Science

A new self-supervised learning framework unlocks accurate and adaptable motion recognition across a wide range of devices and users, minimizing the need for labeled data.

Researchers have developed a self-supervised learning method that allows AI to learn how to simplify complex mathematical expressions by reversing the process of ‘scrambling’ them.

A new system architecture aims to bring the power of large language models to dynamic clinical workflows with enhanced safety and coordination.

A new benchmark reveals that while AI code review tools can find many potential issues, prioritizing accuracy over sheer volume is crucial for effective defect detection.

Researchers have developed a novel system that generates complex, multi-story 3D environments from natural language descriptions, enabling more realistic and challenging tests for embodied AI agents.

Researchers have developed a novel deep learning framework to more accurately predict how proteins bind to DNA and regulate gene expression.

A new system leverages cloud-edge collaboration and efficient data compression to enable accurate and robust multi-robot simultaneous localization and mapping.

A new framework combines the power of artificial intelligence with causal inference to better understand the factors driving legal decisions, going beyond simple pattern recognition.

Researchers have created a comprehensive evaluation framework to assess how well AI-powered robots can identify and avoid unsafe actions in typical household environments.

Researchers have developed a machine learning approach to identify and estimate the magnetic field strengths of white dwarf stars, revealing previously hidden objects.