Local Intelligence: Time To Learn From Ai

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Date

2025

Authors

Journal Title

Journal ISSN

Volume Title

Publisher

Taylor and Francis Ltd.

Open Access Color

Green Open Access

No

OpenAIRE Downloads

OpenAIRE Views

Publicly Funded

No
Impulse
Average
Influence
Average
Popularity
Top 10%

Research Projects

Journal Issue

Abstract

AI research in architecture is flourishing, and there are plausible and praiseworthy experiments using generative models. These experiments could result in intelligence that uses architectural knowledge and opens new learning opportunities, although guidance is still required in this area. With the Local Intelligence (LI) framework, we hypothesize a web of distributed networks to connect different forms of knowledge linked with architectural context. We assume that the tacit knowledge of vernacular architecture corresponds to the implicit knowledge of folklore music when the anonymous designer and the user are the same- the local people. With a multimodal AI model, we call ‘music2architecture’ – ‘architecture2music’, we argue that sharing the same locality/localness may lead to the emergence of a typical, previously hidden pattern (of wisdom) that we can learn from. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

Description

Keywords

Architecture To Music, Generative Adversarial Networks, Local Intelligence, Localness, Music To Architecture, Pix2Pix

Fields of Science

Citation

WoS Q

N/A

Scopus Q

Q1
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OpenCitations Citation Count
N/A

Source

Architectural Science Review

Volume

68

Issue

1

Start Page

13

End Page

28
PlumX Metrics
Citations

Scopus : 1

Captures

Mendeley Readers : 12

SCOPUS™ Citations

1

checked on Mar 19, 2026

Web of Science™ Citations

2

checked on Mar 19, 2026

Page Views

8

checked on Mar 19, 2026

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1.3109

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