Machine Learning News and Research

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Machine learning is a subfield of artificial intelligence that focuses on developing algorithms and models capable of automatically learning and making predictions or decisions from data without being explicitly programmed. It involves training models on labeled datasets to recognize patterns and make accurate predictions or classifications in new, unseen data.

AI Learns Metal Fingerprints To Accelerate the Discovery of Extreme-Environment Alloys

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18 Nov 2025

Agentic AI: Autonomy, Opportunity, and Emerging Risks

Agentic AI systems can interpret intent and independently perform complex online tasks, offering major productivity benefits but also introducing new security, privacy, and compliance risks. Researchers at Saint Louis University are stress-testing these agents to identify vulnerabilities and strengthen safeguards before widespread deployment.

18 Nov 2025

Globus Unveils New Automation and Compute Features to Accelerate AI-Driven Research

Globus has unveiled major platform enhancements that automate secure data workflows, expand compute capabilities for AI-driven research, and extend cloud storage interoperability. These upgrades strengthen Globus’s role as a trusted foundation for data-intensive science and institutional research infrastructure.

12 Nov 2025

Chiba University Researchers Develop Topology-Aware AI To Decode Brain Signals For Thought-Controlled Devices

Researchers at Chiba University have developed the Topology-Aware Multiscale Feature Fusion (TA-MFF) network, an advanced deep learning model that enhances the decoding of motor imagery EEG signals for brain-computer interface (BCI) applications. By integrating spectral, topological, and spatiotemporal features, the system achieves superior accuracy in interpreting neural activity linked to imagined movement.

11 Nov 2025

Tokyo Scientists Use AI to Decode Material Structures From X-Ray Spectra

Researchers at Tokyo University of Science have developed an AI-based method to automatically analyze X-ray absorption spectroscopy (XAS) data, achieving precise and objective identification of material structures and defects. Using machine learning, particularly the UMAP algorithm, the approach enables rapid, scalable, and noise-resistant characterization of boron-based materials.

10 Nov 2025

AI Controls Satellite In Orbit For The First Time In Historic Space Milestone

In a world first, researchers at Julius-Maximilians-Universität Würzburg (JMU) have successfully tested an AI-based satellite attitude controller directly in orbit aboard the nanosatellite InnoCube. Using Deep Reinforcement Learning, the AI autonomously performed precise attitude maneuvers, proving that self-learning controllers can safely operate in space.

10 Nov 2025

AI Predicts Plant Invaders Before They Arrive: Adapting Astrophysics For Ecology

An interdisciplinary team at the University of Connecticut has developed an AI-driven framework that predicts which plant species are most likely to become invasive, —before they arrive in new ecosystems. By adapting astrophysics algorithms for ecology, the model achieves over 90% accuracy, offering a data-driven complement to traditional, time-intensive biosecurity assessments.

2 Nov 2025

AI Offers Hope For Coral Reefs: Machine Learning Predicts Resilience Amid Climate Change

A new study led by the Wildlife Conservation Society shows that AI and machine learning can predict coral reef futures with greater accuracy and optimism than traditional climate models. By integrating decades of field data, the models reveal that local management of fishing, pollution, and coastal impacts can foster reef resilience even under global warming.

2 Nov 2025

AI Revives Ancient Art: Finger Flutings Reveal Clues About Their Creators

Griffith University researchers developed a digital archaeology framework combining tactile experiments, VR, and machine learning to analyse ancient finger flutings—prehistoric markings made in cave moonmilk. Their models showed promising accuracy in identifying the sex of modern creators from tactile flutings, offering new scientific methods to understand who made some of humanity’s oldest art.

20 Oct 2025

AI Accelerates Green Chemistry

Researchers at Universitat Jaume I have developed Reac-Discovery, an AI-driven robotic platform that accelerates catalytic reactor design and optimization, reducing processes from years to weeks. By integrating 3D printing, automation, and real-time machine learning, the platform transforms CO₂ and other feedstocks into sustainable, high-value chemical products with unprecedented efficiency.

20 Oct 2025

Robotic Coaches Aim To Revolutionize Physical Therapy By Accelerating Motor Skill Recovery

A $799,000 NSF-funded project led by FAMU-FSU researchers is developing robotic coaching systems that adapt to human learning, using unicycle training as a model to accelerate motor skill acquisition and transform physical therapy for patients with mobility impairments.

22 Sep 2025

UC Davis Wins NSF Grants To Advance AI-Powered Protein Engineering For Industry And Health

UC Davis has secured nearly $1 million in NSF funding for two AI-driven protein engineering projects—one to create enzymes for low-cost acrylate production and another to synthesize complex sugars found in human milk, advancing U.S. biotechnology and health innovation.

