One of the most fascinating aspects of physics is that nature often behaves in ways that seem completely counterintuitive. A good example comes from ultrathin materials. If I take a sheet of material and make it thinner and thinner, most people would expect it to become weaker. After all, there is less material left to bear a load.
Yet over the last decade, experiments and simulations have repeatedly shown something surprising: when certain materials become extremely thin—only a few nanometers or even a few atomic layers thick—they can become dramatically more resistant under extreme mechanical loading.
This phenomenon has been observed in systems as different as graphene, graphene oxide, and ultrathin polymer films. The effect was clear, but the reason behind it remained unclear. Why should materials with completely different chemistry and structure all exhibit a similar trend?
Pack enough string-like objects together, and they will begin to align with one another. But replace the strings with worms or bacteria living in your gut, and this self-organization becomes much more difficult. A team of University of Amsterdam (UvA) researchers has demonstrated that activity can fundamentally alter one of the most important phase transitions in soft matter physics.
Many systems in nature spontaneously organize themselves: Bird flocks align their flight directions, schools of fish move collectively, snakes and worms protect themselves by forming tight entangled clusters, and even molecules can coordinate their orientation to form ordered phases.
For string-like objects, or filaments, a key transition happens when you increase how densely they are packed together. If the density is low, they point in random directions, much like a crowd of people walking aimlessly through a city square. Physicists call this the isotropic phase. As more filaments are added, however, they begin to align with one another. Eventually, most filaments point roughly in the same direction, creating an ordered state known as a nematic phase.
Birds in flocks, bacteria and cells: In many collective systems, individual elements respond to only part of their surroundings, seemingly defying Newton’s third law of motion—action equals reaction. These exceptions are known as nonreciprocal interactions. A Dresden physics team working with Roderich Moessner, a founding member of the Würzburg–Dresden Cluster of Excellence ctd.qmat, has now developed a theory that makes it possible to describe these interactions efficiently and simulate them far more precisely.
Our universe’s expansion is still accelerating despite recent claims suggesting otherwise, an international team of astrophysicists says.
They refuted a study published last year claiming the growth of the universe is slowing and insist there is no flaw in the widely accepted theory that a mysterious force known as dark energy is driving the expanding cosmos.
The researchers, who include two Nobel laureates and represent institutions worldwide, say the debate that followed last November’s revelations was the result of a scientific misunderstanding rather than a cosmic grenade threatening to blow apart everything we know about the universe.
A 90 minute interview about AI and our human future.
Dr. Hugo de Garis is a computer scientist, AI researcher, and former professor known for his early work on evolvable hardware, artificial brains, and the long-term risks of superintelligent machines. He coined and popularized the idea of the “Artilect War,” a future conflict between those who want to build godlike artificial intellects and those who believe such systems pose an existential threat to humanity. In the interview, he describes himself as trained in pure mathematics and theoretical physics, formerly a computer science professor, and now focused on broader questions about AI, cosmology, civilization, and the future of humanity.
The interview with Prof. Hugo de Garis centers on his long-standing warning that humanity may face an “Artilect War,” a civilizational conflict over whether to build godlike artificial intellects vastly superior to humans. De Garis argues that future computation, potentially extending from nanotech to femtotech and beyond, could produce minds trillions of trillions of times more capable than ours. He distinguishes between Cosmists, who want to build such beings to expand intelligence into the universe, and Terrans, who oppose them because superintelligence may eliminate or marginalize humanity. He personally remains torn, admiring the cosmic grandeur of posthuman intelligence while recognizing the existential danger.
The conversation also covers AI timelines, recursive self-improvement, AI alignment, the U.S.-China race, the Fermi paradox, simulation theory, cyborgs, cryonics, AI-generated content, the decline of universities, and the future of work. De Garis is impressed by current AI systems, treating them almost as intellectual companions, but he doubts that humanity can guarantee long-term control over recursively improving machines. The central theme is that the question “Should humanity build artilects?” may become the defining political and moral problem of the twenty-first century.
A new theoretical study may have cracked one of the most puzzling discoveries of the James Webb Space Telescope (JWST): Little Red Dots, spotted across the early universe. The paper, posted to the arXiv preprint server on May 29, argues that these objects could be black holes caught in rare, violent bursts of feeding at a rate exceeding theoretical limits.
Since JWST began its survey of the deep universe, astronomers have been puzzled by a class of tiny, faint objects appearing in the early universe in far greater numbers than expected. They have a distinctive V-shaped spectrum—bright in both ultraviolet and optical light, but with a dip in between—along with broad emission lines hinting at active black holes. They also show an absence of X-ray, radio and infrared emission.
They don’t look like ordinary galaxies, and they don’t completely look like quasars, either. What they are has been an open question. Some researchers argue that Little Red Dots may need some outside-the-box physics to explain their origin and nature.
Using a novel simulation model based on machine learning, an international research team at GSI/FAIR has succeeded in gaining a deeper understanding of element formation in stellar events such as neutron star mergers. For the first time, the scientists used deep learning with a neural network to model the energy release during r-process nucleosynthesis in hydrodynamic simulations. The results are published in the journal Physical Review D.
Many of the chemical elements we know are created in massive stellar events such as exploding stars or neutron star mergers. These events release incredible amounts of energy, allowing for the production of heavy nuclides. One key nuclear production process is the so-called rapid neutron-capture process, or r-process, in which free neutrons are captured by existing nuclei and converted into protons—thus creating larger, heavier atomic nuclei.
“Researchers around the world strive to make these complex reactions understandable through theoretical simulations. However, modeling all parameters requires incredible computing power, which is why the models often have to be simplified,” said Dr. Oliver Just, first author of the publication and a researcher in the Nuclear Astrophysics & Structure Department at GSI/FAIR. “Our new model, RHINE, which uses artificial intelligence, offers an efficient alternative.”
Every commercial nuclear reactor in the world runs on uranium. Uranium brings three undeniable problems. It creates weapons-grade plutonium. It melts down under pressure. Its radioactive waste lasts for tens of thousands of years.
Thorium solves all three. Physicists have known this since the 1960s. The United States actually built a working thorium reactor. They proved the technology was viable. Then they deliberately abandoned it.
