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Human behavior in image-based Road Health Inspection Systems despite the emerging AutoML

INTRODUCTION: The emergence of automated machine learning or AutoML has raised an interesting trend of no-code and low-code machine learning where most tasks in the machine learning pipeline can possibly be automated without support from human data scientists. While it sounds reasonable that we shou...

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Autor principal: Siriborvornratanakul, Thitirat
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9299412/
https://www.ncbi.nlm.nih.gov/pubmed/35879937
http://dx.doi.org/10.1186/s40537-022-00646-8
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author Siriborvornratanakul, Thitirat
author_facet Siriborvornratanakul, Thitirat
author_sort Siriborvornratanakul, Thitirat
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description INTRODUCTION: The emergence of automated machine learning or AutoML has raised an interesting trend of no-code and low-code machine learning where most tasks in the machine learning pipeline can possibly be automated without support from human data scientists. While it sounds reasonable that we should leave repetitive trial-and-error tasks of designing complex network architectures and tuning a lot of hyperparameters to AutoML, leading research using AutoML is still scarce. Thereby, the overall purpose of this case study is to investigate the gap between current AutoML frameworks and practical machine learning development. CASE DESCRIPTION: First, this paper confirms the increasing trend of AutoML via an indirect indicator of the numbers of search results in Google trend, IEEE Xplore, and ACM Digital Library during 2012–2021. Then, the three most popular AutoML frameworks (i.e., Auto-Sklearn, AutoKeras, and Google Cloud AutoML) are inspected as AutoML’s representatives; the inspection includes six comparative aspects. Based on the features available in the three AutoML frameworks investigated, our case study continues to observe recent machine learning research regarding the background of image-based machine learning. This is because the field of computer vision spans several levels of machine learning from basic to advanced and it has been one of the most popular fields in studying machine learning and artificial intelligence lately. Our study is specific to the context of image-based road health inspection systems as it has a long history in computer vision, allowing us to observe solution transitions from past to present. DISCUSSION AND EVALUATION: After confirming the rising numbers of AutoML search results in the three search engines, our study regarding the three AutoML representatives further reveals that there are many features that can be used to automate the development pipeline of image-based road health inspection systems. Nevertheless, we find that recent works in image-based road health inspection have not used any form of AutoML in their works. Digging into these recent works, there are two main problems that best conclude why most researchers do not use AutoML in their image-based road health inspection systems yet. Firstly, it is because AutoML’s trial-and-error decision involves much extra computation compared to human-guided decisions. Secondly, using AutoML adds another layer of non-interpretability to a model. As these two problems are the major pain points in modern neural networks and deep learning, they may require years to resolve, delaying the mass adoption of AutoML in image-based road health inspection systems. CONCLUSIONS: In conclusion, although AutoML’s utilization is not mainstream at this moment, we believe that the trend of AutoML will continue to grow. This is because there exists a demand for AutoML currently, and in the future, more demand for no-code or low-code machine learning development alternatives will grow together with the expansion of machine learning solutions. Nevertheless, this case study focuses on selected papers whose authors are researchers who can publish their works in academic conferences and journals. In the future, the study should continue to include observing novice users, non-programmer users, and machine learning practitioners in order to discover more insights from non-research perspectives.
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spelling pubmed-92994122022-07-21 Human behavior in image-based Road Health Inspection Systems despite the emerging AutoML Siriborvornratanakul, Thitirat J Big Data Case Study INTRODUCTION: The emergence of automated machine learning or AutoML has raised an interesting trend of no-code and low-code machine learning where most tasks in the machine learning pipeline can possibly be automated without support from human data scientists. While it sounds reasonable that we should leave repetitive trial-and-error tasks of designing complex network architectures and tuning a lot of hyperparameters to AutoML, leading research using AutoML is still scarce. Thereby, the overall purpose of this case study is to investigate the gap between current AutoML frameworks and practical machine learning development. CASE DESCRIPTION: First, this paper confirms the increasing trend of AutoML via an indirect indicator of the numbers of search results in Google trend, IEEE Xplore, and ACM Digital Library during 2012–2021. Then, the three most popular AutoML frameworks (i.e., Auto-Sklearn, AutoKeras, and Google Cloud AutoML) are inspected as AutoML’s representatives; the inspection includes six comparative aspects. Based on the features available in the three AutoML frameworks investigated, our case study continues to observe recent machine learning research regarding the background of image-based machine learning. This is because the field of computer vision spans several levels of machine learning from basic to advanced and it has been one of the most popular fields in studying machine learning and artificial intelligence lately. Our study is specific to the context of image-based road health inspection systems as it has a long history in computer vision, allowing us to observe solution transitions from past to present. DISCUSSION AND EVALUATION: After confirming the rising numbers of AutoML search results in the three search engines, our study regarding the three AutoML representatives further reveals that there are many features that can be used to automate the development pipeline of image-based road health inspection systems. Nevertheless, we find that recent works in image-based road health inspection have not used any form of AutoML in their works. Digging into these recent works, there are two main problems that best conclude why most researchers do not use AutoML in their image-based road health inspection systems yet. Firstly, it is because AutoML’s trial-and-error decision involves much extra computation compared to human-guided decisions. Secondly, using AutoML adds another layer of non-interpretability to a model. As these two problems are the major pain points in modern neural networks and deep learning, they may require years to resolve, delaying the mass adoption of AutoML in image-based road health inspection systems. CONCLUSIONS: In conclusion, although AutoML’s utilization is not mainstream at this moment, we believe that the trend of AutoML will continue to grow. This is because there exists a demand for AutoML currently, and in the future, more demand for no-code or low-code machine learning development alternatives will grow together with the expansion of machine learning solutions. Nevertheless, this case study focuses on selected papers whose authors are researchers who can publish their works in academic conferences and journals. In the future, the study should continue to include observing novice users, non-programmer users, and machine learning practitioners in order to discover more insights from non-research perspectives. Springer International Publishing 2022-07-20 2022 /pmc/articles/PMC9299412/ /pubmed/35879937 http://dx.doi.org/10.1186/s40537-022-00646-8 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Case Study
Siriborvornratanakul, Thitirat
Human behavior in image-based Road Health Inspection Systems despite the emerging AutoML
title Human behavior in image-based Road Health Inspection Systems despite the emerging AutoML
title_full Human behavior in image-based Road Health Inspection Systems despite the emerging AutoML
title_fullStr Human behavior in image-based Road Health Inspection Systems despite the emerging AutoML
title_full_unstemmed Human behavior in image-based Road Health Inspection Systems despite the emerging AutoML
title_short Human behavior in image-based Road Health Inspection Systems despite the emerging AutoML
title_sort human behavior in image-based road health inspection systems despite the emerging automl
topic Case Study
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9299412/
https://www.ncbi.nlm.nih.gov/pubmed/35879937
http://dx.doi.org/10.1186/s40537-022-00646-8
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