CLC number:
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2023-08-29
Cited: 0
Clicked: 1696
Citations: Bibtex RefMan EndNote GB/T7714
Yingbo LI, Zhao LI, Yucong DUAN, Anamaria-Beatrice SPULBER. Physical artificial intelligence (PAI): the next-generation artificial intelligence[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(8): 1231-1238.
@article{title="Physical artificial intelligence (PAI): the next-generation artificial intelligence",
author="Yingbo LI, Zhao LI, Yucong DUAN, Anamaria-Beatrice SPULBER",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="24",
number="8",
pages="1231-1238",
year="2023",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2200675"
}
%0 Journal Article
%T Physical artificial intelligence (PAI): the next-generation artificial intelligence
%A Yingbo LI
%A Zhao LI
%A Yucong DUAN
%A Anamaria-Beatrice SPULBER
%J Frontiers of Information Technology & Electronic Engineering
%V 24
%N 8
%P 1231-1238
%@ 2095-9184
%D 2023
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2200675
TY - JOUR
T1 - Physical artificial intelligence (PAI): the next-generation artificial intelligence
A1 - Yingbo LI
A1 - Zhao LI
A1 - Yucong DUAN
A1 - Anamaria-Beatrice SPULBER
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 24
IS - 8
SP - 1231
EP - 1238
%@ 2095-9184
Y1 - 2023
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2200675
Abstract: Artificial intelligence (AI) has been a driving force for innovation and social progress in various domains (Pan, 2017). However, most of its industrial applications have focused on the signal processing domain, which relies on data generated and collected by different sensors. Recently, some researchers have suggested combining digital AI (DIAI) and physical AI (PAI), which could lead to a significant advancement in the theoretical foundation of AI. In this paper, we explore the concept of PAI and propose two subdomains: integrated PAI (IPAI) and distributed PAI (DPAI). We also discuss the challenges and opportunities for the sustainable development and governance of PAI. Since PAI requires continuous processing of signals from distributed sources across the edge, fog, and Internet of Things (IoT), it can be seen as an extension of the distributed computing continuum system in the field of AI.
[1]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
[2]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
[3]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.
[4]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.
[5]Cheng JF, Chen WH, Tao F, et al., 2018. Industrial IoT in 5G environment towards smart manufacturing. J Ind Inform Integr, 10:10-19.
[6]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.
[7]Costeira JP, Lima P, 2020. A Simple Guide to Physical AI. https://www.ai4europe.eu/research/simple-guide-physical-ai [Accessed on Jan. 14, 2023].
[8]Creswell A, White T, Dumoulin V, et al., 2018. Generative adversarial networks: an overview. IEEE Signal Process Mag, 35(1):53-65.
[9]Dafoe A, 2018. AI Governance: a Research Agenda. Centre for the Governance of AI, Future of Humanity Institute, University of Oxford, Oxford, UK.
[10]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.
[11]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.
[12]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.
[13]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.
[14]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 [Accessed on Jan. 14, 2023].
[15]Deng L, 2016. Deep learning: from speech recognition to language and multimodal processing. APSIPA Trans Signal Inform Process, 5(1):e1.
[16]Frické M, 2019. The knowledge pyramid: the DIKW hierarchy. Knowl Organiz, 46(1):33-46.
[17]Gil L, Liska A, 2019. Security with AI and Machine Learning. O’Reilly Media, Sebastopol, USA.
[18]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.
[19]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.
[20]Janebäck E, Kristiansson M, 2019. Friendly Robot Delivery: Designing an Autonomous Delivery Droid for Collaborative Consumption. Chalmers University of Technology, Gothenburg, Sweden.
[21]Karppi T, Granata Y, 2019. Non-artificial non-intelligence: Amazon’s Alexa and the frictions of AI. AI Soc, 34(4):867-876.
[22]LeCun Y, Bottou L, Bengio Y, et al., 1998. Gradient-based learning applied to document recognition. Proc IEEE, 86(11):2278-2324.
[23]Li H, Zhang ZE, Liu ZJ, 2017. Application of artificial neural networks for catalysis: a review. Catalysts, 7(10):306.
[24]Liao RZ, Chen LP, 2022. An evolutionary note on smart city development in China. Front Inform Technol Electron Eng, 23(6):966-974.
[25]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.
[26]Mahesh B, 2020. Machine learning algorithms—a review. Int J Sci Res, 9:381-386.
[27]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.
[28]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.
[29]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.
[30]Miriyev A, Kovač M, 2020. Skills for physical artificial intelligence. Nat Mach Intell, 2(11):658-660.
[31]Pan YH, 2017. Special issue on artificial intelligence 2.0. Front Inform Technol Electron Eng, 18(1):1-2.
[32]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.
[33]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.
[34]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.
[35]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.
[36]Yadav N, Yadav A, Kumar M, 2015. An Introduction to Neural Network Methods for Differential Equations. Springer, Dordrecht, the Netherlands.
[37]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.
[38]Zhang L, Zhang B, 1999. A geometrical representation of McCulloch-Pitts neural model and its applications. IEEE Trans Neur Netw, 10(4):925-929.
[39]Zhang QS, Zhu SC, 2018. Visual interpretability for deep learning: a survey. Front Inform Technol Electron Eng, 19(1):27-39.
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