Articles tagged with Artificial Intelligence
A new study found that Black patients are six times more likely to have advanced vision loss after a glaucoma diagnosis, highlighting the need for early eye screening in this population. The research analyzed nearly 210,000 participants and used artificial intelligence to identify visual field loss patterns.
Researchers at USC's Information Sciences Institute developed a method to train AI to understand analogies in Aesop's fables, enabling it to make creative connections between familiar and novel situations. The study found that humans approach analogical reasoning subjectively and interpretively, influencing the outcome.
Researchers have developed a novel simulation method to calculate free energy using deep learning artificial intelligence, providing accurate models of molten salts' thermodynamic properties. The study could help examine corrosion in metal containers and improve the design of next-generation nuclear reactors.
Researchers developed an AI-driven device that detects and predicts hemodynamic instability using a single ECG lead, outperforming traditional vital sign measurements with nearly 97% sensitivity. The technology has the potential to provide continuous dynamic monitoring capabilities in patients with intermittent static vital sign measur...
A new study uses machine learning models to predict cancer patients' responses to immunotherapy based on their gut microbiome features. The research identifies common gut bacterial taxa associated with responders versus non-responders, providing a potential tool for distinguishing and predicting immunotherapy responders.
The University of Hong Kong's Faculty of Dentistry has developed an AI-powered approach to automate denture design, allowing for high accuracy and reducing treatment time. The new method uses a 3D Generative Adversarial Network (3D-GAN) algorithm to reconstruct the shape of natural teeth and generate prosthetic teeth.
A University of Ottawa-led team has developed an AI-based deep learning model to identify cystic hygroma, a rare and life-threatening disorder, from first-trimester ultrasound scans with high sensitivity and specificity. The approach may be applied to other fetal anomalies identified by ultrasonography.
Developed a dataset of over 2,000 clinically relevant questions written by medical experts to train AI models. The model asked high-quality and authentic questions over 60% of the time, compared to real questions from medical experts.
Researchers from Johannes Gutenberg University Mainz used AlphaFold to predict the structures of new protein knots, discovering the most complex knot and composite knots. These findings provide insight into folding mechanisms and evolutionary processes in proteins.
Researchers developed an AI system that classifies IBDN lesions accurately, displaying image-based diagnostic ability with 64.5% sensitivity and 89.5% specificity. The correct diagnosis rate of the AI system was 79.0, surpassing that of endoscopists, who achieved a 77.8% accuracy rate.
Researchers discovered that search results yield male-dominated output despite neutral searches, promoting gender bias and potentially affecting hiring decisions. Exposure to such outputs influences perceptions and decisions, reinforcing societal inequalities.
Researchers from North Carolina State University have developed a new approach to federated learning that allows them to develop accurate AI models more quickly and accurately. By reformating updates sent to the centralized server, they can resolve the heterogeneity problem in data, improving model performance.
Researchers from Osaka University found that facial similarity plays a crucial role in ratings of trustworthiness for observers of the same sex, but not for observers of opposite sex. The study suggests that facial similarity is an important factor affecting social judgments for same-sex interactions.
A study by University of Sydney neuroscientists found that people's brains can detect AI-generated fake faces with a 54% accuracy rate. However, verbal identification was only successful 37% of the time, highlighting the potential for using brain activity to flag deepfakes on digital platforms.
Researchers found that mere exposure to a human-like robot is not enough to make people believe it is capable of thoughts and emotions. Human-like behavior is crucial for being perceived as an intentional agent. The study aims to inform the design of social robots in the future.
Researchers developed a convolutional neural network to identify fetuses with Down Syndrome from ultrasound images. The model achieved high accuracy, improving detection by over 15% compared to existing methods. Non-invasive screening could become a convenient and inexpensive tool for early pregnancy diagnosis.
Researchers at Saarland University are developing orthopaedic implants that can actively stimulate bone healing by undergoing controlled micromotions. These innovative implants, which use shape-memory wires, can provide real-time information on fracture progression and identify potential complications.
Researchers developed a neural network algorithm that recognizes emotions and engagement from video images of faces, outperforming existing models in accuracy. The system can be integrated into video conferencing tools and online learning systems to analyze participant engagement and emotions.
A new theory developed by a collaboration between a former cosmologist and a computational neuroscientist has identified essential connections between brain cells. The theory, published in Physical Review Research, uses geometric framework to predict structure from function in neural networks.
