Articles tagged with Artificial Intelligence
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.
Academics argue AI systems like DABUS need new IP laws as existing ones are inadequate to deal with cases where machines are named sole inventors. The authors recommend introducing a new 'AI-IP' form of law that would be specifically tailored to AI-generated inventiveness, addressing ownership and novelty issues.
The Collaborative Research Centre Hearing Acoustics is developing smart hearing aids that use artificial intelligence to adjust to different environments. The project, which aims to create more adaptable hearing devices, has received $8.1 million in funding for another four years.
The CONCERN initiative uses AI to analyze nursing documentation and predict patient outcomes, such as organ failure or cardiac arrest. By leveraging nurse expertise, the tool aims to improve patient care and reduce preventable deaths.
Researchers use AI software to identify invasive species and native trees threatened by disease from photos taken by on-train cameras. This technology enables railway workers to take action to better manage lineside vegetation and achieve biodiversity net gain by 2035.
MIT researchers develop an interactive design pipeline enabling users to create customized robotic hands with tactile sensors. The platform streamlines the process, allowing users to adjust palm and fingers and integrate tactile sensors, resulting in complex tasks like picking delicate items or using tools being performed flawlessly.
A new training program at MUSC and Clemson University aims to make future data scientists aware of health inequities, particularly in rural communities. The SC BIDS4HEALTH program will build on existing relationships with HBCUs and community groups across the state.
A machine learning algorithm has outperformed astronomers in analyzing microlensing data to find new exoplanets, revealing connections hidden in complex mathematics from general relativity. The AI algorithm uncovered a degeneracy that had been missed by experts, suggesting a broader theory is likely incomplete.
Researchers have successfully processed sequences with a large neural network while consuming significantly less energy on neuromorphic hardware. This breakthrough showcases the potential of neuromorphic technology to improve the energy efficiency of AI workloads.
Veterinarians at UC Davis have developed an AI model that can predict Leptospirosis in dogs with high accuracy, outperforming traditional testing methods. The model uses machine-learning algorithms to analyze historical data and provide timely predictions, which can be an online resource for veterinarians.
A new computer-aided diagnostic tool, Deep-Lung Parenchyma-Enhancing (DLPE), uses artificial intelligence to reveal signs of pulmonary fibrosis in COVID long-haulers, helping to explain respiratory symptoms and improve disease management.
Researchers found that computer assistance in design leads to better solutions but compromises creativity and user agency. In a virtual reality experiment, novice designers outperformed their human-led counterparts when using an optimized approach.
Researchers at MIT developed a technique that enables an autonomous vehicle to plot a provably safe trajectory in highly uncertain situations. The algorithm considers probability of observing different environmental conditions and obstacles, and formulates trajectory planning as a probabilistic optimization problem.
A new smartphone app using AI technology has been found to be as accurate as expert gastroenterologists in characterizing stool specimens. The app's ability to provide detailed descriptions of constipation, diarrhea, and normal stools surpasses that of patient reports.
A new method for 3D mapping uses artificial intelligence to detect correspondences and correct gaps in laser-point clouds, eliminating the need for manual data corrections. This approach enables faster, cheaper, and more accurate maps, with potential applications in construction, climate change monitoring, and road safety.
A new distributed learning technique, GD-SEC, reduces communication requirements in wireless architecture, improving efficiency and reducing computational cost. The method employs data compression to transmit only meaningful, usable data, enhancing the impact of machine learning while minimizing its limitations.
Researchers at ETH Zurich have developed a novel memristor design that can switch between two operation modes, enabling greater efficiency in machine learning applications. This breakthrough component is made of halide perovskite nanocrystals and simulates complex neural networks with high accuracy.
A new approach using artificial intelligence generates designs automatically, allowing researchers to create complex metasurfaces with billions of nanopillars. This enables the development of larger, more complex metalenses for virtual reality and augmented reality systems.
A team from the University of Missouri is using artificial intelligence to accelerate the discovery process in materials science. By integrating machine learning algorithms and AI into traditional laboratory processes, they aim to reduce time and cost while increasing the rate of material development. Associate Professor Derek T. Ander...
A new algorithm forecasts which hospitalized patients are at the highest risk of death from COVID-19, regardless of immune protection status or virus variant. The tool, called COVID-19 Disease Outcome Predictor (CODOP), uses blood measurements to predict prognosis and can help direct critical care resources.
