Current perspective on artificial intelligence, machine learning and deep learning

Research Article
Open access

Current perspective on artificial intelligence, machine learning and deep learning

Hang Yuan 1*
  • 1 University of Glasgow    
  • *corresponding author ender.sher@foxmail.com
Published on 23 October 2023 | https://doi.org/10.54254/2755-2721/19/20231019
ACE Vol.19
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-029-5
ISBN (Online): 978-1-83558-030-1

Abstract

Artificial intelligence has exploded in the past few years, especially after 2015. Much of it is due to the widespread use of GPUs, which has made parallel computing faster, cheaper, and more efficient. Of course, the combination of infinite expansion of storage capacity and sudden explosion of data torrent (big data) also makes image data, text data, transaction data, mapping data comprehensive and massive explosion. The wave of artificial intelligence has swept the world, and many words still plague us: artificial intelligence, machine learning, and deep learning. Many people do not have a deep understanding of the meaning of these high-frequency words and the relationship behind them. In order to better understand artificial intelligence, this article explains the meaning of these words in the simplest language to clarify the relationship between them, hoping to be helpful to the beginners. Deep learning expands the scope of artificial intelligence by enabling a wide range of applications for machine learning. Deep learning can overwhelmingly accomplish a variety of tasks, making all machine access capabilities available. For more complex applications, many implementations do not have to rely on supercomputing environments and big data. Data is indispensable, but too much data will also lead to overfitting. Algorithms are the key to solving learning problems. Efficient algorithms make artificial intelligence and machine learning less dependent on big data and supercomputer environment. Data, algorithms and computing power (computing speed, space) maintain a dynamic triangle in the implementation of artificial intelligence.

Keywords:

artificial intelligence, machine learning, deep learning

Yuan,H. (2023). Current perspective on artificial intelligence, machine learning and deep learning. Applied and Computational Engineering,19,116-122.
Export citation

References

[1]. He Kaiming, Zhang Xiangyu, Ren Shaoqing, et al. Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition.[J]. IEEE transactions on pattern analysis and machine intelligence,2015(9).

[2]. LeCun Yann, Bengio Yoshua, Hinton Geoffrey. Deep learning[J]. Nature, 2015(7553).

[3]. Nishio Mizuho. Special Issue on Machine Learning/Deep Learning in Medical Image Processing[J]. Applied Sciences,2021,11(23).

[4]. Wang Yi, Sun Junhai. Design and Implementation of Virtual Reality Interactive Product Software Based on Artificial Intelligence Deep Learning Algorithm[J]. Advances in Multimedia, 2022.

[5]. Garland Jack, Hu Mindy, Kesha Kilak, Glenn Charley, Duffy Michael, Morrow Paul, Stables Simon, Ondruschka Benjamin, Da Broi Ugo, Tse Rexson. An overview of artificial intelligence/deep learning[J]. Pathology,2021,53(S1).

[6]. Yongzhang Zhou, Jun Wang, Renguang Zuo, Fan Xiao, Wenjie Shen, Shugong Wang. Machine Learning Deep Learning and Implementation Language in Geological Field[J]. Journal of Autonomous Intelligence,2021,4(1).

[7]. Kaluarachchi Tharindu, Reis Andrew, Nanayakkara Suranga. A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning[J]. Sensors,2021,21(7).

[8]. Xiaolei Sun. Study on Speech Recognition Method of Artificial Intelligence Deep Learning[J]. Journal of Physics: Conference Series, 2021, 1754(1).

[9]. Xiaolei Sun. Study on Speech Recognition Method of Artificial Intelligence Deep Learning[J]. Journal of Physics: Conference Series, 2021, 1754(1).

[10]. Meng XiangHe. Combining artificial intelligence - deep learning with Hi-C data to predict the functional effects of noncoding variants.[J]. Bioinformatics (Oxford, England),2020,37(10).

