Why it’s critical to move beyond overly aggregated machine-learning metrics
New research detects hidden evidence of mistaken correlations — and provides a method to improve accuracy.
New research detects hidden evidence of mistaken correlations — and provides a method to improve accuracy.
“MorphoChrome,” developed at MIT, pairs software with a handheld device to make everyday objects iridescent.
Founded by two MIT alumni, Samsara’s platform gives companies a central hub to learn from their workers, equipment, and other infrastructure.
New technique could improve the scalability of trapped-ion quantum computers, an essential step toward making them practically useful.
With support from the Siegel Family Endowment, the newly renamed MIT Siegel Family Quest for Intelligence investigates how brains produce intelligence and how it can be replicated to solve problems.
“MechStyle” allows users to personalize 3D models, while ensuring they’re physically viable after fabrication, producing unique personal items and assistive technology.
While the growing energy demands of AI are worrying, some techniques can also help make power grids cleaner and more efficient.
The program recognizes outstanding mentorship of graduate students.
The inaugural MIT Human Insight Collaborative (MITHIC) Annual Event showcased the breadth of projects supported in the first year of the presidential initiative.
Nanoparticles coated with molecular sensors could be used to develop at-home tests for many types of cancer.
New research demonstrates how AI models can be tested to ensure they don’t cause harm by revealing anonymized patient health data.
A new method could enable users to design portable medical devices, like a splint, that can be rapidly converted from flat panels to a 3D object without any tools.
From robotics to apps like “NerdXing,” senior Julianna Schneider is building technologies to solve problems in her community.
CSAIL researchers find even “untrainable” neural nets can learn effectively when guided by another network’s built-in biases using their guidance method.
MIT-IBM Watson AI Lab researchers developed an expressive architecture that provides better state tracking and sequential reasoning in LLMs over long texts.