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. ...

In the world of artificial intelligence, Continual Learning is one of the biggest challenges. The goal is to enable AI models to learn new things sequentially without forgetting what they have learned before. This is a key ability that brings us closer to creating truly intelligent systems capable of adapting to a dynamically changing world. Unfortunately, traditional neural networks suffer from so-called catastrophic forgetting. When they learn a new task, they tend to overwrite the knowledge gained from previous tasks. The publication “Monte Carlo Functional Regularisation for Continual Learning” (arXiv:2508.13006) by Pengcheng Hao, Menghao Waiyan William Zhu, and Ercan Engin Kuruoglu presents an innovative approach to this problem. ...

We are in the midst of an AI gold rush, where companies are investing billions to build increasingly intelligent models. The final, crucial step in this process is often Reinforcement Learning (RL), the “finishing school” where an AI agent learns to master complex tasks through trial and error. However, this training process at an industrial scale is plagued by two crippling problems: crippling inefficiency and maddening complexity. It’s like trying to run a state-of-the-art factory where half the machines are always idle and every product requires a complete retooling of the assembly line. ...

Modern systems based on Machine Learning (ML) are ubiquitous, from credit scoring to fraud detection. The conventional wisdom is that more data leads to better models. However, this data-centric approach directly conflicts with a fundamental legal principle: data minimization (DM). This principle, enshrined in key regulations like the GDPR in Europe and the CPRA in California, mandates that personal data collection and processing must be “adequate, relevant and limited to what is necessary in relation to the purposes for which they are processed”. ...

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”. ...

In the era of Big Data, data has become an organization’s most valuable asset. However, access to it is often limited by a technical barrier: the need to use query languages like SQL. For years, analysts and engineers have dreamed of a system that would allow them to “talk” to a database in natural language. Text-to-SQL systems aim to realize this vision, but their path has been challenging. Older models, though promising, often failed in real-world scenarios: they were “brittle,” struggled with unseen database schemas, and required costly fine-tuning for each new domain. ...

In an era where Large Language Models (LLMs) like GPT-4 or Llama seem to understand the world, a fundamental challenge remains: how to teach them effectively and efficiently? The standard method is Supervised Fine-Tuning (SFT), which involves “feeding” the model thousands of examples of correct responses. However, as the groundbreaking paper “On the Generalization of SFT: A Reinforcement Learning Perspective with Reward Rectification” (arXiv:2508.05629) points out, SFT has a hidden flaw that limits its true potential. ...

In a world where robots and AI systems increasingly learn through observation and interaction with humans, the efficiency of this process remains a key challenge. Traditional Imitation Learning methods often require a human teacher to constantly supervise and correct errors, which is time-consuming and costly. A team of researchers led by Jelle Luijkx proposes a groundbreaking solution in their latest paper, “ASkDAgger: Active Skill-level Data Aggregation for Interactive Imitation Learning.” ...

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. ...