Large language models

Machines work with words by embedding their relationships with other words in a string of numbers.

Common sense has been viewed as one of the hardest challenges in AI. That said, ChatGPT4 has acquired what some believe is an impressive sense of humanity. How is this possible? Listen to this week’s “The Joy of Why” with co-host Steven Strogatz.

The computer scientist Ellie Pavlick is translating philosophical concepts such as “meaning” into concrete, testable ideas.

Large language models do better at solving problems when they show their work. Researchers are beginning to understand why.

A new study suggests that so-called emergent abilities actually develop gradually and predictably, depending on how you measure them.

Far from being “stochastic parrots,” the biggest large language models seem to learn enough skills to understand the words they’re processing.

To better understand how neural networks learn to simulate writing, researchers trained simpler versions on synthetic children’s stories.

Researchers uncover striking parallels in the ways that humans and machine learning models acquire language skills.

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.