MIT engineers developed a tag that can reveal with near-perfect accuracy whether an item is real or fake. The key is in the glue on the back of the tag.

A system designed at MIT could allow sensors to operate in remote settings, without batteries.

With the PockEngine training method, machine-learning models can efficiently and continuously learn from user data on edge devices like smartphones.

The system could improve image quality in video streaming or help autonomous vehicles identify road hazards in real-time.

Mens, Manus and Machina (M3S) will design technology, training programs, and institutions for successful human-machine collaboration.

The receiver chip efficiently blocks signal interference that slows device performance and drains batteries.

A new method uses optics to accelerate machine-learning computations on smart speakers and other low-power connected devices.

A new technique enables AI models to continually learn from new data on intelligent edge devices like smartphones and sensors, reducing energy costs and privacy risks.

The new design is stackable and reconfigurable, for swapping out and building on existing sensors and neural network processors.

Engineers build a lower-energy chip that can prevent hackers from extracting hidden information from a smart device.