Articles tagged with Artificial Intelligence
Researchers at Ohio State University are testing the use of Synthetic Aperture Radar to help with wildfire detection and improve first responders' ability to predict and respond to deadly forest fires. The new tool has potential for tracking wildfires from start to finish, monitoring soil moisture, and discerning flame-prone areas.
Researchers at ETH Zurich have developed a new technology using satellite images and artificial intelligence to determine snow depth with high accuracy. The system can provide detailed snow distribution patterns, even for areas without existing measurements, and offers an indication of uncertainty.
Researchers from Florida Atlantic University's College of Engineering and Computer Science have developed a new AI method to accurately count manatee aggregations in real-time. The method uses images captured from CCTV cameras and outperforms existing baselines, offering potential ways to aid endangered species.
Researchers developed a machine-learning model to predict the risk of visual impairment in people with severe shortsightedness. The study used a dataset of 967 Japanese patients and found that the logistic regression-based model performed well at predicting visual impairment at 5 years.
The researchers developed an AI-based generative neural network called GAN that can generate highly resolved radar precipitation films from coarsely resolved maps. This higher resolution is required to better forecast heavy local precipitation and the resulting natural disasters in future.
A new paper examines the challenges of adopting AI in the food industry, citing liability risks and potential costs, and proposes a temporary on-ramp to allow companies to trial AI technology while exploring benefits and mitigation strategies.
Researchers developed AI tools to systematically explore metamaterials' design and mechanical properties, predicting optimal structures for desired deformation responses. The tools can generate and optimize new structures using large datasets and variational autoencoders.
Researchers found that a large language model chatbot demonstrated more accuracy in making diagnoses than human clinicians, especially when test results were negative. This suggests the potential for AI to serve as a useful diagnostic partner for physicians, reducing overtreatment and improving decision-making.
The ILSI 2024 Annual Meeting will feature scientific presentations, professional development, and networking opportunities focused on innovations for safe, nutritious, and sustainable food manufacturing systems. Press registration is now available for media outlets covering the event.
Researchers from the University of Technology Sydney have developed a portable, non-invasive system that can decode silent thoughts and turn them into text. The technology has been shown to achieve state-of-the-art performance in EEG translation, with an accuracy score of around 40% on BLEU-1.
Researchers from Osaka Metropolitan University investigated the credibility and efficiency of generative AI tools in collecting medical literature. The study found that Elicit outperformed ChatGPT, suggesting multiple references with high accuracy within minutes.
A study by University of Michigan researchers found that CLIP performs poorly on images from low-income and non-Western households, potentially exacerbating digital technology inequality. The model's bias can propagate into downstream applications and tools, excluding diverse representations and perpetuating socioeconomic disparities.
Researchers from SFU and UBC introduce MCS-DETECT, an AI-driven algorithm that detects membrane contact sites in large microscopy volumes without segmentation. This innovation enhances super-resolution microscopy capabilities, contributing to a better understanding of cellular interactions and complex diseases.
Researchers create Automatic Surface Reconstruction framework to estimate all possible variations of material surfaces, providing detailed information on catalysts, semiconductors, and battery components. The method reduces human intuition and provides dynamic information on surface properties over time.
Researchers leverage AI to analyze healthcare data and identify new targets for effective therapies and accelerate drug development in aging research. AI can tailor cancer treatment more precisely to individual patients' unique aging profiles, optimizing treatment outcomes and minimizing risks.
Researchers have developed an algorithm to train an analog neural network just as accurately as a digital one, decreasing energy consumption and eliminating the need for a digital twin. This approach is more biologically plausible and shows improved speed, robustness, and reduced power consumption compared to other methods.
Researchers at West Virginia University are using artificial intelligence to analyze habanero peppers and develop new methods for predicting genetic traits. The goal is to improve crop yields and prevent genetic diseases, with potential applications in human health.
Researchers use AI to develop dynamic modeling of brain graphs, capturing dynamics in continuous time for more accurate predictions and personalized treatment of brain diseases. The project aims to track disease development in individual patients and identify biomarkers associated with brain disorders.
A new AI-based risk model evaluates mammographic images to identify women at high risk of developing breast cancer. The study confirms that the method works well in different European populations, with 6.2% of women classified as high-risk having almost seven times the risk of developing breast cancer.
Scientists developed an AI method to track neurons in moving and deforming animals using convolutional neural networks with targeted augmentation. This breakthrough reduces manual annotation efforts by three times, enabling faster analysis of brain activity in model organisms like Caenorhabditis elegans.
