DeepMind, Harvard and Stanford paper: visual world models may be a path to AGI
rohanpaul_ai · x · 2026-09-12
A paper from Google DeepMind with Harvard, Stanford and other top labs argues that visual AI could be a path to AGI: systems that build world models, remember changes, predict outcomes, and act. Key points:
- Most multimodal AI treats vision merely as input to a language model; the paper wants vision to do more of the thinking itself
- A capable visual system should learn directly from images, video, 3D structure, and interaction — understanding what exists, what changed, what is hidden, what might happen next, and what to look at before acting
- Video generation, reconstruction, persistent memory, continual learning, multimodal sensing, and robotics could be pieces of the same system
- Stop judging visual AI mainly by image Q&A, captions, or realistic video
Related event: DeepMind, Harvard and Stanford Say Visual AI May Be the Path to AGI(2 posts)→
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