Full Text:   <2093>

Summary:  <2>

CLC number: 

On-line Access: 2022-06-17

Received: 2021-05-04

Revision Accepted: 2022-06-20

Crosschecked: 2021-08-11

Cited: 0

Clicked: 3855

Citations:  Bibtex RefMan EndNote GB/T7714




Liriam Michi ENAMOTO


Denise Leyi LI


Geraldo Pereira ROCHA FILHO


-   Go to

Article info.
Open peer comments

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


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

Key Words: 

Share this article to: More <<< Previous Article|

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.

@article{title="New directions for artificial intelligence: human, machine, biological, and quantum intelligence",
author="Li WEIGANG, Liriam Michi ENAMOTO, Denise Leyi LI, Geraldo Pereira ROCHA FILHO",
journal="Frontiers of Information Technology & Electronic Engineering",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T New directions for artificial intelligence: human, machine, biological, and quantum intelligence
%A Liriam Michi ENAMOTO
%A Denise Leyi LI
%A Geraldo Pereira ROCHA FILHO
%J Frontiers of Information Technology & Electronic Engineering
%V 23
%N 6
%P 984-990
%@ 2095-9184
%D 2022
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2100227

T1 - New directions for artificial intelligence: human, machine, biological, and quantum intelligence
A1 - Liriam Michi ENAMOTO
A1 - Denise Leyi LI
A1 - Geraldo Pereira ROCHA FILHO
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 23
IS - 6
SP - 984
EP - 990
%@ 2095-9184
Y1 - 2022
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2100227

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.


李伟钢1,Liriam Michi ENAMOTO1,Denise Leyi LI2,Geraldo Pereira ROCHA FILHO1
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。


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


[1]Arute F, Arya K, Babbush R, et al., 2020. Hartree-fock on a superconducting qubit quantum computer. Science, 369(6507):1084-1089.

[2]Baltrušaitis T, Ahuja C, Morency LP, 2019. Multimodal machine learning: a survey and taxonomy. IEEE Trans Patt Anal Mach Intell, 41(2):423-443.

[3]Bhattacharyya S, Pal P, Bhowmick S, 2014. Binary image denoising using a quantum multilayer self organizing neural network. Appl Soft Comput, 24:717-729.

[4]Bostrom N, 2006. How long before superintelligence? Linguist Philos Investig, 5(1):11-30.

[5]Brown TB, Mann B, Ryder N, et al., 2020. Language models are few-shot learners. https://arxiv.org/abs/2005.14165

[6]Campbell M, Hoane AJ Jr, Hsu FH, 2002. Deep blue. Artif Intell, 134(1-2):57-83.

[7]Devlin J, Chang MW, Lee K, et al., 2018. BERT: pre-training of deep bidirectional transformers for language understanding. https://arxiv.org/abs/1810.04805

[8]Dunjko V, Briegel HJ, 2018. Machine learning & artificial intelligence in the quantum domain: a review of recent progress. Rep Prog Phys, 81(7):074001.

[9]Evans R, Grefenstette E, 2018. Learning explanatory rules from noisy data. J Artif Intell Res, 61:1-64.

[10]Floreano D, Mattiussi C, 2008. Bio-inspired Artificial Intelligence: Theories, Methods, and Technologies. MIT Press, Cambridge, UK.

[11]Goodfellow I, Bengio Y, Courville A. 2016. Deep Learning. MIT press, Cambridge, MA, USA.

[12]Gottfredson LS, 1997. Mainstream science on intelligence: an editorial with 52 signatories, history, and bibliography. Intelligence, 24(1):13-23.

[13]Jumper J, Evans R, Pritzel A, et al., 2021. Highly accurate protein structure prediction with AlphaFold. Nature, 596(7873):583-589.

[14]Kohonen T, 1990. The self-organizing map. Proc IEEE, 78(9):1464-1480.

[15]Konar D, Bhattacharyya S, Panigrahi BK, et al., 2016. A quantum bi-directional self-organizing neural network (QBDSONN) architecture for binary object extraction from a noisy perspective. Appl Soft Comput, 46:731-752.

