论文标题
天真的人工智能
Naive Artificial Intelligence
论文作者
论文摘要
在认知科学中，通常是区分晶体智能，利用过去的学习或经验获得的知识的能力以及流体智能，即在不依赖先验知识的情况下解决新颖问题的能力。使用这种认知能力区别在两种类型的智能，经过广泛训练的深网络之间，可以下棋或展示晶体，但不能展示晶体，而不是流体智能。在人类中，通常使用智能测试对流体智能进行研究和量化。先前的研究表明，深层网络可以解决某些形式的智能测试，但只有经过广泛的培训。在这里，我们提出了一个计算模型，该模型在没有任何事先培训的情况下解决了智能测试。该能力基于持续的归纳推理，并由深度无监督的潜在预测网络实施。我们的工作证明了深网的潜在流体智能。最后，我们建议我们方法基础的计算原理可用于对认知科学中的流体智能进行建模。
In the cognitive sciences, it is common to distinguish between crystal intelligence, the ability to utilize knowledge acquired through past learning or experience and fluid intelligence, the ability to solve novel problems without relying on prior knowledge. Using this cognitive distinction between the two types of intelligence, extensively-trained deep networks that can play chess or Go exhibit crystal but not fluid intelligence. In humans, fluid intelligence is typically studied and quantified using intelligence tests. Previous studies have shown that deep networks can solve some forms of intelligence tests, but only after extensive training. Here we present a computational model that solves intelligence tests without any prior training. This ability is based on continual inductive reasoning, and is implemented by deep unsupervised latent-prediction networks. Our work demonstrates the potential fluid intelligence of deep networks. Finally, we propose that the computational principles underlying our approach can be used to model fluid intelligence in the cognitive sciences.
