Articles tagged with Artificial Intelligence
Researchers at UC Santa Cruz find that popular link prediction metrics are flawed and do not accurately measure algorithm performance. They recommend using a new metric, VCMPR, to benchmark link prediction tasks and highlight the importance of accurate metrics in machine learning decision-making.
A new depth from focus/defocus approach, DDFS, combines model-based and learning-based strategies to achieve notable improvements in performance and applicability. The proposed method outperformed state-of-the-art methods in various metrics for several image datasets.
A new paper argues that LLMs can interpret and analyze neuroscientific data, unlocking new insights and potential treatments. Lead author Danilo Bzdok suggests that scientists may not always fully understand the mechanism behind biological processes discovered by LLMs.
A new tumor spatial mapping tool will help clinicians assess the aggressiveness of cancer and personalize treatment. The tool maps the functional state of oncoproteins onto tumour images, allowing for more accurate diagnosis and informed treatment decisions.
Researchers developed sensors using aerogels to detect formaldehyde, a common indoor air pollutant, with real-time detection capabilities. The sensors require minimal power and can distinguish between different gases.
Researchers developed an AI model to detect low blood sugar levels in diabetics while driving, improving safety and preventing accidents. The model uses driving behavior and head/gaze motion data, showing promise for future self-driving cars.
A clinical trial found that patients who received care recommendations from an AI-based system had fewer recurrent strokes, heart attacks, or vascular death within three months. The study suggests that this technology can improve patient outcomes and aid neurologists in making informed decisions.
Researchers developed an AI algorithm to analyze brain scans and clinical data, predicting treatment response with 8-week accuracy.
Assistant Professor Santiago Segarra at Rice University has won the NSF CAREER Award to develop a new approach for AI-powered climate prediction by leveraging structural properties in real-world data. The research aims to create more effective learning algorithms for structured domains.
Researchers at Forschungszentrum Jülich have programmed an AI that can recognize patterns in complex data sets and formulate them in a physical theory. The AI uses physics for machine learning to simplify complex interactions and build up a new theory.
The JGU Center for Lifelong Learning is developing personalized AI-based learning experiences for adult learners, aiming to improve motivation and learning outcomes. The project will also discuss the challenges of AI technologies in teaching modern foreign languages.
A new study uses GPT-3 to simplify chemical analysis, achieving accuracy surpassing state-of-the-art models. The approach fine-tunes the language model with curated Q&As, enabling easy and fast discovery in low-data chemistry.
A new study from MIT researchers found that doctors are less accurate in diagnosing skin diseases based on images of patients with darker skin. The researchers also discovered that an artificial intelligence algorithm can assist doctors in improving their diagnosis, although the improvements were more pronounced for lighter skin tones.
Researchers from Indiana University School of Medicine examined the use of ChatGPT in writing scientific review articles. They found that while AI-assisted approaches decreased writing time, they required more extensive fact-checking and produced incorrect references up to 70% of the time.
The multidisciplinary team, led by Nihar Mahapatra, is developing sustainable and impactful accessible voice AI solutions for people who stutter. The goal is to provide high-impact deliverables that will be sustainable beyond their support.
Researchers at ISTA have discovered the composition of poxviral cores, a key factor in their infectivity. The study's findings could lead to the development of new therapeutics targeting the viral core.
Smarter eco-cities harness real-time data to address environmental challenges and implement innovative approaches for ecological conservation. However, high energy demands associated with AI and AIoT applications pose a challenge to attaining environmental objectives.
Researchers from King's College London developed an AI model to predict brain cancer patient outcomes after radiotherapy. The model showed improved performances when trained on 10,000s of brain MRI scans and can give clinicians accurate predictions with a single routine scan, allowing for early treatment decisions.
A neural network trained on a photographic database of olive fruit endocarps can identify olive varieties with high accuracy. The OliVaR tool automates the traditional morphological classification process, allowing growers to quickly identify olive tree varieties and advancing general knowledge of all existing olive varieties.
A new study published in JAMA highlights the need for federal regulations that prioritize patient outcomes when it comes to AI-powered tools in healthcare. The researchers argue that AI products in healthcare do not universally undergo evaluation for potential harm or benefit to patients, and propose a revision to the White House Execu...
AI-based personalized medicine is developing rapidly, enabling tailored therapies for individual patients. However, existing regulatory frameworks create significant challenges for approving these novel treatments.