Researchers from MIT and ETH Zurich developed a filtering technique to simplify a key intermediate step in MILP solvers, speeding up the process by 30-70% without compromising accuracy. A machine-learning model is then used to pick the best combination of algorithms for a specific optimization problem.
A novel bio-inspired computing approach using Biologically-Inspired Experience Replay (BIER) has improved the reliability of Unmanned Underwater Vehicles (UUVs) and other adaptive control systems. The method leverages recent experiences to stabilize UUVs in challenging conditions, outperforming conventional methods.
A new study reveals AI tools are more vulnerable than thought to targeted attacks that force AI systems to make bad decisions. Researchers developed a software called QuadAttac K to test for vulnerabilities in deep neural networks.
Researchers have developed an AI algorithm that uses people's flavor impressions to make accurate predictions of individual wine preferences. The algorithm combines data from wine labels, user reviews, and sensory tastings to provide personalized recommendations.
A researcher has developed a chatbot with expertise in nanomaterials, leveraging document-retrieval method to provide accurate context. The bot uses embedding to categorize and link information quickly, generating factual responses sourced from trusted documents.
A team of researchers at North Carolina State University has developed a new experiment to better understand human moral judgments in traffic scenarios. The study aims to collect data for training autonomous vehicles to make
The executive order promotes safe, secure and trustworthy AI by establishing standards, tools and tests to regulate the field. ORNL's AI Initiative connects subject matter experts with resources to develop secure, trustworthy and energy-efficient AI for scientific discovery and national security applications.
Researchers developed a novel approach to integrate multiple functions into a single chip using monolithic 3D integration of layered 2D materials. This technology offers unprecedented efficiency and performance in AI computing tasks, enabling faster processing, less energy consumption, and enhanced security.
A study led by Keck School of Medicine of USC used AI detection technology to analyze influencer content on TikTok between 2019 and 2022, finding an increase in posts that promote e-cigarettes. The prevalence of pod devices, e-juice flavor names, and nicotine warning labels increased significantly over time.
A study published in Nature found that remote teams are less likely to make breakthrough discoveries compared to those who work onsite. The researchers analyzed over 20 million research papers and four million patents, concluding that geographical proximity is essential for conceiving groundbreaking ideas.
Researchers developed an AI model to predict borylation positions in drug molecules, improving chemical synthesis efficiency. The method enables late-stage drug diversification by high-throughput experimentation with geometric deep learning.
The contribution grows the open-access resource that scientists use to invent new materials for future technologies. Researchers can now focus on promising materials with improved fuel economy in cars, more efficient solar cells, or faster transistors.
Researchers at Princeton University and Google have developed a new technique to teach robots to ask for help when they're unsure. The method uses large language models to gauge uncertainty in complex environments, allowing robots to reduce the amount of help required while maintaining high accuracy.
Researchers found that AI-generated content is perceived as higher quality than human-created content, but reveals the source of production can reduce the gap. Human oversight is still necessary to ensure AI-generated content is appropriate in sensitive contexts.
A new AI tool combines cancerous and non-cancerous cell patterns to predict breast cancer outcomes, allowing for more precise treatment plans. This could lead to reduced chemotherapy duration and intensity for some patients.
A team of researchers used AI to optimize thermal aging schedules for nickel-aluminum alloys, resulting in stronger materials at high temperatures. By analyzing unconventional heat treatment patterns, the team discovered a two-step schedule that outperformed conventional methods.
Dr. Ning Zhang's AntiFake tool uses adversarial techniques to make it difficult for AI tools to read voice recordings, preventing synthesis of deceptive speech. The tool has achieved over 95% protection rate against state-of-the-art speech synthesizers and is accessible to diverse populations.
A multi-institutional team led by Columbia Engineering aims to develop AI systems that better communicate with people and react to unforeseen circumstances. They will integrate causal modeling techniques with traditional AI decision-making methods, focusing on real-world applications in public health and robotics.
A new framework for using AI in healthcare considers medical knowledge, practices, and procedures to improve patient care. The proposed framework provides practical guidance for designers, funders, and users on how to integrate AI systems with the greatest potential to help patients.
Researchers will incorporate advanced semiconductor technologies and AI into a millimeter-wave radio system to increase bandwidth while reducing energy consumption. The project aims to save tens to hundreds of terawatt-hours of energy per year, contributing to climate change mitigation.
