论文标题
统一的学习理论
A unified theory of learning
论文作者
论文摘要
最近已经开发了使用神经网络（NN）的机器学习，并提出了许多新方法。这些方法针对输入数据的类型进行了优化并非常有效地工作，但不能普遍使用任何类型的输入数据。另一方面，对于任何形式的问题，人的大脑都是普遍的，如果我们可以模仿人类大脑的工作原理，我们将能够构建人工通用智能。我们考虑人类大脑如何统一学习事物，并发现学习的本质是信息的压缩。我们建议使用模仿人脑系统的玩具NN模型，我们表明NN只能通过正确设置损失功能来压缩输入信息而无需临时处理。损失函数表示为要记住的自我信息的总和以及信息的损失以及压缩，其最小值对应于原始数据的自我信息。为了评估要记住的自我信息，我们提供了记忆的概念。内存表示压缩信息，学习通过参考以前的记忆来进行。这种NN和人脑之间有许多相似之处，而这种NN是对自由能原理的实现，被认为是人类大脑的统一理论。这项工作可以应用于任何类型的数据分析和认知科学。
Recently machine learning using neural networks (NN) has been developed, and many new methods have been suggested. These methods are optimized for the type of input data and work very effectively, but they cannot be used with any kind of input data universally. On the other hand, the human brain is universal for any kind of problem, and we will be able to construct artificial general intelligence if we can mimic the system of how the human brain works. We consider how the human brain learns things uniformly, and find that the essence of learning is the compression of information. We suggest a toy NN model which mimics the system of the human brain, and we show that the NN can compress the input information without ad hoc treatment, only by setting the loss function properly. The loss function is expressed as the sum of the self-information to remember and the loss of the information along with the compression, and its minimum corresponds to the self-information of the original data. To evaluate the self-information to remember, we provided the concept of memory. The memory expresses the compressed information, and the learning proceeds by referring to previous memories. There are many similarities between this NN and the human brain, and this NN is a realization of the free-energy principle which is considered to be a unified theory of the human brain. This work can be applied to any kind of data analysis and cognitive science.
