物理人工智能：下一代人工智能

李颖博; 李朝; 段玉聪; Anamaria-Beatrice SPULBER

doi:10.1631/FITEE.2200675

Your Location：

Home >

Browse articles >

物理人工智能：下一代人工智能

常规文章 | Updated：2023-08-29

- 物理人工智能：下一代人工智能
  Enhanced Publication
- Physical artificial intelligence (PAI): the next-generation artificial intelligence
- 信息与电子工程前沿(英文版) 2023年24卷第8期页码：1231-1238
- Affiliations：
  
  1.School of Computer Science and Technology, Hainan University, Haikou 570228, China
  2.Zhejiang Lab, Hangzhou 311121, China
- Author bio：
  
  †E-mail: xslwen@outlook.com
  lzjoey@gmail.com
  ‡Corresponding author
- Funds：
  
  the Hainan Provincial Key R&D Program of China(ZDYF2022GXJS007;ZDYF2022GXJS010);the Hainan Provincial Natural Science Foundation of China(620RC561);the Hainan Provincial Higher Education and Teaching Reform Research Project of China(Hnjg2021ZD-3);the Open Fund Project of the Hainan Provincial Key Laboratory of Meteorological Disaster Prevention and Mitigation in the South China Sea, China(SCSF202210)
- DOI：10.1631/FITEE.2200675
  中图分类号：
- 纸质出版日期：2023-08-0 ，
  
  收稿日期：2022-12-31，
  
  修回日期：2023-04-18，
- Accepted：
Scan QR Code
李颖博, 李朝, 段玉聪, 等. 物理人工智能：下一代人工智能[J]. 信息与电子工程前沿(英文版), 2023,24(8):1231-1238.

YINGBO LI, ZHAO LI, YUCONG DUAN, et al. Physical artificial intelligence (PAI): the next-generation artificial intelligence. [J]. Frontiers of information technology & electronic engineering, 2023, 24(8): 1231-1238.
李颖博, 李朝, 段玉聪, 等. 物理人工智能：下一代人工智能[J]. 信息与电子工程前沿(英文版), 2023,24(8):1231-1238. DOI： 10.1631/FITEE.2200675.

YINGBO LI, ZHAO LI, YUCONG DUAN, et al. Physical artificial intelligence (PAI): the next-generation artificial intelligence. [J]. Frontiers of information technology & electronic engineering, 2023, 24(8): 1231-1238. DOI： 10.1631/FITEE.2200675.

摘要

人工智能（AI）已经成为各领域创新和社会进步的驱动力。然而，其大多数工业应用集中在信号处理领域，这依赖于不同传感器产生和收集的数据。最近，一些研究人员提出将数字人工智能和物理人工智能结合，这可能带来人工智能理论基础的重大进步。在本文中，我们探讨了物理人工智能的概念并提出两个子领域：集成式物理人工智能和分布式物理人工智能。我们还讨论了物理人工智能可持续发展和治理所面临的挑战和机遇。由于物理人工智能需要连续处理来自边缘、雾和物联网的分布式信号，它可以被看作分布式计算连续系统在人工智能领域的延伸。

Abstract

关键词

Keywords

references

Alom Z, Taha TM, Yakopcic C, et al., 2018. The history began from AlexNet: a comprehensive survey on deep learning approaches. https://arxiv.org/abs/1803.01164https://arxiv.org/abs/1803.01164

Arrieta AB, Díaz-Rodríguez N, Del Ser J, et al., 2020. Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inform Fus, 58:82-115. https://doi.‍org/10.‍1016/j.‍inffus.2019.12.012https://doi.‍org/10.‍1016/j.‍inffus.2019.12.012

Asenjo JC, 2017. Data Masking, Encryption, and Their Effect on Classification Performance: Trade-offs Between Data Security and Utility. PhD Thesis, Nova Southeastern University, Fort Lauderdale, USA.

Belu R, 2013. Artificial intelligence techniques for solar energy and photovoltaic applications. In: Anwar S, Efstathiadis H, Qazi S (Eds.), Handbook of Research on Solar Energy Systems and Technologies. IGI Global, Pennsylvania, USA, p.376-436. https://doi.org/10.4018/978-1-4666-1996-8.ch015https://doi.org/10.4018/978-1-4666-1996-8.ch015

Cheng JF, Chen WH, Tao F, et al., 2018. Industrial IoT in 5G environment towards smart manufacturing. J Ind Inform Integr, 10:10-19. https://doi.org/10.1016/j.jii.2018.04.001https://doi.org/10.1016/j.jii.2018.04.001

Cheng LF, Yu T, 2019. A new generation of AI: a review and perspective on machine learning technologies applied to smart energy and electric power systems. Int J Energy Res, 43(6):1928-1973. https://doi.org/10.1002/er.4333https://doi.org/10.1002/er.4333

Costeira JP, Lima P, 2020. A Simple Guide to Physical AI. https://www.ai4europe.eu/research/simple-guide-physical-aihttps://www.ai4europe.eu/research/simple-guide-physical-ai [Accessed on Jan. 14, 2023].

