What is Origin Trail?
Origin Trail is an ecosystem that bridges physical assets into the Web3 realm. At its core is the OriginTrail Decentralized Knowledge Graph (DKG), a permissionless network serving as a trusted index for Web3 assets. The DKG enables creation and management of interconnected asset graphs, facilitating data sharing and asset discovery across entities. Complementing the DKG is the OriginTrail Parachain, a tailor-made layer 1 blockchain designed for seamless integration with the knowledge graph. It provides scalability, incentives, and interoperability within the Polkadot ecosystem. The ecosystem leverages two utility tokens: TRAC for DKG operations like publishing assets and staking, and OTP for transaction fees, governance, and incentivizing DKG growth on the Parachain.
OriginTrail transforms real-world assets like cars, luxury items, and credentials into Web3-native entities with verifiable identities. This unlocks new business models and use cases by enabling the discoverability, verification, and connectivity of these assets across Web3 applications.
OriginTrail transforms real-world assets like cars, luxury items, and credentials into Web3-native entities with verifiable identities. This unlocks new business models and use cases by enabling the discoverability, verification, and connectivity of these assets across Web3 applications.
What role does AI play in Origin Trail?
OriginTrail envisions a "Verifiable Internet for AI" where artificial intelligence plays a pivotal role. At the heart of this vision lies the Decentralized Knowledge Graph (DKG), a novel approach to ensure the provenance, integrity, and verifiability of information consumed by AI systems. The DKG introduces Knowledge Assets as the primary resource for AI consumption, acting as ownable, discoverable, and verifiable containers of knowledge.
The DKG enables the Decentralized Retrieval Augmented Generation (dRAG) framework, a powerful amalgamation of neural and symbolic AI methodologies. This hybrid approach allows AI solutions to leverage the strengths of both large language models (neural AI) and knowledge graphs (symbolic AI), thereby enhancing the reliability and accuracy of AI systems. A key aspect of the dRAG framework is the creation of autonomous "paranets" (para-networks). These paranets are self-governed communities that curate collections of Knowledge Assets, AI services, and incentivization models tailored to specific topics or domains. Paranets serve as a robust substrate, empowering AI systems to gather verifiable information from both public and private knowledge sources.
The DKG enables the Decentralized Retrieval Augmented Generation (dRAG) framework, a powerful amalgamation of neural and symbolic AI methodologies. This hybrid approach allows AI solutions to leverage the strengths of both large language models (neural AI) and knowledge graphs (symbolic AI), thereby enhancing the reliability and accuracy of AI systems. A key aspect of the dRAG framework is the creation of autonomous "paranets" (para-networks). These paranets are self-governed communities that curate collections of Knowledge Assets, AI services, and incentivization models tailored to specific topics or domains. Paranets serve as a robust substrate, empowering AI systems to gather verifiable information from both public and private knowledge sources.
What are the main use cases enabled by Origin Trail?
Here are the key use cases enabled by OriginTrail:
- Enhancing the reliability and trustworthiness of AI systems by ensuring verifiable and traceable information sources. The DKG's Knowledge Assets provide cryptographic proofs of their content's integrity and provenance, allowing AI systems to validate the authenticity and traceability of the information they consume. This addresses issues like hallucinations and misinformation, increasing trust in AI-generated outputs.
- Enabling AI systems to access and leverage a global, decentralized knowledge base without censorship or bias. The DKG is an open, permissionless network where knowledge can be freely published and discovered, promoting a censorship-resistant and inclusive knowledge ecosystem. AI systems can tap into this vast, decentralized knowledge base, mitigating biases that may arise from centralized data sources.
- Incentivizing the creation and sharing of high-quality knowledge through the paranet incentivization models. Paranet operators can define incentive mechanisms to reward knowledge miners and service providers who contribute to the growth and maintenance of their respective paranets. These incentives, powered by the NEURO token, foster a vibrant economy around knowledge creation and sharing, encouraging the continuous expansion of high-quality, verifiable knowledge resources.
- Facilitating the development of autonomous AI systems that can continuously expand and enrich the DKG through deductive and inductive reasoning on the available knowledge. As the DKG accumulates annotated, ontology-fit data within paranets, AI systems can leverage deductive reasoning to derive new knowledge deterministically based on existing information and ontological rules. Additionally, inductive reasoning techniques, such as graph neural networks, can identify patterns and regularities, enabling AI systems to make predictions and autonomously generate new knowledge, further enriching the DKG.
- Supporting various domains and industries by creating paranets specific to their needs, such as Industry 4.0, sustainability, public company reports, metaverse, and more. The modular nature of paranets allows for the creation of specialized knowledge ecosystems tailored to the unique requirements of different industries and domains. Paranets can be established around topics like Industry 4.0, decentralized science, sustainability, public company reports, metaverse, video content networks, social media, art discovery networks, prediction markets, sports and betting, entertainment, and many more, providing AI systems with access to domain-specific knowledge resources
