Lifeboat Foundation: Safeguarding Humanity
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STANFORD, Calif. — A groundbreaking “superhero” vaccine inspired by the DNA code of Olympic athletes could help transform society over the next decade, a top genetic scientist claims.

The vaccine would provide lifelong protection against three of the top ten leading causes of death, according to Euan Ashley, professor of medicine and genetics at Stanford University. The so-called “superhero” jab could offer simultaneous, long-term protection against heart disease, stroke, Alzheimer’s disease, and liver disease, thanks to advances in genetic engineering.

This breakthrough treatment would deliver the blueprint of “ideal” cells from men and women whose genes are more disease-resistant than those of the average person, together with an “instruction manual” to help the body “repair, tweak and improve” its own versions. A single dose could lead to a “body-wide genetic upgrade” that would cut the risk of premature death in some adults by as much as 50 percent.

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Tech billionaires Elon Musk and Jack Dorsey have agreed to discuss bitcoin with each other at an event in July.

In a bizarre Twitter thread, Musk responded to a tweet from Dorsey promoting an event called “The B Word,” which aims to encourage companies and institutional investors to adopt bitcoin.

“Bicurious?” the Tesla CEO said, seemingly referring to the “B” word in question.

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Researchers at Google Brain announced a deep-learning computer vision (CV) model containing two billion parameters. The model was trained on three billion images and achieved 90.45% top-1 accuracy on ImageNet, setting a new state-of-the-art record.

The team described the model and experiments in a paper published on arXiv. The model, dubbed ViT-G/14, is based on Google’s recent work on Vision Transformers (ViT). ViT-G/14 outperformed previous state-of-the-art solutions on several benchmarks, including ImageNet, ImageNet-v2, and VTAB-1k. On the few-shot image recognition task, the accuracy improvement was more than five percentage-points. The researchers also trained several smaller versions of the model to investigate a scaling law for the architecture, noting that the performance follows a power-law function, similar to Transformer models used for natural language processing (NLP) tasks.

First described by Google researchers in 2017, the Transformer architecture has become the leading design for NLP deep-learning models, with OpenAI’s GPT-3 being one of the most famous. Last year, OpenAI published a paper describing scaling laws for these models. By training many similar models of different sizes and varying the amount of training data and computing power, OpenAI determined a power-law function for estimating a model’s accuracy. In addition, OpenAI found that not only do large models perform better, they are also more compute-efficient.

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It’s hard to see more than a handful of stars from Princeton University, because the lights from New York City, Princeton, and Philadelphia prevent our sky from ever getting pitch black, but stargazers who get into more rural areas can see hundreds of naked-eye stars — and a few smudgy objects, too.

The biggest smudge is the Milky Way itself, the billions of stars that make up our spiral galaxy, which we see edge-on. The smaller smudges don’t mean that you need glasses, but that you’re seeing tightly packed groups of stars. One of the best-known of these “clouds” or “clusters” — groups of stars that travel together — is the Pleiades, also known as the Seven Sisters. Clusters are stellar nurseries where thousands of stars are born from clouds of gas and dust and then disperse across the Milky Way.

For centuries, scientists have speculated about whether these clusters always form tight clumps like the Pleiades, spread over only a few dozen lightyears.

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A team of researchers working at Johannes Kepler University has developed an autonomous drone with a new type of technology to improve search-and-rescue efforts. In their paper published in the journal Science Robotics, the group describes their drone modifications. Andreas Birk with Jacobs University Bremen has published a Focus piece in the same journal issue outlining the work by the team in Austria.

Finding people lost (or hiding) in the forest is difficult because of the tree cover. People in planes and helicopters have difficulty seeing through the canopy to the ground below, where people might be walking or even laying down. The same problem exists for thermal applications—heat sensors cannot pick up readings adequately through the canopy. Efforts have been made to add drones to search-and–, but they suffer from the same problems because they are remotely controlled by pilots using them to search the ground below. In this new effort, the researchers have added new technology that both helps to see through the tree canopy and to highlight people that might be under it.

The new technology is based on what the researchers describe as an airborne optical sectioning algorithm—it uses the power of a computer to defocus occluding objects such as the tops of . The second part of the new device uses thermal imaging to highlight the heat emitted from a warm body. A machine-learning application then determines if the heat signals are those of humans, animals or other sources. The new hardware was then affixed to a standard autonomous . The computer in the drone uses both locational positioning to determine where to search and cues from the AOS and thermal sensors. If a possible match is made, the drone automatically moves closer to a target to get a better look. If its sensors indicate a match, it signals the research team giving them the coordinates. In testing their newly outfitted drones over 17 field experiments, the researchers found it was able to locate 38 of 42 people hidden below tree canopies.

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The AIR program was run by a company called Persistent Surveillance Systems with funding from two Texas billionaires. The city police department admitted to using planes to surveil Baltimore residents in 2016 but approved a six-month pilot program in 2020, which was active until October 31st.

The city of Baltimore’s spy plane program was unconstitutional, violating the Fourth Amendment protection against illegal search, and law enforcement in the city cannot use any of the data it gathered, a court ruled Thursday. The Aerial Investigation Research (or AIR) program, which used airplanes and high-resolution cameras to record what was happening in a 32-square-mile part of the city, was canceled by the city in February.

Local Black activist groups, with support from the ACLU, sued to prevent Baltimore law enforcement from using any of the data it had collected in the time the program was up and running. The city tried to argue the case was moot since the program had been canceled. That didn’t sit well with civil liberties activists. “Government agencies have a history of secretly using similar technology for other purposes — including to surveil Black Lives Matter protests in Baltimore in recent years,” the ACLU said in a statement Thursday.

In an en banc ruling, the US Court of Appeals for the Fourth Circuit found that “because the AIR program enables police to deduce from the whole of individuals’ movements, we hold that accessing its data is a search and its warrantless operation violates the Fourth Amendment.” Chief Judge Roger Gregory wrote that the AIR program “is like a 21st century general search, enabling the police to collect all movements,” and that “allowing the police to wield this power unchecked is anathema to the values enshrined in our Fourth Amendment.”

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