Amid an early start to the Northern Hemisphere influenza season a new variant of the virus is rapidly gaining ground — but vaccination remains the “most effective defence”, the UN health agency said on Tuesday.
Get the latest international news and world events from around the world.
Everything in the universe is a quantum wave
A radical new interpretation of quantum mechanics is offered here. Professor of Quantum Information Science at the University of Oxford, Vlatko Vedral, argues that everything in the universe is a quantum wave. The difficulty of uniting the classical world and the quantum world is overcome; everything is quantum, and the quantum gives rise to the classical. His theory also overcomes the measurement problem, the observer problem, and the problem of quantum entanglement (spooky action at a distance). Poof goes the classical world!
There are, I believe, two main reasons why physics seems stuck at present. The last revolution was quantum mechanics and it began with Heisenberg’s famous paper exactly 100 years ago. And since then, not a single experiment has challenged the quantum description of reality. Not one. The first reason for this century-long absence of a new fundamental theory is that we simply haven’t had the appropriate experimental technology to probe regions where something could go wrong. This has now changed rapidly with the ongoing worldwide race to build a universal quantum computer. The technologies that go into this enterprise and that are being pursued by all the major industrial players are becoming sophisticated enough to test fundamental physics in a non-trivial way. However, there is a second reason for being stuck. It is the fact that we still haven’t agreed on the way to understand quantum mechanics. It is for this reason that I’d like to offer my own interpretation.
Watch: Vine-like robot lifts delicate cargo — including human bodies
Although they’re constantly improving, robots aren’t necessarily known for their gentle touch. A new robotic system from MIT and Stanford takes a unique stab at changing that, with a robot that uses vine-like tendrils to do its lifting.
The system the engineers developed consists of a series of pneumatic tubes that deploy from a pressurized box on one side of a robotic arm, use air pressure to snake under or around a specific object, then rejoin the arm on the other side where they are clamped in place. Once clamped, the arm itself can move, or the tube can be wound up to lift or rotate the object in its grasp. The ability to deploy the tubes and then recapture them is the real breakthrough here, improving on previous vine-based robots by allowing the system to close its own loops.
“People might assume that in order to grab something, you just reach out and grab it,” says study co-author Kentaro Barhydt, from MIT’s Department of Mechanical Engineering. “But there are different stages, such as positioning and holding. By transforming between open and closed loops, we can achieve new levels of performance by leveraging the advantages of both forms for their respective stages.”
Ignorance Is the Greatest Evil: Why Certainty Does More Harm Than Malice
The most dangerous people are not the malicious ones. They’re the ones who are certain they’re right.
Most of the harm in history has been done by people who believed they knew what was right — and acted on that belief without recognizing the limits of their own knowledge.
Socrates understood this long ago: the most dangerous is not *not knowing*, but *not knowing that we don’t know* — especially when paired with power.
Read on to find why:
* certainty often does more harm than malice * humility isn’t weakness, it’s discipline * action doesn’t require certainty, only responsibility * and why, in an age of systems, algorithms, and institutions, has quietly become structural.
This isn’t an argument for paralysis or relativism.
It’s an argument for acting without pretending we are infallible.
Quality control: Neatly arranging crystal growth to make fine thin films
Table salt and refined sugar look white to our eyes, but that is only because their individual colorless crystals scatter visible light. This feature of crystals is not always desirable when it comes to materials for optical and electrical devices, however.
Metal-organic frameworks are one such material. Crystalline with micropores, thin films of these nanomaterials have been attracting attention as a next-generation material that could also have an impact on environmental issues such as hydrogen storage and carbon dioxide capture.
An Osaka Metropolitan University Graduate School of Engineering team has found a way to control the growth of crystals on such thin films so that light scattering is reduced significantly.
New way to read data in antiferromagnets unlocks their use as computer memory
Scientists led by Nanyang Technological University, Singapore (NTU Singapore) investigators have made a significant advance in developing alternative materials for the high-speed memory chips that let computers access information quickly and that bypass the limitations of existing materials.
They have discovered a way that allows them to make sense of previously hard-to-read data stored in these alternative materials, known as antiferromagnets.
Researchers consider antiferromagnets to be attractive materials for making computer memory chips because they are potentially more energy efficient than traditional ones made of silicon. Memory chips made of antiferromagnets are not subject to the size and speed constraints nor corruption issues that are inherent to chips made with certain magnetic materials.
AI learns to build simple equations for complex systems
A research team at Duke University has developed a new AI framework that can uncover simple, understandable rules that govern some of the most complex dynamics found in nature and technology.
The AI system works much like how history’s great “dynamicists”—those who study systems that change over time—discovered many laws of physics that govern such systems’ behaviors. Similar to how Newton, the first dynamicist, derived the equations that connect force and movement, the AI takes data about how complex systems evolve over time and generates equations that accurately describe them.
The AI, however, can go even further than human minds, untangling complicated nonlinear systems with hundreds, if not thousands, of variables into simpler rules with fewer dimensions.