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This paper discusses how intelligent machines have replaced humans in tasks requiring, heavy, and repetitive labor, whilst being better suited to the requirements of these jobs. The increased capacity for brute force computation has facilitated increased collaborative innovation between man and machines. For example, the intelligent farming machines have overcome the confines of computational power, algorithms, and data, and the next generation of intelligent farming machines is expected to interact, learn, and grow autonomously. In the future, in addition to self enhancement, humans are expected to teach machines to learn and work. Scientists and engineers will collaborate with machines to accomplish invention, discovery, and creation. For “embodied intelligence” in the farming machine context, we propose (1) deep learning should be performed iteratively via real-time interactions with the external world; (2) embodied control and self-regulation can ensure coordination between behaviors of machines and their environment; (3) intelligent farming machines are characterized by the ability to interact, learn, and grow autonomously.


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Interactive Embodied Intelligence of Machines

Show Author's information Deyi Li1( )
Deparment of Computer Science and Technology, Tsinghua University, Beijing 100084, China.

Abstract

This paper discusses how intelligent machines have replaced humans in tasks requiring, heavy, and repetitive labor, whilst being better suited to the requirements of these jobs. The increased capacity for brute force computation has facilitated increased collaborative innovation between man and machines. For example, the intelligent farming machines have overcome the confines of computational power, algorithms, and data, and the next generation of intelligent farming machines is expected to interact, learn, and grow autonomously. In the future, in addition to self enhancement, humans are expected to teach machines to learn and work. Scientists and engineers will collaborate with machines to accomplish invention, discovery, and creation. For “embodied intelligence” in the farming machine context, we propose (1) deep learning should be performed iteratively via real-time interactions with the external world; (2) embodied control and self-regulation can ensure coordination between behaviors of machines and their environment; (3) intelligent farming machines are characterized by the ability to interact, learn, and grow autonomously.

Keywords: human-machine interaction, embodied intelligence, machine intelligence

References(6)

[1]
S. Bubeck, V. Chandrasekaran, R. Eldan, J. Gehrke, E. Horvitz, E. Kamar, P. Lee, Y. T. Lee, Y. Li, S. Lundberg et al. , Sparks of artificial general intelligence: Early experiments with GPT-4, arXiv preprint arXiv: 2303.12712, 2023.
[2]

A. Mehrabian and M. Wiener, Decoding of inconsistent communications, J. Pers. Soc. Psychol. , vol. 6, no. 1, pp. 109–114, 1967.

[3]

A. Mehrabian and S. R. Ferris, Inference of attitudes from nonverbal communication in two channels, J. Consult. Psychol. , vol. 31, no. 3, pp. 248–252, 1967.

[4]
Y. LeCun, A path towards autonomous machine intelligence version 0.9. 2, https://openreview.net/forum?id=BZ5a1r-kVsf, 2022.
[5]
A. M. Turing, Computing machinery and intelligence, in Parsing the Turing Test, R. Epstein, G. Roberts, and G. Beber eds. Dordrecht, the Netherlands: Springer, 2009, pp. 23–65.
DOI
[6]
N. Wiener, Cybernetics, or, Control and Communication in the Animal and the Machine. Cambridge, MA, USA: MIT Press, 2019.
DOI
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Publication history

Received: 01 June 2023
Accepted: 04 August 2023
Published: 08 May 2024
Issue date: December 2024

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© The author(s) 2024.

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The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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