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:在本文中,我们的目标是帮助弥合人类流体智能(无需事先培训即可完成新任务的能力)与深神经网络(通常需要大量的事先培训)的运行之间的差距。解决人类中用于测量流体智能的智能测试的基本认知部分,是确定规律性序列的能力。这促使我们构建了基准任务,我们称之为\ textitit{序列一致性评价}(SCE),其解决方案要求能够确定序列的规律性。鉴于深网络已经证实的能力,预计它们有能力在广泛培训后完成此类任务。但令人惊讶的是,我们展示了在深网络中受过培训的天真(随机初始化)深层次学习模式。我们用一个textit{single}SCE(优化步骤)来讨论我们的工作对于构建流体机的影响。我们将我们的调查结果扩大到在未经任何事先培训的情况下解决视觉和听力模式中真实世界异常的检测任务。这些结果显示了流体智能的计算能力。我们讨论了我们的工作对于构建流体机的影响。