Science

Artificial intelligence has already transformed many industries, but the world of scientific research has been waiting for a true game-changer. While general AI models are powerful, they often lack the specialized knowledge needed for deep scientific inquiry. Enter Intern-S1, a new multimodal foundation model that’s set to bridge this gap and accelerate a new era of discovery. Developed by the Shanghai AI Laboratory, Intern-S1 is not just another large language model. It’s a specialized generalist, designed from the ground up to understand and process complex scientific data in various formats, from text and images to time-series data. ...

Imagine you’re learning to play chess. You master all the rules, strategies, and openings. You become a pretty good player. Now, someone introduces a new piece with completely new rules of movement. As you learn to play with this new piece, do you forget how to move a pawn or a knight? Of course not. Your brain can integrate new knowledge without losing what it has already acquired. Unfortunately, for many artificial intelligence systems, this is a huge challenge, known as “catastrophic forgetting”. ...

Can computers learn to “hear” the rhythm in a stream of data, much like we hear the rhythm in music? And by using this skill, can they better protect us from equipment failures, financial fraud, or health problems? A new scientific paper titled “CaPulse: Detecting Anomalies by Tuning in to the Causal Rhythms of Time Series” attempts to answer these questions. The Problem with Anomalies We live in a world of data. From our heartbeats and stock market fluctuations to energy consumption in a smart city—all of this is time series data, collected at regular intervals. Often lurking within this data are anomalies: strange, unexpected events that can signal a problem. This could be a sudden cardiac arrhythmia, a suspicious bank transaction, or an impending engine failure in a factory. ...