🤖3.0: Amplifying NFT discovery through AI self-learning

The more NFTs are analyzed and paired with success or failure data from accuracy of search, forms a dataset the AI can train on. The Pulsr Foundation can then further improve its ability to sophisticatedly and accurately label existing and future NFTs based on its growing, and openly available, dataset of enriched NFT metadata. The computer vision AI can then train on this flywheel of growing labelling data, furthering its ability to recognize and label on a compounding level, thus further improving visibility across the ecosystem via network effects.

The Pulsr Foundation has spent two years building a comprehensive NFT metadata database for AI training with over 60m+ labels for NFTs across video, image and audio. This paired with search to labelling accuracy testing on Pulsr's discovery ecosystem has made fertile ground to start machine learning algorithms to begin training. Pulsr's ability to scan and label NFTs can now learn from The Foundation's growing database and begin generalizing labelling accuracy to unseen data, and perform accurate labelling without human instructions.

This next phase is crucial as NFTs become more sophisticated in their digital expression, and requirements for accurate labelling on subjective rather than objective searches are required.