An algorithm that is trained to predict views of a spatial environment also infers its 3D structure The ability to understand spatial environments based on visual perception arguably is a key function of the cognitive system of many animals, including mammalians and others. A common presumption about artificial intelligence is that its goal is to build machines with a similar capacity of “understanding.” The research community in artificial intelligence, however, has settled on a more pragmatic approach. Instead of attempting to model or quantify understanding directly, the objective is to construct machines that merely solve tasks that seem to require understanding. Understanding can only be measured indirectly, for example, by analyzing the ability of a system to generalize the solving of new tasks, which is sometimes called transfer learning (1). Transfer learning is particularly appealing in an unsupervised setting, which means that the objective of the original task is defined in terms of the input data itself, without requiring additional, task-specific information (see the figure). On page 1204 of this issue, Eslami et al. (2) present an important step toward building machines that learn to understand spatial environments using unsupervised transfer learning. Remarkably, they develop a system that relies only on inputs from its own image sensors, and that learns autonomously and without human supervision.

中文翻译：

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从图像中理解空间环境

经过训练以预测空间环境视图的算法也可以推断其 3D 结构基于视觉感知理解空间环境的能力可以说是许多动物（包括哺乳动物和其他动物）认知系统的关键功能。关于人工智能的一个普遍假设是，它的目标是制造具有类似“理解”能力的机器。然而，人工智能研究界已经确定了一种更务实的方法。目标不是尝试直接建模或量化理解，而是构建仅解决似乎需要理解的任务的机器。理解只能间接测量，例如，通过分析系统概括解决新任务的能力，这有时被称为迁移学习 (1)。迁移学习在无监督环境中特别有吸引力，这意味着原始任务的目标是根据输入数据本身定义的，不需要额外的、特定于任务的信息（见图）。在本期第 1204 页，Eslami 等人。(2) 向构建使用无监督迁移学习学习理解空间环境的机器迈出了重要的一步。值得注意的是，他们开发了一个系统，该系统仅依赖于来自其自身图像传感器的输入，并且可以在没有人工监督的情况下自主学习。特定于任务的信息（见图）。在本期第 1204 页，Eslami 等人。(2) 向构建使用无监督迁移学习学习理解空间环境的机器迈出了重要的一步。值得注意的是，他们开发了一个系统，该系统仅依赖于来自其自身图像传感器的输入，并且可以在没有人工监督的情况下自主学习。特定于任务的信息（见图）。在本期第 1204 页，Eslami 等人。(2) 向构建使用无监督迁移学习学习理解空间环境的机器迈出了重要的一步。值得注意的是，他们开发了一个系统，该系统仅依赖于来自其自身图像传感器的输入，并且可以在没有人工监督的情况下自主学习。