Articles tagged with Machine Learning
A recent study published in Angewandte Chemie found that AI models struggle to predict reaction yields due to biased data, mainly caused by a lack of reported failed experiments. The researchers attribute this failure to three possible causes: experimental error, personal bias, and underreporting of negative results.
Researchers designed a modular AI chip that can be easily upgraded by swapping out layers, reducing the need for new devices. The chip uses optical communication to transmit information between layers, enabling high versatility in edge computing applications.
The study found that precipitation during cold seasons is a major contributor to energy consumption in Tibet, particularly when temperatures fall below freezing. Additionally, there is a marked disparity between urban and rural areas in how climate affects energy consumption, with urban areas experiencing higher rates on cold, snowy days.
Researchers developed an AI model to analyze thyroid ultrasound images, identifying nodules unlikely to be cancerous with a sensitivity of 97%. This technology could reduce the number of unnecessary biopsies and assist radiologists in choosing which nodules should undergo biopsy.
A new machine learning algorithm called 'ikarus' has found a gene signature characteristic of tumors, distinguishing between healthy and tumor cells in various types of cancer. The algorithm was trained on single-cell sequencing data sets and demonstrated an extraordinarily high success rate in distinguishing between different cell types.
A study by UFZ researchers used satellite data and artificial intelligence to determine the land-use intensity of meadows and pastures in Germany. The results show that grassland was used less intensively in 2018 due to drought, with 64% not mowed compared to 36% in 2017.
Researchers developed a machine learning model to predict NAFLD development based on gut microbiome data, showing 90% of subjects who developed the disease had subtle differences in their samples. The model combines easily measurable information from blood and microbiome data with high accuracy.
Researchers at MIT identified a flawed analysis of website-fingerprinting attacks and developed new techniques to prevent them. They found that attackers can use machine-learning algorithms to decode signals leaked between software programs, enabling them to obtain private information.
PaSh parallelizes Unix shell scripts automatically and accurately, boosting execution speeds by hundreds of times. Researchers tested the system on hundreds of scripts and found no errors, making it accessible to data scientists, biologists, engineers, and economists.
Cong Shen earns CAREER award to develop 6G wireless communications systems supporting machine learning and AI technologies. His research aims to boost the performance of machine-learning models driving the Internet of Things while protecting data privacy.
A new study published in the Proceedings of the National Academy of Sciences offers fresh insights into infant word learning. Researchers found that infants between 7 and 11 months old learn words by building up memory representations over time, rather than through repeated connections between words and objects.
Researchers at MIT found that explanation methods used to aid human decision-makers in high-stakes situations often have lower accuracy for minoritized subgroups. The fidelity of these explanations varies dramatically between subgroups, with the quality often significantly lower for women and Black people.
Researchers at the University of Rochester have created an automated scanning device that detects monolayers with high accuracy, reducing processing time and costs. The system utilizes AI-powered image processing to analyze images of materials, identifying monolayers with near 100% accuracy in just nine minutes.
A team of researchers from Waseda University developed a novel solution to efficiently solve complex optimization problems using Ising machines. Their hybrid algorithm reduces residual energy and reaches more optimal results in shorter time, increasing the machine's applicability across industries and sustainability practices.
A new AI-powered technique updates fuel inventories to better predict fire behavior and spread. The method, developed at NCAR, uses satellite imagery to account for pine beetle damage and was tested on the 2020 East Troublesome Fire in Colorado.
A cohort study found that moderate coffee consumption, especially unsweetened coffee, is associated with a lower risk of death. The results suggest that adults who drink 1.5 to 3.5 cups of coffee per day may be less likely to die during a 7-year follow-up period compared to non-coffee drinkers.
A study by University of Alberta researchers found that language related to depression on social media did not increase in response to COVID-19 waves, but rather decreased as people adapted. Instead, they found spikes in language related to the Black Lives Matter movement and other global events.
A new AI method can distinguish between the overall sounds of healthy and unhealthy coral reefs, making it a valuable tool for monitoring reef health. The technique uses machine learning to analyze sound recordings and track the progress of reef restoration projects.
Researchers at University of Tokyo used machine learning-based analysis of scalp-recorded EEG to see when and where odors are processed in the brain. Unpleasant odors were found to be processed earlier than pleasant ones, suggesting potential early warning system against dangers.
Researchers use AI software to identify invasive species and native trees threatened by disease from photos taken by on-train cameras. This technology enables railway workers to take action to better manage lineside vegetation and achieve biodiversity net gain by 2035.
