Articles tagged with Artificial Intelligence
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...
Researchers from Tokyo University of Science have designed a tunable physical reservoir device based on dielectric relaxation at an electrode-ionic liquid interface. The system can store and process analog signals, enabling real-time processing of signals in living environments.
A new case study reveals significant differences between human and AI perception in breast-cancer screenings. Researchers found that AI systems consider tiny details in mammograms that are irrelevant to radiologists, highlighting the need for understanding and correcting AI decision-making before trusting it for life-critical medical d...
Researchers at Johns Hopkins Medicine developed an AI training strategy to capture images of mouse brain cells in action, allowing scientists to understand how the brain functions and is affected by disease. The technology combines ultra-small microscopes with AI to enhance image resolution up to 52 frames per second.
A new AI-based method distinguishes between stable and progressive cognitive impairment, enabling accurate diagnosis with an accuracy rate of 85%. This technique overcomes the complexity of subtle structural changes in mild cognitive impairment, contributing to achieving Sustainable Development Goal 3.
Researchers from the University of Cambridge have developed a hybrid, data-driven approach to the Travelling Salesperson Problem that significantly outperforms current methods. The new approach converges to optimal solutions at a faster rate and produces high-quality solutions under restricted computation time.
A new study indicates that artificial intelligence can accurately predict who will develop pancreatic cancer based on pre-diagnostic CT scan images. The AI tool was trained to analyze textural differences in the pancreas and achieved an accuracy rate of 86% in identifying those who would eventually develop the disease.
Researchers at MIT developed an AI method that constrains machine-learning models to suggest molecules with producible chemical structures. The approach guarantees quality and speed, outperforming existing methods in proposing high-quality molecular structures.
Researchers used AI technologies to conclude that male characters are four times more prevalent in literature than female characters. The study's findings also showed that the discrepancy between male and female characters decreases under female authorship.
Researchers developed an AI system using swarm learning to predict cancer from medical images of tissue samples without accessing patient data. The technique improved the detection of genetic changes in colon tumors with high accuracy.
Researchers developed a machine-learning method that allows robots to pick up and place never-before-seen objects in random poses, requiring only 10 human demonstrations. The system uses a neural network specifically designed to reconstruct 3D shapes, enabling the robot to generalize to new object orientations.
A recent study found that an artificial intelligence-based system improved providers' assessments of bladder cancer patients' response to chemotherapy. The AI tool helped doctors from different specialties and experience levels make more accurate diagnoses, with those in academia showing the greatest gains.
Researchers at Michigan State University have designed a learning-enabled safe controller for systems operating in uncertain environments. The new method, which combines control barrier functions and Lyapunov functions, allows the system to quickly learn uncertainties while achieving maximum safe performance.
Researchers developed a new decision-support tool to optimize ship channel dredging and disposal activities, considering factors like navigability condition deterioration and economic values. The algorithm weighs input from local professionals and projects costs with interest and inflation included.
A new deep learning method, PDD-Net, uses 3D deep convolutional neural networks to extract deep metabolic imaging indices from PET scans for differential diagnosis of parkinsonian diseases. The method achieved high sensitivity and specificity rates for Parkinson's disease and other parkinsonian syndromes.
Researchers developed an AI algorithm to model first impressions and accurately predict how people will be perceived based on a photograph of their face. The algorithm's findings align with common intuitions or cultural assumptions, such as people who smile being seen as more trustworthy.
A study published in Gastroenterology found that artificial intelligence reduced the rate of missed precancerous polyps in colorectal cancer screening by twofold compared to standard colonoscopy. The AI detection method identified smaller, flatter polyps and improved false negative rates.
Researchers at Columbia University have developed an algorithm that generates whisper-quiet sounds to block smart devices from spying on users. The system works by predicting the characteristics of spoken language and generating a signal that can disrupt automatic speech recognition models.
A new study by Hiroshima University introduces a novel AI-based technique for estimating site amplification factors from microtremor data. The model demonstrated potential as a predictive tool for characterizing site amplification factors, but notes are needed for further optimization with a larger dataset.
Researchers develop a new AI agent that uses four strategies to solve single-agent stochastic puzzles like Minesweeper, achieving comparable performance to state-of-the-art studies. The approach defines solvability in this context and establishes the boundary between puzzle-solving and game-playing paradigms.
Researchers at UCF create brain-like devices that enable AI to function independently, allowing technology like robots and voice assistants to operate in remote areas or space. The devices use parallelism and in-memory computing, similar to the brain, for AI and unsupervised learning.
A study found that trainee teachers who received AI-generated feedback improved their diagnostic reasoning, identifying potential learning difficulties in pupils more accurately. The AI system analyzed the trainees' work and provided clear, adaptive feedback.
Scientists develop models that complement simulations using reinforcement learning and numerical methods to predict climate change, turbulent flows, and morphogenesis. This approach enables faster and more energy-efficient predictions, solving complex problems in engineering and climate applications.