In this paper, we aimed to help bridge the gap between human fluid intelligence - the ability to solve novel tasks without prior training - and the performance of deep neural networks, which typically require extensive prior training. An essential cognitive component for solving intelligence tests, which in humans are used to measure fluid intelligence, is the ability to identify regularities in sequences. This motivated us to construct a benchmark task, which we term \textit{sequence consistency evaluation} (SCE), whose solution requires the ability to identify regularities in sequences. Given the proven capabilities of deep networks, their ability to solve such tasks after extensive training is expected. Surprisingly, however, we show that naive (randomly initialized) deep learning models that are trained on a \textit{single} SCE with a \textit{single} optimization step can still solve non-trivial versions of the task relatively well. We extend our findings to solve, without any prior training, real-world anomaly detection tasks in the visual and auditory modalities. These results demonstrate the fluid-intelligent computational capabilities of deep networks. We discuss the implications of our work for constructing fluid-intelligent machines.

翻译：本文旨在弥合人类流体智能——即无需预先训练即可解决新任务的能力——与通常需要大量预先训练的深度神经网络性能之间的鸿沟。解决智力测试（在人类中用于测量流体智能）的关键认知组成部分是识别序列规律的能力。这促使我们构建了一个基准任务，称为“序列一致性评估”（SCE），其解决方案要求具备识别序列规律的能力。鉴于深度网络已被证明的能力，经过大量训练后它们能够解决此类任务是在意料之中的。然而，令人惊讶的是，我们展示了仅经过单次优化步骤在单个SCE上训练的朴素（随机初始化）深度学习模型，仍能相对较好地解决该任务的非平凡版本。我们将研究扩展至无需任何预先训练即可解决视觉和听觉模态下的真实世界异常检测任务。这些结果证明了深度网络具有流体智能的计算能力。我们讨论了本研究对构建流体智能机器的启示。