Articles tagged with Artificial Intelligence
A new generative AI model, DiffSMol, has been developed to generate realistic 3D structures of small molecules with promising drug properties. The study achieved a 61.4% success rate in creating novel molecules, outperforming prior research attempts.
A Carnegie Mellon University researcher critiques an article on large language models' ability to analyze sensitive discourse, including hate speech. The commentary highlights the need for a nuanced approach to human-AI collaboration in critical discourse analysis.
A new AI model, EVE-Vax, uses evolutionary data to predict viral proteins and design future-proof vaccines against rapidly mutating viruses like SARS-CoV-2. By analyzing millions of years of genetic information, the model can create brand-new proteins with the same immune response as real viruses.
The TUCAN6-CM project aims to transform next-generation mobile networks through efficient integration of communication, computing, and sensing technologies. Advanced solutions will enhance quality of life, sustainability, and safety in smart cities and industrial environments.
Researchers at Pohang University of Science & Technology (POSTECH) have developed an achromatic metagrating that handles all colors in a single glass layer, eliminating the need for multiple layers. This breakthrough enables vivid full-color images using a 500-µm-thick single-layer waveguide.
A study published in Cell describes a powerful AI tool that can design synthetic molecules to control gene expression in specific types of cells. The model creates DNA regulatory sequences not seen before in nature, allowing for ultra-selective switches to be turned on or off in healthy mammalian cells.
A study by Aalto University researchers found that people's perception of AI creativity increases with the amount of process and producer information revealed. This discovery has implications for designing creative AI systems, as well as understanding human biases towards creativity in others.
A new AI tool, FaceAge, estimates a person's biological age from their facial characteristics, which is associated with cancer survival outcomes. The tool outperforms clinicians in predicting short-term life expectancies for patients receiving palliative radiotherapy.
A team of Brown University researchers has developed an artificial intelligence model called MotionGlot that can generate movement in robots and animated figures. The model enables users to type an action and generates accurate representations of that motion, translating across different robot and figure types.
A portable device can instantly detect dangerous street drugs at extremely low concentrations, highlighting their dangers. The device, being trialled by drug-checking services in the UK, Norway, and New Zealand, allows for cheap and on-the-spot analysis of substances.
Researchers developed an intelligent autonomous robot capable of automating hospital disinfection processes through dual disinfection system, increasing efficiency and precision. The robot's performance was validated through real-world testing, reducing the risk of infection in hospitals.
A new study from the University of Surrey explores how metaverse platforms like Roblox and ZEPETO are changing consumer engagement forever. Digital doppelgängers, using AR and VR technologies, create immersive experiences that enhance brand engagement and emotional connections.
Researchers have discovered nearly 35,000 publicly downloadable deepfake image generators, downloaded over 15 million times since late 2022, primarily targeting women. The study highlights the need for robust technical safeguards and regulatory approaches to address the creation and distribution of these harmful AI models.
Researchers at Oregon State University have developed a new chip that consumes half the energy of traditional designs, using AI principles to reduce power consumption for signal processing in data centers. The chip can recover corrupted data more efficiently than conventional systems, which are power-hungry.
Researchers have used AI and brain activity to study the Other-Race-Effect (ORE), a phenomenon where people recognize faces of their own race more easily than others. They found that faces from other races are processed more generally and with less detail, leading to less accurate recognition.
Researchers found that while AI models showed promise in assessing weld quality, they performed significantly better analyzing curated online images compared to actual welds. Context-specific prompts may enhance the performance of AI models in some cases.
The journal is inviting submissions on AI applications in biomedical engineering, focusing on medical device development, personalized diagnostics, and patient outcome prediction. Accepted articles will be published as part of a themed section showcasing current research on AI applications in biomedical engineering.
The new Data Science Institute will support researchers in harnessing vast amounts of data to advance scientific discovery and patient care. With seed funding, the institute aims to foster interdisciplinary collaborations and provide cutting-edge tools and academic knowledge to extract valuable insights from massive datasets.
Researchers found AI outperformed physicians in answering GI cancer questions, with oncologists preferring ChatGPT's responses nearly 80% of the time. Additionally, fewer patients are getting surgery for benign colon polyps as endoscopic techniques rise, reducing serious complications and mortality rates.
A recent study from Drexel University found that AI companion chatbots can lead to sexual harassment and unwanted behavior, with over 800 reviews mentioning such incidents. The study suggests that companies must implement safeguards and ethical guidelines to protect users who are vulnerable to emotional harm.
