Articles tagged with Artificial Intelligence
Researchers at Duke University developed an artificial intelligence platform that analyzes potentially cancerous lesions in mammography scans. The algorithm is interpretable, showing physicians how it came to its conclusions, making it more trustworthy and useful for training students and aiding physicians in sparsely populated regions.
A large international evaluation shows AI systems can identify and grade prostate cancer in tissue samples from different countries equally well as human pathologists. The results suggest AI can be a complementary tool in prostate cancer care, improving diagnostic quality and consistency.
An AI-driven solution identifies sites on RNA and DNA molecules where interaction with potential drug candidates can occur. This allows pharmaceutical companies to discover new medications in a more focused and efficient manner.
Researchers found that two commonly used atomic fingerprints, ACSF and SOAP, are insensitive to certain movements, leading to the failure of machine learning in resolving four-body interactions. This limitation affects the accuracy of reproducing these interactions with limited success.
Scientists have developed a mathematical method to interpret data on underground water flows, providing more efficient and accurate imaging for planning construction works and inspecting dams. The technique has great potential for locating water reservoirs in dry areas and tapping into this resource for agricultural and industrial needs.
The European Research Council has awarded €1.5 million in grants to three Saarbrücken-based researchers. Their projects focus on artificial intelligence and cybersecurity, with aims to develop fairer machine learning algorithms and secure computing methods.
A team of researchers from the University of Tokyo found that public trust in AI varies greatly depending on the application and demographic factors. They developed an octagonal visual metric to quantify these attitudes and hope it can lead to a universal scale for measuring ethical issues around AI.
A new machine learning algorithm can accurately identify fish calls from hydrophone data, enabling efficient analysis of marine environments. The method has implications for understanding changes in ocean conditions and coral reef health.
Researchers found that three algorithms - Multilayer Perceptron, Fuzzy Cognitive Map, and Deep Neural Network - outperformed others in diagnosing COVID-19 at early stages. These findings can guide software development to create intelligence-based tools for early diagnosis.
A study found that consumers trust companies more when interacting with chatbots that exhibit high perceived humanness, whether powered by AI or humans. This suggests that companies should focus on creating conversational interfaces that convey a sense of investment and relationship, rather than just relying on human agents.
A pilot study suggests that machine learning algorithms combining EKG and electronic health record data can more effectively screen for pulmonary embolisms than current tests. The fusion model was found to be 15-30% more effective at accurately identifying cases, especially severe ones.
A new neural net technique combines optical imaging and synthetic-aperture radar to provide a single comprehensive data source, enhancing disaster response capabilities. The approach achieves up to a 96% accuracy rate in detecting objects and can be applied to various tasks such as crop health monitoring and building identification.
The NYU Tandon team is developing an AI 'virtual assistant' to provide just-in-time visual and audio feedback for complex tasks. The project aims to create a dynamic intelligent agent that engages with users, responding to their emotional state, location, and surrounding conditions.
A new study has generated a massive dataset of artificial voice recordings, revealing subtle differences in high frequencies between real and fake files. The researchers developed algorithms that can distinguish between deepfakes and real speech, providing a starting point for other researchers to improve detection methods.
The Penn Artificial Intelligence and Technology Collaboratory for Healthy Aging (PennAITech) aims to explore the use of artificial intelligence to improve in-home care for older adults and individuals with Alzheimer’s disease. The collaboratory will leverage expertise in AI, consumer informatics, and aging from various departments acro...
Researchers analyzed hundreds of thousands of secure email messages between doctors and patients to find that most doctors use language too complex for their patients' low health literacy. Effective communication can improve patient outcomes by tailoring electronic messages to match the complexity of the patient's language.
Bielefeld University researcher Yaochu Jin is working on developing artificial intelligence that mimics nature's principles to tackle complex problems. His research aims to create more flexible AI systems that can adapt to uncertainty and fuzziness, with potential applications in healthcare and other fields.
A new study published in JAMA Network Open found that artificial intelligence can predict which people attending memory clinics will develop dementia within two years with 92% accuracy. The machine learning algorithm analyzed data from over 15,300 patients and identified around eight per cent of dementia diagnoses as being made in error.
Researchers developed an AI model that can diagnose COVID-19 with high accuracy, using federated learning to preserve patient data privacy. The model was trained on over 9,000 CT scans from 23 hospitals in the UK and China, and validated against a panel of radiologists.