Hawks use a unique flight path to slow down to a safe speed while minimizing the distance from the perch at which they stall. This allows them to control their landings effectively, even when slowing down risks stall, leading to sudden loss of flight control.
A team of researchers led by Danilo Vasconcellos Vargas has developed a new method called 'Raw Zero-Shot' to evaluate the robustness of artificial neural networks in image recognition. The study found that Capsule Networks produced the densest clusters, indicating improved transferability and potential solutions for improving AI robust...
By using the brain's visual response as feedback, researchers can reconstruct images of simple objects in real-time. The technique has potential applications in augmenting human capabilities and could one day be used to bring together human and artificial intelligence.
A new robotic system, FuseBot, has been developed to efficiently retrieve buried objects in piles. The system uses radio frequency signals and computer vision to reason about the probable location and orientation of objects under the pile, enabling it to find more hidden items than a state-of-the-art robotics system in half the time.
A team of experts presents a new framework for understanding the complex relationship between AI and climate change, suggesting ways to better align AI with climate goals. The study highlights the need for holistic evaluation of AI's impact on greenhouse gas emissions.
A joint research group developed a robotic AI system that autonomously determines optimal conditions for growing replacement retina layers. The system achieved a 90% rate of differentiation efficiency in 185 days, compared to months of work by humans.
Researchers found that two types of computer models, deep neural networks and the Basel Face Model, are surprisingly accurate at replicating human facial similarity judgments. These models were tested on a dataset of realistic computer-generated faces and ranked by students in terms of similarity.
Three African vulture species have significantly larger home ranges than existing protected areas, increasing exposure to human-related food sources and environmental toxins. Conservation efforts require addressing challenges outside protected areas.
Researchers found that people are less morally outraged when gender discrimination occurs due to an algorithm rather than direct human involvement. The study's findings have broader implications for efforts to combat discrimination and may affect how companies are held liable.
A new deep learning model can accurately identify disease-related features from images of eyes, even in low-quality images. The lightweight model is deployable on mobile devices and has better segmentation accuracy than other commercial software.
Researchers at the University of Tokyo have developed a new method to detect deepfakes, using self-blended images that improve detection accuracy by 5-12%. The team created novel synthesized images with controlled artifacts to train algorithms and found significant improvements in detecting deepfake images and videos.
A Rutgers team created an AI-powered framework that detects trespassing events, differentiates violators, and generates video clips of infractions. The system analyzed 1,632 hours of archival footage and found nearly 70% of trespassers were men, with most incidents occurring on Saturdays around 5 p.m.
Researchers at NIST developed LANTERN, a new statistical tool that can predict protein function while providing interpretable explanations. The tool has shown promise in predicting genetic edits for COVID-19, E. coli, and GFP proteins, achieving high predictive accuracy.
A new AI system uses artificial neural networks to recognize objects more accurately and stably, despite changing visual inputs. The system mimics human eye movements to improve machine vision capabilities, reducing errors in self-driving cars and other applications.
A team of researchers from Kyushu University has developed an olfactory sensor capable of identifying individuals by analyzing the compounds in their breath. The system, combined with machine learning, achieved an average accuracy of over 97% in authenticating up to 20 individuals.
A new optimization tool can quickly improve performance of various autonomous systems, including walking robots and self-driving vehicles. The tool uses automatic differentiation to identify tweaks that achieve desired outcomes, reducing trial-and-error simulations.
Researchers at MIT created insect-scale robots that can emit light during flight, allowing for precise motion tracking and potential communication between robots. The ability to emit light also enables the robots to call for help in search-and-rescue missions.
Researchers at the University of Rochester have discovered a novel neural mechanism involved in causal inference that helps the brain detect object motion during self-motion. This discovery may have applications in designing artificial intelligence devices and developing treatments for brain disorders such as autism and schizophrenia.
Computer scientists have developed a system to analyse noise produced by individual cameras, linking videos or images to specific cameras. The tool extracts camera-specific noise from video frames, achieving high accuracy rates and potential practical use for law enforcement.
A team of researchers developed a framework using artificial intelligence to improve long-term life satisfaction. The model, called a self-organizing map, estimates an individual's psychological age, future well-being, and risk of depression, providing personalized daily tips for improved well-being.
Researchers developed an AI method to automatically detect plaque erosion in heart arteries using OCT images. The new technique uses neural networks and post-processing algorithms to predict regions of possible plaque erosion and refine the initial prediction based on clinically interpretable features.
