Machines Imitating Human Thinking Using Bayesian Learning and Bootstrap

Sunghae Jun

doi:10.3390/sym13030389

Abstract

In the field of cognitive science, much research has been conducted on the diverse applications of artificial intelligence (AI). One important area of study is machines imitating human thinking. Although there are various approaches to development of thinking machines, we assume that human thinking is not always optimal in this paper. Sometimes, humans are driven by emotions to make decisions that are not optimal. Recently, deep learning has been dominating most machine learning tasks in AI. In the area of optimal decisions involving AI, many traditional machine learning methods are rapidly being replaced by deep learning. Therefore, because of deep learning, we can expect the faster growth of AI technology such as AlphaGo in optimal decision-making. However, humans sometimes think and act not optimally but emotionally. In this paper, we propose a method for building thinking machines imitating humans using Bayesian decision theory and learning. Bayesian statistics involves a learning process based on prior and posterior aspects. The prior represents an initial belief in a specific domain. This is updated to posterior through the likelihood of observed data. The posterior refers to the updated belief based on observations. When the observed data are newly added, the current posterior is used as a new prior for the updated posterior. Bayesian learning such as this also provides an optimal decision; thus, this is not well-suited to the modeling of thinking machines. Therefore, we study a new Bayesian approach to developing thinking machines using Bayesian decision theory. In our research, we do not use a single optimal value expected by the posterior; instead, we generate random values from the last updated posterior to be used for thinking machines that imitate human thinking.

Highlights

Many researchers have discussed whether machines can think like humans [1,2,3,4,5].At the same time, some studies have been performed on whether machines understand the human mind [5,6,7,8,9]
To develop a machine that imitates human thinking, we proposed a learning methodology using Bayesian decision theory and Bayesian bootstrap
Through Bayesian learning with prior, likelihood and posterior, we build a posterior distribution to develop thinking machines which reflect humans

Summary

Introduction

Many researchers have discussed whether machines can think like humans [1,2,3,4,5].At the same time, some studies have been performed on whether machines understand the human mind [5,6,7,8,9]. Deep learning provides the dominant performance in most tasks of current connectionist AI [12] This is a machine learning algorithm that can better describe the information processing structure of the human brain, composed of numerous neurons, by using much more hidden layers than existing neural network algorithms [12]. In various categories of AI, thinking like humans is the cognitive computing approach to making thinking machines because thinking is related to the mind [13,14]. Since the advent of AI, we have been constantly trying to develop a thinking machine with human emotions [16,17] Such AI is called the artificial mind (AM) [2,6,8,9,18]. Unlike classical AI approaches based on symbolic or connectionist concepts, our proposed method does not always make optimal decisions

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Discussion

Conclusion