Creswell A, White T, Dumoulin V, et al., 2018. Generative adversarial networks: an overview. IEEE Signal Process Mag, 35(1):53-65. https://doi.org/10.1109/MSP.2017.2765202https://doi.org/10.1109/MSP.2017.2765202

Dafoe A, 2018. AI Governance: a Research Agenda. Centre for the Governance of AI, Future of Humanity Institute, University of Oxford, Oxford, UK.

Dalenogare LS, Benitez GB, Ayala NF, et al., 2018. The expected contribution of Industry 4.0 technologies for industrial performance. Int J Prod Econ, 204:383-394. https://doi.org/10.1016/j.ijpe.2018.08.019https://doi.org/10.1016/j.ijpe.2018.08.019

Dattner B, Chamorro-Premuzic T, Buchband R, et al., 2019. The legal and ethical implications of using AI in hiring. Harv Busi Rev, 25:1-7.

Deb D, Wiper S, Gong SX, et al., 2018. Face recognition: primates in the wild. Proc IEEE 9th Int Conf on Biometrics Theory, Applications and Systems, p.1-10. https://doi.org/10.1109/BTAS.2018.8698538https://doi.org/10.1109/BTAS.2018.8698538

de Fazio R, Giannoccaro NI, Carrasco M, 2021. Wearable devices and IoT applications for symptom detection, infection tracking, and diffusion containment of the COVID-19 pandemic: a survey. Front Inform Technol Electron Eng, 22(11):1413-1442. https://doi.org/10.1631/FITEE.2100085https://doi.org/10.1631/FITEE.2100085

Dekhne A, Hastings G, Murnane J, et al., 2019. Automation in Logistics: Big Opportunity, Bigger Uncertainty. https://www.mckinsey.com/industries/travel-logistics-and-infrastructure/our-insights/automation-in-logistics-big-opportunity-bigger-uncertaintyhttps://www.mckinsey.com/industries/travel-logistics-and-infrastructure/our-insights/automation-in-logistics-big-opportunity-bigger-uncertainty [Accessed on Jan. 14, 2023].

Deng L, 2016. Deep learning: from speech recognition to language and multimodal processing. APSIPA Trans Signal Inform Process, 5(1):e1. https://doi.org/10.1017/ATSIP.2015.22https://doi.org/10.1017/ATSIP.2015.22

Frické M, 2019. The knowledge pyramid: the DIKW hierarchy. Knowl Organiz, 46(1):33-46. https://doi.org/10.5771/0943-7444-2019-1-33https://doi.org/10.5771/0943-7444-2019-1-33

Gil L, Liska A, 2019. Security with AI and Machine Learning. O'Reilly Media, Sebastopol, USA.

Güera D, Delp EJ, 2018. Deepfake video detection using recurrent neural networks. Proc 15th IEEE Int Conf on Advanced Video and Signal Based Surveillance, p.1-6. https://doi.org/10.1109/AVSS.2018.8639163https://doi.org/10.1109/AVSS.2018.8639163

Hecht-Nielsen R, 1992. Theory of the backpropagation neural network. In: Wechsler H (Ed.), Neural Networks for Perception. Academic Press, Boston, USA, p.65-93. https://doi.org/10.1016/B978-0-12-741252-8.50010-8https://doi.org/10.1016/B978-0-12-741252-8.50010-8

Janebäck E, Kristiansson M, 2019. Friendly Robot Delivery: Designing an Autonomous Delivery Droid for Collaborative Consumption. Chalmers University of Technology, Gothenburg, Sweden.

