Scientists used AI to uncover two new biological subtypes of multiple sclerosis

By analysing MRI scans and a simple blood test from hundreds of patients, researchers identified patterns that reveal how ...

Understanding Mixture Of Experts: Scaling AI Capability Without Scaling Cost Or Risk

As more organizations consider a mixture of experts strategy, it's important to understand its benefits, challenges and how ...
Security Boulevard

2026 Kubernetes Playbook: AI at Scale, Self‑Healing Clusters, & Growth

In 2026, the question isn’t whether Kubernetes wins – it already has. And yet, many organizations are running mission-critical workloads on a platform they still treat as plumbing, not the operating ...
Tech Xplore

AI models stumble on basic multiplication without special training methods, study finds

These days, large language models can handle increasingly complex tasks, writing complex code and engaging in sophisticated ...
Morning Overview on MSN

Uranus and Neptune might be misclassified and their cores tell the story

For decades, Uranus and Neptune have been filed neatly into the “ice giant” drawer, shorthand for worlds built mostly from ...

How 2025 Recalibrated AI Models Race

In 2025, large language models moved beyond benchmarks to efficiency, reliability, and integration, reshaping how AI is ...
Scientific Research Publishing

Human Gut Microbiome in Relation to Food, Nutrition, Health and Disease ()

Diets, Food Health Benefits, Diseases, Therapy Share and Cite: Menkovska, M. and Raza, S. (2025) Human Gut Microbiome in ...
Scientific Research Publishing

Early Alzheimer’s Disease Detection from Short Speech Samples Using Lightweight, Interpretable Linguistic Markers ()

Based Detection, Linguistic Biomarkers, Machine Learning, Explainable AI, Cognitive Decline Monitoring Share and Cite: de Filippis, R. and Al Foysal, A. (2025) Early Alzheimer’s Disease Detection from ...
IEEE

Learning With Partial-Label and Unlabeled Data: Contrastive With Negative Example Separation

Abstract: Semi-Supervised Partial Label Learning (SSPLL) is an important branch of weakly supervised learning, where the data consists of both partial label examples and unlabeled ones. In SSPLL, the ...
Microsoft

Rethinking Machine Learning Collective Communication as a Multi Commodity Flow problem

Cloud operators utilize collective communication optimizers to enhance the efficiency of the single-tenant, centrally managed training clusters they manage. However, current optimizers struggle to ...
IEEE

Enhancing Label Noise Robustness for Hyperspectral Image Classification by Neighborhood Contrastive Learning

Abstract: Recent advancements in hyperspectral image classification (HIC) rely on high-quality annotations and thus inevitably suffer from noisy labels. To address the negative effects of noisy labels ...