Natural language processing

How do we know if a large language model is lying? Letting AI systems argue with each other may help expose the truth.

To better understand machine learning algorithms, Lenka Zdeborová treats them like physical materials.

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.

Neural networks and other forms of machine learning ultimately learn by trial and error, one improvement at a time.

Researchers are drawing on ideas from game theory to improve large language models and make them more consistent.

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.

Erasing key information during training results in machine learning models that can learn new languages faster and more easily.