A new software uses natural language processing to predict vaccine hesitancy on a community level, providing an early warning system for policymakers. The study shows that publicly available Twitter data can be sufficient to detect zip code-level vaccine hesitancy with reasonable accuracy.
A new study demonstrates a machine-learning approach that can learn to control a fleet of autonomous vehicles as they approach and travel through a signalized intersection. The technique reduces fuel consumption and emissions while improving average vehicle speed, with benefits seen even when only 25% of cars use the control algorithm.
Researchers have developed CAS(ME)3, a large-scale micro-expression database with depth information and high ecological validity. The database contains 1,109 manually labeled micro-expressions and 3,490 macro-expressions, allowing for more accurate emotional analysis and recognition.
Researchers at UC San Diego School of Medicine found that COVID-19, MIS-C and Kawasaki disease share similar underlying molecular patterns. The study reveals that MIS-C is a more severe version of the immune response than KD, with potential implications for improved disease diagnosis and treatment.
Researchers at Stevens Institute of Technology developed an underwater robot capable of mapping its environment, tracking its location, and planning safe routes in complex marine environments. The breakthrough enabled the robot to accurately map a busy harbor in real-time using active SLAM algorithms.
Xie aims to develop algorithms that reflect human domain knowledge and reasoning patterns, increasing trust in machine learning models. He also explores algorithmic fairness, considering multiple perspectives on what's considered fair, to address concerns about biased decision-making.
An artificial intelligence model trained on histological images of surgical specimens accurately classified patients with and without Crohn disease recurrence. The model revealed previously unrecognized differences in adipose cells and mast cell infiltration, enabling stratification by prognosis for postoperative Crohn disease patients.
Researchers used game theory to create models of cooperative and competitive behaviors in multi-agent systems, focusing on distributed online optimization, federated optimization, and static/dynamic games. The findings have potential applications in smart cities, market competition, information security, and drug development.
The university's new Robotics and Autonomous Systems Teaching and Innovation Center (RASTIC) will provide students with hands-on experience in robotics, autonomous systems, and self-driving technology. The lab aims to boost Massachusetts' competitiveness in the tech sector by supporting innovative projects and startups.
A new study uses artificial intelligence to predict bone fractures in cancer patients by creating a digital twin of the vertebra. The AI-assisted framework, ReconGAN, simulates how tumors affect the spine and predicts fracture risks, offering medical experts better treatment strategies and patient decisions.
Researchers at the University of Missouri are applying AI to analyze protein dynamics, identifying potential target sites for new drug therapies. The approach can simulate protein changes related to conditions like cancer, enhancing the chances of successful therapies.
Artificial Intelligence can now identify legendary batting techniques used by Sir Donald Bradman and modern players. Researchers developed a deep learning computer vision AI model to detect lateral backlift batters from straight ones.
MIT researchers develop ExSum, a framework to formalize explanations of machine-learning models into quantifiable rules. This allows for testing assumptions about model behavior and reveals unexpected insights, such as negative words having sharper contributions to model decisions.
Researchers at Texas A&M University used an Artificial Intelligence Materials Selection framework to discover a new shape memory alloy with the highest efficiency ever recorded. The alloy's narrow transformation temperature window and high cyclic stability make it ideal for thermal energy harvesting and storage.
A new $5 million center, MIXR, aims to develop, test and certify XR technologies in medicine and healthcare. The collaboration between academia, industry and government will improve medical training, patient management and health outcomes.
A new study from Mayo Clinic suggests that AI-enabled electrocardiograms (ECGs) can predict atrial fibrillation, a cardiac rhythm abnormality linked to one-third of ischemic strokes. The algorithm also detects other cardiac disease markers and correlates with small vessel cerebrovascular disease and cognitive decline.
Researchers developed an AI algorithm that can detect weak heart pumps in patients using single-lead Apple Watch ECGs. The study found high participation rates and high-quality data from over 46 states and 11 countries, demonstrating the potential for this tool to be widely used.
Researchers from Dartmouth College used artificial intelligence to draft wine and beer reviews, finding agreement between human and machine-generated reviews. The team also developed a system to write review syntheses, aggregating elements from existing reviews to provide limited but relevant information about products.
Researchers at Cedars-Sinai have developed an AI tool that accurately predicts pancreatic cancer patients based on CT scan images. Lowering blood cholesterol has also been shown to slow the growth of prostate cancer by enhancing immune cell action. Additionally, a new study found that an ultrasound probe plugged into a smartphone can b...