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Large Language Objects: The Design of Physical AI and Generative Experiences

Author(s)
Coelho, Marcelo; Labrune, Jean-Baptiste
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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Abstract
Large language models (LLMs) have shown an unprecedented ability to generate text, images, and code, surpassing in many ways our own human capabilities and promising to have a profound impact on design and creativity. While powerful, however, these new forms of intelligence remain largely ignorant of the world outside of natural language, lacking knowledge of our objects, bodies, and physical environments. Large languages objects (LLOs) are a new class of artifacts that extend the capabilities of LLMs onto the physical world. Through general-purpose language understanding and generation, these design objects revisit and challenge traditional definitions of form and function, heralding the design of physical AI as a new frontier for design. In this article, we describe a series of LLOs developed at the MIT Design Intelligence Lab (https://designintelligence.mit.edu/) that combine generative and discriminative models to reveal a host of new applications for AI, from new ways of experiencing music or creating and telling stories to new forms of play, communication, and creating physical forms. These LLOs provide a glimpse at a new kind of creative process that weaves together the capabilities of human and artificial intelligence from the early stages of concept development through form-finding, fabrication, and interaction.
Date issued
2024-06-26
URI
https://hdl.handle.net/1721.1/155549
Department
Massachusetts Institute of Technology. Department of Architecture; Massachusetts Institute of Technology. Media Laboratory
Journal
Interactions
Publisher
Association for Computing Machinery
Citation
Coelho, Marcelo and Labrune, Jean-Baptiste. 2024. "Large Language Objects: The Design of Physical AI and Generative Experiences." Interactions, 31 (4).
Version: Final published version
ISSN
1072-5520
1558-3449

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