JEPA (Joint Embedding Predictive Architecture)
Yann LeCun's Joint Embedding Predictive Architecture (JEPA) enhances AI by emphasizing world models, self-supervised learning, and abstract representations. JEPA predicts future states by transforming inputs into abstract representations, handling uncertainty, and enabling complex predictions through multistep or hierarchical structures. Several models like I-JEPA, MC-JEPA, and V-JEPA have been developed to process visual data and improve AI's understanding of images and videos, moving towards human-like interaction with the world.
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Joint Embedding Predictive Architecture (JEPA) is a concept proposed by Yann LeCun to advance AI towards human-level intelligence. It aims to address the limitations of current AI models like Transformers by focusing on world models, self-supervised learning, and abstract representations. JEPA works by taking related inputs, transforming them into abstract representations, and predicting future states based on these representations. It handles uncertainty by dropping irrelevant information during encoding and using latent variables to simulate different scenarios. JEPA can be combined into multistep or hierarchical structures for more complex predictions. Several models have been developed based on JEPA, including I-JEPA for images, MC-JEPA for multitasking, and V-JEPA for videos. These models use self-supervised learning to process visual data and improve AI's understanding of images and videos. JEPA represents a step towards creating AI systems that can interact with the world more like humans do, focusing on essential information while ignoring irrelevant details.
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