BitTensor: A Peer-to-Peer Intelligence Market

TL;DR

BitTensor introduces a peer-to-peer market for machine intelligence where peers train neural networks to evaluate each other, with a regularization mechanism to prevent collusion and ensure reliable valuation of contributions.

Contribution

This paper presents a novel decentralized intelligence market system with a connectivity-based regularization to resist collusion, enhancing robustness and incentivizing truthful peer evaluation.

Findings

The system can resist collusion of up to 50% of the network weight.

Peers are rewarded based on their learned value and trustworthiness.

The market continually produces trained models and incentivizes information creation.

Abstract

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.