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BitTensor: A Peer-to-Peer Intelligence Market

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arxiv 2003.03917 v3 pith:SIE5WB3E submitted 2020-03-09 cs.AI cs.LGcs.MA

Yuma Rao , Jacob Steeves , Ala Shaabana , Daniel Attevelt , Matthew McAteer This is my paper
classification cs.AIcs.LGcs.MA
keywords intelligencemarketotherpeerscollusionnetworkpeer-to-peerresistant
verification ladder T0 review T1 audit T2 compute T3 formal
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As with other commodities, markets could help us efficiently produce machine intelligence. We propose a market where intelligence is priced by other intelligence systems peer-to-peer across the internet. Peers rank each other by training neural networks which learn the value of their neighbors. Scores accumulate on a digital ledger where high ranking peers are monetarily rewarded with additional weight in the network. However, this form of peer-ranking is not resistant to collusion, which could disrupt the accuracy of the mechanism. The solution is a connectivity-based regularization which exponentially rewards trusted peers, making the system resistant to collusion of up to 50 percent of the network weight. The result is a collectively run intelligence market which continual produces newly trained models and pays contributors who create information theoretic value.

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