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:机器智能的能力取决于从过去的数据中预测未来的潜力。深度学习工具用于寻找可用数据中的结构,以对未来做出预测。这些结构必须先存在于可用的数据中,并且必须在未来得到应用。预测遍历性是从过去的数据中预测未来事件的能力的一种度量。我们通过可用数据的算法复杂性来模拟这种限制。