AI Flash News

EnCompass executes AI agent programs by backtracking and making multiple attempts, finding the best set of outputs generated by an LLM. It could help coders work with AI agents more efficiently.

He joins Nikos Trichakis in guiding the cross-cutting initiative of the MIT Schwarzman College of Computing.

Torralba’s research focuses on computer vision, machine learning, and human visual perception.

Professor James Collins discusses how collaboration has been central to his research into combining computational predictions with new experimental platforms.

The MIT senior will pursue a master’s degree at Cambridge University in the U.K. this fall.

Inspired by the shape-shifting skin of octopuses, Penn State researchers developed a smart hydrogel that can change appearance, texture, and shape on command. The material is programmed using a special printing technique that embeds digital instructions directly into the skin. Images and information can remain invisible until triggered by heat, liquids, or stretching.

A new light-based breakthrough could help quantum computers finally scale up. Stanford researchers created miniature optical cavities that efficiently collect light from individual atoms, allowing many qubits to be read at once. The team has already demonstrated working arrays with dozens and even hundreds of cavities. The approach could eventually support massive quantum networks with millions of qubits.

Scientists warn that rapid advances in AI and neurotechnology are outpacing our understanding of consciousness, creating serious ethical risks. New research argues that developing scientific tests for awareness could transform medicine, animal welfare, law, and AI development. But identifying consciousness in machines, brain organoids, or patients could also force society to rethink responsibility, rights, and moral boundaries. The question of what it means to be conscious has never been more urgent—or more unsettling.

NASA’s Perseverance rover has just made history by driving across Mars using routes planned by artificial intelligence instead of human operators. A vision-capable AI analyzed the same images and terrain data normally used by rover planners, identified hazards like rocks and sand ripples, and charted a safe path across the Martian surface. After extensive testing in a virtual replica of the rover, Perseverance successfully followed the AI-generated routes, traveling hundreds of feet autonomously.