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A Lab Version of Planetary Atmospheres

Researchers recreate key features of atmospheric turbulence in a meter-sized rotating cylinder.

Atmospheric turbulence encompasses a wide range of flow patterns, from 10-m-wide eddies to 1000-km-long wind streams. Geoscientists want to understand how energy and rotational motion transfer (or “cascade”) from one length scale to another, but atmospheric observations have not provided clear answers. A new model of the atmosphere consisting of fluid in a rotating, meter-wide cylinder is able to reproduce key features of observed turbulence [1]. Using video tracking, researchers mapped out the flow velocity in this system, uncovering the dominant role of a “vorticity” transfer that distributes rotational motion from large vortices into smaller ones. This form of cascade may explain the energy distribution in large-scale turbulence on Earth as well as on other planets.

Turbulence can be characterized by a kinetic energy spectrum, which indicates the amount of energy found in fluctuations at each length scale. The typical turbulence spectrum has a mathematical form called a power law, in which the energy density steadily decreases from large to small scales. Fluid dynamics models of Earth’s atmosphere have predicted that the power law should be relatively flat at large scales (with an exponent of −5÷3) and steeper at small scales (with an exponent of −3). However, these predictions aren’t supported by observations. “The basic shape of the spectrum is all wrong,” says Peter Read from the University of Oxford in the UK. Data taken by airplanes have revealed a spectrum that starts out steep at large scales (greater than 500 km) and becomes flatter at small scales.

Quantum computers could have a fundamental limit after all

The performance of quantum computers could cap out after around 1,000 qubits, according to a new analysis published in the Proceedings of the National Academy of Sciences. Through new calculations, Tim Palmer at the University of Oxford has reconsidered the mathematical foundations underlying the quantum principles behind the technology, concluding that restrictions on the information-carrying capacity of large quantum systems could make their computing power far more limited than many researchers predict.

For some time, quantum physicists have been growing increasingly excited—and concerned—about the seemingly limitless potential of quantum computers. In a classical computer, information content generally grows linearly as the number of bits increases. But in a quantum computer, each extra qubit doubles the number of quantum states the system can occupy.

Since these states can encode multiple possibilities at the same time, the overall system appears to become exponentially more powerful with each added qubit—at least according to our current understanding of quantum mechanics.

Why Does 2 + 2 = 4? What Math Teaches Us About Deep Reality

Is math something humans invent—or something we discover? And why does it describe the universe so uncannily well?

In this episode of Uncommon Knowledge, Peter Robinson sits down with mathematicians David Berlinski, Sergiu Klainerman, and Stephen Meyer to explore one of the deepest mysteries in science and philosophy: the reality of mathematics.

From the simple certainty that 2 + 2 = 4 to the mind-bending mathematics behind black holes and quantum physics, the conversation asks why abstract numbers—created in the human mind—map so perfectly onto the physical world. Is mathematics purely logical, or does it point to a deeper structure of reality that isn’t material at all? Along the way, the panel explores beauty in science, the “unreasonable effectiveness” of math, and whether the concept of materialism can really explain the world we live in.

This wide-ranging discussion blends mathematics, physics, philosophy, and metaphysics into a fascinating conversation about truth, beauty, and the nature of reality itself.

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The opinions expressed are those of the authors and do not necessarily reflect the opinions of the Hoover Institution or Stanford University.

Could ChatGPT be conscious? | Roger Penrose, Sabrina Gonzalez, Max Tegmark

Roger Penrose, Sabrina Gonzalez Pasterski, and Max Tegmark discuss consciousness, quantum physics, and the possibility of a sentient superintelligent A.I.

Could ChatGPT be conscious?

With a free trial, you can watch the full debate NOW at https://iai.tv/video/cracking-the-code-for-thought?utm_sourc…ed-comment.

The idea that the brain is computational has, from the outset, been central to neuroscience. Like a computer, the brain is a problem-solving machine that stores memories and processes information. But despite the advances in AI, many challenge whether this analogy captures the essence of the mind. Computers use transistors to build elementary logic gates, enabling them to store files exactly, in 0s and 1s. They are precise and repeatable. Human brains, in contrast, are biological—the neurons do not operate as simple logic gates, but have thousands of inputs, and their output is dependent on past activity and their current internal state. Remove a computer’s processor, and it breaks. But humans can survive with only one brain hemisphere. Fundamentally, brains think, they have perception, and are conscious.

