A unified theory of learning

TL;DR

This paper proposes a unified neural network model inspired by the human brain that learns through information compression, aiming to achieve universal applicability across various data types and providing insights into cognitive science.

Contribution

It introduces a toy neural network model that mimics human brain learning by optimizing a loss function based on information compression and self-information, aligning with the free-energy principle.

Findings

The neural network can compress diverse input data without ad hoc adjustments.

The model aligns with the free-energy principle of the human brain.

It provides a framework applicable to data analysis and cognitive science.

Abstract

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.