The symbolism, connectionism and behaviorism approaches of artificial
intelligence have achieved a lot of successes in various tasks, while we still
do not have a clear definition of "intelligence" with enough consensus in the
community (although there are over 70 different "versions" of definitions). The
nature of intelligence is still in darkness. In this work we do not take any of
these three traditional approaches, instead we try to identify certain
fundamental aspects of the nature of intelligence, and construct a mathematical
model to represent and potentially reproduce these fundamental aspects. We
first stress the importance of defining the scope of discussion and granularity
of investigation. We carefully compare human and artificial intelligence, and
qualitatively demonstrate an information abstraction process, which we propose
to be the key to connect perception and cognition. We then present the broader
idea of "concept", separate the idea of self model out of the world model, and
construct a new model called world-self model (WSM). We show the mechanisms of
creating and connecting concepts, and the flow of how the WSM receives,
processes and outputs information with respect to an arbitrary type of problem
to solve. We also consider and discuss the potential computer implementation
issues of the proposed theoretical framework, and finally we propose a unified
general framework of intelligence based on WSM.

文章拟探讨智能的本质，并构建一个称之为‘世界 - 自我的模型’的数学模型，以此表征人工智能和人类智能的基本方面，特别地，提出了信息抽象的过程和概念的生成和关联机制等方面的内容，以及相应于任意类型的问题解决的信息输入和输出的流程，同时，还对所提倡的理论框架的计算机实现问题进行讨论。最终，作者提出了一个基于 WSM 的智能统一通用框架。

一个世界自我模型：理解智能

A World-Self Model Towards Understanding Intelligence

This paper reports a novel deep architecture referred to as Maxout network In
Network (MIN), which can enhance model discriminability and facilitate the
process of information abstraction within the receptive field. The proposed
network adopts the framework of the recently developed Network In Network
structure, which slides a universal approximator, multilayer perceptron (MLP)
with rectifier units, to exact features. Instead of MLP, we employ maxout MLP
to learn a variety of piecewise linear activation functions and to mediate the
problem of vanishing gradients that can occur when using rectifier units.
Moreover, batch normalization is applied to reduce the saturation of maxout
units by pre-conditioning the model and dropout is applied to prevent
overfitting. Finally, average pooling is used in all pooling layers to
regularize maxout MLP in order to facilitate information abstraction in every
receptive field while tolerating the change of object position. Because average
pooling preserves all features in the local patch, the proposed MIN model can
enforce the suppression of irrelevant information during training. Our
experiments demonstrated the state-of-the-art classification performance when
the MIN model was applied to MNIST, CIFAR-10, and CIFAR-100 datasets and
comparable performance for SVHN dataset.

本文介绍了 Maxout network In Network (MIN) 这一新型深度学习结构的架构和实现方式，其应用了批量归一化和 dropout 等一系列优化方式，成功解决了在使用 rectifier units 时可能出现的梯度消失问题，同时通过 average pooling 方式进一步提高了信息抽象能力以及遏制了无关信息的干扰，最后在实验数据中取得了卓越的分类表现。