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MIT researchers developed an efficient approach for training more reliable reinforcement learning models, focusing on complex tasks that involve variability. This could enable the leverage of reinforcement learning across a wide range of applications.

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A hybrid AI approach known as hybrid autoregressive transformer can generate realistic images with the same or better quality than state-of-the-art diffusion models, but that runs about nine times faster and uses fewer computational resources. The new tool uses an autoregressive model to quickly capture the big picture and then a small diffusion model to refine the details of the image.

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Ben Vinson III, president of Howard University, made a compelling call for AI to be “developed with wisdom,” as he delivered MIT’s annual Karl Taylor Compton Lecture.

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MIT CSAIL researchers have introduced a human-labeled dataset of pareidolic faces found in everyday objects, revealing key differences between human and AI face detection and discovering a mathematical “Goldilocks zone” where pareidolia is most likely to occur.

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The MIT Generative AI Impact Consortium is a collaboration between MIT, founding member companies, and researchers across disciplines who aim to develop open-source generative AI solutions, accelerating innovations in education, research, and industry.

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