The infrastructure behind AI agents isn't static—it’s a living, evolving system. Designing effective data pipelines means ...

When someone starts a new job, early training may involve shadowing a more experienced worker and observing what they do ...

A digital innovation initiative about fault anomalies has been selected as one of the first projects for the new Microsoft AI ...

I am a computational biologist interested in interpretable machine learning for genomics and health care. Interpretable ...

Researchers have used machine learning to dramatically speed up the processing time when simulating galaxy evolution coupled with supernova explosion. This approach could help us understand the ...

In recent years, with the public availability of AI tools, more people have become aware of how closely the inner workings of ...

Predicting wildfire spread is critical for land management and disaster preparedness. To this end, we present “Next Day Wildfire Spread,” a curated, large-scale, multivariate dataset of historical ...

AI initiatives fail for a handful of well-known reasons. Developers, data scientists, and technology leaders hold the keys to ...

A Tribune reporter and data nerd went looking for a smarter way to evaluate and draft NBA players. From Cooper Flagg to a few ...

What if I told you that the secret to dominating the markets wasn’t a hot tip or gut instinct… but a machine? Thanks to AI, machines are now driving massive gains. In 2022, for instance, Citadel made ...

Start implementing post-quantum cryptography, keep an eye on adversarial quantum programs and secure the quantum supply chain.

Multimodal machine learning (MML) is a tempting multidisciplinary research area where heterogeneous data from multiple modalities and machine learning (ML) are combined to solve critical problems.