Researchers have found that mutational signatures, which reflect a collection of mutations across the genome, can accurately predict drug response in cancer cells. The study suggests that these signatures may hold the key to better cancer therapies and could be used to predict treatment response.
A new ECG-based AI model developed by Tempus and Geisinger can predict patients at increased risk of undiagnosed structural heart disease. The rECHOmmend study found that the model achieved excellent performance, exceeding previous models in predicting any single disease.
A Concordia study analyzed gender patterns in AI over two decades, finding women are making strides despite underrepresentation. Notably, female-male collaboration has increased, while core positions held by women remain scarce, tied to factors like family obligations and male-dominated environments.
Researchers have successfully processed sequences with a large neural network while consuming significantly less energy on neuromorphic hardware. This breakthrough showcases the potential of neuromorphic technology to improve the energy efficiency of AI workloads.
A machine learning algorithm has outperformed astronomers in analyzing microlensing data to find new exoplanets, revealing connections hidden in complex mathematics from general relativity. The AI algorithm uncovered a degeneracy that had been missed by experts, suggesting a broader theory is likely incomplete.
Researchers found that computer assistance in design leads to better solutions but compromises creativity and user agency. In a virtual reality experiment, novice designers outperformed their human-led counterparts when using an optimized approach.
A new distributed learning technique, GD-SEC, reduces communication requirements in wireless architecture, improving efficiency and reducing computational cost. The method employs data compression to transmit only meaningful, usable data, enhancing the impact of machine learning while minimizing its limitations.
The 27th North American Meeting will focus on technological challenges, breakthrough discoveries, and state-of-the-art research in catalysis. The meeting features plenary lectures by renowned experts in the field.
Researchers at ETH Zurich have developed a novel memristor design that can switch between two operation modes, enabling greater efficiency in machine learning applications. This breakthrough component is made of halide perovskite nanocrystals and simulates complex neural networks with high accuracy.
Researchers at Duke University have developed a machine learning algorithm that incorporates known physics into neural networks, allowing for new insights into material properties and more efficient predictions. The approach helps the algorithm attain transparency and accuracy, even with limited training data.
A new nanosensor platform uses machine learning to analyze spectral signatures of carbon nanotubes for early detection of ovarian cancer. The approach detects biomarkers and recognizes the cancer itself, offering a promising alternative to traditional methods.
A study by HBP scientists found that wakefulness, non-REM sleep, and REM sleep have complementary functions for learning: experiencing stimuli, solidifying experiences, and discovering semantic concepts. This research suggests that unusual dreams, simulated using Generative Adversarial Networks, can improve brain learning by introducin...
Researcher Jundong Li aims to improve machine recommendations and predictions based on cause and effect. He develops a suite of algorithms and mathematical models informed by human experience and intuition to find cause-and-effect relationships in big data.
Researchers developed a data-driven robotic experiment system to identify electrolyte materials with desirable properties. They discovered a multi-component electrolyte that enhances the cycle life of lithium–air batteries, accelerating the development of next-generation rechargeable batteries.
Xie aims to develop algorithms that reflect human domain knowledge and reasoning patterns, increasing trust in machine learning models. He also explores algorithmic fairness, considering multiple perspectives on what's considered fair, to address concerns about biased decision-making.
A new machine-learning framework has been developed to improve the design of catalysts, which speed up chemical reactions. The approach analyzes the conversion of carbon monoxide to methanol using a copper-based catalyst and identifies key steps that need to be tweaked to increase productivity.
An artificial intelligence model trained on histological images of surgical specimens accurately classified patients with and without Crohn disease recurrence. The model revealed previously unrecognized differences in adipose cells and mast cell infiltration, enabling stratification by prognosis for postoperative Crohn disease patients.
The university's new Robotics and Autonomous Systems Teaching and Innovation Center (RASTIC) will provide students with hands-on experience in robotics, autonomous systems, and self-driving technology. The lab aims to boost Massachusetts' competitiveness in the tech sector by supporting innovative projects and startups.
Researchers at Oak Ridge National Laboratory developed an algorithm that guides breakthroughs in new materials using self-driving microscopes. The approach combines physics and machine learning to automate microscopy experiments, enabling faster discoveries of functional materials' properties.
CHOP researchers developed CancerVar, an artificial intelligence-empowered platform for interpreting somatic cancer mutations. The tool provides standardized procedures for assessing the clinical impacts of over 13 million somatic cancer mutations.
