Articles tagged with Machine Learning
Researchers developed machine learning frameworks that guarantee robots' performance in unfamiliar settings, with a guaranteed success rate of 88.4% in obstacle avoidance trials. The approach expands generalization theory to robotics, providing more broadly applicable guarantees on robot control policies.
The VEDLIoT project is developing a new generation of IoT platforms that use machine learning to improve the performance and energy efficiency of devices. The platform aims to enable autonomous vehicles, smart homes, and industrial applications to learn and adapt to their environments.
Researchers from Bar-Ilan University demonstrate the application of physical concepts in physics to solve key challenges in artificial intelligence. By adopting power-law scaling, they show that learning each example once is equivalent to learning examples repeatedly, enabling rapid decision-making and ultrafast learning.
Researchers created a new type of machine learning model to predict power-conversion efficiency of materials for next-generation organic solar cells. The approach is quick and easy to use, providing important data on chemical fragments that affect performance.
Georgia State University researchers have developed a software tool called COINSTAC, which allows for local data analysis and sharing of results, protecting patient privacy and anonymity. The platform is designed to be compatible with deep learning models, enabling the training of analyses on decentralized databases.
Researchers used machine learning to predict water stability in metal-organic frameworks (MOFs), accelerating the development of new materials. The model, trained on over 200 existing MOFs, enables predictions for other important properties, expanding applications in chemical separations, adsorption, and sensing.
Researchers study insect brain computation to develop faster, more efficient machine learning algorithms. The fruit fly's ability to generalize experiences in complex environments informs the creation of an AI model capable of rapid adaptation.
A new research project at Aarhus University will use Bayesian Neural Networks to model, analyse and understand trading behaviour in the world of finance. This technology is expected to improve our understanding of financial data by providing probability distributions based on complex data.
A group of Skoltech scientists created machine learning algorithms that can predict oil viscosity based on nuclear magnetic resonance (NMR) data. This method has the potential to revolutionize the way oil is processed and understood.
Scientists developed an AI tool called DeepFRET that analyzes protein motion and interaction, speeding up research and making it accessible to more labs. The tool's accuracy exceeds 95%, outperforming human operators and requiring minimal human input.
A research team at POSTECH developed a machine learning technique that uses transcriptome information from artificial organoids to predict anti-cancer drug response. This method increases predictive accuracy by selecting only relevant biomarkers, which was previously limited by false signals in conventional machine learning.
ESEC/FSE 2020, a leading software engineering conference, presents outstanding research results and trends in virtual format. The conference features keynote addresses on human-computer collaboration, diversity, and inclusion initiatives.
A machine-learning algorithm detects early stages of Alzheimer's disease using functional magnetic resonance imaging (fMRI). The algorithm uses a convolutional neural network (CNN) to analyze fMRI data and classify patients as healthy, mild cognitive impairment, or Alzheimer's disease.
A new AI-based tool can help clinicians predict which hospitalized patients are at high risk of developing acute kidney injury, allowing for earlier treatment and potentially better outcomes. The Dascena algorithm outperformed the standard method in predicting AKI 72 hours prior to onset.
Purdue University is leading a $3.7 million research project to create more secure machine learning algorithms for autonomous systems. The goal is to develop a robust, distributed and usable software suite to prevent AI hacking and ensure the accuracy of autonomous machines on the battlefield.
A new AI-based method has been developed to detect small, imperceptibly tiny earthquakes that occur on the same faults as bigger earthquakes. This technology could provide insights into how earthquakes interact and spread out along the fault, allowing for a clearer view of earthquake patterns.
Researchers from FAU's College of Engineering and Computer Science are developing new theory and methods to curate training data sets for AI learning and screen real-time operational data for safe field deployment. The project aims to identify faulty, unusual and irregular information for AI operations.
Researchers at the University of Chicago have developed a new method to design polymers with specific properties, such as degradable plastic bags or super-strong aircraft materials. By combining modeling and machine learning, they created a large database of hypothetical polymers and trained a neural network to predict their properties.
Researchers developed new AI models inspired by nature, reducing complexity and enhancing interpretability. These models can control vehicles with just a few artificial neurons, outperforming previous deep learning models in tasks such as autonomous lane keeping.
A new deep learning model inspired by tiny animals has shown decisive advantages over previous models in tasks such as autonomous driving. The model achieves better performance with fewer neurons and is more interpretable than complex 'black box' systems.
