Technical Architecture
Noosphere AI’s infrastructure is built on three core layers:
1. Dynamic Knowledge Graph (DKG) – The semantic backbone.
2. Privacy-Preserving AI – Federated learning + ZKPs.
3. Decentralized Governance – On-chain coordination.
4.1 Dynamic Knowledge Graph (DKG)
Objective: Structure fragmented knowledge into an adaptive, queryable graph while preserving privacy.
Components
1. Knowledge Nodes
o Types:
§ Public Nodes: Openly contributed (e.g., scientific facts).
§ Private Nodes: E2E-encrypted (user/enterprise-owned).
§ Hybrid Nodes: NFT-gated or shared via multi-sig.
o Format:
json
{"id":"QmXyZ...",// IPFS CID"type":"concept|data|relationship","privacy":"public|private|restricted","relationships":["supports","contradicts","cites"],"zkProof":"0x..."// Optional validity proof}2. AI Parsing Engine
o Workflow:
1. User uploads unstructured data (text, PDFs, etc.).
2. On-device NLP (e.g., Llama 3) extracts entities/relationships.
3. Output is structured into nodes/edges + tagged with metadata.
o Privacy: Raw data never leaves the device; only encrypted nodes sync to IPFS.
2. Storage Layer
o IPFS: Immutable node storage (content-addressed).
o Filecoin: Long-term persistence via incentivized nodes.
o Local Vaults: User-controlled encrypted backups (AES-256).
Diagram:
User Device → [Local AI Parsing] → Encrypted Nodes → IPFS/DKG ↑ [Zero-Knowledge Proofs] → Blockchain (Validation)4.2 Privacy-Preserving AI
Objective: Train AI models without centralized data collection.
Components
1. Federated Learning
o Local models fine-tune on user data → Only weight updates (not raw data) are aggregated.
o Example: A researcher’s private notes improve the global model’s medical knowledge without exposing patient details.
2. Zero-Knowledge Proofs (ZKPs)
o zk-SNARKs verify:
§ A node’s semantic validity (e.g., "This contribution is logically consistent").
§ A user’s right to edit a restricted node (without revealing identity).
3. Query Execution
o Public Queries: Pull from the DKG (e.g., "Show all nodes about quantum computing").
o Private Queries: Processed locally (e.g., "Cross-reference my private notes with public cancer research").
Example Flow:
1. User asks, "What’s the link between Alzheimer’s and sleep?"
2. Device checks local vault + requests public DKG nodes.
3. Response blends local private data and public graph nodes → Displayed only to the user.
4.3 Decentralized Governance
Objective: Transparent, anonymous coordination for graph evolution.
Components
1. $NOS Token Mechanics
o Staking: Lock $NOS to vote on proposals (e.g., "Add a new relationship type ‘replicates’").
o Slashing: Penalize malicious edits (caught via ZK-arbitration).
2. Proposal Types
o Content-Level: Edit node validity (e.g., flag misinformation).
o Protocol-Level: Upgrade AI models or privacy rules.
3. ZK-Arbitration
o Disputes resolved via:
1. Private submission of evidence (ZK-proofs).
2. AI validator checks logic without seeing raw data.
3. On-chain verdict executed via smart contract.
Governance Diagram:
User A → [Private Proposal] → ZK-Proof → [AI Validator] → Approved? → Update DKG ↓ [Consensus via $NOS Stakers]
Key Innovations
1. Hybrid Privacy: Choose between anonymity (ZKPs) or reputation (staking).
2. Local-First AI: No central training data → Compliance with GDPR/CCPA.
3. Semantic Interoperability: Nodes can link to external graphs (e.g., Wikidata).
Last updated