The study successfully integrates probabilistic sampling and deterministic computation in generative AI hardware within a single ferroelectric memory array. The technology enables the generation of diverse images reflecting facial attributes, improving area and power efficiency in applications.
The EU project BRISA aims to test safe and clean industrial technologies that reduce risks to human health and biodiversity. The project will protect over 45,000 workers by 2035 and prevent hundreds of premature deaths each year.
Researchers at the University of Washington created PaperTok, a tool that uses AI to help scientists and researchers create short-form videos from research papers. The system generates scripts and video clips in 45 seconds, making it easier for non-experts to engage with scientific content.
JMIR Publications and Jisc have expanded their Flat-Fee Unlimited Open Access Partnership, making APCs free for researchers affiliated with participating institutions. The agreement allows researchers to prioritize open access publishing without financial burdens.
Researchers developed a tandem neural network that rapidly infers key semiconductor material properties from simple transistor measurements, outperforming conventional approaches. The system produces results in under one millisecond with near-perfect accuracy.
A new study has unveiled a high-resolution global map of seagrass, revealing areas of loss and recovery, and providing valuable insights for conservation efforts. The map shows that seagrass ecosystems store approximately 640 trillion grams of carbon, equivalent to the yearly emissions of 500 million cars.
A new article by Trevor van Mierlo highlights the importance of trust in digital behavioral health. Key findings include the need for dynamic governance and verifiable audit trails to address mistrust issues.
Researchers Anna Galler and Bettina Könighofer at Graz University of Technology will use FWF's Astra awards to identify quantum materials for future electronics and develop trustworthy AI systems. Their projects focus on protecting AI systems from risky actions and exploring new materials with unique electronic properties.
A new commission, launched at the Texas Brain Economy Summit, aims to define, measure, and operationalize brain capital, a concept encompassing both brain health and critical cognitive skills. The initiative seeks to address the gap in economic planning and promote brain health as a driver of economic resilience.
Researchers at Tohoku University have created a clearer map for searching for hydrogen storage materials, identifying key physical factors that control their performance. The study suggests adjusting geometry and lattice flexibility to raise capacity while tuning stiffness to keep equilibrium pressure near everyday conditions.
Researchers used AI to analyze mammograms and found that women who developed breast cancer had increasing risk scores over time, while those who did not had stable scores. The study suggests that image-based AI risk scores can predict future breast cancer risk in women without a known genetic mutation or family history.
A self-driving chemistry lab called Flex-Cat has been developed to autonomously search for faster and more selective ways to make important industrial chemicals. The platform combines robotics, high-pressure reactors, and artificial intelligence to identify high-performing catalysts and those that can be programmed to produce different...
Researchers developed RNovA algorithm to identify new PTMs in human cells, expanding capabilities of machine learning in basic biological research. The discovery aims to advance diagnostics and broaden biologists' horizons for cancer and other diseases.
Researchers developed an algorithm, RAPID, to re-identify wild animals using their coat patterns. The algorithm achieved high accuracy rates and demonstrated its speed on various datasets, making it a promising module for wildlife monitoring and ecological analyses.
Researchers have developed a versatile uncertainty-aware AI framework called TRUECAM that provides customizable accuracy guarantees for cancer subtype classifications. TRUECAM outperforms existing approaches to digital pathology AI uncertainty quantification, detecting out-of-scope inputs and improving fairness across sex and race.
A University of Houston engineering professor developed a mathematical model to help decision-makers decide where to spend limited dollars on infrastructure resilience. The model accounts for real-world uncertainty and identifies critical assets to invest in, providing the greatest benefit before disaster strikes.
Thomas Pock's ERC Advanced Grant aims to develop novel generative learning methods and algorithms for computer vision, improving medical imaging by understanding and realistically generating images. The project seeks to establish a close link between data analysis and generation.
A study of funded AI startups reveals occupations with high AI exposure, including office clerks and data scientists, while manual tasks like construction work are less affected. The Occupational AI Startup Exposure (AISE) index also notes that jobs requiring social skills or ethical decision-making may be less likely to automate.
Researchers developed a method combining AI and mathematical models to accelerate MRI scans for breast cancer imaging, achieving improved tumor visibility and high diagnostic sensitivity. The ELITE method has the potential to improve not only MRI scans but also other imaging platforms.
MIT researchers developed a new system-on-a-chip called Gleanmer, which generates highly accurate 3D maps of the robot's environment using Gaussians to represent obstacles. This approach reduces memory and power consumption by up to 99%, making it suitable for lightweight augmented reality headsets.
The US retains its scientific edge but is losing the race to translate discoveries into cures, warns a new report from Cure Innovation Index. Without immediate renewed investment and policy changes, the US scientific edge will not hold.
Researchers develop AI framework using principles of superposition and entanglement to tailor cancer treatment to patients' entire molecular background. The technique predicts health outcomes and suggests genes to target, outperforming standard biomarkers in clinical trials.
The report reveals cutting-edge technologies affecting physical systems, from energy to medicine and manufacturing. These innovations have the potential to make a meaningful impact on pressing challenges like climate change and food insecurity.
Primary and secondary schools in Shanghai are using an AI-based classroom analysis system to support instructional improvement. The platform automatically processes video data and provides theory-informed analytic reports, enabling teachers to track student agency and professional growth over time.
A landmark international competition has revealed that AI systems designed to spot fake faces perform unevenly across demographic groups, with lighter-skinned individuals enjoying higher accuracy while darker-skinned faces are more frequently misclassified. The top-ranked solution combined data curation, mixture-of-experts architecture...
The new report identifies six principles shaping trust in AI: reliability and competence, contextual awareness, transparency and accountability, fairness and integrity, resilience, and relational dynamics. The paper argues that trust must be grounded in demonstrated system performance, governance, and institutional responsibility.
Researchers developed a data-driven method combining GA-BP neural network and chaotic particle swarm optimization to predict and optimize screen-printing parameters for thick-film resistors. The approach achieved an R² of 0.991, recommending optimal settings in 2.38 seconds and reducing resistance deviation within 5%.
A groundbreaking technology called Time to Move (TTM) offers unprecedented control over object and character movement in AI-generated videos. TTM eliminates the need for complex infrastructure or training on millions of videos, making AI video creation more accessible.
A new framework proposes a third way to balance AI innovation and safety: accelerating responsible innovation through technical, organizational, and ethical advancements. The Telus GenAI customer support agent demonstrates how risks can drive further innovation, reducing the need for restrictive safety constraints.
A new research direction proposes building machine-learning systems on top of AI models to detect hidden information and predict behavior. This enables users to supervise the model, control its behavior without understanding the entire mechanism.
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.