DeepMind's synthetic data for geometry problems and Google's video generation model

DeepMind's approach generates a synthetic dataset for training a system to solve geometry problems without human demonstrations. This marks a step towards neuro-symbolic systems and the potential for future AGI to utilize tools for solving complex problems. Google's Lumiere model for video generation takes a unique approach by generating the entire video in one pass, as opposed to frame-by-frame generation. This ensures more consistency in the overall picture, compared to the traditional approach where small issues can lead the model astray. The model uses a diffusion-based system, showing promise for continued progress in the text-to-video space.

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