Natural language processing

By imbuing enormous vectors with semantic meaning, we can get machines to reason more abstractly — and efficiently — than before.

Large language models like ChatGPT are now big enough that they’ve started to display startling, unpredictable behaviors.

Large language models still struggle with basic reasoning tasks. Two new papers that apply machine learning to math provide a blueprint for how that could change.

A wave of research improves reinforcement learning algorithms by pre-training them as if they were human.

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

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

It’s simple enough for AI to seem to comprehend data, but devising a true test of a machine’s knowledge has proved difficult.

Tiny amounts of artificial noise can fool neural networks, but not humans. Some researchers are looking to neuroscience for a fix.

AI tools are shaping next-generation theorem provers, and with them the relationship between math and machine.