Envisioning a future where health care tech leaves some behind
The winning essay of the Envisioning the Future of Computing Prize puts health care disparities at the forefront.
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The winning essay of the Envisioning the Future of Computing Prize puts health care disparities at the forefront.
A team of MIT researchers founded Themis AI to quantify AI model uncertainty and address knowledge gaps.
SketchAgent, a drawing system developed by MIT CSAIL researchers, sketches up concepts stroke-by-stroke, teaching language models to visually express concepts on their own and collaborate with humans.
This new machine-learning model can match corresponding audio and visual data, which could someday help robots interact in the real world.
Words like “no” and “not” can cause this popular class of AI models to fail unexpectedly in high-stakes settings, such as medical diagnosis.
The CausVid generative AI tool uses a diffusion model to teach an autoregressive (frame-by-frame) system to rapidly produce stable, high-resolution videos.
New type of “state-space model” leverages principles of harmonic oscillators.
A new method helps convey uncertainty more precisely, which could give researchers and medical clinicians better information to make decisions.
New phase will support continued exploration of ideas and solutions in fields ranging from AI to nanotech to climate — with emphasis on educational exchanges and entrepreneurship.
Researchers have created a unifying framework that can help scientists combine existing ideas to improve AI models or create new ones.