Articles tagged with Artificial Intelligence
A team of MIT researchers has developed a machine-learning approach that captures the diversity of atomic environments in chemically disordered materials. This allows for more accurate predictions of material properties and opens up possibilities for creating new sustainable steels and materials for aerospace, energy, and computing.
Optical approaches offer unique advantages for chiral analysis, including non-contact operation and ease of integration. Recent advances in optical sorting and detection of chiral particles have improved sensitivity, selectivity, and practicality through engineered light fields and AI-assisted strategies.
Researchers developed an AI system called SmartTrap that uses optical tweezers to capture particles, take measurements, and load new samples autonomously. This technology accelerates the analysis of life's smallest components, potentially transforming laboratories in the near future.
Researchers found that human brains predict word sequences similar to AI language models' processes, suggesting a shared information processing principle. The study's results corroborate key assumptions in cognitive neuroscience, shedding light on the effectiveness of AI language models in various applications.
Researchers designed artificial proteins that simultaneously form pentagonal and hexagonal arrangements to create virus-like structures. These structures can stably carry drugs, genetic materials, and enzymes within their interior space.
The University of Michigan has successfully implanted the first-in-human Paradromics wireless brain-computer interface, designed to restore communication for patients with difficulty speaking. The clinical trial will focus on the device's long-term safety and assess its ability to restore communication through synthesized text and speech.
A new study uses AI to identify promising chemical compounds that could develop into effective antibiotics against multi-drug resistant Neisseria gonorrhoeae. The approach has the potential to address the growing crisis of antimicrobial resistance in this fast-evolving pathogen.
A landmark collaboration between the University of Reading and Royal Berkshire NHS Foundation Trust developed an AI forecasting tool predicting staff resignations. The tool highlights specific factors driving an individual's risk of leaving, enabling HR teams to intervene early.
Researchers developed BRIDGE, a multilingual benchmark that assesses large language models' understanding of clinical patient-care text. The benchmark reveals significant gaps in LLM performance on real-world clinical tasks, particularly in nuanced clinical language.
A new spatial memory system allows robots to rapidly form and recall detailed mental models of large-scale environments, enabling fast and accurate object recognition. This framework combines advanced map representations with rich descriptions of the environment, enabling robots to answer complex queries in plain language.
Researchers at University of Michigan Health have implanted the first wireless brain-computer interface (BCI) to restore communication in a patient with motor neuron disease. The study, called Connect-One Early Feasibility Study, aims to assess the device's long-term safety and effectiveness in synthesizing text and speech.
MDPI's Ethicality system automatically screens manuscripts for research integrity issues, with human editors reviewing flagged cases. The tool detects potential problems such as plagiarism, manipulated images, and AI-generated content.
Researchers Iris van Rooij and Olivia Guest warn against relying on AI to replace human thinking in psychology research. They argue that AI systems lack the complexity of human cognition and cannot produce meaningful results.
The AI model ARTIMES measures tumor volume rather than diameter, allowing for more accurate predictions of patient survival. This enables physicians to make informed decisions about treatment, potentially leading to improved patient outcomes and reduced healthcare costs.
A recent study comparing AI's diagnostic reasoning to physicians shows promising results, but limitations exist due to lack of non-text inputs. Digital fatigue is a growing concern among healthcare workers, affecting their productivity and well-being.
A new study revealed that retinal photographs can accurately predict many common risk factors associated with developing Alzheimer's disease. The AI model identified regions of the retina linked to Alzheimer's risk factors, such as arteries and optical nerve, and predicted lifestyle factors like smoking and alcohol use.
A new light-sensitive device developed at Oregon State University combines sensing and memory while controlling how digital memories strengthen or fade over time. This innovation could enable more efficient processing of information directly at the sensor level, improving AI systems' energy efficiency.
Researchers propose a Digital Twin Optical Computing System that reduces dependence on physical hardware for task development. The DT-OCS framework enables offline simulation, training, and optimization of computational tasks, improving research efficiency and application flexibility.
A new national publication offers instructors strategies for teaching in the age of AI, discussing the benefits and risks of AI use in higher education. The guide provides guidance on values, learning outcomes, and how to implement regulations that set expectations for students.
A Concordia-led team developed an AI-based method for detecting toxic online content, which outperformed existing tools in accuracy and throughput. The Proximal Policy Optimization-based Cascaded Inference System (PPO-CIS) layers scanning tasks to quickly identify harmful material.
