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On Understanding and Machine Understanding

Chern, Tong...

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On Understanding and Machine Understanding by Chern, Tong ( Author )

Content Provider	Lemonjar Open Access
Publisher	Australian National University
Publication Date	27-07-2023
Summary	In the present paper, we try to propose a self-similar network theory for the basic understanding. By extending the natural languages to a kind of so called idealy sufficient language, we can proceed a few steps to the investigation of the language searching and the language understanding of AI. Image understanding, and the familiarity of the brain to the surrounding environment are also discussed. Group effects are discussed by addressing the essense of the power of influences, and constructing the influence network of a society. We also give a discussion of inspirations.
ISBN	-
Item Type	Article
File Type	pdf
File Size	29.34 KB
Language	English
Subjects	-
Unlimited	MYR 0.01
Total read	-
URL	http://arxiv.org/abs/1103.5034

Citation

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AMA 11th EditionReferences

Chern, Tong. On Understanding and Machine Understanding. Australian National University. July 27, 2023. Accessed November 10, 2024. https://online.provdatabase.com/cite/4920

APA 7th EditionReferences

Chern, Tong. (2023). On Understanding and Machine Understanding. Australian National University. https://online.provdatabase.com/cite/4920

Chicago 17th EditionReferences

Chern, Tong. On Understanding and Machine Understanding. Australian National University. 2023. https://online.provdatabase.com/cite/4920

HarvardReferences

Chern, Tong. (2023). On Understanding and Machine Understanding. [Online]. Australian National University. Available from: https://online.provdatabase.com/cite/4920. (Accessed 10 November 2024).

Harvard AustraliaReferences

Chern, Tong, 2023, On Understanding and Machine Understanding, Australian National University, viewed 10 November 2024, <https://online.provdatabase.com/cite/4920>

MLA 9th EditionWorks Cited

Chern, Tong. On Understanding and Machine Understanding. Australian National University, 2023. PROV Database, https://online.provdatabase.com/cite/4920.

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