Articles tagged with Artificial Intelligence
A research team developed an optical chip that can train machine learning hardware, improving AI performance and reducing energy consumption. This innovation uses photonic tensor cores and electronic-photonic application-specific integrated circuits to speed up the training step in machine learning systems.
A new research project at Aarhus University aims to develop intelligent drones that can detect ice on turbine blades, optimizing energy production and expanding market opportunities. The project has the potential to reduce energy losses by up to 80% and enable wind farms to operate in colder climates.
A new AI tool helps governments decide whether to bail out a bank by predicting if the intervention will save money for taxpayers. The algorithm assesses financial implications and suggests optimal bailout strategies.
Researchers at Singapore University of Technology and Design (SUTD) have developed a novel phase-change key for new hardware security. The device, known as the physical unclonable function (PUF), is scalable, energy-efficient, and secure against AI attacks compared to traditional silicon PUFs.
Researchers used weather radar to track bird movements and found peak roosting stages shifting earlier due to warmer temperatures. This shift may lead to a shortened pre-migratory season, impacting birds' survival during migration.
Researchers at Oak Ridge National Laboratory have discovered genetic markers for autism, developed recyclable composites to drive the net-zero goal, and created a tool for real-time building evaluation. Additionally, they have made significant progress in growing hydrogen-storage crystals using a novel nano-reactor material.
Researchers have developed wearable electronics paired with artificial intelligence to detect emerging health problems, such as heart disease and cancer, before symptoms appear. The device can perform personalized analysis of tracked health data while minimizing wireless transmission.
Researchers created a Facebook-like prototype platform, called Trustnet, where users rate posts as accurate or inaccurate before sharing. This approach showed that people effectively assess misinforming posts and share their assessments with others.
Researchers at Mayo Clinic developed an AI algorithm that accurately detects weak heart pumps using single-lead Apple Watch ECGs. The system has shown promising results, rivaling medical treadmill diagnostic tests in accuracy, and holds potential for scalable screening and prevention of heart failure.
Researchers developed an AI tool that analyzes plaque features on coronary CT scans to predict reduced blood flow, potentially reducing invasive tests. The tool could help doctors determine the next step in treatment plans and risk-stratify patients correctly.
A team of researchers used AI pattern recognition to re-analyze footprints from the Dinosaur Stampede National Monument and concluded that they were made by an ornithopod dinosaur, a herbivorous species. The results contradicted the long-held assumption of a vicious dinosaur predator.
The study developed an extended deep Q-network (EDQN)-incorporated context-based meta-RL model that can autonomously detect traffic states, classify regimes, and assign signal phases. The model outperformed existing algorithms in simulation experiments and showed adaptability to new tasks without adjusting parameters.
Researchers created a new set of standards, called FAIR, to manage AI models, making them findable, accessible, interoperable and reusable. This standardization enables cross-pollination across teams and reduces duplication of effort, ultimately facilitating scientific discovery.
Experts warn that poorly functioning IT systems are a clear and present threat to patient safety, resulting from inadequate investment and lack of prioritization. The British Medical Association estimates that 27% of clinicians lose over four hours a week due to inefficient IT systems, highlighting the need for urgent improvement.
The DUNE project uses deep learning techniques and distributed computing to improve indoor positioning accuracy. The system combines various sensor technologies and adapts to different industrial needs, providing real-time updates and reducing margins of error.
Researchers have demonstrated a power-efficient component for demultiplexing operation using silicon photonic MEMS, enabling efficient wavelength demultiplexing for fiber-optic communications. The compact footprint of the add-drop filter allows fast operation compared to established MEMS products.
The University of Tsukuba researchers developed a machine-learning model to predict undertriage in phone-based triage systems. The model identified risk factors such as age, sex, and comorbid conditions, which can be used to update protocols and improve patient outcomes.
A new AI technology has been developed to automatically diagnose lung diseases such as tuberculosis and pneumonia, freeing up radiographers and reducing waiting times for test results. The system, which uses X-ray imaging and deep learning algorithms, achieved a 98% accuracy rate during extensive testing.
The National Science Foundation has awarded $800,000 to support the development of Edge AI applications in a proposed wearable diabetes management device. The project aims to create a smart, wearable device that monitors blood sugar levels without the need for frequent blood draws, reducing delays and increasing privacy and security.
NJIT Assistant Professor Salam Daher is using augmented reality technology to train caretakers for the elderly. The project aims to improve communication and empathy skills through a digital model of an older person that responds to emotions and environment. The system will be tested to investigate its impact on patient outcomes.