A new paper outlines a path for AI and machine learning to help build tomorrow’s batteries by maximizing three components: ionic conductivity, oxidative stability, and Coulombic efficiency. The team used a dataset compiled from 250 research papers to identify promising candidates for scientists to test in the lab.
Researchers at the University of Tokyo developed a framework to enable decentralized artificial intelligence-based building automation, allowing devices to cooperate directly without central servers. The system learns user preferences over time, adjusting automatically, and provides fine-grained control without programming.
Researchers used generative AI to design diverse mitochondrial targeting sequences, achieving a 50-100% success rate in yeast, plant cells, and mammalian cells. The AI-generated sequences showed improved targeting abilities compared to existing ones, with potential applications in metabolic engineering and therapeutics.
Researchers developed a new AI text-to-video model, MagicTime, that learns real-world physics knowledge from time-lapse videos. The model can simulate biological metamorphosis, building construction, and other complex processes with high accuracy.
This year's ACM awards recognize groundbreaking innovations in autonomous systems, cryptography, and software for parallel computers. Peter Stone receives the Allen Newell Award for contributions to AI, while William Gropp and his team receive the Software System Award for developing MPICH.
Researchers connected AI and wearables, showing a 33.1% decrease in heart failure patients and improved medication adherence. A triage algorithm also reduced unnecessary testing and improved care decisions by 30%.
Researchers developed a novel vote-based model for accurate hand-held object pose estimation, addressing issues with existing approaches. The new framework achieves significant improvements in accuracy and robustness, enabling robots to handle complex objects and advancing AR technologies.
A Dartmouth team's AI model recognized Navajo with near-perfect accuracy, identifying related languages such as Apache and Native Alaskan languages. The study suggests that this technology could be a bridge to including smaller languages in online translation services.
A recent study by the University of Surrey suggests that hotel managers may need to adapt to AI-driven system management, shifting their focus from controlling to coaching staff. Effective communication, emotional intelligence, and creativity are key strategies for managers to navigate this transformation.
Researchers developed an AI system to classify tiny powdery pollen grains produced by fir, spruce, and pine trees, enhancing speed and accuracy. The tool can aid allergy sufferers, urban planners, farmers, and wildlife conservation efforts.
Researchers at MIT developed a technique to improve the reliability of conformal classification, which can produce impractably large prediction sets. By combining test-time augmentation with conformal prediction, they reduced prediction set sizes by up to 30 percent while maintaining probability guarantees.
Researchers at the University of Utah have developed RiskPath, an Explainable AI toolkit that predicts disease progression years before symptoms manifest. The technology analyzes health data to identify high-risk individuals with unprecedented accuracy, offering new insights into risk factor interactions and disease development.
Researchers trained AI to predict protein clumping linked to 50 human diseases. The new tool, CANYA, revealed specific chemical patterns driving or preventing harmful protein folding.
César A. Uribe, a Rice University professor, has received an NSF CAREER Award to develop mathematical tools for decentralized learning in AI and data science. His research aims to create more efficient networks of computers that can process massive amounts of data without relying on centralized coordination.
Researchers developed a data-driven AI framework that identifies potential solid-state electrolyte candidates and predicts their performance. The framework uses large language models, multiple linear regression, and genetic algorithm to optimize battery design.
A new review advocates for building confidence in AI applications by implementing robust data governance frameworks, enhancing transparency, and involving stakeholders. The authors emphasize the importance of addressing ethical implications and ensuring equitable access to AI-driven innovations in clinical oncology.
A study evaluated AI-ECG and AI-powered digital stethoscope tools for detecting early signs of heart dysfunction in women aged 18-49. The findings showed promising results, with a high negative predictive value and low risk of false positives, indicating potential for quick and cost-effective screening during primary care visits.
Kavraki's interdisciplinary research in robotics and biomedicine has been recognized for its impact on manufacturing, space exploration, and medicine. Her work bridges theory and application, with contributions to novel robot motion planning, personalized cancer treatments, and drug discovery.
A study by University of East Anglia compared 145 real student essays with 145 ChatGPT-generated ones, finding that AI essays were coherent but lacked engagement markers like questions and personal commentary. This reflects the limitations of AI in replicating human writing's conversational nuance.
A new curriculum framework helps train future family physicians to integrate AI into their practice, addressing a critical gap in medical training. The AIFM-ed framework provides a structured road map for medical educators to bridge the AI gap, ensuring that future doctors are clinically competent and technologically fluent.
A new study offers fresh perspective on how education systems can adapt to the challenges and opportunities of artificial intelligence. The study proposes a spatiotemporal framework for transforming education, including innovative strategies such as self-directed learning, student-centered pedagogy, and personalized assessments.