[16]Li F, Zhao SM, Zheng BY, 2004. Research on the characteristics of quantum neurons. J Circ Syst, 9(4):76-80 (in Chinese).

[17]Li FF, Fergus R, Perona P, 2003. A Bayesian approach to unsupervised one-shot learning of object categories. Proc 9th IEEE Int Conf on Computer Vision, p.1134-1141.

[18]Li FF, Fergus R, Perona P, 2006. One-shot learning of object categories. IEEE Trans Patt Anal Mach Intell, 28(4):594-611.

[19]Li JZ, Tang J, 2019. Report of Artificial Intelligence Development. Tsinghua University, Beijing, China (in Chinese).

[20]Liu W, Anguelov D, Erhan D, et al., 2016. SSD: single shot multibox detector. 14th European Conf on Computer Vision, p.21-37.

[21]Luger GF, 2005. Artificial Intelligence: Structures and Strategies for Complex Problem Solving (5th Ed.). Addison-Wesley, Harlow, USA.

[22]Miller EG, Matsakis NE, Viola PA, 2000. Learning from one example through shared densities on transforms. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.464-471.

[23]Pan YH, 2016. Heading toward artificial intelligence 2.0. Engineering, 2(4):409-413.

[24]Pan YH, 2017. Special issue on artificial intelligence 2.0. Front Inform Technol Electron Eng, 18(1):1-2.

[25]Pan YH, 2018. 2018 special issue on artificial intelligence 2.0: theories and applications. Front Inform Technol Electron Eng, 19(1):1-2.

[26]Ramesh A, Pavlov M, Goh G, et al., 2021. DALL·E: Creating Images from Text. OpenAI Blog. https://openai.com/blog/dall-e/

[27]Redmon J, Divvala S, Girshick R, et al., 2016. You only look once: unified, real-time object detection. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.779-788.

[28]Russell S, Norvig P, 2003. Artificial Intelligence: a Modern Approach (2nd Ed.). Prentice Hall, Upper Saddle River, USA.

[29]Sgarbas KN, 2007. The road to quantum artificial intelligence. https://arxiv.org/abs/0705.3360

[30]Silva TC, Zhao L, 2016. Machine Learning in Complex Networks. Springer International Publishing, Springer Nature, Switzerland.

[31]Silver D, Schrittwieser J, Simonyan K, et al., 2017. Mastering the game of Go without human knowledge. Nature, 550(7676):354-359.

[32]Valova I, Gueorguieva N, Kempka M, 2005. ParaGro: a learning algorithm for growing parallel self-organizing maps with any input/output dimensions. Int J Gen Syst, 34(6):735-757.

[33]Vaswani A, Shazeer N, Parmar N, et al., 2017. Attention is all you need. Proc 31st Conf on Neural Information Processing Systems, p.6000-6010.

[34]Wiśniewska J, Sawerwain M, Obuchowicz A, 2020. Basic quantum circuits for classification and approximation tasks. Int J Appl Math Comput Sci, 30(4):733-744.

[35]Weigang L, 1998. A study of parallel self-organizing map. https://arxiv.org/abs/quant-ph/9808025v2

[36]Weigang L, Da Silva NC, 1999. A study of parallel neural networks. Int Joint Conf on Neural Networks, p.1113-1116.

[37]Wu F, 2020. Introduction to Artificial Intelligence: Models and Algorithms. Higher Education Press, Beijing, China (in Chinese).

[38]Yang B, Sun H, Huang CJ, et al., 2020. Cooling and entangling ultracold atoms in optical lattices. Science, 369(6503):550-553.

[39]Yuan S, Zhao HY, Du ZX, et al., 2021. WuDaoCorpora: a super large-scale Chinese corpora for pre-training language models. AI Open, 2:65-68.

[40]Zhuang YT, Wu F, Chen C, et al., 2017. Challenges and opportunities: from big data to knowledge in AI 2.0. Front Inform Technol Electron Eng, 18(1):3-14.

Open peer comments: Debate/Discuss/Question/Opinion


Please provide your name, email address and a comment

Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2022 Journal of Zhejiang University-SCIENCE