A Brief Overview of ChatGPT:The History,Status Quo and Potential Future Development

被引：10

作者：

Tianyu Wu ^{[1
]}

Shizhu He ^{[2
,3
]}

Jingping Liu ^{[4
]}

Siqi Sun ^{[5
]}

Kang Liu ^{[2
,3
]}

QingLong Han ^{[6
,7
]}

Yang Tang ^{[6
,1
]}

机构：

[1] the Key Laboratory of Smart Manufacturing in Energy Chemical Process, East China University of Science and Technology

[2] the Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences

[3] the School of Artificial Intelligence, University of Chinese Academy of Sciences

[4] the School of Information Science and Engineering, East China University of Science and Technology

[5] the Research Institute of Intelligent Complex Systems,Fudan University

[6] IEEE

[7] the School of Science, Computing and Engineering Technologies, Swinburne University of Technology

来源：

IEEE/CAAJournalofAutomaticaSinica | 2023年 / 10卷 / 05期

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

ChatG PT,an artificial intelligence generated content (AIGC) model developed by OpenAI,has attracted worldwide attention for its capability of dealing with challenging language understanding and generation tasks in the form of conversations.This paper briefly provides an overview on the history,status quo and potential future development of ChatGPT,helping to provide an entry point to think about ChatGPT.Specifically,from the limited open-accessed resources,we conclude the core techniques of ChatGPT,mainly including large-scale language models,in-context learning,reinforcement learning from human feedback and the key technical steps for developing ChatGPT.We further analyze the pros and cons of ChatGPT and we rethink the duality of ChatGPT in various fields.Although it has been widely acknowledged that ChatGPT brings plenty of opportunities for various fields,mankind should still treat and use ChatG PT properly to avoid the potential threat,e.g.,academic integrity and safety challenge.Finally,we discuss several open problems as the potential development of ChatGPT.

引用

页码：1122 / 1136

页数：15

共 27 条

[1] DAO to HANOI via DeSci:AI Paradigm Shifts from AlphaGo to ChatGPT [J].

Qinghai Miao ;

Wenbo Zheng ;

Yisheng Lv ;

Min Huang ;

Wenwen Ding ;

FeiYue Wang .

IEEE/CAAJournalofAutomaticaSinica, 2023, 10 (04) :877-897

[2] CDP-GAN:Near-Infrared and Visible Image Fusion Via Color Distribution Preserved GAN [J].

Jun Chen ;

Kangle Wu ;

Yang Yu ;

Linbo Luo .

IEEE/CAAJournalofAutomaticaSinica, 2022, 9 (09) :1698-1701

[3] Efficient Exploration for Multi-Agent Reinforcement Learning via Transferable Successor Features [J].

Wenzhang Liu ;

Lu Dong ;

Dan Niu ;

Changyin Sun .

IEEE/CAA Journal of Automatica Sinica, 2022, 9 (09) :1673-1686

[4] Glioma Segmentation-Oriented Multi-Modal MR Image Fusion With Adversarial Learning [J].

Yu Liu ;

Yu Shi ;

Fuhao Mu ;

Juan Cheng ;

Xun Chen .

IEEE/CAAJournalofAutomaticaSinica, 2022, 9 (08) :1528-1531

[5] Price-Based Residential Demand Response Management in Smart Grids:A Reinforcement Learning-Based Approach [J].

Yanni Wan ;

Jiahu Qin ;

Xinghuo Yu ;

Tao Yang ;

Yu Kang .

IEEE/CAA Journal of Automatica Sinica, 2022, 9 (01) :123-134

[6] Robotic Knee Tracking Control to Mimic the Intact Human Knee Profile Based on Actor-Critic Reinforcement Learning [J].

Ruofan Wu ;

Zhikai Yao ;

Jennie Si ;

HeHelen Huang .

IEEE/CAAJournalofAutomaticaSinica, 2022, 9 (01) :19-30

[7] 基于无监督域自适应的计算机视觉任务研究进展 [J].

孙琦钰 ;

赵超强 ;

唐漾 ;

钱锋 .

中国科学:技术科学, 2022, 52 (01) :26-54

[8] Total Variation Constrained Non-Negative Matrix Factorization for Medical Image Registration [J].

Chengcai Leng ;

Hai Zhang ;

Guorong Cai ;

Zhen Chen ;

Anup Basu .

IEEE/CAAJournalofAutomaticaSinica, 2021, 8 (05) :1025-1037

[9] A Novel Automatic Classification System Based on Hybrid Unsupervised and Supervised Machine Learning for Electrospun Nanofibers [J].

Cosimo Ieracitano ;

Annunziata Paviglianiti ;

Maurizio Campolo ;

Amir Hussain ;

Eros Pasero ;

Francesco Carlo Morabito .

IEEE/CAAJournalofAutomaticaSinica, 2021, 8 (01) :64-76

[10] An Adaptive Strategy via Reinforcement Learning for the Prisoner's Dilemma Game [J].

Lei Xue ;

Changyin Sun ;

Donald Wunsch ;

Yingjiang Zhou ;

Fang Yu .

IEEE/CAA Journal of Automatica Sinica, 2018, (01) :301-310

← 1 2 3 →