Lifeboat Foundation

For decades, researchers around the world have searched for ways to use solar power to generate the key reaction for producing hydrogen as a clean energy source—splitting water molecules to form hydrogen and oxygen. However, such efforts have mostly failed because doing it well was too costly, and trying to do it at a low cost led to poor performance.

Now, researchers from The University of Texas at Austin have found a low-cost way to solve one half of the equation, using sunlight to efficiently split off oxygen molecules from water. The finding, published recently in Nature Communications, represents a step forward toward greater adoption of hydrogen as a key part of our energy infrastructure.

As early as the 1970s, researchers were investigating the possibility of using solar energy to generate hydrogen. But the inability to find materials with the combination of properties needed for a device that can perform the key chemical reactions efficiently has kept it from becoming a mainstream method.

Nobody tell Steve Ballmer.

CBL-Mariner, for internal use with cloud-and edge-based projects.

It’s reported the Pegasus spyware can capture a user’s keystrokes, intercept communications, track their device and tap into their camera and microphone.

Check out our new promo for #transvision #future Summit 2021! Get your tickets! -> www.TransVisionMadrid.com There will be talks about #longevity #artificialintelligence #cryonics and much much more. You will also be able to network with speakers and attendees during 5 optional dinner/cocktails, and 2 tours of several UNESCO heritage sites and historical places: Ávila, Segovia, Monsaterio de El Escorial, Valle de los Caídos (Valley of the Fallen), Aranjuez & Toledo.

Humanity Plus Humanity Plus Humanity Plus Magazine MUTISHAN Interactive Vivian Francos #SEOHashtag Alcor Life Extension Foundation Cryonics Institute Cryonics Institute SENS Research Foundation SENS Research Foundation Posthuman Network Posthuman Network Cryonics4U Longevity Conferences Longevity for All U.S. Transhumanist Party Transhumanist Party Australia Transhumanist Party Virtual Rational Transhumanism Singularity University Ray Kurzweil Singularity Singularity Hub Ray Kurzweil’s Singularity Singularity Network Transhumanismo Brasil Transhumanismo Brasil TRANSHUMANISMO Christian Transhumanist Association Mormon Transhumanist Association SingularityNET Singularitarianism Foresight Institute Lifeboat Foundation Lifeboat Foundation Machine Intelligence Research Institute KrioRus The Hedonistic Imperative — Paradise Engineering.

Continue reading “#TransVision Future Summit 2021 — Promo 1 (English)” »

Paper referenced in the video:

Potential reversal of epigenetic age using a diet and lifestyle.
intervention: a pilot randomized clinical trial.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8064200/

Continue reading “Epigenetic Age Reduction Within 8 Weeks: Real Effect Or Statistical Noise?” »

With help from Microsoft’s artificial intelligence systems, the BeachBot learns how to better find the strewn filters, even if they’re partially buried in the sand. It then scoops these cigarette butts up from the sand and disposes of them in an internal bin. Later, people empty that bin into a trash container. Rolling atop the sand on four puffy-looking wheels, the beach-cleaning robot uses two onboard cameras to look ahead (to avoid people and objects) and to look down.

The BeachBot is still in early learning via the software giant’s Trove AI system, which helps provide image sets for this kind of machine learning task. Teaching the bot how to find its prey requires a lot of people. TechTics must show the beach rover (and, specifically, the AI system) thousands of photos of cigarette butts, all lying about in various states, such as partially hidden, so it can recognize and remember them.

Continue reading “AI-enabled BeachBot robot to clean up cigarette butts on beaches” »

A new wearable brain-machine interface (BMI) system could improve the quality of life for people with motor dysfunction or paralysis, even those struggling with locked-in syndrome—when a person is fully conscious but unable to move or communicate.

A multi-institutional, international team of researchers led by the lab of Woon-Hong Yeo at the Georgia Institute of Technology combined wireless soft scalp electronics and virtual reality in a BMI system that allows the user to imagine an action and wirelessly control a wheelchair or robotic arm.

The team, which included researchers from the University of Kent (United Kingdom) and Yonsei University (Republic of Korea), describes the new motor imagery-based BMI system this month in the journal Advanced Science.

One of the scientists prodding and poking the Kelly brothers is Prof Christopher E Mason, the lead geneticist on the Twins Study. Mason’s lab at Cornell University is nothing if not ambitious. Its work centres on a “500-year plan for the survival of the human species on Earth, in space, and on other planets.”

As well as studying what happens to astronauts, it involves laying the genetic groundwork for humans to live among the stars. Mason envisions a future in which the human genome can be bioengineered to adapt to almost any environment, augmented with genes from other species that allow us to explore and settle the farthest corners of the Universe.

The prediction of protein structures from amino acid sequence information alone, known as the “protein folding problem,” has been an important open research question for more than 50 years. In the fall of 2020, DeepMind’s neural network model AlphaFold took a huge leap forward in solving this problem, outperforming some 100 other teams in the Critical Assessment of Structure Prediction (CASP) challenge, regarded as the gold-standard accuracy assessment for protein structure prediction. The success of the novel approach is considered a milestone in protein structure prediction.

This week, the DeepMind paper Highly Accurate Protein Structure Prediction with AlphaFold was published in the prestigious scientific journal Nature. The paper introduces AlphaFold2, a completely redesigned and open-sourced model that can predict protein structures with atomic-level accuracy.

Although machine learning researchers have long sought to develop computational methods for predicting 3D protein structures from protein sequences, there had been limited progress along this path, chiefly due to the computational intractability of molecular simulation, the context-dependence of protein stability, and the difficulty of producing sufficiently accurate models for protein physics.