Karppi T, Granata Y, 2019. Non-artificial non-intelligence: Amazon's Alexa and the frictions of AI. AI Soc, 34(4):867-876. https://doi.org/10.1007/s00146-019-00896-whttps://doi.org/10.1007/s00146-019-00896-w

LeCun Y, Bottou L, Bengio Y, et al., 1998. Gradient-based learning applied to document recognition. Proc IEEE, 86(11):2278-2324. https://doi.org/10.1109/5.726791https://doi.org/10.1109/5.726791

Li H, Zhang ZE, Liu ZJ, 2017. Application of artificial neural networks for catalysis: a review. Catalysts, 7(10):306. https://doi.org/10.3390/catal7100306https://doi.org/10.3390/catal7100306

Liao RZ, Chen LP, 2022. An evolutionary note on smart city development in China. Front Inform Technol Electron Eng, 23(6):966-974. https://doi.org/10.1631/FITEE.2100407https://doi.org/10.1631/FITEE.2100407

Ma Y, Tsao D, Shum HY, 2022. On the principles of Parsimony and Self-consistency for the emergence of intelligence. Front Inform Technol Electron Eng, 23(9):1298-1323. https://doi.org/10.1631/FITEE.2200297https://doi.org/10.1631/FITEE.2200297

Mahesh B, 2020. Machine learning algorithms—a review. Int J Sci Res, 9:381-386.

Marikyan D, Papagiannidis S, Alamanos E, 2019. A systematic review of the smart home literature: a user perspective. Technol Forecast Soc Change, 138:139-154. https://doi.org/10.1016/j.techfore.2018.08.015https://doi.org/10.1016/j.techfore.2018.08.015

May Z, Amaran MH, 2011. Automated oil palm fruit grading system using artificial intelligence. Int J Video Image Process Netw Secur, 11(3):30-35. https://doi.org/10.3390/catal7100306https://doi.org/10.3390/catal7100306

Meyer T, Schmitt M, Dietzek B, et al., 2013. Accumulating advantages, reducing limitations: multimodal nonlinear imaging in biomedical sciences—the synergy of multiple contrast mechanisms. J Biophoton, 6(11-12):887-904. https://doi.org/10.1002/jbio.201300176https://doi.org/10.1002/jbio.201300176

Miriyev A, Kovač M, 2020. Skills for physical artificial intelligence. Nat Mach Intell, 2(11):658-660. https://doi.org/10.1038/s42256-020-00258-yhttps://doi.org/10.1038/s42256-020-00258-y

Pan YH, 2017. Special issue on artificial intelligence 2.0. Front Inform Technol Electron Eng, 18(1):1-2. https://doi.org/10.1631/FITEE.1710000https://doi.org/10.1631/FITEE.1710000

Ryman-Tubb NF, Krause P, Garn W, 2018. How artificial intelligence and machine learning research impacts payment card fraud detection: a survey and industry benchmark. Eng Appl Artif Intell, 76:130-157. https://doi.org/10.1016/j.engappai.2018.07.008https://doi.org/10.1016/j.engappai.2018.07.008

Srinivasan CR, Rajesh B, Saikalyan P, et al., 2019. A review on the different types of Internet of Things (IoT). J Adv Res Dynam Contr Syst, 11(1):154-158.

Wilson G, Pereyda C, Raghunath N, et al., 2019. Robot-enabled support of daily activities in smart home environments. Cogn Syst Res, 54:258-272. https://doi.org/10.1016/j.cogsys.2018.10.032https://doi.org/10.1016/j.cogsys.2018.10.032

Xu YZ, Shieh CH, van Esch P, et al., 2020. AI customer service: task complexity, problem-solving ability, and usage intention. Austr Market J, 28(4):189-199. https://doi.org/10.1016/j.ausmj.2020.03.005https://doi.org/10.1016/j.ausmj.2020.03.005

Yadav N, Yadav A, Kumar M, 2015. An Introduction to Neural Network Methods for Differential Equations. Springer, Dordrecht, the Netherlands. https://doi.org/10.1007/978-94-017-9816-7https://doi.org/10.1007/978-94-017-9816-7

Yu W, Liang F, He XF, et al., 2017. A survey on the edge computing for the Internet of Things. IEEE Access, 6:‍6900-6919. https://doi.org/10.1109/ACCESS.2017.2778504https://doi.org/10.1109/ACCESS.2017.2778504

Zhang L, Zhang B, 1999. A geometrical representation of McCulloch-Pitts neural model and its applications. IEEE Trans Neur Netw, 10(4):925-929. https://doi.org/10.1109/72.774263https://doi.org/10.1109/72.774263

Zhang QS, Zhu SC, 2018. Visual interpretability for deep learning: a survey. Front Inform Technol Electron Eng, 19(1):27-39. https://doi.org/10.1631/FITEE.1700808https://doi.org/10.1631/FITEE.1700808

浏览量

Downloads

CSCD

文章被引用时，请邮件提醒。

Submit

工具集

关联资源

暂无数据