MIT researchers develop ExSum, a framework to formalize explanations of machine-learning models into quantifiable rules. This allows for testing assumptions about model behavior and reveals unexpected insights, such as negative words having sharper contributions to model decisions.
A new study uses artificial intelligence to predict bone fractures in cancer patients by creating a digital twin of the vertebra. The AI-assisted framework, ReconGAN, simulates how tumors affect the spine and predicts fracture risks, offering medical experts better treatment strategies and patient decisions.
Artificial Intelligence can now identify legendary batting techniques used by Sir Donald Bradman and modern players. Researchers developed a deep learning computer vision AI model to detect lateral backlift batters from straight ones.
Scientists have developed a machine learning algorithm that can accurately predict the lifetimes of different battery chemistries using as little as a single cycle of experimental data. The technique could reduce costs and accelerate the development of new battery materials, enabling researchers to quickly evaluate and test multiple ma...
Researchers at the University of Missouri are applying AI to analyze protein dynamics, identifying potential target sites for new drug therapies. The approach can simulate protein changes related to conditions like cancer, enhancing the chances of successful therapies.
A recent Dartmouth study using mobile technology compares emotional well-being of college students before and during the pandemic, finding significant differences in mental health and behavior. The research identifies two groups with differing experiences, with one group showing poorer mental health, anxiety, and stress.
Researchers at Caltech developed Neural-Fly, a deep-learning method that enables drones to adapt to wind conditions in real-time. The method achieved significant improvements in drone performance compared to existing adaptive control algorithms.
Researchers at DTU Compute and DIKU have developed a machine learning model that can map the potential of proteins, enabling the biotech industry to accelerate the development of new proteins. The model generates a picture of how proteins are linked, allowing for the identification of closely related proteins with desirable properties.
Researchers from Dartmouth College used artificial intelligence to draft wine and beer reviews, finding agreement between human and machine-generated reviews. The team also developed a system to write review syntheses, aggregating elements from existing reviews to provide limited but relevant information about products.
Researchers from Bar-Ilan University discovered that brain learning occurs mainly in dendritic trees, where the trunk and branches modify their strength. This finding paves the way for a new type of AI hardware and algorithms with comparable success rates to existing parallel GPUs.
Agricultural nitrous oxide emissions are estimated to be 300 times more powerful than carbon dioxide in trapping heat. The new knowledge-guided machine learning model, KGML-ag, is 1,000 times faster and more accurate than current systems, providing a promising solution for reducing greenhouse gas emissions from agriculture.
Researchers developed an enzyme that can break down plastic waste in hours, making it a promising solution for the world's plastic problem. The enzyme, called FAST-PETase, has the potential to revolutionize recycling and reduce global landfill waste by billions of tons.
Researchers from the University of Cambridge have developed a hybrid, data-driven approach to the Travelling Salesperson Problem that significantly outperforms current methods. The new approach converges to optimal solutions at a faster rate and produces high-quality solutions under restricted computation time.
A team of researchers developed a prognostic model using automated brain scans and machine learning to predict survival and recovery in severe TBI patients. The model accurately predicted risk of death and unfavorable outcomes at six months after the injury, potentially improving clinical decision-making and patient care.
Scientists at the University of Oxford have developed an 'optomemristor' device that facilitates three-factor learning and emulation of biological computations, making it possible to perform complex machine learning tasks. The device uses both light and electrical signals to interact and consume very little energy.
Researchers at MIT developed an AI method that constrains machine-learning models to suggest molecules with producible chemical structures. The approach guarantees quality and speed, outperforming existing methods in proposing high-quality molecular structures.
Researchers developed an AI system using swarm learning to predict cancer from medical images of tissue samples without accessing patient data. The technique improved the detection of genetic changes in colon tumors with high accuracy.
Researchers developed a machine-learning method that allows robots to pick up and place never-before-seen objects in random poses, requiring only 10 human demonstrations. The system uses a neural network specifically designed to reconstruct 3D shapes, enabling the robot to generalize to new object orientations.
Deep learning is being adopted in optical metrology to solve ill-posed inverse problems, such as model mismatch and error accumulation. This approach leverages large datasets and active control strategies to produce accurate reconstruction results.
Researchers developed a new decision-support tool to optimize ship channel dredging and disposal activities, considering factors like navigability condition deterioration and economic values. The algorithm weighs input from local professionals and projects costs with interest and inflation included.