsville, Alabama. “The upcoming fall test series builds off previous hot fire testing already conducted at NASA Stennis to help certify a new design that will make this storied spaceflight engine even more powerful.”

Elon Musk says SpaceX is ready for Starship’s next flight. In a new keynote, he talked about what’s next, and how SpaceX will ready for Mars exploration.

Scientists worldwide are already using artificial intelligence to sort through huge amounts of data, suggesting that the future of astronomy belongs to AI.

That looks promising. 90% accuracy isn’t bad. Now the trick is getting there though we have options on our own solar system possibly. You never know until you try. I doubt we’ll find high level life remnants but perhaps something much less like at most insect level but more likely microbial. I’m just guessing of course.

A team of scientists supported in part by NASA have outlined a simple and reliable method to search for signs of past or present life on other worlds that employs machine learning techniques. The results show that the method can distinguish both modern and ancient biosignatures with an accuracy of 90 percent.

The method is able to detect whether or not a sample contains materials that were tied to biological activity. What the research team refers to as a “routine analytical method” could be performed with instruments on missions including spacecraft, landers, and rovers, even before samples are returned to Earth. In addition, the method could be used to shed light on the history of ancient rocks on our own planet.

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