The approach could help animators to create realistic 3D characters or engineers to design elastic products.
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.
Words like “no” and “not” can cause this popular class of AI models to fail unexpectedly in high-stakes settings, such as medical diagnosis.
A new method helps convey uncertainty more precisely, which could give researchers and medical clinicians better information to make decisions.
The CausVid generative AI tool uses a diffusion model to teach an autoregressive (frame-by-frame) system to rapidly produce stable, high-resolution videos.
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Assistant Professor Sara Beery is using automation to improve monitoring of migrating salmon in the Pacific Northwest.
MIT CSAIL researchers used AI-generated images to train a robot dog in parkour, without real-world data. Their LucidSim system demonstrates generative AI’s potential for creating robotics training data.
Researchers propose a simple fix to an existing technique that could help artists, designers, and engineers create better 3D models.
New dataset of “illusory” faces reveals differences between human and algorithmic face detection, links to animal face recognition, and a formula predicting where people most often perceive faces.