DAO to HANOI via DeSci: AI Paradigm Shifts from AlphaGo to ChatGPT

Qinghai Miao; Wenbo Zheng; Yisheng Lv; Min Huang; Wenwen Ding; Fei-Yue Wang

doi:10.1109/JAS.2023.123561

Volume 10 Issue 4

Apr. 2023

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2023 > 10(4): 877-897

Q. Miao, W. Zheng, Y. Lv, M. Huang, W. Ding, and F.-Y. Wang, "DAO to HANOI via DeSci: AI paradigm shifts from AlphaGo to ChatGPT, " IEEE/CAA J. Autom. Sinica, vol. 10, no. 4, pp. 877–897, Apr. 2023. doi: 10.1109/JAS.2023.123561

Citation:

Q. Miao, W. Zheng, Y. Lv, M. Huang, W. Ding, and F.-Y. Wang, "DAO to HANOI via DeSci: AI paradigm shifts from AlphaGo to ChatGPT, " IEEE/CAA J. Autom. Sinica, vol. 10, no. 4, pp. 877–897, Apr. 2023. doi: 10.1109/JAS.2023.123561

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DAO to HANOI via DeSci: AI Paradigm Shifts from AlphaGo to ChatGPT

doi: 10.1109/JAS.2023.123561

1.
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
2.
School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan 430070, China
3.
Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
4.
Institute of System Engineering, Macau University of Science and Technology, Macao, China

Funds:

the National Key Research and Development Program of China 2020YFB2104001

the National Natural Science Foundation of China 62271485

the National Natural Science Foundation of China 61903363

the National Natural Science Foundation of China U1811463

Open Project of the State Key Laboratory for Management and Control of Complex Systems 20220117

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Author Bio:
Qinghai Miao (Senior Member, IEEE) received his Ph.D. degree in control theory and control engineering from the Graduate University of Chinese Academy of Sciences, Beijing, China, in 2007. From 2017 to 2018, he was a Visiting Scholar with the School of Informatics, the University of Edinburgh, UK. He is currently an Associate Professor with the School of Artificial Intelligence, University of Chinese Academy of Sciences. His research interests include parallel intelligence, machine learning, computer vision, intelligent transportation systems. e-mail: miaoqh@ucas.ac.cn

Wenbo Zheng (M'22) received his bachelor degree in software engineering from Wuhan University of Technology, Wuhan, China, in 2017. He received his Ph.D. degree in computer science and technology from the Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China, in 2021. He is currently an Associate Professor at School of Computer Science and Artificial Intelligence, Wuhan University of Technology. His research interests include computer vision and machine learning. e-mail: zwb2022@whut.edu.cn

Yisheng Lv (Senior Member, IEEE) received B.S. and M.S. degrees from Harbin Institute of Technology, Harbin, China, in 2005 and 2007, respectively, and Ph.D. degree from Chinese Academy of Sciences, Beijing, China, in 2010. He is a Professor with the State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences. His research interests include AI for transportation, autonomous driving, and intelligent transportation systems

Min Huang (Member, IEEE) received her Ph.D. degree in computer sciences from Wuhan University, Wuhan, China, in 2007. From 2017 to 2018, she was a Visiting Scholar with the School of Informatics, the University of Edinburgh, UK. She is currently an Associate Professor with the School of Artificial Intelligence, University of Chinese Academy of Sciences. Her research interests include image processing, knowledge engineering, big data and deep learning. e-mail: huangm@ucas.ac.cn

Wenwen Ding received the M.E. degree in administrative management from Zhengzhou University, Zhengzhou, China, in 2018. She is currently pursuing the Ph.D. degree with the Institute of System Engineering, Macau University of Science and Technology, Macao, China. She is a Research Assistant with the Institute of System Engineering, Macau University of Science and Technology. Her research interests include parallel intelligence, parallel governance, blockchain, and decentralized autonomous organizations (DAOs). e-mail: savanna.wen@gmail.com