Researchers have developed machine learning algorithms to predict which RNA-based toehold switches function well, enabling the identification and optimization of these tools. The algorithms analyzed a massive dataset of over 100,000 toehold switch sequences and predicted their behavior with high accuracy.
Machine learning transforms traditional science assessment by tapping into complex constructs, improving functionality and facilitating automatic scoring. The technology is expected to redefine science assessment practices and change the future of education.
The inaugural ACM International Conference on AI in Finance (ICAIF) will explore how artificial intelligence is transforming the finance industry. The conference features a dynamic program of work from top researchers, including papers on machine learning methods to detect money laundering and algorithms in future capital markets.
The ACM International Conference on AI in Finance (ICAIF) will explore the effects of AI on the finance world through a dynamic program of work from top researchers. Key topics include machine learning methods for detecting money laundering and algorithms for future capital markets.
Researchers created machine learning models to predict patients' post-surgical pain and opioid needs. The models accurately identified high-risk patients 80% of the time, suggesting more effective pain management strategies with non-opioid alternatives.
Researchers at Cold Spring Harbor Laboratory have developed an AI tool that can efficiently recognize neurons in microscope images, significantly improving the accuracy of automated tracing and analysis. This breakthrough aims to untangle the mysteries of brain connectivity and enable humans to think about how brains work.
Researchers are developing a novel deep learning technique to identify relationships between brain networks and Alzheimer's disease using algorithms mimicking neural networks. The goal is to pinpoint specific areas in the brain to slow and treat disease progression.
Berkeley Lab scientists develop a tool using machine learning algorithms to guide synthetic biology development systematically. The Automated Recommendation Tool (ART) predicts how changes in a cell's DNA or biochemistry will affect its behavior and recommends the next engineering cycle.
Swiss Center for Electronics and Microtechnology engineers developed an approach to overcome the initial trial-and-error phase of reinforcement learning. This allows computers to quickly find the right path without extreme fluctuations, slashing energy use by over 20% in complex systems.
The DOE has awarded $2.16 million to two physicists at Jefferson Lab for AI-assisted experiment control and calibration, as well as improved SRF operation at the CEBAF accelerator facility. These projects aim to optimize operations and generate better-quality data, potentially shaving off months of research labor.
A team led by Lydia Kavraki used machine learning to predict scaffold material quality, controlling print speed is critical in making high-quality implants. The collaboration could lead to better ways to quickly print customized implants.
Researchers at UMass Amherst and Baylor College of Medicine developed a new method to protect deep neural networks from catastrophic forgetting, inspired by the brain's 'replay' ability. The method, called generative replay, generates high-level representations of previously seen data, preventing the network from forgetting earlier lea...
Researchers used AI to analyze stool samples from over 1,000 individuals, identifying clusters of bacteria that could indicate existing or non-existent cardiovascular disease. The study suggests fecal microbiota composition could serve as a convenient diagnostic screening method for CVD.
Researchers at University of California San Diego use artificial intelligence to identify a DNA activation code called the downstream core promoter region (DPR) that's used as frequently as the TATA box in humans. The discovery could be used to control gene activation in biotechnology and biomedical applications.
A team of researchers from the University of California, San Francisco, has made a significant breakthrough in developing a 'plug and play' brain prosthesis that enables individuals with paralysis to control devices using their brain activity. The device uses machine learning algorithms to match brain signals to desired movements, allo...
Scientists have developed an artificial intelligence system that autonomously learns how to grip and move individual molecules, overcoming the complexity of nanoscale manipulation. The system uses reinforcement learning to find optimal movement patterns, enabling targeted assembly and separation of molecules.
A UTA researcher is developing an AI-based system to refine traditional measurements of bridge structural health by accounting for variables like truck types and traffic conditions. The goal is to provide more accurate load parameters and improve the overall integrity of bridges.
A team of engineers and computer scientists are developing a theory of deep learning based on rigorous mathematical principles to improve reliability and predictability in AI systems. They will use three perspectives: local to global understanding, statistical analysis, and formal verification.
Researchers are combining different types of brain imaging data to capture patterns indicative of brain disorders. The goal is to develop a compact framework to map brain disorders, providing a signature that tells us how mental illnesses impact the brain.
The University of Texas at Austin has been selected to lead the NSF AI Institute for Foundations of Machine Learning, aiming to develop new classes of algorithms for more sophisticated AI technologies. The institute will focus on addressing challenges such as noise and diffuseness in modern datasets.
