Academic papers, research breakthroughs, and scientific advances in machine learning and AI.
201 articles
Traditional drug discovery used to take years, starting with identifying a biological target and then screening thousands of compounds. New approaches are now shortening that painstaking process significantly. Source: today.ucsd.edu
Detecting treatment-resistant schizophrenia (TRS) early remains difficult for clinicians. A new study uses metabolomics and machine learning to identify biomarkers linked to TRS and its cognitive effects. Source: nature.com
A study published August 18, 2026 in Quantum Science used Fourier analysis to map out the properties of quantum machine learning ansatzes. The research aims to better understand how these quantum algorithms behave and perform. Source: quantumzeitgeist.com
A new machine-learning model provides a detailed map of ice volume hidden within the world's glaciers, estimating enough ice to raise global sea levels by roughly 32.3 centimeters. The findings offer more precise data than previous glacier ice assessments. Source: thebrighterside.news
Researchers at the DIII-D National Fusion Facility, home to North America's largest operating tokamak, developed a machine learning framework to predict plasma behavior changes between experimental shots. The work involves scientists from Thomas Jefferson National Accelerator Facility. Source: ans.org
Researchers introduced PhotoScan, a deep-learning method that estimates body composition from ordinary smartphone photos rather than relying solely on BMI. Early results suggest it can help predict insulin resistance, a key marker of cardiometabolic risk. Source: research.google
Researchers from Harvard Medical School, working with Dana-Farber Cancer Institute and Massachusetts General Hospital, built a machine-learning algorithm capable of estimating a patient's risk for hundreds of diseases. The model relies on data already present in standard medical records, without requiring new tests. Source: hms.harvard.edu
Lung cancer remains one of the deadliest and most common cancers worldwide, with clinical care long hampered by fundamental diagnostic and treatment challenges. A new machine-learning approach that merges data from multiple sources aims to improve how doctors manage lung cancer cases. Source: news-medical.net
MIT researchers have developed a new machine-learning method that lets AI models simulate a broader range of physical phenomena. The approach also cuts down the amount of training data needed to achieve accurate results. Source: scientific-computing.com
Researchers have developed a new method that reduces prediction errors by 80%, allowing fusion scientists to detect potential hardware issues before they escalate into serious problems. Source: interestingengineering.com
AI has demonstrated it can handle one of the essential operational tasks in modern astronomy by autonomously observing the sky. Scientists see this as an early sign of how AI could reshape the future of astronomical research. Source: digitaljournal.com
Black Book's fourth annual U.S.-EU benchmark report finds that 78% of healthcare organizations now run AI/ML systems in ongoing production use. Yet only 19% have full lifecycle governance controls in place, exposing a widening gap between deployment and oversight. Source: bhpioneer.com
Researchers developed an AI system that analyzes solar magnetic field data, brightness patterns, and acoustic activity to detect early signals of active region formation on the Sun. The model can flag these hidden precursors hours before sunspots and eruptions actually appear on the surface. Source: eurekalert.org
Scientists found that subtle changes occur beneath the Sun's surface long before visible sunspots form, and an AI model can now detect these hidden patterns. Once this underground activity stabilizes, it typically precedes the emergence of an active region and potential eruption. Source: techexplorist.com
As machine learning faces growing privacy scrutiny, researchers are exploring ways to make models 'forget' specific training data efficiently. A new approach identifies data points with low influence on the model, allowing their removal at a much lower computational cost than standard unlearning methods. Source: machinelearning.apple.com
A new study points to a promising path for a longstanding AI challenge: getting machine learning models to genuinely grasp scientific principles instead of just spotting surface patterns. Researchers trained neural networks on simulations built from underlying mechanistic models, resulting in improved inference on scientific problems. Source: bioengineer.org
Progress in genome sequencing is giving more families access to prenatal genetic testing and deeper insight into an unborn baby's health. A newly developed machine learning model aims to make the results of these tests more reliable. Source: news-medical.net
A research team is combining AI, data science and advanced mathematics to model how fires spread in modern homes. The goal is to give firefighters better tools for predicting fire behavior and making faster, safer decisions. Source: dpaonthenet.net
Researchers used a machine learning approach based on quantum principles to identify two new superconducting materials, YRu3B2 and LuRu3B2. The method significantly speeds up the search for new superconductors compared to traditional approaches. Source: quantumzeitgeist.com
Advances in genome sequencing are opening new possibilities for examining fetal health, letting clinicians look for DNA changes linked to genetic conditions in an unborn baby. Machine learning is now being explored as a way to improve the accuracy of these findings. Source: bioengineer.org