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Build trusted neuro-symbolic AI with Knowledge Assets and the Decentralized Knowledge Graph

“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 building a verifiable knowledge layer for AI, where knowledge is traceable, memory is decentralized, and humans remain in control. It aims to achieve this by organizing all human knowledge in a Decentralized Knowledge Graph (DKG) through a collective neuro-symbolic AI approach.

A collective neuro-symbolic AI combines structured and connected information from symbolic AI (DKG) with the creativity of neural AI technologies (LLMs), building a robust decentralized AI infrastructure.

This provides a powerful substrate for trusted, human-centric AI solutions to tackle some of humanity's most pressing challenges. It also drives AI agents’ autonomous memories and trusted intents, as both AI agents and robots become potent enough to act on behalf of humans.

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