Meta Platforms Inc.’s artificial intelligence (AI) researchers are making progress on developing a new architecture for machines that can learn internal models of the world. The goal is to enable AI models to learn faster, plan complex tasks, and adapt to unfamiliar situations. Meta’s AI team has introduced the Image Joint Embedding Predictive Architecture (I-JEPA), the first AI model based on this vision. I-JEPA learns by creating an internal model of the world, comparing abstract representations of images instead of pixels. It aims to replicate how humans passively observe and learn background knowledge about the world. I-JEPA predicts missing information by focusing on abstract prediction targets, unlike generative AI models that fill in missing information indiscriminately. I-JEPA has shown strong performance on computer vision benchmarks and is computationally efficient. Meta plans to open-source I-JEPA’s training code and model checkpoints and expand its application to other domains. The research represents a significant step towards scaling self-supervised methods for learning a general model of the world.


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