Lifeboat Foundation: Safeguarding Humanity
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The rapidly spreading COVID-19 epidemic has created an unusual situation: in each population that is infected by the virus, large parts of the population will be infected within a well-defined short time period, with near certainty. A major question is thus: Can the unusual predictability of the infections’ timing be utilized to mitigate the imminent infection’s length, severity, and probability of complications?

We suggest that priming the immune system for attack shortly before it is expected to occur, e.g. via a vaccine that elicits a broad anti-viral immune response, may have this desired effect. Early activation of the immune system would allow it to clear the infection faster and with less complications than otherwise. This would alleviate adverse clinical outcomes at the individual level, and mitigate population-level risk by reducing need for hospitalizations and decreasing the infectious period of individuals, thus slowing the epidemic’s spread and reducing its impact.

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It’s hard to piece together the full history of human evolution from piles of old bones. But now, scientists have made use of a new method to study proteins in dental enamel of an 800,000-year-old human species, helping place it in the family tree.

Although Homo sapiens is the only human species still alive today, the road to get here is paved with extinct relatives. And untangling how they’re all related to each other is a task that scientists continue to wrestle with. The timeline is usually determined through various dating processes, both on the bones themselves and the sediment layers they’re found in. Relationships between species are then determined from this timeline, and by examining the structures and features of the bones to track the progress of evolution.

For the new study, researchers at the University of Copenhagen have used a new tool called palaeoproteomics to get a more precise picture. This involves sequencing proteins from ancient remains, and it works on samples that are far too old to have intact DNA. In this case, the team applied it to the 800,000-year-old teeth of a mysterious, archaic human species called Homo antecessor.

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Social roboticist, Heather Knight, sees robots and entertainment a research-rich coupling. So she programmed a charming humanoid robot named DATA with jokes, and equipped it with sensors and algorithmic capabilities to help with timing and gauging a crowd. Then Knight and DATA hit the road on an international robot stand-up comedy tour. Their act landed stage time at a TED conference and Knight was profiled in Forbes 30 Under 30. Watching Data perform is much like watching an amateur stand-up comedian cutting her/his chops at an open mic night doing light comedy with a sweet but wooden delivery.

Knight’s goal is specific:

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In satellite photos of the Earth, clouds of bright green bloom across the surface of lakes and oceans as algae populations explode in nutrient-rich water. From the air, the algae appear to be the primary players in the ecological drama unfolding below.

But those we credit for influencing the aquatic environment at the base of the food chain may be under the influence of something else: whose can reconfigure their hosts’ metabolism.

In a new study published in Nature Communications, a research team from Virginia Tech reported that they had found a substantial collection of genes for metabolic cycles—a defining characteristic of cellular life—in a wide range of “.”

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“It’s good to hear your voice, you know it’s been so long If I don’t get your calls, then everything goes wrong… Your voice across the line gives me a strange sensation” — Blondie, “Hanging on the Telephone”

In 1978, Debbie Harry propelled her new wave band Blondie to the top of the charts with a plaintive tale of yearning to hear her boyfriend’s from afar and insisting he not leave her “hanging on the telephone.”

But the questions arises: What if it were 2020 and she was speaking over VOIP with intermittent packet losses, audio jitter, network delays and out-of-sequence packet transmissions?

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Researchers have designed a machine learning method that can predict battery health with 10x higher accuracy than current industry standard, which could aid in the development of safer and more reliable batteries for electric vehicles and consumer electronics.

The researchers, from Cambridge and Newcastle Universities, have designed a new way to monitor batteries by sending electrical pulses into them and measuring the response. The measurements are then processed by a to predict the ’s health and useful lifespan. Their method is non-invasive and is a simple add-on to any existing battery system. The results are reported in the journal Nature Communications.

Predicting the state of health and the remaining useful lifespan of lithium-ion batteries is one of the big problems limiting widespread adoption of : it’s also a familiar annoyance to mobile phone users. Over time, battery performance degrades via a complex network of subtle chemical processes. Individually, each of these processes doesn’t have much of an effect on battery performance, but collectively they can severely shorten a battery’s performance and lifespan.

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In order to better solve complex challenges at the dawn of the third decade of the 21st century, Alphabet Inc. has tapped into relics dating to the 1980s: video games.

The parent company of Google reported this week that its DeepMind Technologies Artificial Intelligence unit has successfully learned how to play 57 Atari video games. And the plays better than any human.

Atari, creator of Pong, one of the first successful video games of the 1970s, went on to popularize many of the great early classic video games into the 1990s. Video games are commonly used with AI projects because they algorithms to navigate increasingly complex paths and options, all while encountering changing scenarios, threats and rewards.

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As of right now, Cortical’s mini-brains have less processing power than a dragonfly brain. The company is looking to get its mouse-neuron-powered chips to be capable of playing a game of “Pong,” as CEO Hon Weng Chong told Fortune, following the footsteps of AI company DeepMind, which used the game to test the power of its AI algorithms back in 2013.

“What we are trying to do is show we can shape the behavior of these neurons,” Chong told Fortune.

READ MORE: A startup is building computer chips using human neurons [Fortune].

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