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Researchers pioneer optical generative models, ushering in a new era of sustainable generative AI

In a major leap for artificial intelligence (AI) and photonics, researchers at the University of California, Los Angeles (UCLA) have created optical generative models capable of producing novel images using the physics of light instead of conventional electronic computation.

Published in Nature, the work presents a new paradigm for generative AI that could dramatically reduce energy use while enabling scalable, high-performance content creation.

Generative models, including diffusion models and , form the backbone of today’s AI revolution. These systems can create realistic images, videos, and human-like text, but their rapid growth comes at a steep cost: escalating power demands, large carbon footprints, and increasingly complex hardware requirements. Running such models requires massive computational infrastructure, raising concerns about their long-term sustainability.

Huawei Unveils Solid State Battery Patent With 1900 Mile Range and 5 Minute Charge

Huawei has shocked the electric vehicle (EV) world by filing a patent for a solid-state battery that claims a potential range of 3,000 kilometres and the ability to recharge to full in just five minutes. Normally, we have to assume this particular patent will go nowhere, and we will forget about it; however, it could be the start of something new.

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Cancer’s iron theft: Research uncovers why bone metastasis often leads to anemia

Cancer specialists have long known that anemia, caused by a lack of healthy red blood cells, often arises when cancer metastasizes to the bone, but it’s been unclear why. Now, a research team led by Princeton University researchers Yibin Kang and Yujiao Han has uncovered exactly how this happens in metastatic breast cancer, and it involves a type of cellular hijacking. The research aims to help slow down bone metastasis—one of cancer’s deadliest forms.

In a study published in the journal Cell on September 3, Kang and Han reveal that effectively commandeer a specialized cell that normally recycles iron in the bone, known as an erythroblast island (EBI) macrophage. This both deprives red blood cells of necessary iron and helps the tumor continue to grow in the bone.

Understanding metastatic cancer—or cancer that grows and spreads in other parts of the body beyond the original tumor site—is critically important. It is one of the deadliest forms of cancer and there is no cure. Of patients who die from breast and , 70% have bone metastasis.

Deep learning method enables efficient Boltzmann distribution sampling across a continuous temperature range

A research team has developed a novel direct sampling method based on deep generative models. Their method enables efficient sampling of the Boltzmann distribution across a continuous temperature range. The findings have been published in Physical Review Letters. The team was led by Prof. Pan Ding, Associate Professor from the Departments of Physics and Chemistry, and Dr. Li Shuo-Hui, Research Assistant Professor from the Department of Physics at the Hong Kong University of Science and Technology (HKUST).

Quantum emitter discovery in diamonds enables a new type of coupling

Researchers at The City College of New York have shown how a quantum emitter, the nitrogen-vacancy (NV) center in diamond, interacts in unexpected ways with a specially engineered photonic structure when moved around with a scanning tip.

The study, led by Carlos A. Meriles—Martin and Michele Cohen Professor of Physics in the Division of Science—and titled “Emission of Nitrogen–Vacancy Centres in Diamond Shaped by Topological Photonic Waveguide Modes,” appears in the journal Nature Nanotechnology.

What has long been considered a drawback of the NV center—its broad and messy emission spectrum—turns out to enable a new type of coupling that reshapes its light in ways not seen before. This discovery has fundamental importance for , since such coupling could help overcome longstanding challenges like spectral diffusion and open pathways toward robust spin–photon and spin–spin entanglement on a chip.

DNA-based neural network learns from examples to solve problems

Neural networks are computing systems designed to mimic both the structure and function of the human brain. Caltech researchers have been developing a neural network made out of strands of DNA instead of electronic parts that carries out computation through chemical reactions rather than digital signals.

An important property of any neural network is the ability to learn by taking in information and retaining it for future decisions. Now, researchers in the laboratory of Lulu Qian, professor of bioengineering, have created a DNA-based neural network that can learn. The work represents a first step toward demonstrating more complex learning behaviors in .

A paper describing the research appears in the journal Nature on September 3. Kevin Cherry, Ph.D., is the study’s first author.

A robot learns to handle bulky objects like humans do after just one lesson

For all their technological brilliance, from navigating distant planets to performing complex surgery, robots still struggle with a few basic human tasks. One of the most significant challenges is dexterity, which refers to the ability to grasp, hold and manipulate objects. Until now, that is. Scientists from the Toyota Research Institute in Massachusetts have trained a robot to use its entire body to handle large objects, much like humans do.

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