Lifeboat Foundation: Safeguarding Humanity
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Humanity Augmented | Science Documentary.

2077 — 10 Seconds to the Future — Mutation: https://youtu.be/qTkHD55kcaw.

With Augmented Humanity we will travel from the US to Japan, into the heart of secret labs of the most borderline scientists in the world, who try to push the boundaries of life through technology. Robotics is an important step, but the future of our species is not in a massive substitution by robots, on the contrary, robotics and technology must be used to improve the human being.
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Free Documentary is dedicated to bringing high-class documentaries to you on YouTube for free. With the latest camera equipment used by well-known filmmakers working for famous production studios. You will see fascinating shots from the deep seas and up in the air, capturing great stories and pictures from everything our beautiful and interesting planet has to offer.

Enjoy stories about nature, wildlife, culture, people, history and more to come.

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“Welcome back to our channel! Today, we’re diving into an extraordinary and futuristic topic: Neural Enhancement: Human 2.0. Imagine a future where AI-driven technologies can enhance human brain functions, creating a new version of humanity with unparalleled cognitive and physical abilities. Let’s explore this revolutionary concept! 🧬🧠 #Science #Tech”

Segment 1: the concept of neural enhancement.

“Imagine a world where humans can enhance their natural abilities through advanced technology. 🧠✨ Neural enhancement uses AI and neural interfaces to boost cognitive functions, improve memory, and enhance physical capabilities, creating ‘Human 2.0.’ 🌟 #NeuralEnhancement #TechInnovation”

Segment 2: how neural enhancement works.

“So, how does neural enhancement work? 🤖🧬 Using brain-computer interfaces (BCIs), neural implants, and AI algorithms, scientists can directly interact with the brain’s neural networks. These technologies can stimulate and enhance brain functions, improving everything from memory and learning speed to physical coordination and strength. 🌐✨ #AI #NeuroTech”

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AI and Human Enhancement:

A groundbreaking new AI system is exploring the limits of human potential, developing technologies that can enhance our physical and cognitive abilities. 🤖 By analyzing biological data and applying advanced engineering principles, the AI can identify ways to improve human performance.

How AI Enhances Human Abilities:

AI-powered human enhancement technologies can:

Enhance Physical Abilities: Increase strength, speed, and endurance.
Improve Cognitive Abilities: Enhance memory, intelligence, and creativity.
Extend Lifespan: Slow down the aging process and increase lifespan.
The Ethical Implications:

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A new analysis of the sky has finally confirmed where the missing half of the Universe’s visible matter has been hiding.

In the space around galaxies, it lurks as huge, invisible clouds of ionized hydrogen. Normally, this would be impossible to see – but a large international team of astronomers and astrophysicists has developed a technique that reveals its hiding places, out there in the darkness amidst the stars.

Survey programs confirm the missing half of the Universe’s material takes the form of an intergalactic mist of hydrogen expelled farther from the active cores of galaxies than anybody previously thought.

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An NIH-funded project leverages advanced synapse imaging to monitor real-time neuronal changes during learning, unveiling new insights that could inspire next-generation brain-like AI systems. How do we learn something new? How do tasks at a new job, the lyrics to the latest hit song, or directio

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Next-generation DNA sequencing (NGS)—the same technology which is powering the development of tailor-made medicines, cancer diagnostics, infectious disease tracking, and gene research—could become a prime target for hackers.

A study published in IEEE Access highlights growing concerns over how this powerful sequencing tool—if left unsecured—could be exploited for , privacy violations, and even future biothreats.

Led by Dr. Nasreen Anjum from the University of Portsmouth’s School of Computing, it is the first comprehensive research study of cyber-biosecurity threats across the entire NGS workflow.

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Perovskite photovoltaics (PV) are poised at the brink of commercialization, yet stability remains the foremost hurdle to overcome for widespread adoption. While extensive research has addressed the degradation of perovskite PV through accelerated indoor testing, outdoor testing remains relatively underexplored and primarily focused on small cells rather than modules.

This gap underscores the urgent need to comprehensively study outdoor degradation processes. Understanding how perovskite PV modules perform under real-world is crucial for advancing toward commercial viability.

In our work published in ACS Energy Letters, we present a two-year outdoor evaluation of perovskite modules, shedding light on their degradation under real-world conditions. Our findings highlight a significant milestone in perovskite PV research, with the most robust module maintaining 78% of its initial performance after one year. Performance loss rates during the burn-in period were found to be about 7%–8% per month.

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A technology for hydrogen (H2) production has been developed by a team of researchers led by Professors Seungho Cho and Kwanyong Seo from the School of Energy and Chemical Engineering at UNIST, in collaboration with Professor Ji-Wook Jang’s team from the Department of Materials Science and Engineering at UNIST.

Their research is published in the journal Nature Communications.

This innovative method utilizes biomass derived from sugarcane waste and silicon photoelectrodes to generate H2 exclusively using sunlight, achieving a production rate four times higher than the commercialization benchmark set by the U.S. Department of Energy (DOE).

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A team of researchers from the Institute for Basic Science, Yonsei University, and the Max Planck Institute have developed a new artificial intelligence (AI) technique that brings machine vision closer to how the human brain processes images. Called Lp-Convolution, this method improves the accuracy and efficiency of image recognition systems while reducing the computational burden of existing AI models.

The is remarkably efficient at identifying key details in complex scenes, an ability that traditional AI systems have struggled to replicate. Convolutional Neural Networks (CNNs)—the most widely used AI model for image recognition—process images using small, square-shaped filters. While effective, this rigid approach limits their ability to capture broader patterns in fragmented data.

More recently, vision transformers have shown superior performance by analyzing entire images at once, but they require massive computational power and large datasets, making them impractical for many .

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