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Open-source software written by MIT Morningside Academy of Design Fellow Jonathan Zong and others in the MIT Visualization Group improves accessibility of online graphics for blind users.
MIT professors Leonid Mirny, Davesh Maulik, Aram Harrow, Virginia Vassilevska Williams, and Vinod Vaikuntanathan have been selected to receive the 2023 Simons Investigator Awards from the Simons Foundation in support of their work on fundamental theoretical questions of math, physics, and computer science.
Drawing inspiration from physics, a new Poisson Flow Generative Model ++ (PFGM++) integrates diffusion and Poisson Flow principles, outperforming existing diffusion models in advanced image generation. This breakthrough in generative AI taps into both the complexity of electric fields and the simplicity of diffusion to create realistic patterns and images with potential applications spanning multiple domains.
An archeological view on biased data could help improve machine-learning models in health care settings.
MIT's “Lightning” system connects photons to the electronic components of computers using a novel abstraction, creating the first photonic computing prototype to serve real-time machine-learning inference requests.