Deep learning uses an increasing amount of computation and data to solve very specific problems. By stark contrast, human minds solve a wide range of problems using a fixed amount of computation and limited experience. One ability that seems crucial to this kind of general intelligence is meta-reasoning, i.e., our ability to reason about reasoning. To make deep learning do more from less, we propose the differentiable logical meta interpreter (DLMI). The key idea is to realize a meta-interpreter using differentiable forward-chaining reasoning in first-order logic. This directly allows DLMI to reason and even learn about its own operations. This is different from performing object-level deep reasoning and learning, which refers in some way to entities external to the system. In contrast, DLMI is able to reflect or introspect, i.e., to shift from meta-reasoning to object-level reasoning and vice versa. Among many other experimental evaluations, we illustrate this behavior using the novel task of "repairing Kandinsky patterns," i.e., how to edit the objects in an image so that it agrees with a given logical concept.

翻译：深层次的学习用越来越多的计算和数据来解决非常具体的问题。 鲜明对比之下, 人类的头脑用固定的计算量和有限的经验解决了各种各样的问题。 一种对一般情报来说至关重要的能力是元理性, 即我们理性的能力。 要让深层次的学习从更少的地方做更多的事, 我们建议不同的逻辑元解释( DLMI ) 。 关键的想法是利用一阶逻辑中不同的前链推理实现元解释。 这直接允许 DLMI 理性甚至了解自己的操作。 这与执行目标层次的深层次推理和学习不同, 以某种方式指系统外部的实体。 相比之下, DLMI 能够反省或反省, 也就是说, 从元理性推理到目标层次的推理和反向转变。 在许多其他实验评估中, 我们用“ 更新 Kandinsky 模式” 的新任务来说明这种行为。 也就是说, 如何在图像中编辑对象, 以便它同意给定的逻辑概念 。