Aurelia Butler

Artificial intelligence (AI) systems are increasingly central to technology, powering everything from facial recognition to language translation. But as AI models grow more complex, they consume vast amounts of electricity — posing challenges for energy efficiency and sustainability. A new chip developed by researchers at the University of Florida could help address this issue by using light, rather than just electricity, to perform one of AI’s most power-hungry tasks. Their research is reported in Advanced Photonics.
The chip is designed to carry out convolution operations, a core function in machine learning that enables AI systems to detect patterns in images, video, and text. These operations typically require significant computing power. By integrating optical components directly onto a silicon chip, the researchers have created a system that performs convolutions using laser light and microscopic lenses — dramatically reducing energy consumption and speeding up processing.
“Performing a key machine learning computation at near zero energy is a leap forward for future AI systems,” said study leader Volker J. Sorger, the Rhines Endowed Professor in Semiconductor Photonics at the University of Florida. “This is critical to keep scaling up AI capabilities in years to come.”
In tests, the prototype chip classified handwritten digits with about 98 percent accuracy, comparable to traditional electronic chips. The system uses two sets of miniature Fresnel lenses — flat, ultrathin versions of the lenses found in lighthouses — fabricated using standard semiconductor manufacturing techniques. These lenses are narrower than a human hair and are etched directly onto the chip.
To perform a convolution, machine learning data is first converted into laser light on the chip. The light passes through the Fresnel lenses, which carry out the mathematical transformation. The result is then converted back into a digital signal to complete the AI task.
“This is the first time anyone has put this type of optical computation on a chip and applied it to an AI neural network,” said Hangbo Yang, a research associate professor in Sorger’s group at UF and co-author of the study.
The team also demonstrated that the chip could process multiple data streams simultaneously by using lasers of different colors — a technique known as wavelength multiplexing. “We can have multiple wavelengths, or colors, of light passing through the lens at the same time,” Yang said. “That’s a key advantage of photonics.”
The research was conducted in collaboration with the Florida Semiconductor Institute, UCLA, and George Washington University. Sorger noted that chip manufacturers such as NVIDIA already use optical elements in some parts of their AI systems, which could make it easier to integrate this new technology.
“In the near future, chip-based optics will become a key part of every AI chip we use daily,” Sorger said. “And optical AI computing is next.” More

What do children’s building blocks and quantum computing have in common? The answer is modularity. It is difficult for scientists to build quantum computers monolithically – that is, as a single large unit. Quantum computing relies on the manipulation of millions of information units called qubits, but these qubits are difficult to assemble. The solution? Finding modular ways to construct quantum computers. Like plastic children’s bricks that lock together to create larger, more intricate structures, scientists can build smaller, higher quality modules and string them together to form a comprehensive system.
Recognizing the potential of these modular systems, researchers from The Grainger College of Engineering at the University of Illinois Urbana-Champaign have presented an enhanced approach to scalable quantum computing by demonstrating a viable and high-performance modular architecture for superconducting quantum processors. Their work, published in Nature Electronics, expands on previous modular designs and paves the way toward scalable, fault-tolerant and reconfigurable quantum computing systems.
Monolithic superconducting quantum systems are limited in size and fidelity, which predicts scientists’ rate of success in performing logical operations. A fidelity of one signifies no mistakes; as such, researchers want to achieve a fidelity as close to one as possible. Compared to these limited monolithic systems, modularity enables system scalability, hardware upgrades, and tolerance to variability, making it a more attractive option for building system networks.
“We’ve created an engineering-friendly way of achieving modularity with superconducting qubits,” said Wolfgang Pfaff, an assistant professor of physics and the senior author of the paper. “Can I build a system that I can bring together, allowing me to manipulate two qubits jointly so as to create entanglement or gate operations between them? Can we do that at a very high quality? And can we also have it such that we can take it apart and put it back together? Typically, we only find out that something went wrong after putting it together. So we would really like to have the ability to reconfigure the system later.”
By constructing a system where two devices are connected with superconducting coaxial cables to link qubits across modules, Pfaff’s team demonstrated ~99% SWAP gate fidelity, representing less than 1% loss. Their ability to connect and reconfigure separate devices with a cable while retaining high quality provides novel insight to the field in designing communication protocols.
“Finding an approach that works has taken a while for our field,” Pfaff said. “Many groups have figured out that what we really want is this ability to stitch bigger and bigger things together through cables, and at the same time reach numbers that are good enough to justify scaling. The problem was just finding the right combination of tools.”
Moving forward, the Grainger engineers will turn their focus toward scalability, attempting to connect more than two devices together while retaining the ability to check for errors.
“We have good performance,” Pfaff said. “Now we need to put it to the test and say, is it really going forward? Does it really make sense?” More