[11]. Nencka Andrew S. Editorial for "Top 10 Reviewer Critiques of Radiology Artificial Intelligence (AI) Articles: Qualitative Thematic Analysis of Reviewer Critiques of Machine Learning / Deep Learning Manuscripts Submitted to JMRI".[J]. Journal of magnetic resonance imaging : JMRI,2020,52(1).

[12]. Misawa M,Kudo S,Mori Y,et al.Current status and future perspective on artificial intelligence for lower endoscopy[J].Digestive Endoscopy,2020.

[13]. Welliver Sara, Chong Jaron. Top 10 Reviewer Critiques of Radiology Artificial Intelligence (AI) Articles: Qualitative Thematic Analysis of Reviewer Critiques of Machine Learning/Deep Learning Manuscripts Submitted to JMRI.[J]. Journal of magnetic resonance imaging: JMRI,2020,52(1).

[14]. Gregory Jules. Promising Artificial Intelligence–Machine Learning–Deep Learning Algorithms in Ophthalmology:Erratum[J]. Asia-Pacific Journal of Ophthalmology,2019,8(5).

[15]. Yan Bicheng. A robust deep learning workflow to predict multiphase flow behavior during geological sequestration injection and Post-Injection periods[J]. Journal of Hydrology,2022,607.

[16]. Li Joshua J.X. Tumour segmentation with deep learning model trained on immunostain-augmented pixel-accurate labels–Application on whole slide images of breast carcinoma[J]. Pathology,2022,54(11).

[17]. Gupta R,Krishnam S P,Schaefer P W,et al.An East Coast Perspective on Artificial Intelligence and Machine Learning Part 2:Ischemic Stroke Imaging and Triage[J].Neuroimaging clinics of North America,2020(4):30.

[18]. Schuhmacher A.Big Techs and startups in pharmaceutical R&D–A 2020 perspective on artificial intelligence[J].Drug Discovery Today,2021.

[19]. Agarwal Piyush, Aghaee Mohammad, Tamer Melih, Budman Hector. A novel unsupervised approach for batch process monitoring using deep learning[J]. Computers and Chemical Engineering,2022,159.

[20]. Li Dongsheng. Frank. Towards automated extraction for terrestrial laser scanning data of building components based on panorama and deep learning[J]. Journal of Building Engineering,2022,50.

[21]. Pan Ning. A sensor data fusion algorithm based on suboptimal network powered deep learning[J]. Alexandria Engineering Journal,2022,61(9).

Cite this article

Yuan,H. (2023). Current perspective on artificial intelligence, machine learning and deep learning. Applied and Computational Engineering,19,116-122.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

Disclaimer/Publisher's Note

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

About volume

Volume title: Proceedings of the 5th International Conference on Computing and Data Science

ISBN：978-1-83558-029-5(Print) / 978-1-83558-030-1(Online)
Editor：Roman Bauer, Marwan Omar, Alan Wang
Conference website: https://2023.confcds.org/
Conference date: 14 July 2023
Series: Applied and Computational Engineering
Volume number: Vol.19
ISSN：2755-2721(Print) / 2755-273X(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).

References

[1]. He Kaiming, Zhang Xiangyu, Ren Shaoqing, et al. Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition.[J]. IEEE transactions on pattern analysis and machine intelligence,2015(9).

[2]. LeCun Yann, Bengio Yoshua, Hinton Geoffrey. Deep learning[J]. Nature, 2015(7553).

[3]. Nishio Mizuho. Special Issue on Machine Learning/Deep Learning in Medical Image Processing[J]. Applied Sciences,2021,11(23).

[4]. Wang Yi, Sun Junhai. Design and Implementation of Virtual Reality Interactive Product Software Based on Artificial Intelligence Deep Learning Algorithm[J]. Advances in Multimedia, 2022.

[5]. Garland Jack, Hu Mindy, Kesha Kilak, Glenn Charley, Duffy Michael, Morrow Paul, Stables Simon, Ondruschka Benjamin, Da Broi Ugo, Tse Rexson. An overview of artificial intelligence/deep learning[J]. Pathology,2021,53(S1).