Researchers use machine learning to automatically analyze Reflection High-Energy Electron Diffraction (RHEED) data, enabling faster and more efficient discovery of new materials. The study focused on surface superstructures in thin-film silicon surfaces and identified optimal synthesis conditions using non-negative matrix factorization.
The device can instantly recognize what it sees, like automatic descriptions of pictures taken by a camera or phone, and outperforms the eye in the number of wavelengths it can see. It's uniqueness comes from its ability to integrate three different operations into one, making it many times faster than current technology.
Researchers at Case Western Reserve and University of Washington develop AI-3D collaboration to gain insights into highly aggressive prostate cancer. The collaboration aims to provide a new view and understanding of prostate cancer cells using light sheet microscopy.
A Cornell University research team used AI to detect eelgrass wasting disease at nearly three dozen sites along the West Coast. The study found that warmer-than-normal water temperatures are associated with eelgrass wasting, regardless of region. Eelgrass is a vital coastal species for fish habitat and biodiversity.
A novel AI-empowered robotic device successfully assisted in percutaneous nephrolithotomy—surgery for removing large kidney stones. The trial showed improved precision and reduced complications, with a significant reduction in needle punctures and puncture duration.
Researchers designed a modular AI chip that can be easily upgraded by swapping out layers, reducing the need for new devices. The chip uses optical communication to transmit information between layers, enabling high versatility in edge computing applications.
Researchers at University of Virginia are developing long-lived sensors for the Internet of Things (IoT) to reduce maintenance costs and upgrade challenges. The goal is to create an ecosystem that enables sensors to adapt to changing environments, reducing electronic waste.
Researchers at the Kosinski Group used a combination of cryo-electron tomography, single particle cryo-EM, and integrative modelling to create the most complete model of the human NPC to date, covering over 90% of its core. This breakthrough enables scientists to understand the NPC's structure and function in greater detail.
The Department of Energy has awarded Early Career Research Program funding to three Oak Ridge National Laboratory scientists. The awardees will receive $500,000 annually for five years to support their research in fusion energy, advanced scientific computing, and biogeochemical controls on phosphorus cycling.
Researchers developed an AI technology that merges physical domain knowledge with AI to accurately predict battery capacity and lifespan. The approach improved prediction accuracy by up to 20% and has significant implications for the widespread adoption of electric vehicles.
PaSh parallelizes Unix shell scripts automatically and accurately, boosting execution speeds by hundreds of times. Researchers tested the system on hundreds of scripts and found no errors, making it accessible to data scientists, biologists, engineers, and economists.
A study found that the brain uses data compression to eliminate irrelevant information and adopt temporary 'tunnel vision' for optimal performance. This principle extends beyond sensory processing to cognitive functions.
The study explored how humans react to AI decision making by studying human interaction with autonomous cars. Researchers found that people show an aversion to AI when asked explicitly, but not when not being asked, and that this rejection is mostly due to incorporating individuals' beliefs about society's opinion.
Researchers used AI to compare brain images of individuals with Autism Spectrum Disorder to computer-generated simulations, revealing links between brain anatomy and symptoms. The discovery could help understand ASD causes and develop targeted interventions.
Researchers have created prototypes that provide text descriptions at varying levels of detail, enabling blind and low-vision individuals to drill down from high-level data to more detailed information using just a few keystrokes. The prototypes improved users' ability to identify patterns in the data and navigate through visualization...
Researchers at Oak Ridge National Laboratory have made significant advancements in recovering rare earth metals, developing safer batteries, and enhancing material properties through tailored molecules and advanced microscopy. These discoveries could lead to more efficient clean energy technologies and reduced carbon impacts.
Researchers at MIT found that explanation methods used to aid human decision-makers in high-stakes situations often have lower accuracy for minoritized subgroups. The fidelity of these explanations varies dramatically between subgroups, with the quality often significantly lower for women and Black people.
Ingmar Weber's research focuses on societal computing, using computer science methods to understand and improve society. He collaborates with various stakeholders, including the UN, to develop solutions for humanitarian crises.
A study conducted by Kaunas University of Technology researchers analyzed how people with Alzheimer's disease process visual information from faces. The study found that brain signals in patients are significantly noisier, making it harder for them to focus and be attentive.
The Frontier supercomputer has achieved an unprecedented level of computing performance, breaking the exascale barrier with 1.1 exaflops of performance. This milestone enables scientists to tackle previously unsolvable problems in fields like energy and national security.