Learning is a process which can update decision rules, based on past experience, such that future performance improves. Traditionally, machine learning is often evaluated under the assumption that the future will be identical to the past in distribution or change adversarially. But these assumptions can be either too optimistic or pessimistic for many problems in the real world. Real world scenarios evolve over multiple spatiotemporal scales with partially predictable dynamics. Here we reformulate the learning problem to one that centers around this idea of dynamic futures that are partially learnable. We conjecture that certain sequences of tasks are not retrospectively learnable (in which the data distribution is fixed), but are prospectively learnable (in which distributions may be dynamic), suggesting that prospective learning is more difficult in kind than retrospective learning. We argue that prospective learning more accurately characterizes many real world problems that (1) currently stymie existing artificial intelligence solutions and/or (2) lack adequate explanations for how natural intelligences solve them. Thus, studying prospective learning will lead to deeper insights and solutions to currently vexing challenges in both natural and artificial intelligences.

翻译：学习是一个基于过往经验更新决策规则、使未来表现得以改善的过程。传统上，机器学习通常假设未来与过去在分布上完全相同或呈现对抗性变化，并据此进行评估。然而，对于现实世界中的许多问题而言，这些假设可能过于乐观或过于悲观。现实场景在多时空尺度上演化，且具有部分可预测的动态特性。本文重新将学习问题聚焦于这一核心思想：未来是动态的且部分可学习的。我们推测，某些任务序列在回顾性学习（数据分布固定）中是不可学习的，但在前瞻性学习（分布可能动态变化）中是可学习的，这表明前瞻性学习本质上比回顾性学习更为困难。我们论证，前瞻性学习更能准确刻画许多现实世界问题，这些问题（1）目前阻碍了现有人工智能解决方案的发展，和/或（2）缺乏对自然智能如何解决它们的充分解释。因此，研究前瞻性学习将为当前自然智能和人工智能中棘手的挑战带来更深刻的见解与解决方案。