论文标题
Bittensor：点对点情报市场
BitTensor: A Peer-to-Peer Intelligence Market
论文作者
论文摘要
与其他商品一样，市场可以帮助我们有效地产生机器智能。我们提出了一个市场，即情报在整个互联网上都由其他情报系统对等点定价。同伴通过训练学习邻居价值的神经网络互相排名。在数字分类帐上积累的分数在网络中获得了额外的重量，在数字分类帐上积累了高级同行。但是，这种形式的同伴级别对勾结不抵抗，这可能会破坏机制的准确性。该解决方案是基于连接性的正则化，该正规化成倍奖励受信任的同行，使该系统抵抗了高达50％的网络重量的勾结。结果是一个集体运行的情报市场，该市场持续产生新训练的模型，并支付创造信息理论价值的贡献者。
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.
