Non-terrestrial networks have their own challenges that cellular networks didn't have. Will AI help solve them dynamically?

Whether a smartphone battery lasts longer or a new drug can be developed to treat incurable diseases depends on how stably ...

QA teams now use machine learning to analyze past test data and code changes to predict which tests will fail before they run. The technology examines patterns from previous test runs, code commits, ...

A machine learning model incorporating functional assessments predicts one-year mortality in older patients with HF and improves risk stratification beyond established scores. Functional status at ...

Supervised learning algorithms like Random Forests, XGBoost, and LSTMs dominate crypto trading by predicting price directions ...

Neel Somani has built a career that sits at the intersection of theory and practice. His work spans formal methods, mac ...

Adaptive systems were supposed to simplify decision-making. Instead of hard-coded rules, engineers built models that could learn from data, respond to change, and improve over time. That promise still ...

From fine-tuning open source models to building agentic frameworks on top of them, the open source world is ripe with projects that support AI development.

Machine learning requires humans to manually label features while deep learning automatically learns features directly from raw data. ML uses traditional algorithms like decision tress, SVM, etc., ...

A rotating cylinder with its side cut away to expose the core, showing patches of purple, blue, green, yellow, and orange that are dense in the middle and more diffuse toward the edges. This rotating ...