ai人工智能面相测试_有趣的IQ测试以衡量AI的智能

One of the long-standing goals of artificial intelligence is to develop machines with abstract reasoning capabilities equal to or better than humans. Though there has also been substantial progress in both reasoning and learning in neural networks, the extent to which these models exhibit anything like general abstract reasoning is the subject of much debate.

人工智能的长期目标之一是开发具有等于或优于人类的抽象推理能力的机器。 尽管在神经网络的推理和学习方面也取得了长足的进步，但这些模型在多大程度上表现出类似于一般抽象推理的能力仍是许多争论的主题。

Neural networks have perfected the technique to identify cats in images and translating from one language to another. Is that intelligence or they are just great at memorizing? How can we measure the intelligence of neural networks?

神经网络完善了识别图像中猫并从一种语言翻译成另一种语言的技术。 那是智慧还是他们擅长记忆？ 我们如何测量神经网络的智能？

Some researchers have been developing ways to evaluate neural networks’ intelligence. It’s not using mean squared error or entropy loss. But they are giving neural networks an IQ test, high school mathematics questions, and comprehension problems.

一些研究人员一直在开发评估神经网络智能的方法。 它没有使用均方误差或熵损失。 但是他们给神经网络一个智商测试，高中数学问题和理解问题。

模式匹配 (Pattern Matching)

A human’s capacity for abstract reasoning can be estimated using a visual IQ test developed by psychologist John Raven in 1936: the Raven’s Progressive Matrices (RPMs). The premise behind RPMs is simple: one must reason about the relationships between perceptually obvious visual features, such as shape positions or line colors, and choose an image that completes the matrix.

可以使用心理学家约翰·拉文(John Raven)在1936年开发的视觉智商测验( Raven's Progressive Matrices ，RPM)来估计人的抽象推理能力。 RPM的前提很简单：必须推理出视觉上明显的视觉特征(例如形状位置或线条颜色)之间的关系，并选择可以完成矩阵的图像。

Since one of the goals of AI is to develop machines with similar abstract reasoning capabilities to humans, researchers at Deepmind proposed an IQ test for AI, designed to probe their abstract visual reasoning ability. In order to succeed in this challenge, models must be able to generalize well for every question.

由于AI的目标之一是开发具有与人类相似的抽象推理能力的机器，因此Deepmind的研究人员提出了一种AI智商测试，旨在探究他们的抽象视觉推理能力。 为了成功应对这一挑战，模型必须能够很好地概括每个问题。

In this study, they compared the performance of several standard deep neural networks and proposed two models that include modules that specially designed for abstract reasoning:

在这项研究中 ，他们比较了几种标准深度神经网络的性能，并提出了两个模型，其中包括专门为抽象推理设计的模块：

• standard