A team of researchers from the University of Groningen developed an AI-based system that can identify individual Holstein cows in a milking station based on their coat pattern. The system achieved a recognition rate of 99.7% and has several advantages, including non-invasiveness, cost-effectiveness, and scalability.
Researchers propose a new model for electricity trade that balances economic interests and considers the stability of economic ties. The model suggests ways to improve cooperation between nations and prioritize transit country interests.
Researchers from Pusan National University have developed an algorithm to restore missing data in event logs, improving restoration accuracy by 10-30% compared to existing algorithms. The high accuracy of the new algorithm ensures its widespread application in industries and potential improvements in AI technologies.
Researchers simulated an attack that falsified mammogram images, fooling both AI breast cancer diagnosis models and human radiologist experts. The study highlights the need to develop ways to make AI models more robust to adversarial attacks, which could lead to incorrect cancer diagnoses.
A new database has been launched to systematically record findings on perovskite semiconductors, featuring over 42,000 individual data sets and analysis tools for interactive exploration. The FAIR principles guide the preparation of the data, enabling easy searching with modern algorithms and artificial intelligence.
Researchers developed an AI system to detect inflammation in ulcerative colitis patients using live colonoscopy videos. The system accurately predicted remission cases with high accuracy, reducing the need for invasive biopsies.
The University of California, Riverside, has been awarded a $980,000 grant from the Department of Energy to develop an AI-driven detector for the future Electron-Ion Collider. The team will use machine learning techniques to optimize detector design and achieve 'co-design,' a new concept in nuclear physics.
A global community of hackers and threat modellers is needed to stress-test the harm potential of new AI products. Companies can harness techniques like red team hacking, audit trails, and bias bounties to prove their integrity and earn public trust. The industry faces a 'crisis of trust' if it doesn't adopt these measures.
Researchers developed AI that extracts hidden equations of motion from observational data, creating models faithful to the laws of physics. This enables physics-based investigations and simulations for ecosystem sustainability, among other applications.
Researchers at MIT and Google Brain developed a system that predicts how changing materials or designs will improve solar cell performance. The new simulator, called differentiable solar cell simulator, provides information on which changes will provide desired improvements, increasing the rate of discovery of new configurations.
Researchers create an artificial intelligence that allows drones to use ocean currents to aid navigation, reducing energy consumption. The AI uses reinforcement learning networks to exploit low-velocity regions in water currents, outperforming even the most efficient swimming strategies found in nature.
Researchers developed an AI-powered system to model disease progression in patients as they age. The model assesses metabolic and cardiovascular biomarkers to calculate health status and disease risks across a patient's lifespan.
Researchers developed an AI program that accurately identified patients at risk of serious illness due to blood infections, with an accuracy rate of 82%. The technology has the potential to serve as an early warning system for doctors, enabling them to rank patients based on their risk level.
A study published in eLife found that rats exhibit efficient coding processes for visual stimuli, similar to those observed in humans. This suggests a universal principle in vision, where the brain adapts to its environment by specializing in the recognition of informative signals, thereby conserving computational resources and energy.
Researchers developed an AI system to predict ICU admission for COVID-19 patients based on over 200 clinical data points. The system achieved greater than 95-per-cent accuracy in predicting ICU need for new cases and identified key factors driving its predictions.
A new machine learning-based algorithm can predict stable material compounds much faster than traditional methods, opening up new avenues for research and discovery. The researchers identified several thousand potential new compounds using the computer, offering a promising breakthrough in materials science.
Researchers at Case Western Reserve University are developing an AI tool to analyze medical images and determine which rectal cancer patients require surgery. By using radiomics and machine learning techniques, the team hopes to identify patients who can avoid invasive surgery and opt for chemotherapy or radiation therapy instead.
A study by the University of Toronto's Rotman School of Management found that predictive analytics can increase revenue by $500,000 to $1 million for manufacturers who invest in IT capital, educate their workforce, and implement high-efficiency manufacturing processes. The research team surveyed over 30,000 manufacturers and found that...
A research team developed an AI framework that analyzes protein interactions to predict effective and low-toxicity cancer drug combinations. The framework, GraphSynergy, outperforms conventional models in identifying synergistic combinations.
Computer scientists and mathematicians have used artificial intelligence to help prove or suggest new mathematical theorems in complex fields. The breakthrough uses DeepMind's AI processes to explore conjectures in mathematics, leading to a completely new theorem in knot theory.
