Aurelia Butler

Forget the cloud.
Northwestern University engineers have developed a new nanoelectronic device that can perform accurate machine-learning classification tasks in the most energy-efficient manner yet. Using 100-fold less energy than current technologies, the device can crunch large amounts of data and perform artificial intelligence (AI) tasks in real time without beaming data to the cloud for analysis.
With its tiny footprint, ultra-low power consumption and lack of lag time to receive analyses, the device is ideal for direct incorporation into wearable electronics (like smart watches and fitness trackers) for real-time data processing and near-instant diagnostics.
To test the concept, engineers used the device to classify large amounts of information from publicly available electrocardiogram (ECG) datasets. Not only could the device efficiently and correctly identify an irregular heartbeat, it also was able to determine the arrhythmia subtype from among six different categories with near 95% accuracy.
The research will be published on Oct. 12 in the journal Nature Electronics.
“Today, most sensors collect data and then send it to the cloud, where the analysis occurs on energy-hungry servers before the results are finally sent back to the user,” said Northwestern’s Mark C. Hersam, the study’s senior author. “This approach is incredibly expensive, consumes significant energy and adds a time delay. Our device is so energy efficient that it can be deployed directly in wearable electronics for real-time detection and data processing, enabling more rapid intervention for health emergencies.”
A nanotechnology expert, Hersam is Walter P. Murphy Professor of Materials Science and Engineering at Northwestern’s McCormick School of Engineering. He also is chair of the Department of Materials Science and Engineering, director of the Materials Research Science and Engineering Center and member of the International Institute of Nanotechnology. Hersam co-led the research with Han Wang, a professor at the University of Southern California, and Vinod Sangwan, a research assistant professor at Northwestern. More

Australian researchers have designed an algorithm that can intercept a man-in-the-middle (MitM) cyberattack on an unmanned military robot and shut it down in seconds.
In an experiment using deep learning neural networks to simulate the behaviour of the human brain, artificial intelligence experts from Charles Sturt University and the University of South Australia (UniSA) trained the robot’s operating system to learn the signature of a MitM eavesdropping cyberattack. This is where attackers interrupt an existing conversation or data transfer.
The algorithm, tested in real time on a replica of a United States army combat ground vehicle, was 99% successful in preventing a malicious attack. False positive rates of less than 2% validated the system, demonstrating its effectiveness.
The results have been published in IEEE Transactions on Dependable and Secure Computing.
UniSA autonomous systems researcher, Professor Anthony Finn, says the proposed algorithm performs better than other recognition techniques used around the world to detect cyberattacks.
Professor Finn and Dr Fendy Santoso from Charles Sturt Artificial Intelligence and Cyber Futures Institute collaborated with the US Army Futures Command to replicate a man-in-the-middle cyberattack on a GVT-BOT ground vehicle and trained its operating system to recognise an attack.
“The robot operating system (ROS) is extremely susceptible to data breaches and electronic hijacking because it is so highly networked,” Prof Finn says. More