[6]. Yongzhang Zhou, Jun Wang, Renguang Zuo, Fan Xiao, Wenjie Shen, Shugong Wang. Machine Learning Deep Learning and Implementation Language in Geological Field[J]. Journal of Autonomous Intelligence,2021,4(1).

[7]. Kaluarachchi Tharindu, Reis Andrew, Nanayakkara Suranga. A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning[J]. Sensors,2021,21(7).

[8]. Xiaolei Sun. Study on Speech Recognition Method of Artificial Intelligence Deep Learning[J]. Journal of Physics: Conference Series, 2021, 1754(1).

[9]. Xiaolei Sun. Study on Speech Recognition Method of Artificial Intelligence Deep Learning[J]. Journal of Physics: Conference Series, 2021, 1754(1).

[10]. Meng XiangHe. Combining artificial intelligence - deep learning with Hi-C data to predict the functional effects of noncoding variants.[J]. Bioinformatics (Oxford, England),2020,37(10).

[11]. Nencka Andrew S. Editorial for "Top 10 Reviewer Critiques of Radiology Artificial Intelligence (AI) Articles: Qualitative Thematic Analysis of Reviewer Critiques of Machine Learning / Deep Learning Manuscripts Submitted to JMRI".[J]. Journal of magnetic resonance imaging : JMRI,2020,52(1).

[12]. Misawa M,Kudo S,Mori Y,et al.Current status and future perspective on artificial intelligence for lower endoscopy[J].Digestive Endoscopy,2020.

[13]. Welliver Sara, Chong Jaron. Top 10 Reviewer Critiques of Radiology Artificial Intelligence (AI) Articles: Qualitative Thematic Analysis of Reviewer Critiques of Machine Learning/Deep Learning Manuscripts Submitted to JMRI.[J]. Journal of magnetic resonance imaging: JMRI,2020,52(1).

[14]. Gregory Jules. Promising Artificial Intelligence–Machine Learning–Deep Learning Algorithms in Ophthalmology:Erratum[J]. Asia-Pacific Journal of Ophthalmology,2019,8(5).

[15]. Yan Bicheng. A robust deep learning workflow to predict multiphase flow behavior during geological sequestration injection and Post-Injection periods[J]. Journal of Hydrology,2022,607.

[16]. Li Joshua J.X. Tumour segmentation with deep learning model trained on immunostain-augmented pixel-accurate labels–Application on whole slide images of breast carcinoma[J]. Pathology,2022,54(11).

[17]. Gupta R,Krishnam S P,Schaefer P W,et al.An East Coast Perspective on Artificial Intelligence and Machine Learning Part 2:Ischemic Stroke Imaging and Triage[J].Neuroimaging clinics of North America,2020(4):30.

[18]. Schuhmacher A.Big Techs and startups in pharmaceutical R&D–A 2020 perspective on artificial intelligence[J].Drug Discovery Today,2021.

[19]. Agarwal Piyush, Aghaee Mohammad, Tamer Melih, Budman Hector. A novel unsupervised approach for batch process monitoring using deep learning[J]. Computers and Chemical Engineering,2022,159.

[20]. Li Dongsheng. Frank. Towards automated extraction for terrestrial laser scanning data of building components based on panorama and deep learning[J]. Journal of Building Engineering,2022,50.

[21]. Pan Ning. A sensor data fusion algorithm based on suboptimal network powered deep learning[J]. Alexandria Engineering Journal,2022,61(9).

For authors
Guide for authors Policies for authors Aims and scope Subscription Open access policy
For editors
Policies for editors Editorial board
For reviewers
Policies for reviewers Privacy policy

Cookies are used by this site.
Copyright © 2024 EWA Publishing, its licensors, and contributors. All rights reserved, including text and data mining, AI training, and similar technologies.