Machine learning

Language processing programs are notoriously hard to interpret, but smaller versions can provide important insights into how they work.

For centuries, mathematicians have tried to prove that Euler’s fluid equations can produce nonsensical answers. A new approach to machine learning has researchers betting that “blowup” is near.

Traditional algorithms power complicated computational tools like machine learning. A new approach, called algorithms with predictions, uses the power of machine learning to improve algorithms.

A simple algorithm that revolutionizes how neural networks approach language is now taking on image classification as well. It may not stop there.

A new computer program fashioned after artificial intelligence systems like AlphaGo has solved several open problems in combinatorics and graph theory.

Algorithms that use the brain’s communication signal can now work on analog neuromorphic chips, which closely mimic our energy-efficient brains.

Two recent collaborations between mathematicians and DeepMind demonstrate the potential of machine learning to help researchers generate new mathematical conjectures.

Two researchers show that for neural networks to be able to remember better, they need far more parameters than previously thought.

If only scientists understood exactly how electrons act in molecules, they’d be able to predict the behavior of everything from experimental drugs to high-temperature superconductors. Following decades of physics-based insights, artificial intelligence systems are taking the next leap.