Researchers used AI to discover 464 types of enzymes in E. coli and verified their predictions through in vitro enzyme assay. The developed AI can predict a total of 5360 enzyme EC numbers, enabling accurate analysis of metabolic processes and development of eco-friendly microbial factories.
Researchers at the University of Konstanz developed an AI-powered method to objectively characterize embryonic development tempo and stages. The Twin Network trained on over 3 million zebrafish embryo images accurately identified developmental stages, temperature dependence, and malformations.
Scientists at ETH Zurich used AI to analyze data from 1,380 borylation reactions and predict optimal synthesis methods for new drugs. The model was tested on six known drug molecules and showed a success rate of five out of six cases.
Researchers developed an AI tool that can identify never-smokers at high risk for lung cancer based on their chest X-ray images. The study found that 28% of non-smokers were deemed high risk by the model, and these patients had a 2.1 times greater risk of developing lung cancer compared to low-risk individuals.
Research analyzing over 1.4 million Americans finds significant disparities in automation job displacement risk across race and gender. The study highlights the importance of education in reducing automation risks and addressing existing racial and gender disparities.
Researchers develop AI methods to analyze photoluminescence data and identify factors influencing coating quality. The findings provide a blueprint for improving production processes and boosting the efficiency of highly efficient solar cells.
Virginia Tech researchers analyzed partisan media sentiment toward AI and found that liberal-leaning media tend to have a more negative tone than conservative media. The study suggests that this opposition can be attributed to concerns over AI amplifying existing social biases, such as racial and income disparities.
Artificial intelligence tools can generate convincing texts, images, voices, and videos, making it difficult to distinguish misinformation from genuine content. To combat this, experts suggest being more alert when consuming online content and relying on trusted sources.
Researchers at Tufts University have created hybrid transistors using silk proteins that can detect changes in humidity, oxygenation levels, and glucose. The transistors have the potential to enable integrated circuits that train themselves and respond to environmental signals.
A new AI program created by researchers at UF and NVIDIA can generate medical records so well that human physicians couldn't tell the difference. The GatorTronGPT model uses a large language model to mimic natural human language, overcoming hurdles such as protecting patients' privacy and being highly technical.
A computer simulation by Nagoya University researchers found that human behavior, such as lockdowns and isolation measures, influenced the evolution of new COVID-19 strains. The study discovered that SARS-CoV-2 variants with higher peak viral loads were more successful at spreading, but also had shorter infection durations.
Scientists at the University of Copenhagen and University of Victoria have developed an AI formula to predict rogue waves, which can split apart ships and damage oil rigs. The new knowledge can make shipping safer by identifying the likelihood of being struck by a monster wave at sea.
A study published in Facial Plastic Surgery & Aesthetic Medicine found that ChatGPT outperformed expert plastic surgeons in answering preoperative and postoperative patient questions. The AI tool received significantly higher ratings for completeness and overall quality.
A recent study by Flinders University researchers found that Generative AI can rapidly create convincing disinformation on health topics, including fake videos and articles.
Researchers suggest a new evaluation framework to assess AI reasoning abilities, comprising psychological experiments, self-reflection, and source code analysis. This approach aims to determine if AI systems genuinely reason like humans.
Scientists at PNNL introduced a new way to evaluate AI system recommendations by incorporating human experts' insights. Human expertise improved the accuracy of predictions and boosted confidence scores, indicating better decision-making capabilities for machine learning systems.
A recent study found that during stringent COVID-19 periods, online searches surged in the health and daily life category. As government policies relaxed, searches shifted towards duty-free and travel-related products. The research team applied PCA to Big Data of internet search activity volume data from NAVER DataLab platform.
A novel technology to manage demands on mobile networks from multiple users has been developed by University of Leicester computer scientists. The study found a 10% power consumption reduction compared to existing technologies, with faster device selection and less resource allocation.
A paper by Anthony Chemero explains how AI thinking differs from human thinking, highlighting the limitations of large language models trained on biased data. Despite generating impressive text, these models can make up facts and produce biased outputs due to their lack of embodiment and understanding of context.
The Python code library snnTorch, developed by UC Santa Cruz's Jason Eshraghian, has surpassed 100,000 downloads and is used in various projects. A new paper published in the Proceedings of the IEEE documents the library and offers a candid educational resource for students and programmers interested in brain-inspired AI.