The Cleveland Discovery and Innovation Forum showcased the impact of AI and quantum computing on biomedical research, from prevention to treatment. The partnership between Cleveland Clinic and IBM's Discovery Accelerator has supported over 50 projects, contributing to multiple publications and education curriculum development.
Researchers have developed a generative AI model called Void-X that can predict protein-protein interactions with high accuracy, enabling the design of new biomolecules for drug discovery and synthetic biology. The model achieves predictive accuracies of 78.3% for intra-chain clusters and 68.2% for inter-chain clusters.
Most Americans, even those who appreciate AI, want more regulation to ensure human interaction in medical, legal, and educational settings. The survey found a significant split in overall attitudes towards AI, with daily users feeling positively about the technology.
Klick Labs is launching a series of clinical studies with Mayo Clinic exploring the use of novel vocal biomarkers in connection with Type 2 diabetes, hypertension, ovulation, and blood glucose. The research collaboration aims to leverage the voice's hidden properties to flag critical health issues and enhance patient care.
A study found that 18% of college students used AI for mental health, with those having more severe symptoms and Asian students being more likely to do so. The use of AI may pose risks, such as undermining emotional regulation or perspective-taking.
Avishek Choudhury, a WVU researcher, has won the NSF CAREER award to study how healthcare providers' trust in artificial intelligence changes over time. His goal is to humanize algorithms behind AI and improve decision-making quality and patient safety.
Researchers developed DigMethpy, an AI-empowered digital catalysis platform to speed up methane pyrolysis catalyst discovery. The platform uses machine learning and large language models to predict promising catalyst candidates, reducing trial-and-error experimentation.
Researchers at Nagoya University developed an AI tool, DiSPAH, to estimate ALS disease progression speed and identify muscle decline patterns. The study found six distinct patterns of disease progression among patients, with some experiencing rapid deterioration while others declined slowly.
Researchers used AI agents to compile data on 31,028 licensed greyhounds, uncovering a 30% increase in on-track fatalities between 2022 and 2024. The study also revealed the high turnover of greyhounds has not changed, with around 40% stopping every year.
A new method analyzes AI models' learned features to group materials by structural and spectral similarity, revealing key factors influencing material properties. This approach opens up new possibilities for designing materials with specific and useful properties.
The review systematically maps out the research landscape of wearable AI doctors, combining power-harvesting approaches with self-powered intelligence. Flexible electronics enable devices to withstand dynamic environments, while on-device AI inference processes data locally for real-time analysis.
Researchers create PhishLumos AI system to detect phishing campaigns by analyzing infrastructure clues, achieving 8-day faster detection than experts. The system uncovered over 190,000 new links, with 92% later flagged as malicious, outperforming content-centric approaches.
A new AI model analyzed 30 years of EHR data to identify patients with primary aldosteronism, a common cause of high blood pressure. The model correctly flagged over 90% of cases while missing fewer than 10%, suggesting it could improve screening for this condition.
The system uses two AI agents to comb through publicly available data and conduct life cycle assessments, achieving an average error rate of 5%-19% similar to expert-led LCAs. The team also developed a new method to bypass detailed data collection and estimate carbon footprints for unknown devices using 'nearest-neighbors' approach.
The study synthesizes recent advances in single-cell and spatial transcriptomics to identify tumor-enriched cell subsets closely related to prognosis and treatment response. The review introduces the
Rockefeller University Press has partnered with Cashmere to enable safe and transparent AI inference use of its journals, protecting authors' work while providing visibility and revenue transparency. The partnership uses Cashmere's infrastructure suite to manage AI-powered research applications.
Researchers at Princeton University used AI to analyze how drugs affect cell structures, finding new shapes linked to disease and discovering a novel drug effect. The neural network identified cap, necklace, and flower shapes, with the latter indicating a previously unknown role of an enzyme in maintaining nucleolar organization.
Researchers developed a new magnetic memory material that can be rewritten using laser light, allowing for faster and more energy-efficient storage and processing of information. This breakthrough could help reduce power consumption in data centers and support future high-speed information systems.
A new AI model has revolutionized molecular simulations, enabling researchers to predict molecular behavior and identify promising drug candidates more quickly. By analyzing over 12,500 organic molecules, the model has demonstrated accuracy and consistency with physical laws.
A team of researchers from The University of Osaka has developed a new approach for depth reconstruction from defocus, estimating distances by analyzing blur in an image. Their method combines a coded-aperture camera with diffusion-model-based AI to accurately estimate depth and produce high-quality images.