Quantum computing uses quantum mechanics to process and store information in a way that is different from classical computers. While classical computers rely on bits like tiny switches that can be either 0 or 1, quantum computers use quantum bits (qubits). Qubits are unique because they can be in a mixture of 0 and 1 simultaneously — a state referred to as superposition. This unique property enables quantum computers to solve specific problems significantly faster than classical ones.
In a recent publication in EPJ Quantum Technology, Le Bin Ho from Tohoku University’s Frontier Institute for Interdisciplinary Sciences has developed a technique called “Time-dependent Stochastic Parameter Shift” in the realm of quantum computing and quantum machine learning. This breakthrough method revolutionizes the estimation of gradients or derivatives of functions, a crucial step in many computational tasks.
Typically, computing derivatives requires dissecting the function and calculating the rate of change over a small interval. But even classical computers cannot keep dividing indefinitely. In contrast, quantum computers can accomplish this task without having to discrete the function. This feature is achievable because quantum computers operate in a realm known as “quantum space,” characterized by periodicity, and no need for endless subdivisions.
One way to illustrate this concept is by comparing the sizes of two elementary schools on a map. To do this, one might print out maps of the schools and then cut them into smaller pieces. After cutting, these pieces can be arranged into a line, with their total length compared. However, the pieces may not form a perfect rectangle, leading to inaccuracies. An infinite subdivision would be required to minimize these errors, an impractical solution, even for classical computers.
A more straightforward method involves weighing the paper pieces representing the two schools and comparing their weights. This method yields accurate results when the paper sizes are large enough to detect the mass difference. This bears resemblance to the parameter shift concept, though operating in different spaces that do not necessitate infinite intervals.
“Our time-dependent stochastic method is applicable to the broader applications for higher-order derivatives and can be employed to compute the quantum Fisher information matrix (QFIM), a pivotal concept in quantum information theory and quantum metrology,” states Le.
“QFIM is intricately linked to various disciplines, including quantum metrology, phase transitions, entanglement witness, Fubini-Study metric, and quantum speed limits, making it a fundamental quantity with various applications. Therefore, calculating QFIM on quantum computers can open doors to utilizing quantum computers across diverse fields such as cryptography, optimization, drug discovery, materials science, and beyond.”
Le also showed how this method can be used in various applications, including quantum metrology with single and multiple magnetic fields and Hamiltonian tomography applied to intricate many-body systems. He also meticulously compared the new approach to the exact theoretical method and another approximation model called the Suzuki-Trotter. Although the method aligned closely with the theoretical approach, the Suzuki-Trotter approximation deviated from the true value. Enhancing the results of the Suzuki-Trotter approximation would necessitate an infinite subdivision of the Suzuki-Trotter steps. More

Artificial intelligence, AI, which finds patterns in complex biological data could eventually contribute to the development of individually tailored healthcare. Researchers at Linköping University, Sweden, have developed an AI-based method applicable to various medical and biological issues. Their models can for instance accurately estimate people’s chronological age and determine whether they have been smokers or not.
There are many factors that can affect which out of all our genes are used at any given point in time. Smoking, dietary habits and environmental pollution are some such factors. This regulation of gene activity can be likened to a power switch determining which genes are switched on or off, witout altering the actual genes, and is called epigenetics.
Researchers at Linköping University have used data with epigenetic information from more than 75,000 human samples to train a large number of AI neural network models. They hope that such AI-based models could eventually be used in precision medicine to develop treatments and preventive strategies tailored to the individual. Their models are of the autoencoder type, that self-organises the information and finds interrelation patterns in the large amount of data.
To test their model, the LiU researchers compared it with existing models. There are already existing models of the effects of smoking on the body, building on the fact that specific epigenetic changes reflect the effect of smoking on the functioning of the lungs. These traces remain in the DNA long after a person has quit smoking, and this type of model can identify whether someone is a current, former or never smoker. Other models can, based on epigenetic markers, estimate the chronological age of an individual, or group individuals according to whether they have a disease or are healthy.
The LiU researchers trained their autoencoder and then used the result to answer three different queries: age determination, smoker status and diagnosing the disease systemic lupus erythematosus, SLE. Although the existing models rely on selected epigenetic markers known to be associated with the condition they aim to classify. However, it turned out that the LiU researchers’ autoencoders functioned better or equally well.
“Our models not only enable us to classify individuals based on their epigenetic data. We found that our models can identify previously known epigenetic markers used in other models, but also new markers associated with the condition we’re examining. One example of this is that our model for smoking identifies markers associated with respiratory diseases, such as lung cancer, and DNA damage,” says David Martínez, PhD student at Linköping University.
The objective of the autoencoder models is to enable compression of extremely complex biological data into a representation of the most relevant characteristics and patterns in data. More

