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It’s not often that messing around in the lab has produced a fundamental breakthrough, à la Michael Faraday with his magnets and prisms. Even more uncommon is the discovery of the same thing by two research teams at the same time: Newton and Leibniz come to mind. But every so often, even the rarest of events does happen. The summer of 2021 has been a banner season for condensed-matter physics. Three separate teams of researchers have created a crystal made entirely of electrons — and one of them actually did it by accident.

The researchers were working with single-atom-thick semiconductors, cooled to ultra-low temperatures. One team, led by Hongkun Park along with Eugene Demler, both of Harvard, discovered that when very specific numbers of electrons were present in the layers of these slivers of semiconductor, the electrons stopped in their tracks and stood “mysteriously still.” Eventually colleagues recalled an old idea having to do with Wigner crystals, which were one of those things that exist on paper and in theory but had never been verified in life. Wigner had calculated that because of mutual electrostatic repulsion, electrons in a monolayer would assume a tri-grid pattern.

Park and Demler’s group was not alone in its travails. “A group of theoretical physicists led by Eugene Demler of Harvard University, who is moving to ETH [ETH Zurich, in Switzerland] this year, had calculated theoretically how that effect should show up in the observed excitation frequencies of the excitons – and that’s exactly what we observed in the lab,” said Ataç Imamoğlu, himself from ETH. Imamoğlu’s group used the same technique to document the formation of a Wigner crystal.

Japan may have just changed the future of space technology! Join us… to find out more!

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What does space travel look like in the future? A recent breakthrough in Japan might’ve changed the direction that science is taking, and in a BIG way! In this video, Unveiled takes a closer look at rotating detonation engines, a new and efficient way to zoom spaceships through the void!

This is Unveiled, giving you incredible answers to extraordinary questions!

Find more amazing videos for your curiosity here:
What If NASA Explored Antarctica Instead? — https://youtu.be/oBPs7lyaHD8
Are We the Creation of a Type V Civilization? — https://youtu.be/T_u4lGDs3dM

0:00 Start.
0:35 The Solar System.
2:00 JAXA’s Rotating Detonation Engine.
4:08 The Future of RDE Technology.
6:10 Future Destinations.
7:31 Conclusions.

What is your take on this Chris Smedley?


Please be sensitive to any artificial intelligence you encounter today. A UK appeals court just ruled that AI systems cannot submit or hold patents, as software is not human and therefore lacks human rights. Several courtrooms around the world have come to the same conclusion, despite the efforts of a very enthusiastic inventor.

Dr. Stephen Thaler has repeatedly filed patents on behalf of his AI, called DABUS. He claims that this AI should be credited for the inventions that it’s helped to produce. But patent offices disagree. After Dr. Thaler refused to resubmit his patents under a real name, the UK Intellectual Property Office pulled him from the registration process.

Our friend Dr. Thaler responded by taking the Intellectual Property Office to court. And predictably, the body rejected his case. So Dr. Thaler made an appeal, and again, he lost.

The key is an innovation that’s being called ‘light beads microscopy’. It improves on current two-photon microscopy, using lasers to trigger introduced fluorescence in living cells. As the cells are lit up, scientists can see how they’re moving and interacting.

With light beads microscopy, scientists can get the speed, scale, and resolution required to map a mouse brain in detail as its neural activity changes. The near-simultaneous tracking can last for as long as the light beads are able to stay illuminated.

Welcome to AI book reviews, a series of posts that explore the latest literature on artificial intelligence.

Recent advances in deep learning have rekindled interest in the imminence of machines that can think and act like humans, or artificial general intelligence. By following the path of building bigger and better neural networks, the thinking goes, we will be able to get closer and closer to creating a digital version of the human brain.

But this is a myth, argues computer scientist Erik Larson, and all evidence suggests that human and machine intelligence are radically different. Larson’s new book, The Myth of Artificial Intelligence: Why Computers Can’t Think the Way We Do, discusses how widely publicized misconceptions about intelligence and inference have led AI research down narrow paths that are limiting innovation and scientific discoveries.

Prof. Chuanfeng Li and Prof. Zongquan Zhou from University of Science and Technology of China (USTC) of the Chinese Academy of Sciences (CAS) innovatively raised and realized noiseless photon echo (NLPE) protocol. The research of entire originality reduced the noise by 670 times compared with previous strategies and achieved solid quantum memory with high fidelity. The results were published in Nature Communications.

First observed by Erwin Hahn in 1,950 photon echo is a fundamental physical interaction between light and matter as well as an essential tool for the manipulation of electromagnetic fields. However, the intense spontaneous noise emission generated has the same frequency as the signal, it is impossible to separate them in principle. Previous protocols, such as atomic frequency comb and the revival of silenced echo, failed to eliminate the spontaneous noise emission as much as needed.

In this study, the researchers implemented NLPE protocol in Eu3+:Y2SiO5 crystal to serve as an optical quantum memory and applied a four-level aromic system to suppress the noise.

Reinforcement learning is an interesting area of machine learning (ML) that has advanced rapidly in recent years. AlphaGo is one such RL-based computer program that has defeated a professional human Go player, a breakthrough that experts feel was a decade ahead of its time.

Reinforcement learning differs from supervised learning because it does not need the labelled input/output pairings for training or the explicit correction of sub-optimal actions. Instead, it investigates how intelligent agents should behave in a particular situation to maximize the concept of cumulative reward.

This is a huge plus when working with real-world applications that don’t come with a tonne of highly curated observations. Furthermore, when confronted with a new circumstance, RL agents can acquire methods that allow them to behave even in an unclear and changing environment, relying on their best estimates at the proper action.