Undergraduate research projects show how students are advancing research in human and artificial intelligence, and applying intelligence tools to other disciplines.

Researchers pinpoint the “neurons” in machine-learning systems that capture specific linguistic features during translation tasks.

Model identifies instances when autonomous systems have learned from examples that may cause dangerous errors in the real world.

Neural network assimilates multiple types of health data to help doctors make decisions with incomplete information.

MIT researchers have developed a model that can assimilate multiple types of a patient’s health data to help doctors make decisions with incomplete information.

Startup Connect member Thunkable and MIT App Inventor gives noncoders the ability to create professional-grade mobile apps.

Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have come up with a way to get a better handle on this understanding of complex motion. 

The new system uses an algorithm that can take 2-D videos and turn them into 3-D-printed “motion sculptures” that show how a human body moves through space.

MIT CSAIL's Vinod Vaikuntanathan aims to improve encryption in a world with growing applications and evolving adversaries.

MIT researchers describe an autonomous system for a fleet of drones to collaboratively search under dense forest canopies. The drones use only onboard computation and wireless communication — no GPS required.

As the largest publicly available collection of recipe data, Recipe1M affords the ability to train high capacity models on aligned, multi-modal data.

Systems have been limited to single color changes, i.e. changes from transparent to colored. In this paper, we present ColorMod, a method to extend this ap-proach to multi-color changes (e.g., red-to-yellow).