Lifeboat Foundation: Safeguarding Humanity
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Blog – Page 9,175

T HE CONTEST between China and America, the world’s two superpowers, has many dimensions, from skirmishes over steel quotas to squabbles over student visas. One of the most alarming and least understood is the race towards artificial-intelligence-enabled warfare. Both countries are investing large sums in militarised artificial intelligence (AI), from autonomous robots to software that gives generals rapid tactical advice in the heat of battle. China frets that America has an edge thanks to the breakthroughs of Western companies, such as their successes in sophisticated strategy games. America fears that China’s autocrats have free access to copious data and can enlist local tech firms on national service. Neither side wants to fall behind.

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The average North American life – which values gregarious personalities, extroverted social styles, clamorous entertainment, bright lights and big sound – is simply too much for many sensitive people.

“This world is not built for sensitive people. In fact, our world is designed perfectly for those who are detached,” wrote Nicole Hollingshead, a Canadian empowerment blogger, in response to a Quora question about the highly sensitive person (HSP) trait. Her statement illustrates the way that most HSPs feel when comparing themselves to their seemingly unfazed non-HSP peers.

The term highly sensitive person was coined in the 1990s by husband and wife psychologist team Elaine Aron and Arthur Aron. The HSP trait is synonymous with the term sensory processing sensitivity (SPS) and is thought to be genetically determined and present at birth. SPS exists in 15–20 percent of the human population, and has been observed in over 100 nonhuman species.

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A talk by Dave Bacon during the Industry session of the 14th Conference on the Theory of Quantum Computation, Communication and Cryptography (TQC 2019), Day 3. TQC 2019 was hosted June 3–5, 2019 by the Joint Center for Quantum Information and Computer Science at the University of Maryland (QuICS). More information about TQC can be found at https://www.tqcconference.org.

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Something odd is stirring in the depths of Canada’s Kidd Mine. The zinc and copper mine, 350 miles northwest of Toronto, is the deepest spot ever explored on land and the reservoir of the oldest known water. And yet 7,900 feet below the surface, in perpetual darkness and in waters that have remained undisturbed for up to two billion years, the mine is teeming with life.

Many scientists had doubted that anything could live under such extreme conditions. But in July, a team led by University of Toronto geologist Barbara Sherwood Lollar reported that the mine’s dark, deep water harbors a population of remarkable microbes.

The single-celled organisms don’t need oxygen because they breathe sulfur compounds. Nor do they need sunlight. Instead, they live off chemicals in the surrounding rock — in particular, the glittery mineral pyrite, commonly known as fool’s gold.

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A team of scientists at Freie Universität Berlin has developed an Artificial Intelligence (AI) method that provides a fundamentally new solution of the “sampling problem” in statistical physics. The sampling problem is that important properties of materials and molecules can practically not be computed by directly simulating the motion of atoms in the computer because the required computational capacities are too vast even for supercomputers. The team developed a deep learning method that speeds up these calculations massively, making them feasible for previously intractable applications. “AI is changing all areas of our life, including the way we do science,” explains Dr. Frank Noé, professor at Freie Universität Berlin and main author of the study. Several years ago, so-called deep learning methods bested human experts in pattern recognition—be it the reading of handwritten texts or the recognition of cancer cells from medical images. “Since these breakthroughs, AI research has skyrocketed. Every day, we see new developments in application areas where traditional methods have left us stuck for years. We believe our approach could be such an advance for the field of statistical physics.” The results were published in Science.

Statistical Physics aims at the calculation of properties of materials or molecules based on the interactions of their constituent components—be it a metal’s melting temperature, or whether an antibiotic can bind to the molecules of a bacterium and thereby disable it. With statistical methods, such properties can be calculated in the computer, and the properties of the material or the efficiency of a specific medication can be improved. One of the main problems when doing this calculation is the vast computational cost, explains Simon Olsson, a coauthor of the study: “In principle we would have to consider every single structure, that means every way to position all the atoms in space, compute its probability, and then take their average. But this is impossible because the number of possible structures is astronomically large even for small molecules.

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When Elon Musk and the team at Tesla unveiled the Tesla Roadster 2.0, a new stake was pounded into the tarmac, cementing the new Roadster and electric cars as the performance kings in nearly every meaningful category. It puts supercars to shame and at a fraction of the price.

With such a high bar being set at such a low price point, a no holds barred electric supercar seemed to be the only thing that could possibly top the high marks set by the new Tesla Roadster. Travel with me over to unlikely Sveta Nedelja, Croatia, where Mate Rimac and his motley crew of twisted engineering geniuses at Rimac Automobili assemble battery powered beasts that shake the boots off even the most seasoned track driver.

A 30 million euro round of fundraising last year from Asia’s largest battery manufacturer, Camel Group Ltd laid the foundation for Rimac’s new Concept_Two and now, finally, the beast is loose with full specs and a photo shoot to get fans drooling.

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Microsoft’s patent filing recently made public has juiced up curiosity over what Microsoft might debut sooner or later as its own version of a folding computing device.

MSPoweruser took the view that “Microsoft is trying hard to bring its first foldable to the market, like every other big tech companies.” It’s apparent now that “the Redmond giant has filed yet another for its much-awaited foldable Windows 10 device.”

The patent “Multi-Sided Electromagnetic Coil Access Assembly” was filed in February last year but only recently made public. It is particularly drawing interest because, as TechRadar said, what would make this foldable idea work would be “a multi-sided electromagnetic coil for wireless charging.”

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Researchers have designed a machine learning algorithm that predicts the outcome of chemical reactions with much higher accuracy than trained chemists and suggests ways to make complex molecules, removing a significant hurdle in drug discovery.

University of Cambridge researchers have shown that an algorithm can predict the outcomes of complex reactions with over 90% accuracy, outperforming trained chemists. The algorithm also shows chemists how to make target compounds, providing the chemical “map” to the desired destination. The results are reported in two studies in the journals ACS Central Science and Chemical Communications.

A central challenge in drug discovery and materials science is finding ways to make complicated organic molecules by chemically joining together simpler building blocks. The problem is that those building blocks often react in unexpected ways.

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