AI-powered systems for wildlife monitoring and conservation intelligence.
AI ZIPs define protocols for machine learning models that monitor wildlife populations, detect poaching activities, analyze habitat health, and power intelligent conservation agents. Our zLLM (training-free) approach enables efficient, decentralized AI deployment.
Jin -- a unified architecture processing vision, language, audio, and 3D within a single transformer, enabling cross-modal reasoning and generation
Protocol for continuous model improvement through active learning, semantic feedback loops, and human-in-the-loop optimization
Hamiltonian mechanics applied to LLM training and inference, using energy-conserving dynamics for stable optimization and interpretable reasoning
Consensus mechanism where validators prove useful AI work (training, inference, verification) to earn block rewards
Systematic pipeline for distilling large Zen models into smaller, deployment-efficient variants while preserving domain expertise
Native multilingual support across 100+ languages for Zen models, with emphasis on languages spoken in biodiversity hotspot regions
Conservation-aware fungible token standard for the Zoo ecosystem with impact tracking and auto-donation extensions
Conservation NFT standard with species metadata, provenance chain, and royalties directed to conservation funds
Multi-token standard for batch operations supporting conservation badges, in-game items, and multi-asset portfolios
ERC-6551 token-bound accounts enabling wildlife NFTs to own assets, receive donations, and accumulate conservation history