MIT CSAIL researchers solve a differential equation behind the interaction of two neurons through synapses to unlock a new type of speedy and efficient AI algorithm.

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

Models trained on synthetic data can be more accurate than other models in some cases, which could eliminate some privacy, copyright, and ethical concerns from using real data.

Computing systems that appear to generate brain-like activity may be the result of researchers guiding them to a specific outcome.

This machine-learning system can simulate how a listener would hear a sound from any point in a room.

A new approach sheds light on the behavior of turbulent structures that can affect the energy generated during fusion reactions, with implications for reactor design.

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

Greater availability of de-identified patient health data would enable better treatments and diagnostics, the researchers say.

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.

Inspired by jellyfish and octopuses, PhD candidate Juncal Arbelaiz investigates the theoretical underpinnings that will enable systems to more efficiently adapt to their environments.