Articles tagged with Artificial Intelligence
The new computer chip uses a transistor-free design that eliminates data transfer time and minimizes energy consumption. It offers up to 100 times faster performance than conventional computing architectures, making it ideal for AI applications.
A Rutgers-led study statistically evaluated government data to determine if COVID-19 vaccination increases the incidence of Guillain-Barré syndrome. The results show no significant association between any COVID-19 vaccinations and the disease.
Physicists at the University of Basel have developed a computational shortcut for neural networks, allowing for faster calculation of optimal solutions without training. This breakthrough provides insight into neural network functioning and could help detect unknown phase transitions in materials and quantum systems.
Researchers developed a machine learning model to quickly recognize predictive risk factors and their importance for undesirable hospitalization outcomes. The model achieved an accuracy of 95.6% and identified modifiable risk factors that can be mitigated through clinical interventions.
Neuronal silencing periods enable efficient temporal sequence identification, allowing the brain to remember phone numbers and PINs. A new AI mechanism utilizing this mechanism also protects against stolen cards by recognizing personal handwriting style and timing.
Researchers have developed a scalable, fully coupled quantum-inspired processor that can solve optimization problems efficiently. The system uses an array calculator approach to divide calculations among multiple chips, reducing data transmission and increasing performance.
Researchers at Mayo Clinic used AI to evaluate patients' ECGs, identifying new cases of atrial fibrillation that would not have been detected through routine care. The study showed that AI can target screening to high-risk patients, potentially reducing undiagnosed strokes and deaths worldwide.
A recent grant will fund a project developing new hardware for machine learning, aiming to curb unsustainable energy use in AI systems. The new algorithms being developed are made available to the research community and compatible with an openly shared computing platform.
The MEMQuD project investigates quantum effects in memristive devices, which could lead to breakthroughs in AI, cryptography, and computation. Researchers analyze fundamentals and applications of quantized conductivity phenomena at room temperature.
Current AI models are restricted by a lack of experience in real-world environments, despite achieving significant advancements in virtual settings. Researchers are now exploring ways to bridge this gap with foundation models that can operate in physical spaces.
A public consultation is launched to develop best-practice standards for diverse and inclusive healthcare datasets used in Artificial Intelligence. The project aims to reduce biases that affect patients from minoritised racial/ethnic groups, ensuring they receive accurate predictions and treatments.
Physicists used machine learning to compress a complex quantum problem into four equations, capturing the physics of electrons on a lattice with high accuracy. The approach could revolutionize how scientists investigate systems containing many interacting electrons and potentially aid in designing materials with sought-after properties.
A new study by Eurac Research confirms negative global trends in mountain areas, with an average decline of 15 days of snow on the ground over 38 years. Snow cover has decreased by 4% globally, with peaks of 20 or more fewer days in Canada's western provinces.
Researchers developed an AI-based screening method that models drug and target protein interactions using natural language processing techniques. The technique achieved high accuracy in identifying promising drug candidates, which can accelerate the exploration of new medicines and repurpose existing ones.
A recent study found that individuals who distrust fellow humans tend to have more trust in artificial intelligence's ability to moderate content online. The researchers suggest personalizing interfaces based on individual differences can positively alter user experience.
Researchers developed an in-home wireless device that monitors a patient's movement and gait speed to track Parkinson's disease progression. The device uses machine-learning algorithms to analyze over 200,000 data points collected from 50 participants, showing that it can effectively track the severity of the disease.
Sooby's research aims to develop an AI algorithm to rapidly provide high-quality data during alloy fabrication, reducing time and cost. Her lab collaborates with Computer Science professor Amanda Fernandez to leverage deep learning approaches for image labeling, increasing efficiency by 99%.
Researchers at POSTECH have developed a method to engineer organs at scale using bioprinting, overcoming previous limitations of small tissue size and functional complexity. This innovation holds promise for personalized treatment of patients with the potential to create more realistic engineered organs.
UC San Diego researchers created the first diagnostic tool to differentiate between Kawasaki Disease and Multisystem Inflammatory Syndrome in Children (MIS-C) using a novel artificial intelligence-guided algorithm. The algorithm achieved accuracy exceeding 90% with simple test results and physical exam features.
A study from the University of Georgia shows people who rely on algorithms for creative tasks don't improve their performance and are more likely to trust low-quality advice. Participants preferred algorithm-derived advice over human-based advice, even when confident in their answers.
