Imitation of Life: A Search Engine for Biologically Inspired Design

Research output: Contribution to journalConference articleResearchpeer-review

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Imitation of Life : A Search Engine for Biologically Inspired Design. / Emuna, Hen; Borenstein, Nadav; Qian, Xin; Kang, Hyeonsu; Chan, Joel; Kittur, Aniket; Shahaf, Dafna.

In: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 38, No. 11, 2024, p. 503-511.

Research output: Contribution to journalConference articleResearchpeer-review

Harvard

Emuna, H, Borenstein, N, Qian, X, Kang, H, Chan, J, Kittur, A & Shahaf, D 2024, 'Imitation of Life: A Search Engine for Biologically Inspired Design', Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, no. 11, pp. 503-511. https://doi.org/10.1609/aaai.v38i1.27805

APA

Emuna, H., Borenstein, N., Qian, X., Kang, H., Chan, J., Kittur, A., & Shahaf, D. (2024). Imitation of Life: A Search Engine for Biologically Inspired Design. Proceedings of the AAAI Conference on Artificial Intelligence, 38(11), 503-511. https://doi.org/10.1609/aaai.v38i1.27805

Vancouver

Emuna H, Borenstein N, Qian X, Kang H, Chan J, Kittur A et al. Imitation of Life: A Search Engine for Biologically Inspired Design. Proceedings of the AAAI Conference on Artificial Intelligence. 2024;38(11):503-511. https://doi.org/10.1609/aaai.v38i1.27805

Author

Emuna, Hen ; Borenstein, Nadav ; Qian, Xin ; Kang, Hyeonsu ; Chan, Joel ; Kittur, Aniket ; Shahaf, Dafna. / Imitation of Life : A Search Engine for Biologically Inspired Design. In: Proceedings of the AAAI Conference on Artificial Intelligence. 2024 ; Vol. 38, No. 11. pp. 503-511.

Bibtex

@inproceedings{cf2c095ec3a14ea8bb81c9d6dfb52b86,
title = "Imitation of Life: A Search Engine for Biologically Inspired Design",
abstract = "Biologically Inspired Design (BID), or Biomimicry, is a problem-solving methodology that applies analogies from nature to solve engineering challenges. For example, Speedo engineers designed swimsuits based on shark skin. Finding relevant biological solutions for real-world problems poses significant challenges, both due to the limited biological knowledge engineers and designers typically possess and to the limited BID resources. Existing BID datasets are hand-curated and small, and scaling them up requires costly human annotations. In this paper, we introduce BARCODE (Biological Analogy Retriever), a search engine for automatically mining bio-inspirations from the web at scale. Using advances in natural language understanding and data programming, BARCODE identifies potential inspirations for engineering challenges. Our experiments demonstrate that BARCODE can retrieve inspirations that are valuable to engineers and designers tackling real-world problems, as well as recover famous historical BID examples. We release data and code; we view BARCODE as a step towards addressing the challenges that have historically hindered the practical application of BID to engineering innovation.",
author = "Hen Emuna and Nadav Borenstein and Xin Qian and Hyeonsu Kang and Joel Chan and Aniket Kittur and Dafna Shahaf",
note = "Publisher Copyright: Copyright {\textcopyright} 2024, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.; 38th AAAI Conference on Artificial Intelligence, AAAI 2024 ; Conference date: 20-02-2024 Through 27-02-2024",
year = "2024",
doi = "10.1609/aaai.v38i1.27805",
language = "English",
volume = "38",
pages = "503--511",
journal = "AAAI Conference on Artificial Intelligence",
issn = "2159-5399",
publisher = "Association for the Advancement of Artificial Intelligence",
number = "11",

}

RIS

TY - GEN

T1 - Imitation of Life

T2 - 38th AAAI Conference on Artificial Intelligence, AAAI 2024

AU - Emuna, Hen

AU - Borenstein, Nadav

AU - Qian, Xin

AU - Kang, Hyeonsu

AU - Chan, Joel

AU - Kittur, Aniket

AU - Shahaf, Dafna

N1 - Publisher Copyright: Copyright © 2024, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

PY - 2024

Y1 - 2024

N2 - Biologically Inspired Design (BID), or Biomimicry, is a problem-solving methodology that applies analogies from nature to solve engineering challenges. For example, Speedo engineers designed swimsuits based on shark skin. Finding relevant biological solutions for real-world problems poses significant challenges, both due to the limited biological knowledge engineers and designers typically possess and to the limited BID resources. Existing BID datasets are hand-curated and small, and scaling them up requires costly human annotations. In this paper, we introduce BARCODE (Biological Analogy Retriever), a search engine for automatically mining bio-inspirations from the web at scale. Using advances in natural language understanding and data programming, BARCODE identifies potential inspirations for engineering challenges. Our experiments demonstrate that BARCODE can retrieve inspirations that are valuable to engineers and designers tackling real-world problems, as well as recover famous historical BID examples. We release data and code; we view BARCODE as a step towards addressing the challenges that have historically hindered the practical application of BID to engineering innovation.

AB - Biologically Inspired Design (BID), or Biomimicry, is a problem-solving methodology that applies analogies from nature to solve engineering challenges. For example, Speedo engineers designed swimsuits based on shark skin. Finding relevant biological solutions for real-world problems poses significant challenges, both due to the limited biological knowledge engineers and designers typically possess and to the limited BID resources. Existing BID datasets are hand-curated and small, and scaling them up requires costly human annotations. In this paper, we introduce BARCODE (Biological Analogy Retriever), a search engine for automatically mining bio-inspirations from the web at scale. Using advances in natural language understanding and data programming, BARCODE identifies potential inspirations for engineering challenges. Our experiments demonstrate that BARCODE can retrieve inspirations that are valuable to engineers and designers tackling real-world problems, as well as recover famous historical BID examples. We release data and code; we view BARCODE as a step towards addressing the challenges that have historically hindered the practical application of BID to engineering innovation.

U2 - 10.1609/aaai.v38i1.27805

DO - 10.1609/aaai.v38i1.27805

M3 - Conference article

AN - SCOPUS:85189348972

VL - 38

SP - 503

EP - 511

JO - AAAI Conference on Artificial Intelligence

JF - AAAI Conference on Artificial Intelligence

SN - 2159-5399

IS - 11

Y2 - 20 February 2024 through 27 February 2024

ER -

ID: 390579448