Twelve teams of students and postdocs across the MIT community presented innovative startup ideas with potential for real-world impact.
New method uses crowdsourced feedback to help train robots
Human Guided Exploration (HuGE) enables AI agents to learn quickly with some help from humans, even if the humans make mistakes.
Accelerating AI tasks while preserving data security
The SecureLoop search tool efficiently identifies secure designs for hardware that can boost the performance of complex AI tasks, while requiring less energy.
Using language to give robots a better grasp of an open-ended world
By blending 2D images with foundation models to build 3D feature fields, a new MIT method helps robots understand and manipulate nearby objects with open-ended language prompts.
Complimentary approaches — “HighLight” and “Tailors and Swiftiles” — could boost the performance of demanding machine-learning tasks.
Making genetic prediction models more inclusive
MIT computer scientists developed a way to calculate polygenic scores that makes them more accurate for people across diverse ancestries.
Five MIT faculty, along with seven additional affiliates, are honored for outstanding contributions to medical research.
System combines light and electrons to unlock faster, greener computing
“Lightning” system connects photons to the electronic components of computers using a novel abstraction, creating the first photonic computing prototype to serve real-time machine-learning inference requests.
AI helps robots manipulate objects with their whole bodies
With a new technique, a robot can reason efficiently about moving objects using more than just its fingertips.
Mens, Manus and Machina (M3S) will design technology, training programs, and institutions for successful human-machine collaboration.