Articles tagged with Machine Learning
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.
Researchers at Cornell University used AI to investigate how reflection changes images, discovering clues like facial features and beards that can differentiate originals from reflections. The study has implications for training machine learning models and detecting faked images.
The researchers aim to create a machine-learning solution that combines pure data-driven reinforcement learning algorithms with domain knowledge. Their technique, Dino-RL, is designed to dynamically adjust to quickly detect and fix network problems without human intervention.
The increasing use of machine learning systems in public policy raises concerns about accountability, bias, and transparency. Explaining complex algorithms can reveal conflicting aims and implicit trade-offs in policy decisions.
A new study by UCLA professors combines AI and folklore analysis to examine the storytelling elements of debunked conspiracy theories and actual news stories. The researchers found that conspiracy theories tend to form around specific elements that act as an adhesive, holding facts and characters together.
Researchers found that humans can complement machine learning in correcting for biases. Vintage-specific skills and domain expertise are key attributes that help humans guide machines in mitigating bias. Human collaboration improves ML productivity but its impact on long-term productivity is unclear.
A team of researchers from the University of Konstanz and Innsbruck explore the role of artificial intelligence in basic research, focusing on agency, creativity, and authorship. They aim to provide a conceptual framework for the development of AI methods in science.
A machine-learning model analyzes histopathology slides to predict cancer prognosis and survival. The study, published in PLOS ONE, shows promise for improving cancer care.
Researchers developed an AI algorithm that uses the 'slowness principle' to estimate age and ethnicity by ignoring rapidly changing facial features. The system achieves impressive accuracy, outperforming even human experts in face recognition.
Researchers have improved Wi-Fi network operation and performance for the 5G/6G ecosystem by applying machine learning techniques. A new algorithm, ε-sticky, is proposed to reduce service disruptions and network instability, benefiting both stations that have found a suitable access point and those that haven't.
Artificial neural networks became unstable after continuous unsupervised learning, but exposure to Gaussian noise mimics slow-wave sleep stabilized them. This finding has implications for the development of biologically realistic AI systems.
A study by Oregon State University found that telepresence robots can enhance student engagement and expression in online classes. In contrast, instructors preferred teaching students in person, but valued telepresence robots as a remote learning solution over distance learning tools.
Researchers developed a novel AI-managed trading strategy that outperforms traditional methods, achieving greater gains and fewer losses. The proposed system utilizes convolutional neural networks to analyze layered images of current and past market data, leading to more accurate predictions and reduced randomness.
A new study by Carnegie Mellon University researchers has found the brain programs that code the sequence of steps in performing a complex procedure. The main findings were that each knot had a distinctive neural signature, so the researchers could tell which knot was being tied from the sequence of brain images collected.
A new NIST formula could significantly improve Wi-Fi and cellular system performance in unlicensed bands by selecting optimal frequency channels. The formula uses machine learning to maximize data rates and reduce interference, with computer simulations showing it can outperform exhaustive trial-and-error methods.
A recent study explores the application of deep learning in ecological resource research, addressing challenges such as multi-source/multi-meta heterogeneity and high dimensional complexity. The study highlights the potential of deep learning in connecting computer science with classical theoretical sciences in ecology.
A new AI-powered algorithm has revolutionized the analysis of deep sleep patterns by automating the detection of K-complexes. The tool, developed by Flinders University researchers, outperforms human scoring methods in speed and accuracy, providing a more comprehensive understanding of sleep health.
Researchers used AI to speed up the search for a key material in a new catalyst that converts carbon dioxide into ethylene with record efficiency. The resulting electrocatalyst has an 80% faradaic efficiency, a new record for this reaction, and shows promise for clean energy storage and carbon capture.
The human brain balances complexity and accuracy when processing patterns, with errors playing a crucial role in learning and cognition. The new model suggests that the brain constantly strives to represent things in simple terms, with participants showing quicker responses to sequences generated by modular networks.
Researchers developed an AI tool to automatically diagnose atrial fibrillation and five common ECG abnormalities, comparable to human diagnosis. The AI was trained on a large database of manually diagnosed ECGs and shows great potential for improved cardiovascular care in low-income countries.
Researchers at Carnegie Mellon University have developed a new AI-powered teaching interface that allows teachers to create intelligent tutoring systems in minutes, rather than hours. This innovation has the potential to increase the adoption of AI-based tutors and provide deeper insights into learning processes.
A team of scientists has developed a system utilizing image analysis and artificial intelligence to analyze the shape of large numbers of seeds from a single image. The trained model detected and segmented individual seeds with high accuracy and analyzed seeds of other crops, accelerating crop breeding and analysis.
Researchers successfully rebuilt bridge between experimental neuroscience and artificial intelligence learning algorithms by demonstrating a new accelerated brain-inspired learning mechanism. This mechanism outperformed commonly-used machine learning algorithms in handwritten digit recognition tasks with small datasets.
Fugu outperforms BBA in terms of least interruption time, highest image resolution and consistency of video quality, keeping viewers engaged for 5-9% longer
Researchers propose Anticipated Learning Machine (ALM) for precise future-state predictions based on short-term data. ALM efficiently reconstructs dynamics even with a small number of samples by constraining to a low-dimension space.
A team of scientists used machine learning to speed up the process of identifying optimal self-assembling peptides for biocompatible electronic devices. By screening 8,000 candidates, they were able to rank each design and pave the way for experimentalists to test the most promising ones.
Researchers used machine learning to analyze nationwide health checkup records and identified a reliable method for predicting diabetes patients. The model accurately predicted future incidence of diabetes with an overall accuracy of 94.9%.
A team of researchers used machine learning to study complex spin models, revealing key similarities between distinct phases. By training an AI on one model and applying it to another, they found that the algorithm could correctly classify phases and identify temperature transitions.