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On-line Access: 2022-06-17

Received: 2021-05-04

Revision Accepted: 2022-07-05

Crosschecked: 2021-08-11

Cited: 0

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Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Li WEIGANG

https://orcid.org/0000-0003-1826-1850

Liriam Michi ENAMOTO

https://orcid.org/0000-0003-0188-5966

Denise Leyi LI

https://orcid.org/0000-0003-0664-3149

Geraldo Pereira ROCHA FILHO

https://orcid.org/0000-0001-6795-2768

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Frontiers of Information Technology & Electronic Engineering  2022 Vol.23 No.6 P.984-990

http://doi.org/10.1631/FITEE.2100227


New directions for artificial intelligence: human, machine, biological, and quantum intelligence


Author(s):  Li WEIGANG, Liriam Michi ENAMOTO, Denise Leyi LI, Geraldo Pereira ROCHA FILHO

Affiliation(s):  Department of Computer Science, University of Brasilia, Brasilia-DF 70910-900, Brazil; more

Corresponding email(s):   weigang@unb.br, liriam.enamoto@gmail.com, denise.leyi@gmail.com, geraldof@unb.br

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Li WEIGANG, Liriam Michi ENAMOTO, Denise Leyi LI, Geraldo Pereira ROCHA FILHO. New directions for artificial intelligence: human, machine, biological, and quantum intelligence[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(6): 984-990.

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Abstract: 
This comment reviews the “once learning” mechanism (OLM) that was proposed byWeigang (1998), the subsequent success of “one-shot learning” in object categories (Li FF et al., 2003), and “you only look once” (YOLO) in objective detection (Redmon et al., 2016). Upon analyzing the current state of research in artificial intelligence (AI), we propose to divide AI into the following basic theory categories: artificial human intelligence (AHI), artificial machine intelligence (AMI), artificial biological intelligence (ABI), and artificial quantum intelligence (AQI). These can also be considered as the main directions of research and development (R&D) within AI, and distinguished by the following classification standards and methods: (1) human-, machine-, biological-, and quantum-oriented AI R&D; (2) information input processed by dimensionality increase or reduction; (3) the use of one/a few or a large number of samples for knowledge learning.

人工智能新方向:类人、机器、仿生和量子智能

李伟钢1,Liriam Michi ENAMOTO1,Denise Leyi LI2,Geraldo Pereira ROCHA FILHO1
1巴西利亚大学计算机科学系,巴西巴西利亚市,70910-900
2圣保罗大学经济、管理、会计和审计学院,巴西圣保罗市,05508-010
摘要:本评论回顾1998年提出的"一次性学习"(once learning,OLM)机制,和随后出现的用于图像分类的"一瞥学习"(one-shot learning)以及用于目标检测的"你仅看一次"(you only look once,YOLO)。基于目前人工智能(AI)研究现状,提出将其划分为以下子学科:人工类人智能、人工机器智能、人工仿生智能和人工量子智能。这些被认为是AI研发的主要方向,并按以下分类标准区分:(1)以类人、机器、仿生或量子计算为本的AI研发;(2)升维或降维的信息输入;(3)小样本或大数据知识学习。

关键词:人工智能;机器学习;一次性学习;一瞥学习;量子计算

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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