Improving AI models’ ability to explain their predictions
A new approach could help users know whether to trust a model’s predictions in safety-critical applications like health care and autonomous driving.
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A new approach could help users know whether to trust a model’s predictions in safety-critical applications like health care and autonomous driving.
To help generative AI models create durable, real-world accessories and decor, the PhysiOpt system runs physics simulations and makes subtle tweaks to its 3D blueprints.
Annual award honors early-career researchers for creativity, innovation, and research accomplishments.
An AI control system co-developed by SMART researchers enables soft robotic arms to learn a broad set of motions once and adapt instantly to changing conditions without retraining.
EnCompass executes AI agent programs by backtracking and making multiple attempts, finding the best set of outputs generated by an LLM. It could help coders work with AI agents more efficiently.
Torralba’s research focuses on computer vision, machine learning, and human visual perception.
As AI technology advances, a new interdisciplinary course seeks to equip students with foundational critical thinking skills in computing.
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.
“MechStyle” allows users to personalize 3D models, while ensuring they’re physically viable after fabrication, producing unique personal items and assistive technology.
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.
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.