Articles tagged with Artificial Intelligence
Researchers are developing AI technology to quantify emotions, improving emotional recognition in fields like healthcare, education, and customer service. This technology can create personalized experiences and enhance comprehension of human emotions, revolutionizing human-computer interactions and mental health assessments.
Scientists used artificial intelligence and molecular dynamics simulations to understand how enzymes fold lasso peptides into a unique structure. They identified key residues important for interaction with the substrate, enabling the design of new cyclase variants that can produce potent lasso peptides.
The integration of artificial intelligence in intratumoral immunotherapy can refine diagnoses, guide interventions, predict treatment responses, and adapt therapeutic strategies. This enables the enhancement of patient outcomes, including improved survival rates and quality of life.
A new AI model called Crystalyze can analyze X-ray crystallography data to determine the structure of powdered crystals. The model was trained on a database of over 150,000 materials and successfully predicted structures for over 100 previously unsolved patterns.
Researchers found that large language models used in home surveillance can make inconsistent decisions about calling the police, even when videos show no crime. Models often disagreed with each other and exhibited inherent biases influenced by neighborhood demographics.
The UC3M-Universia Chair aims to address the challenges of large tech companies' data use and create a new, transparent and fair personal data economy. Researchers will develop models and algorithms linked to AI that are interpretable and unbiased.
Researchers aim to advance training methodologies for superintelligent systems, ensuring they learn from imperfect and evolving data. The project seeks to develop new techniques to improve AI reliability, reduce biases, and increase accuracy.
Two new AI institutes will harness AI to analyze vast amounts of astronomical data, accelerating discoveries and democratizing access to research. Researchers at all career levels will gain access to trustworthy AI tools, enabling them to explore the cosmos with newfound insights.
Researchers analyzed 2 million Google Street View images to explore the utility of digital data in informing public health decision-making. They found that neighborhoods with more crosswalks had lower rates of obesity and diabetes, but no significant link was found between sidewalks and health outcomes.
A new AI-based system has been shown to reduce the risk of unexpected deaths in hospitalized patients by identifying those at high risk of deteriorating health. The system, CHARTWatch, uses real-time alerts and enhanced communication between nurses and physicians to support high-quality care.
CREME, a new AI-powered virtual laboratory, allows scientists to run thousands of virtual experiments with the click of a button to identify key regions of the genome. This breakthrough may lead to discovering new therapeutic targets and giving scientists access to cutting-edge technology without a real laboratory.
Researchers at Purdue University found that autonomous vehicles can interpret and respond to commands from passengers using large language models like ChatGPT. This technology allows the vehicle to personalize its driving to a passenger's satisfaction and take into consideration traffic rules, road conditions, and weather.
A new study by Boston University researchers found that the majority of social media posts from e-cigarette brands left out health warnings, despite a federal requirement to include them. The study used AI to analyze over 2,000 Instagram posts and discovered that only 13% complied with FDA health warning requirements.
Researchers at Peking University developed a dual-IMC scheme to accelerate machine learning and improve energy efficiency. The new computing scheme stores both neural network weights and inputs in memory, reducing data movement and power consumption.
Genethon has developed an innovative gene therapy vector that effectively targets muscle tissue while reducing the risk of liver penetration. The new capsid design uses AI predictive methodology to improve efficacy and safety, paving the way for more effective treatments for neuromuscular diseases.
Researchers at Indian Institute of Science develop a neuromorphic platform that stores and processes data in 16,500 conductance states, cutting energy consumption by a huge margin. This breakthrough could enable complex AI tasks on personal devices, transforming the development of AI tools.
Using AI and the connectome, researchers can now predict individual neuron activity in living brains. The new model predicts neural activity in response to visual input and accurately reproduces over two dozen experimental studies.
A $3.9 million grant from the National Institutes of Health supports a study on wearable sleep trackers and AI in predicting blood biomarkers of Alzheimer's disease in at-risk individuals. The research aims to create an 'early warning system' for flagging individuals with a genetic predisposition to Alzheimer's.
Researchers at Klick Labs developed an AI technique using vocal biomarkers to predict chronic high blood pressure with up to 84% accuracy. The study used machine learning to analyze hundreds of indiscernible vocal biomarkers, including pitch variability and speech energy distribution patterns.
The study reveals that local field potential events play a crucial role in processing information, even without external stimuli. The findings offer new avenues for research, diagnosis, and treatment of brain diseases, as well as the development of AI inspired by neuroscience.
A new AI model generates detailed images of cancer tissue that imitate what its staining would look like, reducing the need for resource-intensive lab analyses. The VirtualMultiplexer uses contrastive unpaired translation to create accurate virtual pictures of diagnostic tissue colorations.