Researchers developed an intelligent compaction technology that integrates into a road roller, assessing real-time the quality of road base compaction. This improves road construction, reducing potholes and maintenance costs, leading to safer and more resilient roads.
A new research group aims to develop an interdisciplinary approach for secure operation of critical infrastructure systems using artificial intelligence. The goal is to create a 'hybrid architecture' that combines AI-based strategic development with rule-based systems, ensuring safe and reliable behavior.
Researchers at the University of Houston are developing a clinical decision support system to predict which patients with diabetes are most likely to experience complications. The tool, called Primary Care Forecast, uses deep learning to consider patient health history and social factors such as employment status and education level.
Researchers from Osaka University have developed an AI-powered method to identify optimal amino acid mutations in enzymes. This approach accelerates the enzyme engineering process, allowing for tailored enzyme designs suitable for various biochemical environments.
Deep learning models can become less accurate in recognizing specific categories of images, sounds, or text after network pruning. Researchers demonstrate a technique to address this challenge, improving the fairness of deep learning models.
The 13th Annual Meeting on Skin Challenges 2022 will address key skin ageing and challenges such as skin microbiome, cancer, inflammation, wound healing, and more. The event will feature experts from industry and academia presenting their research on these topics.
A new AI framework, M2YOLOF, has been proposed for fast and high-precision target detection. The framework reduces model complexity and inference time overhead by using a multi-input single-output approach.
A Mayo Clinic study found that clinicians who were high adopters of an AI-enabled clinical decision support tool were twice as likely to diagnose low left ventricular ejection fraction as those who were low adopters. The tool's accuracy was critical in detecting serious health conditions before symptoms appeared.
Researchers can now study microplankton at an individual level using holographic microscopy and AI, gaining a deeper understanding of their movement, growth, reproduction, and interactions. This breakthrough provides new insights into the ocean's oxygen production and carbon cycle.
The US Department of Energy's Oak Ridge National Laboratory has developed a massive geographic dataset, USA Structures, using deep learning to forecast potential damage and accelerate emergency response. The dataset provides critical information on building outlines and attributes, enabling FEMA to prioritize response efforts.
Researchers used AI to predict compounds that could neutralize reactive oxygen species causing baldness. They successfully regenerated hair on mice using microneedle patches, providing a promising new treatment for androgenic alopecia.
A study of 9,303 US adults found that people with cardiovascular disease were less likely to use wearable devices, highlighting disparities in access and use. The research emphasizes the need to ensure equitable access to wearables to benefit high-risk populations.
Researchers at the University of Illinois developed an AI-powered system that uses a molecule-making machine to find optimal reaction conditions for synthesizing chemicals. The system doubled the average yield of a challenging class of reactions, paving the way for faster innovation and automation in biomedical and materials research.
Researchers aim to identify electrical activity in brain consolidation and test targeted brain stimulation to strengthen memories. The study could advance understanding of neural mechanisms involved in memory formation, potentially benefiting people with Alzheimer's disease.
An AI-based model has been developed to assist radiologists in detecting and identifying leadless implanted electronic devices (LLIEDs) on chest X-ray images. The model achieved high detection and classification accuracy, even with suboptimal image quality, and showed promise for real-world deployment.
Researchers at Purdue University and the University of Tennessee, Knoxville, have developed a metamaterial that can learn to adapt to its surroundings on its own. The material uses shape to store information in microseconds, allowing drones to quickly recall patterns associated with dangerous conditions.
The Beckman Institute has established a new national collaborative Biomedical Technology Research Resource to develop label-free optical imaging technologies. The center aims to create optical and computational imaging technologies that can serve as a resource for clinicians and researchers.
Researchers aim to use quantum computer-based AI to accelerate drug discovery, cutting costs and time by exponentially increasing processing power for complex problems. Quantum AI models have higher capability to approximate desired functionality compared to classical neural networks.
A study by Professor Dennis Murphy Odo found that automatic text simplification (ATS) software makes authentic materials more comprehensible for L2 learners with high reading proficiency. However, the tool's effectiveness is limited for learners with lower reading proficiencies. The researcher suggests that ATS tools need to be further...
Lei Yang is conducting research on developing fair AI-assisted mobile dermatology diagnosis technology using unsupervised federated learning. The goal is to ensure participation from all socioeconomic populations and find the best network while considering hardware constraints.
