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Glossary

Key Terms and Concepts in Noosphere AI

A. Foundational Concepts

  1. Noosphere

  2. Definition: A term coined by geochemist Vladimir Vernadsky, describing the "sphere of human thought" — a collective layer of intelligence formed by human cognition, communication, and technological systems.

  3. Noosphere AI’s Usage: The protocol aims to digitize this concept by creating a decentralized, privacy-preserving network where human knowledge is dynamically mapped and interconnected.

  4. Dynamic Knowledge Graph (DKG)

  5. Definition: A real-time, evolving web of structured data where concepts (nodes) and relationships (edges) are continuously updated by AI and human contributors.

  6. Key Features:

  7. Supports semantic tagging (e.g., "quantum physics → related to → entanglement").

  8. Privacy-aware: Nodes can be public, private, or shared under granular permissions.

  9. Zero-Knowledge Proofs (ZKPs)

  10. Definition: Cryptographic methods that allow one party to prove the validity of a statement without revealing the underlying data (e.g., proving you contributed to a research topic without disclosing the content).

  11. Noosphere Use Case:

  12. Anonymous peer-review of knowledge nodes.

  13. Private voting in governance (e.g., staking $NOS without revealing identity).


B. Privacy & Security

  1. Federated Learning

  2. Definition: A machine learning approach where AI models are trained across decentralized devices (e.g., user phones/laptops) without raw data ever leaving the device.

  3. Advantage: Noosphere AI trains its models on user data locally, then aggregates only anonymized model updates (never personal data).

  4. Client-Side Encryption

  5. Definition: Data is encrypted on the user’s device before being stored on decentralized networks (IPFS/Filecoin). Only the user holds the decryption keys.

  6. Example: Your private research notes are encrypted into unreadable ciphertext — not even Noosphere nodes can access them.

  7. NFT-Gated Access

  8. Definition: A permissioning system where users must hold a specific NFT (non-fungible token) to view or edit parts of the knowledge graph.

  9. Use Case: A research consortium restricts access to a sub-graph to NFT holders (e.g., paid members).


C. Governance & Economics

  1. ZK-Arbitration

  2. Definition: A dispute-resolution mechanism where AI validators assess the accuracy of contested knowledge nodes using zero-knowledge proofs (to protect sensitive context).

  3. Process:

  4. User A flags a node as "misleading."

  5. Validators analyze encrypted metadata (e.g., citation trails) via ZKPs.

  6. Consensus is reached without exposing private data.

  7. $NOS Token

  8. Role: The native cryptocurrency of Noosphere AI, used for:

§ Staking: Earn rewards for validating knowledge nodes.

§ Governance: Vote on protocol upgrades (e.g., adjusting privacy settings).

§ Payments: Tip contributors or purchase premium features.

Knowledge Cred (KC)

Definition: A reputation score awarded to users whose contributions are consistently validated by peers/AI.

Utility: Higher KC increases voting weight and unlocks governance privileges.


D. Technical Infrastructure

  1. IPFS (InterPlanetary File System)

  2. Definition: A peer-to-peer hypermedia protocol for storing/sharing data in a decentralized manner (alternative to HTTP).

  3. Noosphere Usage: Hosts encrypted knowledge nodes to ensure censorship-resistant storage.

  4. Semantic Parsing

  5. Definition: AI-driven extraction of meaning from unstructured text (e.g., converting a research paper into a graph of connected concepts).

  6. Example: The sentence "Einstein discovered relativity" → nodes: [Einstein] → [discovered] → [relativity].


E. Comparative Terms

  1. Noosphere vs. Traditional Mind-Mapping Tools

Feature

Traditional Tools (e.g., Obsidian, Notion)

Noosphere AI

Data Ownership

Vendor-controlled servers

User-owned, encrypted

Interoperability

Walled gardens

Open protocol

Monetization

None

$NOS rewards for contributions

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