Advanced endoscopy combined with AI and digital pathology offers a deeper understanding of inflammatory bowel diseases (IBD). This leads to more accurate diagnoses and improved patient outcomes.
Researchers used Google AI tool to map proteins' configurations and their ability to resist pressure changes, offering insights into protein design and life on other planets. The findings shed light on deep ocean life and could lead to new targets for structural and biophysical studies.
Researchers at Cornell University found that integrating AI into environmental control systems can reduce energy consumption in indoor agriculture by up to 25%. By optimizing lighting and climate regulation, AI helps create an energy-efficient solution for optimal plant growth, carbon dioxide levels, and ventilation requirements.
Researchers developed an AI model to detect lung disease in premature babies by analyzing their breathing patterns while sleeping. The Long Short-Term Memory (LSTM) model achieved 96% accuracy in classifying flow values as belonging to a patient with BPD or not, enabling early diagnosis and treatment.
A study found that ChatGPT performed better than trainee doctors in assessing complex cases of respiratory disease in areas such as cystic fibrosis and asthma. The chatbot's responses were also scored higher for being human-like, while Google's Bard and Microsoft's Bing performed similarly to or worse than trainees.
A deep-learning algorithm developed by astronomer David Harvey can untangle the complex signals of self-interacting dark matter and AGN feedback in galaxy cluster images. The Inception model achieved an accuracy of 80% under ideal conditions, showcasing its potential for analyzing vast amounts of space data.
Researchers at the Florida Museum of Natural History are studying bagworms' unique life cycle and their potential to inform understanding of modern climate change. They'll also analyze fossils from ancient mammal communities affected by rapid warming, as well as use AI to model future disease outbreaks.
A new AI tool called CAR-E is being developed to provide personalized coaching conversations for medical students and residents, encouraging reflective practice and enhancing their skills. The web-based tool will use a coaching approach to prompt students to think back on their clinical encounters and knowledge gaps.
Researchers Maria Eichlseder and Fariba Karimi will study keyless encryption and AI's impact on online social networks to promote fair algorithms. Their projects aim to address open problems in cryptographic systems and quantify intersectional inequality.
Researchers at McGill University developed AI-guided feedback systems that outperformed human instructors in teaching neurosurgical skills. The study found that expert instruction alone led to poorer surgical learning outcomes, highlighting the potential of AI to enhance learner surgical skills acquisition.
Researchers will investigate the psychological influence of online communications developed with artificial intelligence, including misinformation and radicalizing messages. The team aims to develop a holistic model of multi-level belief resonance that can counter foreign influence on campaigns and radicalization.
A team at NUS Yong Loo Lin School of Medicine leveraged an artificial intelligence-derived platform, CURATE.AI, to guide treatment for a patient with Waldenström macroglobulinemia, a rare blood disorder. The trial demonstrated substantial improvement in red blood cell levels and minimised side effects.
A Mass General Brigham-led study found that a large language model improves the accuracy of immune-related adverse event detection, identifying additional cases not picked up by manual adjudication. The model demonstrated excellent specificity and sensitivity, opening up opportunities for collaboration among institutions.
A machine-learning algorithm called xFakeSci has been developed to detect AI-generated scientific articles, with a success rate of nearly 94%. The tool analyzes word patterns and bigrams to distinguish between real and fake papers, highlighting the need for comprehensive detection methods as AIs become increasingly sophisticated.
A study at the European Society of Cardiology Congress found that an AI-enabled digital stethoscope helped doctors identify twice as many cases of pregnancy-related heart failure compared to a control group. The tool was 12 times more likely to flag heart pump weakness, leading to better diagnosis and potentially life-saving treatment.
A new machine-learning model developed by a University of Arkansas student improves upon existing genotype-by-environmental interaction models, achieving higher prediction accuracy. The model uses feature engineering to process environmental data, leading to a 7% improvement in mean prediction accuracy.
Artificial intelligence (AI) is set to revolutionize scientific publication by assisting editors in selecting impactful papers and reviewing manuscripts. AI will help increase the influence of journals by providing an initial 'score' for articles, making triaging faster and more objective.
Researchers found that AI-integrated apps often struggle with mixed dishes, such as Asian cuisine, which can lead to errors in energy content calculations. Manual food-logging apps also overestimated or underestimated energy intake for certain diets, highlighting the need for improved accuracy and cultural diversity.
Researchers at Heidelberg University developed an AI-supported method to map mosquito populations by analyzing satellite and street view images. This approach helps assess environmental conditions favoring Aedes aegypti presence, enabling targeted disease control measures.
