The capabilities of machine intelligence are bounded by the potential of data from the past to forecast the future. Deep learning tools are used to find structures in the available data to make predictions about the future. Such structures have to be present in the available data in the first place and they have to be applicable in the future. Forecast ergodicity is a measure of the ability to forecast future events from data in the past. We model this bound by the algorithmic complexity of the available data.

翻译：机器智能的能力受限于过去数据预测未来的潜力。深度学习工具用于在现有数据中寻找结构以对未来进行预测。这些结构必须首先存在于可用数据中，且需在未来仍然适用。预测遍历性是一种衡量基于过去数据预测未来事件能力的指标。我们通过可用数据的算法复杂度来建模这一边界。