Neuroscience professor and Science Hub investigator Ted Adelson explains how simulating the sense of touch with a camera can make robots smarter.

“Interpretability methods” seek to shed light on how machine-learning models make predictions, but researchers say to proceed with caution.

MIT researchers create KineCAM, an instant camera that yields images that appear to move.

With FabO, PhD student Dishita Turakhia wants to empower students to learn digital fabrication by making video game objects and characters come alive.

A geometric deep-learning model is faster and more accurate than state-of-the-art computational models, reducing the chances and costs of drug trial failures.

Researchers created Exo for writing high-performance code on hardware accelerators.

Rapid increases in the speed and power of microchips have fueled innovation in many industries, but the future trajectory of that incredible progress may be in jeopardy.

MIT alumni-founded Overjet analyzes and annotates dental X-rays to help dentists offer more comprehensive care.

A new system lets robots manipulate soft, deformable material into various shapes from visual inputs, which could one day enable better home assistants.

MIT scientists unveil the first open-source simulation engine capable of constructing realistic environments for deployable training and testing of autonomous vehicles.