Researchers warn that open-source AI could increase environmental pressures, deepen technological inequalities, and spread misinformation. To mitigate these risks, the authors propose four governance actions to ensure AI contributes positively to the Sustainable Development Goals.
The ACM Technology Policy Council's TechBrief examines agentic AI's legal liability, security risks, and workforce impacts. Existing frameworks fall short in addressing accountability questions, highlighting the need for defined authentication and delegation standards, robust audit trails, and sector-specific guidance.
Researchers at the University of Washington are using AI and quantum computing to design new materials with unique properties, such as superconductivity and entanglement. The tools are helping to power the growing field of quantum computing and could lead to breakthroughs in energy-efficient electronics.
Researchers at MSK discovered that tumors with low levels of NF2, a tumor suppressor gene, are less likely to respond to immunotherapy. They also found that combinations of VEGFR-TKIs and immunotherapies can be effective but have limited long-term benefits due to paradoxical effects of hypoxia on treatment response.
Researchers have developed a digital brain twin using multimodal data, providing anatomically accurate brain models that can be used in experimental and clinical settings. The models were reconstructed simultaneously with brain anatomy and dynamics from neural data.
The Luxembourg AI Factory released an open-source tool to accelerate testing for trustworthy artificial intelligence. The AI Assessment Sandbox Configurator enables rigorous AI testing at scale, addressing regulatory requirements and ensuring trustworthiness.
A new study explores how transfer learning reduces computational costs in cosmological simulations while revealing risks of negative transfer, which can hinder learning new physics. Transfer learning can accelerate inference but may also push AI systems toward incorrect interpretations of new effects.
The Acceleration Consortium and Structural Genomics Consortium collaborate to develop new drugs using AI-driven lab capabilities. The partnership aims to speed up the discovery of bioactive molecules, advancing human health and disease understanding.
The new project aims to build self-regulating AI agents that can recognize uncertainty, explain their decisions using past experience, and improve their understanding of the world through corrective feedback. The goal is to make future physical AI systems more trustworthy and useful in real-world settings.
Researchers found that friendly, choice-oriented language in AI chatbots boosts patient engagement, while aggressive phrasing and blurred human-AI boundaries put them off. Patients appreciate personalized interactions, but are wary of data security concerns.
FireANTs, an open-source algorithm, combines AI optimization and geometry to quickly match complex medical images. The new method can accomplish what took weeks in minutes, detecting subtle changes that signal disease or cognitive decline, making it practical for clinical practice.
Teachers tend to accept overly harsh AI grades more than human grades, resulting in a 22% larger gap between their score and the fair score. Teachers are more likely to accept strict AI grades if they view the system as competent and accountable.
A machine learning model, trained on 19 key parameters, can predict wind shear events with a minimum of 15 seconds warning. The model's outputs showed deviations from real outcomes within 5% across all forecast horizons, suggesting improved aviation safety.
A study published in Radiology found that three commercially available AI-based computer-assisted detection (AI-CAD) systems can identify early mammographic signs of breast cancer up to 6 years before diagnosis. This could help radiologists spot potential future cancers and allow for earlier intervention.
TurboLynx, developed by POSTECH researchers, analyzes complex, interconnected data up to 184 times faster than existing systems. The engine groups similar data together and processes them collectively, reducing unnecessary memory usage and enabling efficient analytical queries.
A new ultra-lightweight AI model, Multinex, advances low-light image enhancement by leveraging classical colour vision theory and Retinex principles. The model outperforms comparable compact systems, recovering detail and clarity from previously unusable images.
Researchers at Tohoku University's Advanced Institute for Materials Research have developed a method to summarize decades of scattered literature data into actionable information for catalyst design. By combining human intelligence, regression models, and AI agents, they can uncover new discoveries hidden in the literature data.
A new study found that AI-powered chatbots can make vaccine-hesitant parents more likely to say they will immunize their children against HPV, but no more than standard written public health materials. Additionally, the effects of the chatbots did not last longer than those of government health materials.
Researchers have developed an AI-powered safety switch for future cell therapies using caffeine. The platform, CODS, rapidly separates engineered proteins in living cells and triggers cellular responses on demand.
Participants trusted AI more for large-scale scanning tasks like identifying red flags in social media posts, while trusting humans for nuanced fact-checking requiring piecing together evidence. The findings suggest that effective fact-checking tools should provide accurate results and explain how they're reached.