Action Flow Matching: A New Approach to Continual Robot Learning
Robots that can adapt to changing environments and tasks in real-time are the holy grail of robotics research. But achieving
A New Framework for Assessing AI Risks Like Nuclear Power Plants
Modern AI systems are advancing at a breakneck pace, but our ability to assess their risks isn't keeping
AI-Powered Early Detection of Multidrug Resistance in ICU Patients Using Interpretable Machine Learning
AI-Powered Early Detection of Multidrug Resistance in ICU Patients Using Interpretable Machine Learning
Multidrug resistance (MDR) is a growing global
Large Language Models Struggle with Non-English Educational Tasks, Study Finds
Large language models (LLMs) like GPT-4o and Gemini are increasingly being used in educational settings, but a new study reveals
How AI is revolutionizing industrial defect detection with statistical guarantees
The problem with traditional defect detection
In industrial manufacturing, surface defects on materials like steel can lead to catastrophic failures—
Rethinking Spiking Neural Networks: How Reset Mechanisms Shape Sequential AI Models
Spiking neural networks (SNNs) have long been considered the "third generation" of neural networks, promising energy-efficient AI through
This AI Can Tell If Text Was Written by a Human — And Which LLM Wrote It
As AI-generated text becomes increasingly indistinguishable from human writing, the need for reliable detection tools has never been greater. A
Latent Diffusion Planning: A New Approach to Imitation Learning That Leverages Suboptimal and Action-Free Data
Latent Diffusion Planning: A New Approach to Imitation Learning That Leverages Suboptimal and Action-Free Data
Imitation learning has made significant
I-Con: The Unified Framework That Ties Together 23 Representation Learning Methods
Representation learning has exploded in recent years, with new techniques emerging daily across domains like contrastive learning, clustering, dimensionality reduction,
How Reinforcement Learning Can Fix LLMs’ Greedy Decision-Making
Large Language Models (LLMs) have shown remarkable capabilities in text generation and reasoning tasks, but their performance in decision-making scenarios
