Machine learning

Researchers got a better look at the thoughts of chatbots, amateurs learned exactly how complicated simple systems can be, and quantum computers passed an essential milestone.

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

Across the social and biological sciences, statisticians use a technique that leverages randomness to deal with the unknown.

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

By tapping into a decades-old mathematical principle, researchers are hoping that Kolmogorov-Arnold networks will facilitate scientific discovery.

By creating digital twins of patients, Amanda Randles wants to bring unprecedented precision to medical forecasts.

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.

Three years ago, Google’s AlphaFold pulled off the biggest artificial intelligence breakthrough in science to date, accelerating molecular research and kindling deep questions about why we do science.