Physical artificial intelligence (PAI): the next-generation artificial intelligence

Yingbo LI; Zhao LI; Yucong DUAN; Anamaria-Beatrice SPULBER

doi:10.1631/FITEE.2200675

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Physical artificial intelligence (PAI): the next-generation artificial intelligence

Regular Papers | Updated：2023-08-29

- Physical artificial intelligence (PAI): the next-generation artificial intelligence
- 物理人工智能：下一代人工智能
- Frontiers of Information Technology & Electronic Engineering Vol. 24, Issue 8, Pages: 1231-1238(2023)
- 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：
- DOI：10.1631/FITEE.2200675
  CLC：
- Received：31 December 2022，
  
  Revised：2023-04-18，
  
  Published：0 August 2023
- Accepted：
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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：

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。

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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/1803.01164 https://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.2019.12.012 https://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.ch015 https://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.2018.04.001 https://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.4333 https://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-ai https://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.2017.2765202 https://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.2018.08.019 https://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 9 th Int Conf on Biometrics Theory, Applications and Systems , p. 1 - 10 . https://doi.org/10.1109/BTAS.2018.8698538 https://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.2100085 https://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-uncertainty https://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.2015.22 https://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/10.5771/0943-7444-2019-1-33 https://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 15 th IEEE Int Conf on Advanced Video and Signal Based Surveillance , p. 1 - 6 . https://doi.org/10.1109/AVSS.2018.8639163 https://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/B978-0-12-741252-8.50010-8 https://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-w https://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/10.1109/5.726791 https://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/catal7100306 https://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.2100407 https://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.2200297 https://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.2018.08.015 https://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/catal7100306 https://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.201300176 https://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-y https://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.1710000 https://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.2018.07.008 https://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.2018.10.032 https://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.2020.03.005 https://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/10.1007/978-94-017-9816-7 https://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.2017.2778504 https://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/10.1109/72.774263 https://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.1700808 https://doi.org/10.1631/FITEE.1700808

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