Machines Imitating Human Thinking Using Bayesian Learning and Bootstrap
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/13/3/389 |
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| author | Sunghae Jun |
| author_facet | Sunghae Jun |
| author_sort | Sunghae Jun |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T06:17:48Z |
| format | Article |
| id | doaj.art-70bd858901634b69af8ed4a953e7b32d |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-09T06:17:48Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-70bd858901634b69af8ed4a953e7b32d2023-12-03T11:51:50ZengMDPI AGSymmetry2073-89942021-02-0113338910.3390/sym13030389Machines Imitating Human Thinking Using Bayesian Learning and BootstrapSunghae Jun0Department of Big Data and Statistics, Cheongju University, Chungbuk 28503, KoreaIn 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.https://www.mdpi.com/2073-8994/13/3/389Bayesian learningartificial intelligencethinking machinesprior and posteriorBayesian bootstrap |
| spellingShingle | Sunghae Jun Machines Imitating Human Thinking Using Bayesian Learning and Bootstrap Symmetry Bayesian learning artificial intelligence thinking machines prior and posterior Bayesian bootstrap |
| title | Machines Imitating Human Thinking Using Bayesian Learning and Bootstrap |
| title_full | Machines Imitating Human Thinking Using Bayesian Learning and Bootstrap |
| title_fullStr | Machines Imitating Human Thinking Using Bayesian Learning and Bootstrap |
| title_full_unstemmed | Machines Imitating Human Thinking Using Bayesian Learning and Bootstrap |
| title_short | Machines Imitating Human Thinking Using Bayesian Learning and Bootstrap |
| title_sort | machines imitating human thinking using bayesian learning and bootstrap |
| topic | Bayesian learning artificial intelligence thinking machines prior and posterior Bayesian bootstrap |
| url | https://www.mdpi.com/2073-8994/13/3/389 |
| work_keys_str_mv | AT sunghaejun machinesimitatinghumanthinkingusingbayesianlearningandbootstrap |