neural networks

A custom-built machine learning algorithm can predict when a complex system is about to switch to a wildly different mode of behavior.

Neural networks originally designed for language processing turn out to be great models of how our brains understand places.

Self-supervised learning allows a neural network to figure out for itself what matters. The process might be what makes our own brains so successful.

Christopher Kanan is building algorithms that can continuously learn over time — the way we do.

Intelligent beings learn by interacting with the world. Artificial intelligence researchers have adopted a similar strategy to teach their virtual agents new tricks.

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.

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

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