Natural language processing

In steganography, an ordinary message masks the presence of a secret communication. Humans can never do it perfectly, but a new study shows it’s possible for machines.

Today’s language models are more sophisticated than ever, but they still struggle with the concept of negation. That’s unlikely to change anytime soon.

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.