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.
Yilun Du, a PhD student and MIT CSAIL affiliate, discusses the potential applications of generative art beyond the explosion of images that put the web into creative hysterics.
Researchers create a method for magnetically programming materials to make cubes that are very picky about what they connect with, enabling more-scalable self-assembly.
A system for monitoring motion and muscle engagement could aid the elderly and athletes during unsupervised physical rehabilitation for injuries or impaired mobility.
Inspired by jellyfish and octopuses, PhD candidate Juncal Arbelaiz investigates the theoretical underpinnings that will enable systems to more efficiently adapt to their environments.