The age of artificial intelligence (AI) has transformed our interactions, but threatens human dignity on a worldwide scale, according to a study led by Charles Darwin University (CDU).
Study lead author Dr Maria Randazzo, an academic from CDU’s School of Law, found the technology was reshaping Western legal and ethical landscapes at unprecedented speed but was undermining democratic values and deepening systemic biases.
Dr Randazzo said current regulation failed to prioritize fundamental human rights and freedoms such as privacy, anti-discrimination, user autonomy, and intellectual property rights – mainly thanks to the untraceable nature of many algorithmic models.
Calling this lack of transparency a “black box problem,” Dr Randazzo said decisions made by deep-learning or machine-learning processes were impossible for humans to trace, making it difficult for users to determine if and why an AI model has violated their rights and dignity and seek justice where necessary.
“This is a very significant issue that is only going to get worse without adequate regulation,” Dr Randazzo said.
“AI is not intelligent in any human sense at all. It is a triumph in engineering, not in cognitive behavior.
“It has no clue what it’s doing or why – there’s no thought process as a human would understand it, just pattern recognition stripped of embodiment, memory, empathy, or wisdom.”
Currently, the world’s three dominant digital powers – the United States, China, and the European Union – are taking markedly different approaches to AI, leaning on market-centric, state-centric, and human-centric models respectively.

Dr Randazzo said the EU’s human-centric approach is the preferred path to protect human dignity but without a global commitment to this goal, even that approach falls short.
“Globally, if we don’t anchor AI development to what makes us human – our capacity to choose, to feel, to reason with care, to empathy and compassion – we risk creating systems that devalue and flatten humanity into data points, rather than improve the human condition,” she said.
“Humankind must not be treated as a means to an end.”
“Human dignity in the age of Artificial Intelligence: an overview of legal issues and regulatory regimes” was published in the Australian Journal of Human Rights.
The paper is the first in a trilogy Dr Randazzo will produce on the topic. More

