Researchers make headway in solving a longstanding problem of balancing curious “exploration” versus “exploitation” of known pathways in reinforcement learning.

MIT researchers have developed a machine-learning technique that accurately captures and models the underlying acoustics of a scene from only a limited number of sound recordings. In this image, a sound emitter is marked by a red dot. The colors show the sound volume if a listener were to stand at different locations — yellow is louder and blue is quieter.

Researchers increase the accuracy and efficiency of a machine-learning method that safeguards user data.

On its own, a new machine-learning model discovers linguistic rules that often match up with those created by human experts.

The Lincoln Laboratory Supercomputing Center has released a dataset containing more than a million jobs run on its TX-GAIA supercomputer. The data can help feed AI research into optimizing data center resources.

An MIT-developed device with the appearance of a Wi-Fi router uses a neural network to discern the presence and severity of one of the fastest-growing neurological diseases in the world.

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MIT researchers have trained a machine-learning model to monitor and adjust the 3D printing process in real-time.

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