Researchers used machine learning to identify patterns in knot theory and representation theory, suggesting new connections that mathematicians were able to prove. This collaboration demonstrates the potential of AI as a tool for guiding intuition in mathematical research.
The University of Texas at Arlington has received a $1.5 million grant from the US Department of Education to support domestic doctoral students specializing in Internet-of-Things study and research. The project aims to increase diversity among computer science researchers, with a focus on women and minorities.
A Japanese study found that a robot's physical texture, such as softness or elasticity, affects perceptions of its personality. The researchers used six humanoid robots with varying arm textures and asked participants to touch and evaluate them.
A novel AI algorithm developed at Mayo Clinic can predict long-term patient survival after cardiac surgery using a single-lead EKG. The study found a clear correlation between long-term mortality and a positive AI ECG screen for reduced left ventricular ejection fraction among patients without apparent severe cardiomyopathy.
Researchers developed transformational machine learning (TML) to learn from multiple problems and improve performance while learning. TML out-performs current machine learning methods for drug design, accelerating the identification and production of new drugs.
Researchers at the University of Gothenburg have successfully combined a memory function with a calculation function in the same component, enabling more efficient technologies like mobile phones and self-driving cars. The discovery opens the way for brain-like computers that can perform tasks effectively and energy efficiently.
A study using GDPR-compliant mobile phone data found significant decreases in household mixing during the first two lockdowns, but increases during the third, coinciding with the vaccine rollout.
Researchers have developed a new AI technique, MuSIC, which combines microscopy, biochemistry techniques and artificial intelligence to reveal approximately 70 components contained within a human kidney cell line, half of which had never been seen before.
A team of scientists from GIST developed an AI-based approach to analyze and extract user behavior data, estimating the optimal demand response management for each household. The study showcases how AI can improve electricity consumption, leading to lower prices and a smaller carbon footprint.
Researchers developed an AI method to correct speed of sound aberrations in photoacoustic images, resulting in improved image quality and resolution. The technique reduced streak artifacts by up to 5% and increased signal-to-noise ratio by about 25 decibels.
A study using Google Trends and Wikipedia search volume found that public interest in earthquakes is dependent on the income level of the affected country. Earthquakes in developing countries receive less international attention than those in developed countries, with interest often dwindling within a week. However, interest persists f...
Fred Martin, a UMass Lowell computer scientist, is being recognized for developing an educational framework that introduces robotics and AI to K-12 students. His work has guided the development of primary and secondary AI education initiatives worldwide.
A new AI model has been developed to classify colorectal polyps, demonstrating accuracy and sensitivity at a level comparable to practicing pathologists. The model was tested in a clinical trial involving 15 pathologists, showing significant improvements in accuracy compared to traditional methods.
A new study by USC researchers uses GANs to generate synthetic neurological data that can be fed into machine-learning algorithms to improve BCI usability. This approach improved BCI training speed by up to 20 times and enabled rapid adaptation to new subjects.
Machine learning enables better understanding of climate-induced hazards, predicting floods and landslides with high accuracy. The technology combines diverse data sources to assess risk extent, considering both triggering hazards and socio-economic vulnerability.
Research reveals AI recommendation tools can lead to a 'information cocoon' effect, where customers see only what they want to see, negatively impacting purchasing decisions. This phenomenon is particularly concerning in e-commerce, highlighting the need for consumers to take independent purchasing decisions.
A research team found that combining human and AI predictions yields more accurate results, especially in atypical cases with unknown factors. Human analysis can fill gaps in big data, leading to better collective predictions.
A new AI application called DeepMReye uses MRI signals to read eye movements and infer thoughts, memories, and goals. It can also diagnose brain diseases by analyzing characteristic eye movement patterns.
Researchers from Okayama University developed an AI-powered image classifier to simplify and speed up the task of image analysis in cell biology. The system achieved high detection accuracy for mitotic cells in plant species, demonstrating its potential for non-experts to use.
Researchers have developed a new approach to studying prostate cancer, allowing them to track the behavior of individual cancer cells from birth to organ spread. The technique uses whole-organ imaging and artificial intelligence to create a 3D reconstruction of the organ at single-cell resolution.
Researchers identified 166 prognostic biomarkers from long non-coding RNAs, with one biomarker, HOXA10-AS, showing high effectiveness in categorizing gliomas as low- or high-risk. The study provides potential therapeutic targets and insights into cancer biology.