Articles tagged with Artificial Intelligence
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...
A team of researchers at USC has developed a system to quantify and fix anti-queer bias in AI-powered text prediction. By training the popular language model BERT on queer-inclusive data from Twitter and news articles, they significantly reduced biases in predictions, with QueerTwitter showing the most effective results.
MIT researchers developed a method to create 3D-printed materials with tunable mechanical properties and embedded sensors, enabling real-time feedback on movement and interaction. The sensing structures use air-filled channels that deform when moved or squeezed, providing accurate feedback for robotics and wearable devices.
Researchers developed a Flashover Prediction Neural Network (FlashNet) model to forecast deadly fire events, beating other AI-based tools with up to 92.1% accuracy across various building floorplans. The model's performance improved when given real-world data, highlighting its potential for saving firefighter lives.
Researchers at UNH will develop and test social assistive robots to aid in the care of individuals with Alzheimer’s disease and related dementia in their own homes. The robots will be equipped with smart home devices, artificial intelligence, and wearables to track patients' activity levels and health management.
A new study by Petra Nieken found that charismatic leadership tactics and consistent video communication lead to better performance in remote work. In contrast, traditional charisma questionnaires did not predict staff performance. The study suggests that managers should convey a consistent impression when using the video channel.
Researchers have developed a proof-of-concept model that uses artificial intelligence to combine multiple types of data from different sources to predict patient outcomes. The models demonstrate the ability to make prognostic determinations while uncovering the predictive bases of features used to predict patient risk.
Researchers at Carnegie Mellon University have developed an AI pilot that can safely navigate crowded airspace by predicting the intent of other aircraft and avoiding collisions. The AI uses vision and natural language processing to communicate with other aircraft, enabling safe and socially compliant navigation.
Researchers developed a flexible, stretchable computing chip that processes information like a human brain to analyze health data. The device aims to change the way health data is processed, enabling continuous tracking of health without sending data wirelessly.
The University of Illinois School is awarding a grant to explore the use of conversational AI in libraries, aiming to strengthen engagement between libraries and diverse audiences. The project will investigate ways to democratize conversational AI for librarians, enhancing workforce development and community service.
Researchers at Washington University in St. Louis developed a new imaging tool that uses optical coherence tomography (OCT) and machine learning to detect and classify cancerous tissue samples with high accuracy. The technique, which was tested on patients in a trial, showed a 93% diagnostic accuracy rate.
A new study used artificial intelligence to create the largest global map of insect diversity, highlighting areas with high ant biodiversity and potential conservation priorities. The researchers also found that only a low percentage of these areas are protected.
Physicists have created a way to simulate quantum entanglement between interacting particles using neural networks and fictitious 'ghost' electrons. This approach enables accurate predictions of molecule behavior, which could lead to breakthroughs in pharmaceutical development and material design.
Researchers at MIT have developed a machine-learning system that uses computer vision to monitor the 3D printing process and correct errors in real-time. The system successfully printed objects more accurately than other 3D printing controllers, enabling engineers to incorporate novel materials into their prints with ease.
A new study introduces a novel epigenetic predictor, PCBrainAge, that captures aging heterogeneity across multiple brain regions. The tool demonstrates stronger associations with AD dementia and pathologic AD compared to existing age predictors.
Researchers evaluated the real-time performance of an AI model to detect COVID-19 from chest X-rays and found that it did not outperform radiologist predictions. The study highlights the limitations of AI in diagnostic accuracy, particularly for certain groups such as women and minority populations.
A new AI-based dynamic brain imaging technology has been introduced by Carnegie Mellon University, which can map out rapidly changing electrical activity in the brain with high precision and speed. The technology uses deep learning approaches to translate scalp EEG signals back to neural circuit activity without human intervention.
MIT researchers have developed a new type of programmable resistor that enables analog deep learning, which promises faster computation with reduced energy usage. The device can process complex AI tasks like image recognition and natural language processing, paving the way for integration into commercial computing hardware.
A Nagoya University research group has developed an AI algorithm that can diagnose idiopathic pulmonary fibrosis with high accuracy, based on non-invasive examinations and medical data. The technology may revolutionize medical care by allowing doctors to request AI-assisted diagnoses instead of specialists.
A study published in Science Robotics journal reveals that humans are unable to distinguish between a humanoid robot's response time variability and that of a human when interacting with the robot. This finding suggests robots can exhibit human-like behavior, raising questions about the blurring line between humans and machines.