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Workshop: NeurIPS 2023 Workshop on Machine Learning for Creativity and Design

BioSpark: An End-to-end Generative System for Biological-Analogical Inspirations and Ideation

Hyeonsu Kang · David Chuan-En Lin · Nikolas Martelaro · Aniket Kittur · Yin-Ying Chen · Matthew Hong

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[ Poster
Sat 16 Dec 1:30 p.m. PST — 2:30 p.m. PST


Nature provides a valuable source of inspirations for novel design solutions to challenging engineering problems. Yet, achieving the full potential of biological-analogical inspirations in engineering and design domains has proven difficult due to the difficulty of discovering relevant analogies and sufficiently understanding them to synthesize novel insights.Here, we introduce an end-to-end system that combines a scalable pipeline for generating biological-analogical mechanisms from nature and an interactive interface that facilitates users’ understanding and synthesis with them.Our dataset generation pipeline starts from a small seed mechanism from human experts and expands it using breadth- and depth-focused expansion prompts based on iteratively constructed taxonomic hierarchies.This approach mitigates the sparsity of data due to high cost of expert curation and the limited conceptual diversity in automated analogy generation using Large Language Models (LLMs).Furthermore, the interactive interface assists designers in recognizing and understanding the applicability of analogs to design problems through four interaction features: Explain, Compare, Combine, and Critique.Our case studies showcase the potential value of our system.We end with avenues for future research.

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