When can we trust the results we get from AI, and when is learning impossible? Researchers have shown that there are some ...

This research assesses data provenance in widely used health datasets, revealing flaws that could undermine clinical prediction models and patient care.

XL, dynamic interest modeling, and distributed stream computing to analyze large-scale e-commerce user behavior. By improving long-sequence prediction, real-time processing, and behavioral clustering, ...

Summary: A new study utilizes Koopman operator learning to prove that certain complex, chaotic systems have fundamental ...

How a team at UC Berkeley devised a multi-sensor smell system and combined it with machine learning to create a more ...

Google's TabFM skips per-dataset training and still predicts on unseen tables, matching tuned baselines and cutting pipeline ...

Three heads are better than one. Versions of this proverb are found worldwide and throughout history. Yet in the race to ...

Promo codes look like a marketing gimmick, but underneath they are a data problem — and a surprisingly hard one. Every code is a small record with a short, unpr ...

ACRouter, a new open-source AI router, learns from execution feedback to pick the best coding model per task, cutting costs 2 ...

A multi-cloud MLOps framework improves AI service reliability through automated deployment, canary releases, and ...

A new analysis of seismic “families” reveals that some large earthquakes may be preceded by hidden patterns in clustering, ...

AI tools are proliferating across pulmonary medicine and critical care, with promising early results in diagnostics and ...