The framework utilizes consortium blockchain architecture for immutable records, minimizing data falsification and cyberattacks. AI-driven tools ensure objective data collection and analysis, reducing human bias.
Researchers at Mayo Clinic developed AI-ECG tools that can detect heart muscle weakness in women of childbearing age, allowing for earlier identification and management. The tools demonstrated high diagnostic performance, with an area under the curve of .94 for AI-ECG and .98 for AI digital stethoscope.
Researchers at Columbia University have developed an AI algorithm that can accurately determine the atomic structure of materials with minimal sample size. The technique uses diffusion generative modeling to augment the diffraction data from nanocrystals, enabling near-perfect reconstruction of the crystal's atomic-scale structure.
The SNU-CMU HCAI Center aims to pioneer innovative AI solutions by combining interdisciplinary expertise in human-centered design, prioritizing human well-being and accessibility. The center has already achieved major recognition at the ACM Conference on Human Factors in Computing Systems (CHI) with a Best Paper award for a project exp...
DSAPS achieves dual scalability by manipulating ∆ E blocks using two structures: a high-capacity structure for increasing spins and a high-precision structure for increasing interaction bit width. The system has shown promising results in solving complex COPs, with validation tests achieving over 99% accuracy.
Researchers at PolyU developed an AI-driven assessment system, LMLPA, to quantify LLM personality traits. The system analyzes linguistic patterns and style in LLM outputs to evaluate their personalities, with applications in education, manufacturing, business, and sustainable development.
A new AI interpretability tool helps explain ECG findings for better diagnoses, even in imperfect scans. Researchers aim to make AI trustworthy and transparent in medicine.
A study by Cornell University finds that AI-based writing assistants can generate generic language that makes non-Western users write in a more American style, leading to cultural stereotyping and language homogenization. Indian participants showed a smaller productivity boost compared to their American counterparts.
Researchers have developed an AI-driven model that can accurately diagnose various subtypes of pediatric sarcoma using only routine pathology images. The model correctly distinguished between nine sarcoma subtypes, demonstrating its potential to provide quick and accurate cancer diagnoses in under-resourced settings.
A new AI-driven biomarker model analyzes imaging and clinical data to predict cancer cachexia with high accuracy, improving detection rates by up to 85% compared to standard methods. The model's skeletal muscle quantification function was trained using annotated CT scans and validated on a separate set of images.
A recent study used AI to discover that phosphoglycerate dehydrogenase (PHGDH) is a causal gene for spontaneous Alzheimer's disease. The researchers found that altering PHGDH expression levels can significantly impact the progression of the disease, suggesting a novel therapeutic target.
A team of researchers at POSTECH has identified a hidden mechanism in Electrochemical Random-Access Memory (ECRAM) technology, enabling faster and more efficient AI computations. This breakthrough could lead to significant improvements in data processing and reduced energy consumption.
Researchers at Johns Hopkins University found that AI systems struggle to understand social dynamics and context necessary for human interaction. Human participants were able to accurately rate features important for understanding social interactions, while AI models failed to match human brain and behavior responses across the board.
FAU CA-AI will acquire NVIDIA infrastructure for generative physical AI and develop state-of-the-art hardware and software for computational T&E of connected AI autonomy. This funding solidifies FAU's role as a national leader in next-gen networked AI autonomous systems research.
Researchers used AI-driven methods to analyze thousands of digital images of melanoma tumor tissue, identifying key immune cell structures that boost immunotherapy effects. The presence of these structures was linked to significantly better overall survival for patients with advanced melanoma.
A new AI algorithm, Viz HCM, can quickly and specifically identify high-risk heart patients with hypertrophic cardiomyopathy (HCM) and provide individualized risk assessments. The algorithm's findings can help doctors prioritize the highest-risk patients for earlier appointments and treatment.
A new robotic system uses cues in a scene to determine a human's objective and quickly identify relevant objects, enabling intuitive assistance in household, workplace, and warehouse settings. The approach achieved 90% accuracy in predicting human objectives and 96% accuracy in identifying relevant objects.
Researchers at Carnegie Mellon University developed a cybersecurity lab, CyberSim Lab, that integrates experiential learning, role play, and collaborative learning to support learning outcomes. The lab is designed to bridge the gap between classroom education and real-world workplace skills.
A new approach, called ESGAN, reduces the need for human-annotated data by one-to-two orders of magnitude in machine-learning models for crop breeding. The tool can differentiate between aerial images of flowering and nonflowering grasses with minimal external guidance, increasing the pace of agricultural field research.