Lifeboat Foundation

Some have described the last several millennia of human dominion over the earth’s resources as the anthropocene, deriving from the Greek “anthropo” meaning human, and “cene” meaning recent. The last century in particular has been dubbed the fourth industrial revolution, due to the pace of technological innovation ushered in by the advent of computers in the middle of the 20th century.

In the past seventy years, computation has transformed every aspect of society, enabling efficient production at an accelerated rate, displacing human labour from chiefly production to services, and exponentially augmenting information storage, generation, and transmission through telecommunications.

How did we get here? Fundamentally, technological advancement draws on existing science. Without an understanding of the nature of electromagnetism and the structure of atoms, we wouldn’t have electricity and the integrated circuitry that power computers. It was only a matter of time, then, before we thought of exploiting the most accurate, fundamental description of physical reality provided by quantum mechanics for computation.

Windows 11 Pro ships with a security feature that could severely hamper your solid-state drive’s performance. Fortunately, it is easy enough to turn off but some might not even know it is enabled by default.

BitLocker encryption in Windows 11 Pro is designed to safeguard data and ensure it is only accessible by authorized individuals, but it comes with a steep performance penalty. To find out how much of an impact it could have, Tom’s Hardware recently conducted tests under three scenarios: unencrypted (no BitLocker), software-enabled BitLocker (the Windows 11 Pro default), and hardware-based BitLocker.

The crew used a 4 TB Samsung 990 Pro SSD running Windows 11 Pro (22H2, with all patches installed) paired with an Intel Core i9-12900K and 32 GB of DDR4 RAM for testing.

This model of computing would use 1/1000th of the energy today’s computers do. So why aren’t we using it?

A new electrical method to conveniently change the direction of electron flow in some quantum materials could have implications for the development of next-generation electronic devices and quantum computers.

A team of researchers from Penn State developed and demonstrated the method in materials that exhibit the quantum anomalous Hall (QAH) effect—a phenomenon in which the flow of electrons along the edge of a material does not lose energy. The team described the work in a paper in the journal Nature Materials.

“As electronic devices get smaller and computational demands get larger, it is increasingly important to find ways to improve the efficiency of information transfer, which includes the control of electron flow,” said Cui-Zu Chang, Henry W. Knerr Early Career Professor and associate professor of physics at Penn State and co-corresponding author of the paper. “The QAH effect is promising because there is no energy loss as electrons flow along the edges of materials.”

The super-special material graphene continues to surprise and fascinate scientists, this time revealing a rare electronic state termed ‘ferro-valleytricity’, which occurs when graphene is stacked up in a particular five-layer combination.

When in this new state, the graphene stack exhibits weird and wonderful magnetic and electronic behavior, as reported by researchers from the Massachusetts Institute of Technology (MIT), Harvard University, and the National Institute for Materials Science in Japan.

Using graphene in this way could help in the development of both classical and quantum computers, according to the team, especially in terms of creating data storage solutions that offer large capacities but that also need relatively little energy to run.

What makes a quantum computer more powerful than a classical computer? It’s a surprisingly subtle question that physicists are still grappling with, decades into the quantum age.

As Shor looked for applications for his quantum period-finding algorithm, he rediscovered a previously known but obscure mathematical theorem: For every number, there exists a periodic function whose periods are related to the number’s prime factors. So if there’s a number you want to factor, you can compute the corresponding function and then solve the problem using period finding — “exactly what quantum computers are so good at,” Regev said.

On a classical computer, this would be an agonizingly slow way to factor a large number — slower even than trying every possible factor. But Shor’s method speeds up the process exponentially, making period finding an ideal way to construct a fast quantum factoring algorithm.

Shor’s algorithm was one of a few key early results that transformed quantum computing from an obscure subfield of theoretical computer science to the juggernaut it is today. But putting the algorithm into practice is a daunting task, because quantum computers are notoriously susceptible to errors: In addition to the qubits required to perform their computations, they need many others doing extra work to keep them from failing. A recent paper by Ekerå and the Google researcher Craig Gidney estimates that using Shor’s algorithm to factor a security-standard 2,048-bit number (about 600 digits long) would require a quantum computer with 20 million qubits. Today’s state-of-the-art machines have at most a few hundred.

From every study they could find, including research that was never published, research by the military and private businesses, and research that had sat dormant on hard drives for decades to find out how personality and intelligence relate to each another.⁠
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Fourteen years later, the massive data catalog has dropped. It contains 79 personality traits and 97 cognitive abilities from 1,300 studies from over 50 countries including over 2 million participants. And an early meta-analysis published in the Proceedings of the National Academy of Sciences shows that personality and intelligence relate in some surprising ways.⁠
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Personality describes how someone generally thinks, feels, and behaves. Intelligence (termed cognitive ability by the researchers) describes how well someone can understand and apply information.⁠
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Here are 3 of the 5 findings:⁠
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1. Extraversion, a measure of sociality and enthusiasm, was only negligibly related to intelligence overall. However, the activity facet more strongly correlated, and (surprisingly) sociability had a small negative relationship with some cognitive abilities. ⁠
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2. Neuroticism encompasses negative emotionality, which can inhibit advanced thinking. Despite the trope of the moody genius, perhaps it’s no surprise that higher levels of neuroticism predicted lower levels of intelligence, albeit weakly. The uneven temper and depression facets were particularly strong predictors of decreased intelligence. ⁠
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3. Conscientiousness, a measure of self-regulation and orderliness, correlated positively with intelligence overall. But some facets, including cautiousness and routine seeking, predicted lower cognitive abilities.⁠
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For the rest of the findings, along with something interesting they learned about extraversion, click here: https://www.freethink.com/society/study-personality-intellig…jjjrtebdkm.

Article by Elizabeth Gilbert.

There is an intense, worldwide search for novel materials to build computer microchips with that are not based on classic transistors but on much more energy-saving, brain-like components. However, whereas the theoretical basis for classic transistor-based digital computers is solid, there are no real theoretical guidelines for the creation of brain-like computers.

Such a theory would be absolutely necessary to put the efforts that go into engineering new kinds of microchips on solid ground, argues Herbert Jaeger, Professor of Computing in Cognitive Materials at the University of Groningen.

Computers have, so far, relied on stable switches that can be off or on, usually transistors. These digital computers are logical machines and their programming is also based on logical reasoning. For decades, computers have become more powerful by further miniaturization of the transistors, but this process is now approaching a physical limit. That is why scientists are working to find new materials to make more versatile switches, which could use more values than just the digitals 0 or 1.