EurekAlert! In the Spotlight - Artificial Intelligence

Welcome to In the Spotlight, where each month we shine a light on something exciting, timely, or simply fascinating from the world of science.

This month, we’re focusing on artificial intelligence (AI), a topic that continues to capture attention everywhere. Here, you’ll find the latest research news, insights, and discoveries shaping how AI is being developed and used across the world.

Latest News Releases

Updates every hour. Last Updated: 2-Nov-2026 23:15 ET (3-Nov-2026 04:15 GMT/UTC)

Exploring the mysteries of plant disease severity through multimodal learning

KeAi Communications Co., Ltd.
Peer-Reviewed Publication

Researchers in China developed AgriMamba, a text-guided multimodal framework for plant disease severity grading in complex field environments. The framework combines disease images with pathological descriptions of lesion color, shape, edge characteristics, and spatial distribution. It follows a coarse-to-fine process that localizes the target leaf, segments lesion regions, and quantifies disease severity using lesion-to-leaf area ratios. Experimental and visualization results showed that AgriMamba achieved competitive performance compared with several representative deep learning methods. The model also progressively focused on target leaves and lesions despite background interference, leaf occlusion, and small or scattered disease regions. These findings provide a new technical approach for automated plant disease assessment in complex field environments.

Journal: The Crop Journal

New AI-enhanced virtual reality program shows promise in helping manage menopause symptoms

The Menopause Society
Reports and Proceedings

CLEVELAND, Ohio (October 20, 2026)—Cognitive behavioral therapy (CBT) has proven effective in managing a number of menopause symptoms, including hot flashes, insomnia, depression, and sexual dysfunction. However, access remains limited. A new study suggests that virtual reality (VR) and artificial intelligence (AI) could be effective in delivering CBT interventions. Results of the study will be presented at the 2026 Annual Meeting of The Menopause Society in San Diego October 20-24, 2026.

Meeting: 2026 Annual Meeting of The Menopause Society

How IoT and machine learning could help factories use less energy

KeAi Communications Co., Ltd.

A review published in Cyber-Physical Energy Systems examines how IoT and machine learning technologies can transform manufacturing energy management. By enabling real-time monitoring, prediction, and optimization, these approaches offer new pathways for energy-efficient and sustainable factories while addressing key challenges in industrial adoption.

Journal: Cyber-Physical Energy Systems

AI and wastewater DNA-based mapping helps identify sewer pipes at risk of corrosion

Tohoku University
Peer-Reviewed Publication

Underground sewer pipes are essential to our cities, but because they're difficult to access, detecting damage before it leads to major problems isn't easy. To address this, researchers at Tohoku University have developed a new method that uses bacteria found in wastewater and computer models to identify pipes at risk of severe corrosion. This could help cities spot problems earlier and prioritize inspections.

Journal: Journal of Environmental Management

Empowering material defect research with machine learning

ELSP
Peer-Reviewed Publication

This review examines how machine learning advances the modeling of material defects, covering point defects, dislocations, surfaces, grain boundaries and interfaces. It summarizes defect-data construction, machine-learning interatomic potentials, configuration searches, active learning, high-throughput screening and multiscale simulations. Emphasis is placed on matching models and training data to defect physics, accounting for finite-temperature and long-range effects, and validating defect energetics, transport and structural evolution through calibrated uncertainty and independent benchmarks.

Journal: AI & Materials

AI and symmetry accelerate the design of sound-absorbing woven materials

Advanced Institute for Materials Research (AIMR), Tohoku University
Peer-Reviewed Publication

The patterns that make up the weaves in textiles are seemingly endless. Certain patterns produce different qualities, such as making a material better at allowing sound to travel through it. But given the vast arrangement possibilities, testing each pattern arrangement can be a never-ending process. Now, a research team has developed an AI-assisted method to sift through the pattern possibilities.

Journal: Communications Materials

Real-time driver warning system helps prevent animal collisions

Virginia Tech
Grant and Award Announcement

A novel wildlife detection and real-time driver warning system piloted along a 14-mile stretch in Southwest Virginia has successfully demonstrated its ability to change driver behavior and improve safety in remote terrain. Led by the Virginia Tech Transportation Institute (VTTI) and sponsored by the Virginia Department of Transportation (VDOT), the year-long pilot project utilized artificial intelligence (AI), thermal imaging, and 5G connectivity to reduce vehicle speeds and protect both motorists and native elk populations.

Funder: Virginia Department of Transportation

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