Sharing Knowledge with Truly Open and Decentralized AI by Zacchaeus Scheffer

Presented by Zacchaeus Scheffer

Machine learning can be thought of as a class of algorithms which distill data into knowledge. This has created huge incentives for companies to hoard massive amounts of (often private) user data in order to obtain the knowledge stored therein. The decentralized web paradigm resists this trend and rightly tries to protect user data. In this talk, Zacchaeus describes this, as well as design considerations when trying to deploy such a framework.

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