“Q. How do you propose to do this?
A. By saving the knowledge of the race. The sum of human knowing is beyond any one man; any thousand men. With the destruction of our social fabric, science will be broken into a million pieces. Individuals will know much of the exceedingly tiny facets of which there is to know. They will be helpless and useless by themselves. The bits of lore, meaningless, will not be passed on. They will be lost through the generations. But, if we now prepare a giant summary of all knowledge, it will never be lost. Coming generations will build on it, and will not have to rediscover it for themselves. One millennium will do the work of thirty thousand.”
Hari Seldon, Foundation series by Isaac Asimov (1951)
OriginTrail is an ecosystem dedicated to the vision of a Verifiable Internet for AI through organizing all human knowledge in a neuro-symbolic Decentralized Knowledge Graph.
It aims to address the challenge of misinformation in the age of AI by tackling the problems of AI hallucinations, data ownership and IP rights, centralization of power, bias and model collapse. It does so by building on core values of information provenance, verifiability and positive incentive alignment through organizing human knowledge in a neuro-symbolic Decentralized Knowledge Graph (DKG) and the Decentralized Retrieval Augmented Generation (DRAG) framework.
To learn more about the vision of the Verifiable Internet for AI and DRAG check out the ecosystem white paper.
There are two active versions of OriginTrail DKG, DKG V6 and V8.
| DKG V8, the upcoming version in development running on the V8 incentivised testnet, designed to increase DKG scalability and usability across devices. Learn more on how you can contribute and test V8 in the DKG V8 section. | DKG V8.jpg | ||
| DKG V6, deployed in production on OriginTrail DKG mainnet on Neuroweb, Base and Gnosis blockchains. Learn more on how to build and utilize in the dedicated DKG V6 section. | DKG V6.jpg |
If you're looking to learn more about the inner workings of the DKG, start here.
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