A new machine learning approach combines computer vision with deep-learning algorithms to pinpoint problem areas in concrete structures. The system enables efficient identification and inspection of cracks using autonomous robots, reducing the overall inspection workload.
Lehigh University researcher Dominic DiFranzo is developing an AI-powered platform, Social Media TestDrive, to equip youth with skills to navigate social media safely. The platform uses conversational AI tools to provide instant feedback and interactive simulations, empowering students to be 'upstanders' against cyberbullying.
Researchers have developed an AI-driven test that accurately diagnoses ovarian cancer in women clinically classified as normal, improving detection of early-stage disease. The test uses machine learning and blood metabolite information to assign a probability of disease presence or absence, offering a more clinically informative approach.
A new study proposes a data-sharing solution based on distributed fountain coding to improve the reliability of transmission in complex vehicle-connected environments. The proposed method allows receivers to accept encoded packets from different vehicles, reducing decoding failure caused by network anomalies.
The American College of Radiology has issued a joint statement with four other radiology societies to address the development and use of AI tools in radiology. The statement emphasizes the need for increased monitoring of AI utility and safety, advocating for collaboration among developers, clinicians, purchasers, and regulators.
Researchers at Universiteit van Amsterdam developed an autonomous chemical synthesis robot with integrated AI, outperforming human chemists in terms of speed and accuracy. The 'RoboChem' system can perform various reactions while producing minimal waste and delivering results quickly.
The TRAILS AI Institute has awarded eight seed grants totaling $1.5 million to advance AI design, development and governance. The funded projects include developing AI chatbots for smoking cessation and designing animal-like robots for autism support.
A study by Lancaster University forecasts exponential growth in Artificial Intelligence over the next 15 years, with experts warning of corners being cut in safe AI development. The experts also predict that advances in technology will make it harder to distinguish truth from fiction, with potential ramifications for democracies.
Researchers at City of Hope and TGen developed a machine-learning tool that can identify half of the cancers in 11 studied types with high accuracy. The innovative 'fragmentomics' approach allows for smaller blood draws, making it more practical for clinical applications.
Researchers from Cleveland Clinic and IBM have developed an AI strategy to identify new targets for immunotherapy treatments. The study, published in Briefings in Bioinformatics, reveals how artificial intelligence can be designed to accurately depict how immune systems recognize target antigens.
Researchers predict an increase in bad-actor AI attacks on mainstream communities by mid-2024, with AI tools being used to spread toxic content daily.
A new study by George Washington University researchers predicts an escalation of daily bad-actor AI activity globally by mid-2024, posing a significant threat to election results. The study provides the first quantitative analysis of the misuse of AI for disinformation during elections.
Researchers at Stanford University have developed a new phase-change memory that could help computers process large amounts of data faster and more efficiently. The technology improves several metrics simultaneously, including speed, endurance, and stability, while operating below 1 volt.
By 2050, at least 34% of Alpine ice volume will be lost even if global warming stops. This is based on a new study predicting near-term ice loss due to climate inertia.
A groundbreaking study uses machine learning to predict treatment resistance in cervical cancer by analyzing genetic mutations. The algorithm identified molecular assemblies driving treatment resistance and pinpointed tumors most susceptible to therapy, resulting in improved patient outcomes.
Scientists at Kyushu University use machine learning to identify promising green energy materials, accelerating the search for hydrogen fuel cell efficiency and expanding material discovery capabilities. Two new candidate materials with unique crystal structures have been successfully synthesized.
Researchers developed an AI-driven algorithm that accurately predicts death and complications after percutaneous coronary intervention (PCI). The tool uses patient feedback to provide a patient-centered, individualized risk prediction score.
A new technique using optical orbital angular momentum lattice (OAML) multiplexed holography boosts information storage capacity and offers novel approaches for implementing high-capacity holographic systems. The research unlocks supplementary encrypted dimensions, enhancing storage capacity and overcoming limitations of traditional me...
Researchers from Osaka University have developed a small sensor-based data logger that automatically detects and records video of infrequent behaviors in wild seabirds. The bio-logger uses low-power sensors and artificial intelligence to capture rare behaviors, such as head-shaking and foraging habits, without needing human supervision.
A study published in Arthroplasty Today found that robotic-assisted knee revision surgery significantly influences the function of the knee replacement. The computer system can help predict implant size and balance the knee for stability, leading to improved patient outcomes.