A University of Missouri researcher and his team are using artificial intelligence to study anatomical research, creating detailed 3D computer models of muscles. This technology is advancing the field by enabling researchers to analyze muscle fiber orientation and develop a better understanding of motor control in animals.
A large-scale experimental study by Harvard, Stanford, and MIT researchers found that weaker social connections on LinkedIn have a greater beneficial effect on job mobility than stronger ties. Weaker ties increased the likelihood of job mobility the most, while strongest ties had the least impact.
Researchers at Kyoto University designed a shared-laughter AI system to build empathy into dialogue between robots and humans. The system was tested on the Japanese android Erica, performing better than baseline models in evaluating empathy, naturalness, and human-likeness.
The MEMEX project promotes social cohesion by using AI and interactive tools to share community stories linked to cultural heritage. The exhibition features audio-visual stories from Lisbon, Barcelona, Paris, and Genoa participants who co-created their own digital content.
A team of researchers from Japan successfully reconstructed common brain input signals from the firing rates of neurons using a method called superposed recurrence plot. This breakthrough has significant implications for artificial intelligence, neuroscience, and potential treatments for mental health disorders.
Researchers developed a mathematical model to analyze cognitive changes and impairment in the brain, applicable to multiple sclerosis and other neurodegenerative diseases. The model uses multilayer networks for comprehensive analysis of CT scans, X-rays, ultrasound, and magnetic resonance imaging data.
Using artificial intelligence, researchers from Tufts University have devised rules for faster and more effective identification of potential new drug cocktails against tuberculosis. The study developed a set of design principles to assemble drug combinations, reducing the amount of testing needed before moving to further study.
Researchers discovered varying attitudes towards AI ethics, legal issues, and social implications in Japan, the US, and Germany. The study found that older respondents were more concerned about AI and ELSI issues, while those familiar with AI prioritized legal concerns.
Researchers designed an integrated system to support joint operations between UAVs and mobile robots for surveillance, epidemic prevention, and rescue operations. The system aims to improve public safety while ensuring privacy and security.
The University of California, San Diego is part of the National Institutes of Health's Bridge to Artificial Intelligence program, aiming to create comprehensive AI-ready datasets. The program will support researchers in developing interpretable and trustworthy AI technologies to improve human health.
A study by Simon Dubé examines the personality traits of people willing to engage with sex robots, revealing that erotophilia, sexual sensation-seeking, and enthusiasm for new experiences are key drivers. The research highlights an opportunity for manufacturers to cater to a female customer base as technology improves and becomes more ...
The NEUROCOV project seeks to understand the interplay between SARS-CoV-2 infection and the nervous system, with a focus on immune response and neuronal functions. The research aims to predict risk for neurological symptoms and develop effective treatments.
A new neural network model developed by Aalto University researchers can accurately predict the occurrence of fires in peatlands. The model identified a suite of interventions that would reduce fire incidence by 50-76%.
Researchers find XAI methods improve AI performance and explain bias in data, enabling accurate applications like insurance decision-making. Implementing XAI helps manage power consumption and optimize AI systems for efficient Industry 4.0 growth.
A study by NTU Singapore found that people have lower trust in health interventions suggested by AI alone, compared to those they perceive as based on human expert opinion. Emphasizing the involvement of a human health expert can improve acceptance and effectiveness of AI-suggested preventive care measures.
A new study from MIT reveals that computer models predicting molecular interactions, like AlphaFold, need improvement to help identify drug mechanisms of action. Researchers improved the performance of these models using machine-learning techniques, but more work is needed.
Machine learning helps researchers discover how bacterial populations adapt to environmental diversity by analyzing growth curves. The analysis reveals distinct decision-making components for lag, growth, and saturation phases, protecting the population from extinction.
Researchers created IGLUE to score ML efficiency in 20 languages, addressing cultural bias and practical implications. The tool aims to improve solutions for visually impaired and reduce performance dropouts outside English-speaking contexts.
A team from Ruhr-Universität Bochum developed a novel neural network that can classify tissue samples as containing tumors or not. The AI also generates an activation map showing where the tumor is detected, based on falsifiable hypotheses.
Researchers analyzed 170,000 vegetation plots from all climate zones to find that small areas can have high biodiversity, like Eastern European steppes and Siberia. This challenges the idea of large-scale conservation, as smaller protected zones may be more effective in preserving ecosystems.
