A hallmark of human intelligence is the ability to ask rich, creative, and
revealing questions. Here we introduce a cognitive model capable of
constructing human-like questions. Our approach treats questions as formal
programs that, when executed on the state of the world, output an answer. The
model specifies a probability distribution over a complex, compositional space
of programs, favoring concise programs that help the agent learn in the current
context. We evaluate our approach by modeling the types of open-ended questions
generated by humans who were attempting to learn about an ambiguous situation
in a game. We find that our model predicts what questions people will ask, and
can creatively produce novel questions that were not present in the training
set. In addition, we compare a number of model variants, finding that both
question informativeness and complexity are important for producing human-like
questions.

本文引入了一个认知模型来构建类似于人类一样的问题，并利用概率分布来选择简洁的程序来帮助该模型在当前情境下学习。通过模拟人类在学习模糊情况下所提出的无尽问题，我们发现该模型可以准确地预测人类所提出的问题，并能够创造性地产生训练集中不存在的新问题。此外，根据不同的模型变量，我们发现问题的信息量和复杂性对于生成类似于人类的问题至关重要。

问题提问作为程序生成

Question Asking as Program Generation

IQ tests are an accepted method for assessing human intelligence. The tests
consist of several parts that must be solved under a time constraint. Of all
the tested abilities, pattern recognition has been found to have the highest
correlation with general intelligence. This is primarily because pattern
recognition is the ability to find order in a noisy environment, a necessary
skill for intelligent agents. In this paper, we propose a convolutional neural
network (CNN) model for solving geometric pattern recognition problems. The CNN
receives as input multiple ordered input images and outputs the next image
according to the pattern. Our CNN is able to solve problems involving rotation,
reflection, color, size and shape patterns and score within the top 5% of human
performance.

本文提出了一种卷积神经网络（CNN）模型，可用于解决几何图案识别问题，包括旋转、反射、颜色、大小和形状的图案。该 CNN 的性能得分处于人类表现前 5%。

神经网络的智商

IQ of Neural Networks

A distinguishing property of human intelligence is the ability to flexibly
use language in order to communicate complex ideas with other humans in a
variety of contexts. Research in natural language dialogue should focus on
designing communicative agents which can integrate themselves into these
contexts and productively collaborate with humans. In this abstract, we propose
a general situated language learning paradigm which is designed to bring about
robust language agents able to cooperate productively with humans.

本文提出一种通用的情境语言学习范式，旨在设计可靠的语言代理以能够与人类进行成功的合作。

面向环境和目标驱动的语言学习模式

A Paradigm for Situated and Goal-Driven Language Learning

A fundamental problem in artificial intelligence is that nobody really knows
what intelligence is. The problem is especially acute when we need to consider
artificial systems which are significantly different to humans. In this paper
we approach this problem in the following way: We take a number of well known
informal definitions of human intelligence that have been given by experts, and
extract their essential features. These are then mathematically formalised to
produce a general measure of intelligence for arbitrary machines. We believe
that this measure formally captures the concept of machine intelligence in the
broadest reasonable sense.

本文旨在将著名的人类智能概念的精髓进行数学形式化，以产生适用于任意机器的智能的普遍度量，这个度量从最广泛的合理意义上正式捕捉了机器智能的概念。