A unique framework measures individual perceptions of common sense and collective beliefs across groups, finding that larger groups have fewer common beliefs. Demographic factors like age or education do not influence common sense, but social perceptiveness does correlate with higher levels.
A nurse practitioner at The Hospital for Sick Children reduced invasive scans by 58% after using an AI model to analyze ultrasounds. She developed a new type of bias called induced belief revision through repeated exposure to the model.
The CACHE Challenge series has identified seven promising molecules with potential for new, more effective drugs for familial Parkinson's disease. The open science competition, funded by The Michael J. Fox Foundation and Conscience, used AI to predict hits and validated experimental results.
Researchers have developed an AI-enabled bio-logger that captures rare bird behaviors, including vigorous head shaking before flight and deep-sea mating rituals. The system uses outlier detection to identify unusual behaviors in real-time, providing valuable insights into animal biology and ecology.
Researchers at UB have developed a new deepfake detection algorithm that reduces biases in facial recognition, with one method classifying videos based on demographics and the other relying on features not visible to the human eye. The algorithms improved fairness metrics and reduced disparities in accuracy across races and genders.
The study introduced a silver-dispersive chalcogenide thin film for use in memristive devices, addressing data retention and endurance challenges. The device demonstrated reliable state retention and endurance, even at high temperatures, and achieved a recognition rate of ~92% in the MNIST database.
Researchers developed a platform combining automated experiments with AI to predict chemical reactivity, greatly accelerating the design process for new drugs. A machine learning model predicts where molecules will react and how reaction sites vary under different conditions, enabling precise tweaks to complex molecules.
AI models predict patient outcomes with high accuracy within their training data, but their performance deteriorates when used outside the original trial or in different clinical settings. Researchers aim to improve accuracy by examining large patient groups and exploring new data modalities.
A new study suggests that using artificial intelligence to scan surgery patients' medical records can identify those with risky drinking habits who may benefit from education or treatment. The AI system analyzed over 53,000 patient records and found three times more patients with high-risk alcohol use than diagnosis codes alone.
A new European research project, SMARTS, aims to improve air travel efficiency by redesigning flexible airspace sectors using artificial intelligence. The €2million project will create accurate predictive models and develop innovative sector configuration plans to reduce passenger delays, increase productivity, and lower emissions.
Researchers developed AI models that can extract information on social determinants of health from clinicians' notes, identifying 93.8% of patients with adverse social factors. The models were less prone to bias than generalist models, offering a potential solution to address health disparities.
Researchers at UMass Amherst have received an NIH grant to create predictive models for Alzheimer's disease using brain MRIs and clinical data. The goal is to enable earlier detection of the condition, ideally two years before onset of symptoms.
Researchers have developed a thermal management technique for photonic packages using glass substrates and thermoelectric vias, enabling precise temperature control. The technology, termed SimTEC, combines through glass vias partially filled with copper and thermoelectric materials to reduce thermal resistance between chips.
Researchers built a new AI that challenges traditional fingerprint comparison methods, revealing similarities between fingerprints from different fingers of the same person. The system achieved high accuracy in detecting intra-person fingerprint matches, potentially increasing forensic efficiency by tenfold.
The partnership aims to leverage AI and cloud computing to accelerate scientific discovery in chemistry and materials science, two key fields for global energy challenges. PNNL researchers are testing Microsoft's Azure Quantum Elements platform to identify promising new materials for energy applications.
Researchers aim to create robots that can change tasks autonomously and explore settings to optimize performance. The project will focus on battery recycling and energy efficiency, with the goal of reducing industrial waste.
Researchers developed a machine learning-based method to estimate chemical attributes of spice extracts by measuring fluorescence emitted by polyphenols and flavonoids. The approach integrated data from multiple dilution levels, achieving highly accurate results.
A study redevelops a framework for teaching artificial intelligence and robotics to preschool children, providing five big ideas that can be grasped by young kids. The framework aims to prepare children to understand and use AI tools effectively, promoting healthy and responsible learning in the rapidly developing digital society.
A recent study published by the Mayo Clinic found that an AI-enabled digital stethoscope can detect peripartum cardiomyopathy, a potentially life-threatening condition, more accurately than regular care. The researchers identified twice as many cases using the AI-powered tool.