Artificial intelligence (AI) and machine learning (ML) systems have become central to modern data-driven decision-making. They are now widely applied in fields as diverse as healthcare, finance, ...

Explore how AI in high-throughput screening improves drug discovery through advanced data analysis, hit identification and ...

Built on a new architecture KumoRFM-2 achieves state-of-the-art results across 41 predictive tasks and four major benchmarks, ...

AI security cameras enhance smart home security using computer vision, behavioral anomaly detection, and facial recognition ...

In a future perhaps not too far away, artificial intelligence and its subfield of machine learning (ML) tools and models, ...

Published in Microplastics, the study titled “Canonical Spectral Transformation for Raman Spectra Enables High Accuracy AI Identification of Marine Microplastics” introduces a novel data processing ...

What was the rationale behind applying machine learning (ML) models to improve identification probability in the absence of ...

Stanford's 2026 AI Index: frontier models fail one in three attempts, lab transparency is declining, and benchmarks are ...

With the rapid development of single-cell RNA sequencing (scRNA-seq), researchers can now examine gene activity in individual ...

Walk through enough industrial AI deployments and a pattern becomes uncomfortable to ignore. The pilot works. The model ...

What do mosquito populations and physical measurement data have in common? Both lead to a central problem in machine learning: the reliable estimation of class prevalence in the face of changing data.