Researchers have developed an easy-to-use optical chip that can configure itself to achieve various functions. The positive real-valued matrix computation they have achieved gives the chip the potential to be used in applications requiring optical neural networks. Optical neural networks can be used for a variety of data-heavy tasks such as image classification, gesture interpretation and speech recognition.
Photonic integrated circuits that can be reconfigured after manufacturing to perform different functions have been developed previously. However, they tend to be difficult to configure because the user needs to understand the internal structure and principles of the chip and individually adjust its basic units.
“Our new chip can be treated as a black box, meaning users don’t need to understand its internal structure to change its function,” said research team leader Jianji Dong from Huazhong University of Science and Technology in China. “They only need to set a training objective, and, with computer control, the chip will self-configure to achieve the desired functionality based on the input and output.”
In the journal Optical Materials Express, the researchers describe their new chip, which is based on a network of waveguide-based optical components called Mach-Zehnder interferometers (MZIs) arranged in a quadrilateral pattern. The researchers showed that the chip can self-configure to perform optical routing, low-loss light energy splitting and the matrix computations used to create neural networks.
“In the future, we anticipate the realization of larger-scale on-chip programmable waveguide networks,” said Dong. “With additional development, it may become possible to achieve optical functions comparable to those of field-programmable gate arrays (FPGAs) — electrical integrated circuits that can be reprogrammed to perform any desired application after they are manufactured.”
Creating the programmable MZI network
The on-chip quadrilateral MZI network is potentially useful for applications involving optical neural networks, which are created from networks of interconnected nodes. To use an optical neural network effectively, the network must be trained with known data to determine the weights between each pair of nodes — a task that involves matrix multiplication. More

What type of brain tumor does this patient have? AI technology helps to determine this as early as during surgery, within 1.5 hours. This process normally takes a week. The new technology allows neurosurgeons to adjust their surgical strategies on the spot. Today, researchers from UMC Utrecht and researchers, pathologists and neurosurgeons from the Princess Máxima Center for pediatric oncology and Amsterdam UMC have published about this study in Nature.
Every year, 1,400 adults and 150 children are diagnosed with a tumor in the brain or spinal cord in the Netherlands. Surgery is often the first step taken in treatment. Currently, during surgery, neurosurgeons do not precisely know what type of brain tumor and what degree of aggressiveness they are dealing with. The exact diagnosis will usually only be available one week after surgery, after the tumor tissue has been visually and molecularly analyzed by the pathologist.
Deep-learning algorithm
Researchers from UMC Utrecht have developed a new ‘deep-learning algorithm’, a form of artificial intelligence, which significantly speeds up diagnosis.
Jeroen de Ridder, research group leader within UMC Utrecht and Oncode Institute: “Recently, Nanopore sequencing became available: a technology that helps to read DNA in real time. For this, we developed an algorithm that is equipped to learn from millions of simulated realistic ‘DNA snapshots’. With this algorithm, we can identify the tumor type within 20 to 40 minutes. And that is fast enough to directly adjust the surgical strategy, if necessary.”
Tested and trained with biobank
Bastiaan Tops is in charge of the Pediatric Oncology Laboratory at the Princess Máxima Center. He brought together the new technology and the needs from the operating room. This was made possible by funding from the KiKa foundation and, more specifically, to the extensive biobank that the Máxima Center has maintained for years. Among other things, this biobank stores tissue from children with brain tumors. The algorithm was trained and tested using the biobank. More