Fei-Yue Wang (Fellow, IEEE) received his Ph.D. degree in computer and systems engineering from the Rensselaer Polytechnic Institute, Troy, NY, USA, in 1990. He joined The University of Arizona in 1990 and became a Professor and the Director of the Robotics and Automation Laboratory and the Program in Advanced Research for Complex Systems. In 1999, he founded the Intelligent Control and Systems Engineering Center at the Institute of Automation, Chinese Academy of Sciences (CAS), Beijing, China, under the support of the Outstanding Chinese Talents Program from the State Planning Council, and in 2002, was appointed as the Director of the Key Laboratory of Complex Systems and Intelligence Science, CAS, and Vice President of Institute of Automation, CAS in 2006. He found CAS Center for Social Computing and Parallel Management in 2008, and became the State Specially Appointed Expert and the Founding Director of the State Key Laboratory for Management and Control of Complex Systems in 2011. His current research focuses on methods and applications for parallel intelligence, social computing, and knowledge automation. He is a Fellow of INCOSE, IFAC, ASME, and AAAS. In 2007, he received the National Prize in Natural Sciences of China, numerous best papers awards from IEEE Transactions, and became an Outstanding Scientist of ACM for his work in intelligent control and social computing. He received the IEEE ITS Outstanding Application and Research Awards in 2009, 2011, and 2015, respectively, the IEEE SMC Norbert Wiener Award in 2014, and became the IFAC Pavel J. Nowacki Distinguished Lecturer in 2021.
Since 1997, he has been serving as the General or Program Chair of over 30 IEEE, INFORMS, IFAC, ACM, and ASME conferences. He was the President of the IEEE ITS Society from 2005 to 2007, the IEEE Council of RFID from 2019 to 2021, the Chinese Association for Science and Technology, USA, in 2005, the American Zhu Kezhen Education Foundation from 2007 to 2008, the Vice President of the ACM China Council from 2010 to 2011, the Vice President and the Secretary General of the Chinese Association of Automation from 2008 to 2018, the Vice President of IEEE Systems, Man, and Cybernetics Society from 2019 to 2021. He was the Founding Editor-inChief (EiC) of the International Journal of Intelligent Control and Systems from 1995 to 2000, IEEE ITS Magazine from 2006 to 2007, IEEE/CAA JOURNAL OF AUTOMATICA SINICA from 2014-2017, China's Journal of Command and Control from 2015-2021, and China's Journal of Intelligent Science and Technology from 2019 to 2021. He was the EiC of the IEEE Intelligent Systems from 2009 to 2012, IEEE TRANSACTIONS on Intelligent Transportation Systems from 2009 to 2016, IEEE TRANSACTIONS ON COMPUTATIONAL Social Systems from 2017 to 2020. Currently, he is the President of CAA's Supervision Council, and the EiC of IEEE Transaction on Intelligent Vehicles. e-mail: feiyue.wang@ia.ac.cn
Corresponding author: Yisheng Lv, e-mail: yisheng.lv@ia.ac.cn
Received Date: 2023-02-09
Revised Date: 2023-03-10
Accepted Date: 2023-03-10

Available Online: 2023-03-31

Abstract

Abstract

From AlphaGo to ChatGPT, the field of AI has launched a series of remarkable achievements in recent years. Analyzing, comparing, and summarizing these achievements at the paradigm level is important for future AI innovation, but has not received sufficient attention. In this paper, we give an overview and perspective on machine learning paradigms. First, we propose a paradigm taxonomy with three levels and seven dimensions from a knowledge perspective. Accordingly, we give an overview on three basic and twelve extended learning paradigms, such as Ensemble Learning, Transfer Learning, etc., with figures in unified style. We further analyze three advanced paradigms, i.e., AlphaGo, AlphaFold and ChatGPT. Second, to enable more efficient and effective scientific discovery, we propose to build a new ecosystem that drives AI paradigm shifts through the decentralized science (DeSci) movement based on decentralized autonomous organization (DAO). To this end, we design the Hanoi framework, which integrates human factors, parallel intelligence based on a combination of artificial systems and the natural world, and the DAO to inspire AI innovations.
- ChatGPT,
- decentralized science (DeSci),
- decentralized autonomous organization (DAO),
- machine learning,
- paradigm shift

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DAO to HANOI via DeSci: AI Paradigm Shifts from AlphaGo to ChatGPT

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