  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.
