BitTensor: A Peer-to-Peer Intelligence Market
This addresses the challenge of efficiently producing and pricing machine intelligence in a decentralized manner, though it appears incremental as it builds on existing market and ranking concepts with a specific collusion-resistance improvement.
The paper tackles the problem of creating a peer-to-peer market for machine intelligence by proposing a system where peers rank each other's intelligence, with scores recorded on a digital ledger and high-ranking peers rewarded. The result is a collusion-resistant mechanism that can withstand up to 50% network weight collusion through connectivity-based regularization.
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.