A new study published in iScience found that humans tend to follow a two-step process when in a crowd, with an initial reflexive imitation followed by a more deliberate strategic decision. The research suggests this influence affects not only social norms but also immediate actions and underlies behaviors such as rioting and mass panic.
Researchers developed an AI-based model that combines artificial intelligence and weather forecast models to predict extreme wildfire danger with high accuracy. The new method can produce forecasts of extreme fire danger out to one week at finer scales (4km x 4km resolution), increasing its utility for fire suppression and management.
Researchers at MIT have developed a new method that uses optics to accelerate machine-learning computations on low-power devices. By encoding model components onto light waves, data can be transmitted rapidly and computations performed quickly, leading to over a hundredfold improvement in energy efficiency.
Researchers at Indiana University have developed a virtual reality therapy to aid in substance use disorder recovery, using 'future-self avatars' to help people choose long-term rewards over immediate gratification. The technology has shown promising results in lowering relapse rates and increasing future self-connectedness.
FathomNet aggregates images from multiple sources to create a publicly available, expertly curated underwater image training database. The database uses artificial intelligence and machine learning to alleviate the bottleneck for analyzing underwater imagery, accelerating important research around ocean health.
A new AI model developed by researchers at the University of Georgia uses artificial intelligence to analyze terabytes of quail call recordings, allowing wildlife managers to gather data in a matter of minutes. The model is accurate, picking up between 80% and 100% of all calls, even in noisy recordings.
Omnipose, a deep learning software, can identify various types of tiny objects in micrographs with high precision, including bacteria of all shapes and sizes. It overcomes limitations of previous approaches by handling object overlap and detecting cell intoxication, making it a game-changer for biological image analysis.
A study found that Italian regions with high levels of environmental pollution have higher cancer mortality rates, even after controlling for lifestyle factors. The analysis identified specific sources of pollution associated with certain types of cancer, highlighting the need for environmental reduction and prevention to combat cancer.
A new study aims to diagnose severe neurodegenerative diseases like ALS and FTD with the help of speech tests. AI can analyze subtle nuances of speech patterns, including pauses, speed, and melodic aspects, to detect early changes.
A new project will develop a technique to quantify uncertainty in AI-based tools used for image analysis and create a questionnaire to assess patients' risk tolerance when using these tools. The goal is to ensure that AI-assisted clinical decisions are informed by the inherent uncertainty of imaging technologies.
Researchers found that AI language models exhibit significant implicit bias against individuals with disabilities, resulting in inaccurate sentiment analysis and potential misclassification of posts as toxic. The models' biases were apparent in various applications, including autocorrect and social media content moderation.
A team of researchers at the University of Pittsburgh is using artificial intelligence to create more efficient and effective organoids. By designing and printing smarter cell structures that mimic human organs, they aim to reduce trial-and-error methods and costs, ultimately contributing to new methods in disease research and human he...
Researchers created living brain cells in a dish that can perform goal-directed tasks and adapt to changes, opening doors for new understanding of brain function. The study also raises possibilities for alternative animal testing and biomimetic research.
Researchers from McGill University and MIT developed an AI system that can learn the rules and patterns of human languages on its own. The model automatically generates higher-level language patterns that can be applied to different languages, achieving better results.
A perspective paper explores the role of clinician-data-scientists in healthcare, emphasizing their need for interdisciplinary knowledge and training. The researchers highlight the importance of integrating data science into conventional medical education to prepare clinicians for the digital health era.
Researchers at North Carolina State University developed a blueprint for incorporating ethical guidelines into AI decision-making programs. The new mathematical formula, based on the Agent, Deed, and Consequence (ADC) Model, considers intent, character, and consequences of actions to make more informed decisions.
A Cleveland Clinic-led research team created a discovery tool outlining interactions between COVID-19 and host proteins, identifying potential host-targeting therapies. The study confirmed over 200 interactions and discovered new ones, highlighting promising approaches for treating COVID-19.
New research highlights the dangers of AI-powered recruitment tools that claim to remove discrimination from hiring. The tools reduce race and gender to trivial data points and often rely on personality analysis that is
The program cuts the average time it took for new patients to get their first treatment from 97 days to just 41 days, while also allowing more than 1,000 new patients to begin treatments. The system has made significant improvements in efficiency and patient satisfaction, with appointment no-shows dropping by 50%.
A University at Buffalo-led research team has been awarded a $5 million grant to develop digital tools that can help older adults recognize and protect themselves from online deceptions. The project, DART, aims to reduce online fraud among older adults, who lose billions of dollars each year due to scams.