Researchers developed a machine learning model that analyzes molecular structure to predict enthalpy of formation with better accuracy than traditional approaches. The model's accuracy improves with more data, enabling the development of fully automated algorithms for predicting complex chemical phenomena.
A new machine learning algorithm confirms 50 new exoplanets, ranging from Neptunes to Earth-sized worlds, by distinguishing between real and false positives. The technique outperforms previous methods, can be automated, and improves with further training.
Researchers have developed two new strategies to automate the assessment of stored red blood cell quality, matching and surpassing expert analysis. Trained machines can analyze vast quantities of images to accurately predict RBC degradation, eliminating human error and improving consistency.
A team of researchers at Tokyo Tech successfully used machine learning with an artificial neural network model to predict two key properties of self-assembled monolayers, enabling advanced material screening and design. This approach opens up new possibilities for the development of biomaterials with desired functions.
KDD 2020 features four keynote talks on meta-provenance, AI for intelligent financial services, state-space multi-taper time-frequency analysis, and computational epidemiology. The conference will also include 18 applied data science invited talks and 217 accepted research papers.
The NASA JPL team is using deep learning to develop software for future Mars rovers, which will enable them to travel farther and explore more of the planet. The team has been training machine learning models on the Maverick2 supercomputer and developing novel capabilities such as Drive-By Science and Energy-Optimal Autonomous Navigation.
Researchers have developed a new approach to machine learning combining ensemble methods and deep learning to diagnose cancer, predict viral attacks, and revolutionize molecular biology. This emerging field has the potential to transform bioinformatics and biomedical sciences.
Researchers developed an algorithm that allows robots to improve navigation systems by watching a human drive, enabling them to navigate more quickly and with fewer failures. This approach uses machine learning from demonstration, leveraging the expertise of soldiers in training environments.
Researchers at Babylon have developed a new approach to AI diagnosis using causal machine learning, which enables the algorithm to consider alternative realities and improve accuracy for written test cases. This technology has the potential to augment the work of clinicians and drive better healthcare outcomes.
A new study by Cornell researchers uses machine learning to assess the effectiveness of mathematical tools in predicting financial markets. The model can also predict future market movements, a task considered extraordinarily difficult due to markets' massive amounts of information and high volatility.
Researchers developed a sensitive and specific early warning system for predicting NEC using stool microbiome features combined with clinical and demographic information. The pilot study showed optimal performance from a gated attention-based multiple instance learning approach.
A team of researchers at UC Riverside used machine learning to understand what chemicals smell like, predicting how any chemical will smell to humans. This breakthrough technology has vast applications in the food, flavor, and fragrance industries, including discovering new flavors and insect repellents.
A new machine learning approach, federated learning, enables hospitals to share patient data securely, improving the accuracy of brain tumor diagnoses. This technique has been successfully tested in a Penn Medicine study, which showed that it can outperform centralized models in identifying brain tumors.
The ERC Proof of Concept project 'AssemblySkills' aims to validate an autonomous, intelligent skill learning system for robots to acquire and improve motor skills. The project builds on the ERC Starting Grant 'SKILLS4ROBOTS', which has yielded a structured control architecture that can scale robot learning to complex tasks.
A new approach uses photons to perform computations required by neural networks, improving speed and efficiency. Photonic tensor cores can process data in parallel, reducing power consumption and increasing throughput.
Researchers at Graz University of Technology developed a new machine learning algorithm called e-prop, which significantly expands the possible applications of AI. This novel approach uses spikes to enable more efficient information processing and reduces energy consumption.
Researchers used 2018 Japan floods to calibrate a machine learning model to predict Typhoon Hagibis' flooding impact. The model accurately identified inundated areas, verifying AI's potential for learning from past disasters.
Researchers at MDC developed Janggu, a universal programming tool converting genomics data into a format compatible with deep learning models. This allows for flexible and efficient analysis of large datasets, enabling the investigation of various biological questions.
Zhi Tian will receive funding to develop communication-efficient approaches for collaborative learning from private data in big data computing. Her goal is to minimize overall runtime, communication costs and total samples used.
The Argonne team has created a machine learning algorithm that approximates how the present detector would respond to the greatly increased data expected with the LHC upgrade. This algorithm simulates detector responses and reconstructs objects from physical processes, enabling faster and more accurate analysis of particle collisions.