22 Sep 2025

New AI Framework LLRL Accurately Detects Plant Disease Severity for Smarter Farming

Researchers at Guizhou University have developed LLRL, a deep learning framework that targets lesion regions directly, boosting the accuracy of plant disease severity assessment across apple, potato, and tomato leaves. The model achieved up to 92.4% accuracy, outperforming 12 benchmark approaches.

22 Sep 2025

Machine Learning Supercharges Biochar Research To Fight Climate Change

A review in Biochar X shows how Machine learning is revolutionizing Biochar research, enabling precise predictions of biochar properties and performance to cut experimentation costs. This integration could accelerate climate mitigation by boosting biochar’s capacity to sequester carbon and remove pollutants.

18 Sep 2025

Hybrid AI Tool Boosts National Flood Forecast Accuracy

Researchers developed Errorcastnet, a neural network tool that significantly improves National Water Model flood predictions by identifying and correcting systematic errors. When combined with the model, accuracy improved four- to six-fold compared to current forecasts.

18 Sep 2025

Why AI Struggles With the Unknown While Humans Adapt Instinctively

Researchers from Bielefeld University and global partners propose a unified framework to explain why humans adapt so well to unfamiliar situations while AI systems often fail. The study explores how differing definitions and mechanisms of “generalization” create a critical divide between human and machine intelligence.

16 Sep 2025

New Optical Chip Slashes AI Energy Use With Light-Powered Computing

Researchers at University of Florida have created a silicon chip that uses laser light and microscopic Fresnel lenses to perform convolution operations, a power-intensive step in AI. This optical approach greatly reduces energy use while maintaining near 98% accuracy in machine learning tasks.

11 Sep 2025

AI And Drone Imaging Predict Wheat Yields With Unmatched Accuracy

This study developed a transformer-based AI model using UAV multispectral and RGB imaging to predict wheat yields with unprecedented accuracy. The approach allows non-destructive, cost-effective, and early identification of high-yield lines, speeding up breeding for climate-resilient wheat.

9 Sep 2025

AI System Translates and Edits Human Thoughts Into Images Using Brain Scans

Researchers have developed DreamConnect, a dual-stream AI system that interprets and edits visual mental imagery directly from fMRI signals using natural language prompts. This novel framework allows users to reshape imagined scenes, bridging brain activity and interactive visualization.

3 Sep 2025

UC Riverside Scientists Develop Certified Unlearning Method To Erase Data From AI Models Without Retraining

UC Riverside researchers have created a certified unlearning method that removes sensitive or copyrighted data from AI models without needing access to the original training data. Their framework uses surrogate datasets and calibrated noise to provide provable guarantees while maintaining model performance.

2 Sep 2025

Machine Learning News and Research

AI Learns Metal Fingerprints To Accelerate the Discovery of Extreme-Environment Alloys

Agentic AI: Autonomy, Opportunity, and Emerging Risks

Globus Unveils New Automation and Compute Features to Accelerate AI-Driven Research

Chiba University Researchers Develop Topology-Aware AI To Decode Brain Signals For Thought-Controlled Devices

Tokyo Scientists Use AI to Decode Material Structures From X-Ray Spectra

AI Controls Satellite In Orbit For The First Time In Historic Space Milestone

AI Predicts Plant Invaders Before They Arrive: Adapting Astrophysics For Ecology

AI Offers Hope For Coral Reefs: Machine Learning Predicts Resilience Amid Climate Change

AI Revives Ancient Art: Finger Flutings Reveal Clues About Their Creators

AI Accelerates Green Chemistry

Robotic Coaches Aim To Revolutionize Physical Therapy By Accelerating Motor Skill Recovery

UC Davis Wins NSF Grants To Advance AI-Powered Protein Engineering For Industry And Health

New AI Framework LLRL Accurately Detects Plant Disease Severity for Smarter Farming

Machine Learning Supercharges Biochar Research To Fight Climate Change

Hybrid AI Tool Boosts National Flood Forecast Accuracy

Why AI Struggles With the Unknown While Humans Adapt Instinctively

New Optical Chip Slashes AI Energy Use With Light-Powered Computing

AI And Drone Imaging Predict Wheat Yields With Unmatched Accuracy

AI System Translates and Edits Human Thoughts Into Images Using Brain Scans

UC Riverside Scientists Develop Certified Unlearning Method To Erase Data From AI Models Without Retraining

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