Lifeboat Foundation

A group of astronomers from the universities of Groningen, Naples and Bonn has developed a method that finds gravitational lenses in enormous piles of observations. The method is based on the same artificial intelligence algorithm that Google, Facebook and Tesla have been using in the last years. The researchers published their method and 56 new gravitational lens candidates in the November issue of Monthly Notices of the Royal Astronomical Society.

When a galaxy is hidden behind another galaxy, we can sometimes see the hidden one around the front system. This phenomenon is called a gravitational lens, because it emerges from Einstein’s general relativity theory which says that mass can bend light. Astronomers search for gravitational lenses because they help in the research of dark matter.

The hunt for gravitational lenses is painstaking. Astronomers have to sort thousands of images. They are assisted by enthusiastic volunteers around the world. So far, the search was more or less in line with the availability of new images. But thanks to new observations with special telescopes that reflect large sections of the sky, millions of images are added. Humans cannot keep up with that pace.

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Particle physics data sorted by quantum machine learning but still needs work.

One of the most defining scientific discoveries in recent decades is the development of induced pluripotent stem cells, which lets scientists revert adult cells back into an embryonic-like blank state and then manipulating them to become a particular kind of tissue.

But now a new model could do away with this time-consuming process, taking out the middle step and directly programming cells to become whatever we want them to be.

“Cells in our body always self-specialise,” explains bioinformatics researcher Indika Rajapakse from the University of Michigan.

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A lobbying group representing top artificial-intelligence companies including Amazon.com Inc., Facebook Inc. and Google issued a warning to lawmakers on Tuesday: hands off our algorithms.

Company will use algorithm similar to new Go champ to find proteins.

An international, interdisciplinary research team of scientists has come up with a machine-learning method that predicts molecular behavior, a breakthrough that can aid in the development of pharmaceuticals and the design of new molecules that can be used to enhance the performance of emerging battery technologies, solar cells, and digital displays.

The work appears in the journal Nature Communications.

“By identifying patterns in molecular behavior, the learning algorithm or ‘machine’ we created builds a knowledge base about atomic interactions within a molecule and then draws on that information to predict new phenomena,” explains New York University’s Mark Tuckerman, a professor of chemistry and mathematics and one of the paper’s primary authors.

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Intel says it is shipping an experimental quantum computing chip to research partners in The Netherlands today. The company hopes to demonstrate that its packaging and integration skills give it an edge in the race to produce practical quantum computers.

The chip contains 17 superconducting qubits—the quantum computer’s fundamental component. According to Jim Clarke, Intel’s director of quantum hardware, the company chose 17 qubits because it’s the minimum needed to perform surface code error correction, an algorithm thought to be necessary to scaling up quantum computers to useful sizes.

Intel’s research partners, at the TU Delft and TNO research center Qutech, will be testing the individual qubits’ abilities as well as performing surface code error correction and other algorithms.

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We are using A.I. and Computer Vision Techniques to Determine Age and Assess the Effect of Therapies Against Aging in Mice, Increasing the Pace of Life Extension Research. Please subscribe, share, and fund our campaign today! ►Campaign Link: https://www.lifespan.io/mouseage ►Subscribe: https://www.youtube.com/user/LifespanIO?sub_confirmation=1

MouseAGE is working to develop the first photographic biomarker of aging in mice to help validate potential anti-aging interventions, save animal lives, and greatly speed up the pace of longevity research.

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Pioneered by Erik Verlinde, the idea is that gravity emerges from a more fundamental phenomenon in the Universe, and that phenomenon is entropy.

“Sound waves emerge from molecular interactions; atoms emerge from quarks, gluons and electrons and the strong and electromagnetic interactions; planetary systems emerge from gravitation in General Relativity. But in the idea of entropic gravity — as well as some other scenarios (like qbits) — gravitation or even space and time themselves might emerge from other entities in a similar fashion. There are well-known, close relationships between the equations that govern thermodynamics and the ones that govern gravitation. It’s known that the laws of thermodynamics emerge from the more fundamental field of statistical mechanics, but is there something out there more fundamental from which gravity emerges? That’s the idea of entropic gravity.”

Continue reading “Are Space, Time, And Gravity All Just Illusions?” »