A purely inter-model version of a machine intelligence benchmark would allow us to measure intelligence directly as information without projecting that information onto labeled datasets. We propose a framework in which other learners measure the informational significance of their peers across a network and use a digital ledger to negotiate the scores. However, the main benefits of measuring intelligence with other learners are lost if the underlying scores are dishonest. As a solution, we show how competition for connectivity in the network can be used to force honest bidding. We first prove that selecting inter-model scores using gradient descent is a regret-free strategy: one which generates the best subjective outcome regardless of the behavior of others. We then empirically show that when nodes apply this strategy, the network converges to a ranking that correlates with the one found in a fully coordinated and centralized setting. The result is a fair mechanism for training an internet-wide, decentralized and incentivized machine learning system, one which produces a continually hardening and expanding benchmark at the generalized intersection of the participants.

中文翻译：

---

BitTensor：模型间智能度量

机器智能基准的纯模型间版本将允许我们直接将智能作为信息来衡量，而无需将该信息投影到标记数据集上。我们提出了一个框架，在该框架中，其他学习者通过网络衡量他们的同龄人的信息重要性，并使用数字分类帐来协商分数。然而，如果基础分数不诚实，则与其他学习者一起衡量智力的主要好处就会消失。作为解决方案，我们展示了如何利用网络中的连接竞争来强制进行诚实投标。我们首先证明使用梯度下降选择模型间分数是一种无遗憾的策略：无论其他人的行为如何，都会产生最佳的主观结果。然后我们凭经验表明，当节点应用此策略时，网络收敛到一个排名，该排名与在完全协调和集中的环境中发现的排名相关。结果是一个公平的机制来训练一个互联网范围的、分散的和激励的机器学习系统，一个在参与者的广义交集上产生一个不断强化和扩展的基准。