Robots learn human-like movement adjustments to prevent object slipping

To effectively tackle a variety of real-world tasks, robots should be able to reliably grasp objects of different shapes, textures and sizes, without dropping them in undesired locations. Conventional approaches to enhancing the ability of robots to grasp objects work by tightening the grip of a robotic hand to prevent objects from slipping.

Filtered data stops openly-available AI models from performing dangerous tasks, study finds

Researchers from the University of Oxford, EleutherAI, and the UK AI Security Institute have reported a major advance in safeguarding open-weight language models. By filtering out potentially harmful knowledge during training, the researchers were able to build models that resist subsequent malicious updates—especially valuable in sensitive domains such as biothreat research.

From terabytes to insights: Real-world AI obervability architecture

GUEST: Consider maintaining and developing an e-commerce platform that processes millions of transactions every minute, generating large amounts of telemetry data, including metrics, logs and traces across multiple microservices. When critical incidents occur, on-call engineers face the daunting task of sifting through an ocean of data to unravel r…Read More