In 2026, neural networks are achieving unprecedented capabilities across industries, yet large-scale tests reveal persistent struggles with generalization. Researchers are exploring adaptive ...

In 2026, AI research is moving beyond raw scaling to focus on efficiency, adaptability, and operational robustness. Advances in architectures, benchmarks, and conferences reflect a growing emphasis on ...

Atlassian Corp. today unveiled a sweeping set of artificial intelligence updates at its annual Team ’26 conference, headlined ...

The financial crime compliance industry has spent two decades building better detection. Transaction monitoring rules have multiplied. Machine learning models have grown more sophisticated. SAR ...

Target identification is a critical and challenging step in drug discovery, with only a small fraction of human genes considered druggable and even fewer successfully targeted by approved therapies.

Microsoft has simplified service mesh scaling and management with an ambient-based service network for AKS. Here’s how to get started. If you’re using Kubernetes, especially a managed version like ...

Abstract: Recent years have witnessed fast developments of graph neural networks (GNNs) that have benefited myriad graph analytic tasks and applications. Most GNNs rely on the homophily assumption ...

ThousandEyes released its list of the 11 most notable Internet outages and application issues of 2025, along with recommendations to help network teams improve resilience. System outages plagued ...

Abstract: Geometric Deep Learning is a modern approach to deep learning that focuses on the assessment of the structure and inherent symmetry of the data. How to best represent Electrocardiogram (ECG) ...

Accurate prediction of protein-protein interactions (PPIs) is crucial for understanding cellular functions and advancing the development of drugs. While existing in-silico methods leverage direct ...