The Multi-Modal Shift: Augmenting AI with Rich Data

The shift toward multi-modal AI training—integrating text, images, audio, and video—significantly enhances model capabilities, enabling richer context understanding and new emergent abilities. The creation of massive datasets such as InternVid, OpenHumanVid, and VALID has substantially improved AI realism, accuracy, and functionality. However, assembling these expansive datasets remains challenging due to issues of scale, cost, and ethical considerations. Grass Protocol addresses these challenges by using decentralized infrastructure that efficiently crowdsources web-based multi-modal data at unprecedented scale—recent network upgrades notably boosted its capacity, solidifying Grass's central role in powering future AI model training.

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