A machine-learning model developed at Mass Eye and Ear accurately diagnosed pediatric ear infections at a rate 30 percent higher than doctors surveyed. The model achieved 95% accuracy in diagnosing an ear infection from test images, surpassing the average clinical accuracy of 65%.
Researchers used data from over 66,000 deliveries to create a prediction model that analyzes patterns of changes in women in labor. The algorithm aims to reduce cesarean delivery rates and maternal/neonatal complications by providing real-time risk predictions.
Rice University's ROBE Array algorithm slashes the size of DLRM memory structures, allowing training on 100 megabytes of memory and a single GPU. The method matches state-of-the-art DLRM training methods with improved inference efficiency.
A new method for generating realistic images in driving simulations uses machine learning to improve visual fidelity. This enables better testing of driverless cars and study of driver distraction, ultimately enhancing safety and interaction between humans and AI on the road.
Researchers at the University of Oldenburg and Fraunhofer IWES collaborate on a new project to develop more accurate wind flow simulations using artificial intelligence. The goal is to reduce computing times and enhance precision, ultimately accelerating innovation in wind turbine design.
Researchers developed an algorithm to identify different regions within tumors using nearly 6,500 digital slide images. The algorithm can predict recurrence-free survival and help guide therapy for patients with colon cancer.
Researchers developed an AI algorithm that can learn independently from multiple medical institutions without compromising data privacy. The algorithm outperformed traditional methods and has the potential to revolutionize digital medicine by reducing radiologists' workloads and making AI-based solutions more affordable.
Researchers at MIT developed an AI model that can detect Parkinson's disease from breathing patterns, using a neural network to assess the presence and severity of the condition. The device is non-invasive and can be used in patients' homes without any bodily contact.
ORNL researchers have won seven 2022 R&D 100 Awards for their advancements in materials science, machine learning, and energy storage. DuAlumin-3D, a high-strength aluminum alloy, and Gremlin, an AI system to identify weaknesses in machine learning models, are among the winning technologies.
Researchers at NC State University have developed a cooperative distributed algorithm that allows autonomous vehicle software to make calculations more quickly, enabling real-time navigation of complex merging scenarios. The approach improves both traffic flow and safety, with zero incidents in simulations.
A new study by the University of Central Florida reveals that workers in countries with greater income inequality are more likely to perceive robots and artificial intelligence as job threats. This association was found despite the potential benefits of these technologies to improve work and increase flexibility.
Researchers developed a machine learning algorithm that can predict how different driving patterns affect battery performance, improving safety and reliability. The algorithm uses non-invasive probing to provide a holistic view of battery health, suggesting routes and driving patterns that minimize degradation and charging times.
A new study found that over 50% of Gen Z respondents were concerned about the use of non-conscious data collection (NCDC), with attitudes varying by gender, income, education level, and religion. The study proposed a "mind-sponge" model-based approach to account for socio-cultural factors in assessing AI technology acceptance.
Researchers at Stanford University have created a new chip architecture called NeuRRAM that performs AI computing directly within memory, reducing energy consumption and increasing efficiency. The chip has been tested on various AI tasks and shown high accuracy rates.
A team of researchers from Osaka University developed an AI algorithm to predict the risk of mortality for trauma patients. They analyzed a large dataset of patient information and blood markers to identify critical factors that guide treatment strategies more precisely.
A new method, XTEC, uses machine learning to analyze large volumes of X-ray data, revealing previously hidden structural changes in materials. This accelerates materials discoveries and unlocks new properties of temperature-sensitive devices.
A new evaluation method at Osaka University accurately compares android and human facial expressions. The study found that androids have significantly less expressiveness than humans, but the new index may aid in developing more lifelike robots.
A new University of Illinois project aims to improve undergraduate students' ability to estimate their knowledge using artificial intelligence methods. The researchers will utilize machine learning to anticipate student performance and provide personalized feedback to enhance studying strategies.
Researchers identified robust metabolic markers of Covid, leading to better understanding and treatments for post-diagnosis symptoms. The study found that effects of Covid changed over time, persisting in some patients months after recovery.
A new AI algorithm has been developed to detect subtle brain abnormalities causing epileptic seizures. The Multicentre Epilepsy Lesion Detection project used over 1,000 patient MRI scans to train the algorithm, which was able to detect focal cortical dysplasia (FCD) in 67% of cases. This could lead to more patients being considered for...