Is it a mistake to see the mind as computational? Are computers, at root, limited machines with little in common with the sophistication of living things? Or have computers and mathematics uncovered the essential character of thought—and perhaps even the cosmos itself?

#consciousness #quantum #neuroscience #quantumphysics #ai #artificialintelligence.

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Is consciousness an illusion? 5 experts explain

Become a Big Think member to unlock expert classes, premium print issues, exclusive events and more: https://bigthink.com/membership/?utm_… “If science aims to describe everything, how can it not describe the simple fact of our existence?” On this episode of Dispatches, Kmele speaks with the scientists, mathematicians, and spiritual leaders trying to do just that:

This video is an episode from ‪@The-Well‬, our publication about ideas that inspire a life well-lived, created with the ‪@JohnTempletonFoundation‬

Watch the full podcast now ► • Dispatches from The Well.

In the newest episode of Dispatches from The Well, we’re diving deep into the “hard problem of consciousness.” Here, Kmele combines the perspectives of five different scientists, philosophers, and spiritual leaders to approach one of humanity’s most pressing questions: what is consciousness?

In the AI age, the question of consciousness is more prevalent than ever. Is every single thing in the universe self-aware? What does it actually mean to be conscious? Are our bodies really just a vessel for our thoughts? Kmele asks these questions, and many more, in the most thought-provoking episode yet. This is Dispatches from The Well.

Featuring: sir roger penrose, christof koch, melanie mitchell, reid hoffman, swami sarvapriyananda.

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Designing better 2D electronics: Addressing anisotropic conductivity to cut contact resistance

The high-performance semiconductor devices powering smartphone displays, AI computing, EV batteries and more are increasingly incorporating 2D materials to overcome silicon’s scaling limits. To optimize these technologies, a University of Michigan Engineering team developed a precise mathematical framework that accounts for anisotropic—or unevenly spreading—conductivity and device geometry.

Accurate models of how currents move through anisotropic thin films, made of layered 2D materials, can enable the design of more reliable, high-performance nanoelectric devices. Specifically, the model can help engineers reduce current crowding and spreading resistance, essentially current traffic jams, that occur at vertical electrical contacts that connect with the top of a 2D surface. The study is published in ACS Applied Electronic Materials.

Introduction to Quantum Electrodynamics (QED)

It’s now time to dig into quantum field theories with considerably more rigor than earlier in the series. First up is quantum electrodynamics, or QED. This was the first successful QFT, combining quantum mechanics and special relativity. Let’s learn what this model is all about, and how to do math with Feynman diagrams.

Script by andrew mattson, physics phd student at johns hopkins university.

Watch the whole Modern Physics playlist: http://bit.ly/ProfDavePhysics2

Classical Physics Tutorials: http://bit.ly/ProfDavePhysics1
Mathematics Tutorials: http://bit.ly/ProfDaveMaths.
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Topology helps build more robust photonic networks

Penn-led researchers have shown for the first time that multiple, information-carrying light signals can be safely guided through chip-based, reconfigurable networks using topology, the esoteric branch of mathematics that says donuts and mugs are identical. Because topological properties remain stable even when objects are deformed—hence the field equating mugs and donuts, since both have one opening—the advance could help make light-based technologies for computing and communications more powerful and reliable.

“We already knew how to guide light using topology,” says Liang Feng, Professor in Materials Science and Engineering (MSE) with a secondary appointment in Electrical and Systems Engineering (ESE) within Penn Engineering and senior author of a study in Nature Physics describing the result. “But we had never been able to guide multiple, concurrent signals before.”

That opens the door to building networks of chips that communicate using light while taking advantage of the robustness topology provides. “Signals guided by these principles can be extremely reliable,” says Feng. “It’s like building a highway for light where even large potholes have no effect on traffic—it’s as if the defects simply aren’t there.”

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