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.
翻译:与其他商品一样,市场可以帮助我们高效地生成机器情报。我们建议建立一个由互联网上其他情报系统对同行定价的市场。 同行通过培训了解邻居价值的神经网络相互排名。 在数字分类账上积累了分数,在网络中,高级同行获得货币上的额外重量。然而,这种同级排名形式并不抵制串通,这可能会破坏机制的准确性。 解决方案是基于连通的正规化,它能给信任的同行带来巨大的回报,使该系统无法连结高达网络重量的50%。 结果是一个集体运行的智能市场,不断生成新培训的模型,并支付创建信息理论价值的捐助方。