Quantum computers of the future hold promise in solving all sorts of problems. For example, they could lead to more sustainable materials, new medicines, and even crack the hardest problems in fundamental physics. But compared to classical computers in use today, rudimentary quantum computers are more prone to errors. Wouldn’t it be nice if researchers could just take out a special quantum eraser and get rid of the mistakes?
Reporting in the journal Nature, a group of researchers led by Caltech is among the first to demonstrate a type of quantum eraser. The physicists show that they can pinpoint and correct for mistakes in quantum computing systems known as “erasure” errors.
“It’s normally very hard to detect errors in quantum computers, because just the act of looking for errors causes more to occur,” says Adam Shaw, co-lead author of the new study and a graduate student in the laboratory of Manuel Endres, a professor of physics at Caltech. “But we show that with some careful control, we can precisely locate and erase certain errors without consequence, which is where the name erasure comes from.”
Quantum computers are based on the laws of physics that govern the subatomic realm, such as entanglement, a phenomenon in which particles remain connected to and mimic each other without being in direct contact. In the new study, the researchers focused on a type of quantum-computing platform that uses arrays of neutral atoms, or atoms without a charge. Specifically, they manipulated individual alkaline-earth neutral atoms confined inside “tweezers” made of laser light. The atoms were excited to high-energy states — or “Rydberg” states — in which neighboring atoms start interacting.
“The atoms in our quantum system talk to each other and generate entanglement,” explains Pascal Scholl, the other co-lead author of the study and a former postdoctoral scholar at Caltech now working at a quantum computing company in France called PASQAL.
Entanglement is what allows quantum computers to outperform classical computers. “However, nature doesn’t like to remain in these quantum entangled states,” Scholl explains. “Eventually, an error happens, which breaks the entire quantum state. These entangled states can be thought of as baskets full of apples, where the atoms are the apples. With time, some apples will start to rot, and if these apples are not removed from the basket and replaced by fresh ones, all the apples will rapidly become rotten. It is not clear how to fully prevent these errors from happening, so the only viable option nowadays is to detect and correct them.”
The new error-catching system is designed in such a way that erroneous atoms fluoresce, or light up, when hit with a laser. “We have images of the glowing atoms that tell us where the errors are, so we can either leave them out of the final statistics or apply additional laser pulses to actively correct them,” Scholl says.
The theory for implementing erasure detection in neutral atom systems was first developed by Jeff Thompson, a professor of electrical and computer engineering at Princeton University, and his colleagues. That team also recently reported demonstrating the technique in the journal Nature.
By removing and locating errors in their Rydberg atom system, the Caltech team says that they can improve the overall rate of entanglement, or fidelity. In the new study, the team reports that only one in 1,000 pairs of atoms failed to become entangled. That’s a factor-of-10 improvement over what was achieved previously and is the highest-ever observed entanglement rate in this type of system.
Ultimately, these results bode well for quantum computing platforms that use Rydberg neutral atom arrays. “Neutral atoms are the most scalable type of quantum computer, but they didn’t have high-entanglement fidelities until now,” says Shaw. More

Artificial intelligence (AI) comes with promises of helping coders code faster, drivers drive safer, and making daily tasks less time-consuming. But in a commentary published October 10 in the journal Joule, the founder of Digiconomist demonstrates that the tool, when adopted widely, could have a large energy footprint, which in the future may exceed the power demands of some countries.
“Looking at the growing demand for AI service, it’s very likely that energy consumption related to AI will significantly increase in the coming years,” says author Alex de Vries, a Ph.D. candidate at Vrije Universiteit Amsterdam.
Since 2022, generative AI, which can produce text, images, or other data, has undergone rapid growth, including OpenAI’s ChatGPT. Training these AI tools requires feeding the models a large amount of data, a process that is energy intensive. Hugging Face, an AI-developing company based in New York, reported that its multilingual text-generating AI tool consumed about 433 megawatt-hours (MWH) during training, enough to power 40 average American homes for a year.
And AI’s energy footprint does not end with training. De Vries’s analysis shows that when the tool is put to work — generating data based on prompts — every time the tool generates a text or image, it also uses a significant amount of computing power and thus energy. For example, ChatGPT could cost 564 MWh of electricity a day to run.
While companies around the world are working on improving the efficiencies of AI hardware and software to make the tool less energy intensive, de Vries says that an increase in machines’ efficiency often increases demand. In the end, technological advancements will lead to a net increase in resource use, a phenomenon known as Jevons’ Paradox.
“The result of making these tools more efficient and accessible can be that we just allow more applications of it and more people to use it,” de Vries says.
Google, for example, has been incorporating generative AI in the company’s email service and is testing out powering its search engine with AI. The company processes up to 9 billion searches a day currently. Based on the data, de Vries estimates that if every Google search uses AI, it would need about 29.2 TWh of power a year, which is equivalent to the annual electricity consumption of Ireland.
This extreme scenario is unlikely to happen in the short term because of the high costs associated with additional AI servers and bottlenecks in the AI server supply chain, de Vries says. But the production of AI servers is projected to grow rapidly in the near future. By 2027, worldwide AI-related electricity consumption could increase by 85 to 134 TWh annually based on the projection of AI server production.
The amount is comparable to the annual electricity consumption of countries such as the Netherlands, Argentina, and Sweden. Moreover, improvements in AI efficiency could also enable developers to repurpose some computer processing chips for AI use, which could further increase AI-related electricity consumption.
“The potential growth highlights that we need to be very mindful about what we use AI for. It’s energy intensive, so we don’t want to put it in all kinds of things where we don’t actually need it,” de Vries says. More