How can data be processed at lightning speed, or electricity conducted without loss? To achieve this, scientists and industry alike are turning to quantum materials, governed by the laws of the infinitesimal. Designing such materials requires a detailed understanding of atomic phenomena, much of which remains unexplored. A team from the University of Geneva (UNIGE), in collaboration with the University of Salerno and the CNR-SPIN Institute (Italy), has taken a major step forward by uncovering a hidden geometry — until now purely theoretical — that distorts the trajectories of electrons in much the same way gravity bends the path of light. This work, published in Science, opens new avenues for quantum electronics.
Future technologies depend on high-performance materials with unprecedented properties, rooted in quantum physics. At the heart of this revolution lies the study of matter at the microscopic scale — the very essence of quantum physics. In the past century, exploring atoms, electrons and photons within materials gave rise to transistors and, ultimately, to modern computing.
New quantum phenomena that defy established models are still being discovered today. Recent studies suggest the possible emergence of a geometry within certain materials when vast numbers of particles are observed. This geometry appears to distort the trajectories of electrons in these materials — much like Einstein’s gravity bends the path of light.
From theory to observation
Known as quantum metric, this geometry reflects the curvature of the quantum space in which electrons move. It plays a crucial role in many phenomena at the microscopic scale of matter. Yet detecting its presence and effects remains a major challenge.
”The concept of quantum metric dates back about 20 years, but for a long time it was regarded purely as a theoretical construct. Only in recent years have scientists begun to explore its tangible effects on the properties of matter,” explains Andrea Caviglia, full professor and director of the Department of Quantum Matter Physics at the UNIGE Faculty of Science.
Thanks to recent work, the team led by the UNIGE researcher, in collaboration with Carmine Ortix, associate professor in the Department of Physics at the University of Salerno, has detected quantum metric at the interface between two oxides — strontium titanate and lanthanum aluminate — a well-known quantum material. ”Its presence can be revealed by observing how electron trajectories are distorted under the combined influence of quantum metric and intense magnetic fields applied to solids,” explains Giacomo Sala, research associate in the Department of Quantum Matter Physics at the UNIGE Faculty of Science and lead author of the study.
Unlocking Future Technologies
Observing this phenomenon makes it possible to characterise a material’s optical, electronic and transport properties with greater precision. The research team also demonstrates that quantum metric is an intrinsic property of many materials — contrary to previous assumptions.
”These discoveries open up new avenues for exploring and harnessing quantum geometry in a wide range of materials, with major implications for future electronics operating at terahertz frequencies (a trillion hertz), as well as for superconductivity and light-matter interactions,” concludes Andrea Caviglia. More

Osaka, Japan — A joint research team from Japan has observed “heavy fermions,” electrons with dramatically enhanced mass, exhibiting quantum entanglement governed by the Planckian time – the fundamental unit of time in quantum mechanics. This discovery opens up exciting possibilities for harnessing this phenomenon in solid-state materials to develop a new type of quantum computer.
Heavy fermions arise when conduction electrons in a solid interact strongly with localized magnetic electrons, effectively increasing their mass. This phenomenon leads to unusual properties like unconventional superconductivity and is a central theme in condensed matter physics. Cerium-Rhodium-Tin (CeRhSn), the material studied in this research, belongs to a class of heavy fermion systems with a quasi-kagome lattice structure, known for its geometrical frustration effects.
Researchers investigated the electronic state of CeRhSn, known for exhibiting non-Fermi liquid behavior at relatively high temperatures. Precise measurements of CeRhSn’s reflectance spectra revealed non-Fermi liquid behavior persisting up to near room temperature, with heavy electron lifetimes approaching the Planckian limit. The observed spectral behavior, describable by a single function, strongly indicates that heavy electrons in CeRhSn are quantum entangled.
Dr. Shin-ichi Kimura of The University of Osaka, who led the research, explains, “Our findings demonstrate that heavy fermions in this quantum critical state are indeed entangled, and this entanglement is controlled by the Planckian time. This direct observation is a significant step towards understanding the complex interplay between quantum entanglement and heavy fermion behavior.”
Quantum entanglement is a key resource for quantum computing, and the ability to control and manipulate it in solid-state materials like CeRhSn offers a potential pathway towards novel quantum computing architectures. The Planckian time limit observed in this study provides crucial information for designing such systems. Further research into these entangled states could revolutionize quantum information processing and unlock new possibilities in quantum technologies. This discovery not only advances our understanding of strongly correlated electron systems but also paves the way for potential applications in next-generation quantum technologies. More