A new algorithm, inspired by the nervous system's matchmaker, pairs drivers with riders in a way that maximizes everyone's happiness. The algorithm creates near-optimal pairings while preserving privacy, making it suitable for everyday applications.
A team of researchers created RENAISSANCE, an AI-based tool that simplifies the creation of kinetic models to accurately depict metabolic states. The tool successfully generated models that matched experimentally observed metabolic behaviors in Escherichia coli, simulating how the bacteria would adjust their metabolism over time.
Researchers aim to develop a voice assistant delivering customized cognitive stimulation therapy to improve the quality of life for persons living with dementia and their care partners. The project has the potential to positively impact a large population, including those in remote areas.
Researchers developed a tool to improve data transparency in large language models, enabling practitioners to find suitable datasets for their models. The tool, Data Provenance Explorer, automatically generates summaries of dataset creators, sources, licenses, and allowable uses.
A new automated scoring system using deep learning and pyramid sampling analyzes morphological features at various spatial scales to enhance the accuracy of HER2 assessment. The system achieved a classification accuracy of 84.70% in blind testing, comparable to experienced pathologists.
The Human AugmentatioN via Dexterity (HAND) center aims to develop robots capable of enhancing human labor through engineered systems of dexterous robotic hands, AI-powered fine motor skills, and human interface. The center's goal is to make robotic assistance accessible and applicable to a wide range of physical actions.
A study by Dr. Shunichi Kasahara found that levels of identification with one's face remain consistent regardless of agency or control over facial movements. The results suggest that a sense of agency does not significantly impact our ability to judge our facial identity, even in scenarios like deepfakes.
Researchers found that large language models (LLMs) can improve systems that detect bots by outperforming state-of-the-art systems by 9%. However, LLMs can also reduce the performance of existing detectors by up to 30% when used to evade automated detection.
A new study published in Scientific Reports confirms a linear relationship between blood glucose levels and voice fundamental frequency, suggesting potential for voice-based glucose monitoring. Researchers at Klick Labs used vocal biomarkers and AI to detect Type 2 diabetes with high accuracy.
A new course launched with funding from the UK Space Agency to address the growing skills gap in software, data, and AI for the UK space sector. The Securing the future of space: Space Software and Data/AI CPD programme will equip mid-career professionals with expertise in AI and data-science.
Researchers found that students who struggle with executive functioning (EF) skills perceive generative AI chatbots as significantly more useful for schoolwork. Using these tools critically can support students with EF challenges, but responsible use is crucial to maintain academic integrity.
Researchers at the University of Illinois have developed a method to understand and improve light-harvesting molecules for solar energy applications. By combining AI with automated chemical synthesis and experimental validation, they were able to produce molecules four times more stable than traditional ones.
Researchers have made a significant breakthrough in vasculitis research using AI-powered big data techniques, enabling more precise identification of disease patterns. The study offers new insights into the diagnosis and treatment of systemic vasculitis, a group of rare autoimmune diseases.
Researchers at Boston University created an AI tool that can determine the cause of dementia using commonly collected patient data, boosting doctor accuracy by 26%. The algorithm identifies 10 types of dementia, including vascular and frontotemporal dementia, to help doctors manage patients more effectively.
Researchers developed AI tool AINU to differentiate cancer cells from normal cells and detect early stages of viral infection. The tool scans high-resolution images of cells at nanoscale resolution, enabling it to detect subtle changes in cell structure that are too small for human observers.
Researchers explore AI's role in addressing challenges in immunotherapy, developing new biomarkers for precise disease characterization and predicting treatment response. A comprehensive review applied AI/radiomics to cross-sectional imaging, showcasing the current landscape in IO treatment.
A recent study found that approximately half of FDA-approved AI medical devices are not trained on real patient data, sparking concerns about device accuracy. The researchers analyzed 500+ medical AI devices and discovered that many lacked clinical validation data, which is essential for ensuring the credibility of these technologies.
A new paper in JCOM describes the development of an automatic translation app for sign language, designed through co-creation with deaf communities. The research team used a theatrical performance and AI tools to gather feedback from audiences, informing the app's design and features.
Researchers developed Stain SAN, a novel domain adaptation technique to correct color differences in stained histopathology images. The method improves consistency and comparability of data, leading to better performance in machine-learning-based classifiers.
A new AI-based digital platform has been developed to analyze tissue sections from lung cancer patients, making diagnosis faster and more accurate. The platform uses algorithms that enable fully automated analysis of digitized tissue samples, allowing for personalized therapy based on molecularly specific genetic changes.