Meta has unleashed a new AI that's more 'human-like.'
that I-JEPA “uses background knowledge about the world to fill in missing pieces of images, rather than looking at nearby pixels like other generative AI models.”how I-JEPA builds upon Meta’s Chief AI Scientist Yann LeCun’s vision for “more human-like AI.”
Meta also highlights I-JEPA’s training efficiency, promising that a 632-million parameter visual transformer model can be trained using 16 A100 GPUs in under 72 hours, which the company claims is two to 10 times faster than other methods while delivering fewer errors. “Illustrating how the predictor learns to model the semantics of the world. For each image, the portion outside of the blue box is encoded and given to the predictor as context. The predictor outputs a representation for what it expects to be in the region within the blue box. To visualize the prediction, we train a generative model that produces a sketch of the contents represented by the predictor output, and we show a sample output within the blue box.
. Meta says that these issues arise because generative methods try to fill in “every bit of missing information,” leading to pixel errors humans wouldn’t make. Meta says generative methods focus on “irrelevant details” instead of “capturing high-level predictable concepts.”
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