When genetic testing reveals a rare DNA mutation, doctors and patients are frequently left in the dark about what it actually means. Now, researchers at the Icahn School of Medicine at Mount Sinai have developed a powerful new way to determine whether a patient with a mutation is likely to actually develop disease, a concept known in genetics as penetrance.
The team set out to solve this problem using artificial intelligence (AI) and routine lab tests like cholesterol, blood counts, and kidney function. Details of the findings were reported in the August 28 online issue of Science. Their new method combines machine learning with electronic health records to offer a more accurate, data-driven view of genetic risk.
Traditional genetic studies often rely on a simple yes/no diagnosis to classify patients. But many diseases, like high blood pressure, diabetes, or cancer, don’t fit neatly into binary categories. The Mount Sinai researchers trained AI models to quantify disease on a spectrum, offering more nuanced insight into how disease risk plays out in real life.
“We wanted to move beyond black-and-white answers that often leave patients and providers uncertain about what a genetic test result actually means,” says Ron Do, PhD, senior study author and the Charles Bronfman Professor in Personalized Medicine at the Icahn School of Medicine at Mount Sinai. “By using artificial intelligence and real-world lab data, such as cholesterol levels or blood counts that are already part of most medical records, we can now better estimate how likely disease will develop in an individual with a specific genetic variant. It’s a much more nuanced, scalable, and accessible way to support precision medicine, especially when dealing with rare or ambiguous findings.”
Using more than 1 million electronic health records, the researchers built AI models for 10 common diseases. They then applied these models to people known to have rare genetic variants, generating a score between 0 and 1 that reflects the likelihood of developing the disease.
A higher score, closer to 1, suggests a variant may be more likely to contribute to disease, while a lower score indicates minimal or no risk. The team calculated “ML penetrance” scores for more than 1,600 genetic variants.
Some of the results were surprising, say the investigators. Variants previously labeled as “uncertain” showed clear disease signals, while others thought to cause disease had little effect in real-world data.

“While our AI model is not meant to replace clinical judgment, it can potentially serve as an important guide, especially when test results are unclear. Doctors could in the future use the ML penetrance score to decide whether patients should receive earlier screenings or take preventive steps, or to avoid unnecessary worry or intervention if the variant is low-risk,” says lead study author Iain S. Forrest, MD, PhD, in the lab of Dr. Do at the Icahn School of Medicine at Mount Sinai. “If a patient has a rare variant associated with Lynch syndrome, for instance, and it scores high, that could trigger earlier cancer screening, but if the risk appears low, jumping to conclusions or overtreatment might be avoided.”
The team is now working to expand the model to include more diseases, a wider range of genetic changes, and more diverse populations. They also plan to track how well these predictions hold up over time, whether people with high-risk variants actually go on to develop disease, and whether early action can make a difference.
“Ultimately, our study points to a potential future where AI and routine clinical data work hand in hand to provide more personalized, actionable insights for patients and families navigating genetic test results,” says Dr. Do. “Our hope is that this becomes a scalable way to support better decisions, clearer communication, and more confidence in what genetic information really means.”
The paper is titled “Machine learning-based penetrance of genetic variants.”
The study’s authors, as listed in the journal, are Iain S. Forrest, Ha My T. Vy, Ghislain Rocheleau, Daniel M. Jordan, Ben O. Petrazzini, Girish N. Nadkarni, Judy H. Cho, Mythily Ganapathi, Kuan-Lin Huang, Wendy K. Chung, and Ron Do.
This work was supported in part by the following grants: National Institute of General Medical Sciences of the National Institutes of Health (NIH) (T32-GM007280); the National Institute of General Medical Sciences of the NIH (R35-GM124836); the National Institute of Diabetes and Digestive and Kidney Diseases (U24-DK062429); the National Human Genome Research Institute of the NIH (R01-HG010365); the National Institute of General Medical Sciences of the NIH (R35-GM138113); and the National Institute of Diabetes and Digestive and Kidney Diseases (U24-DK062429).
* Mount Sinai Health System member hospitals: The Mount Sinai Hospital; Mount Sinai Brooklyn; Mount Sinai Morningside; Mount Sinai Queens; Mount Sinai South Nassau; Mount Sinai West; and New York Eye and Ear Infirmary of Mount Sinai More