2026-09-19T12:59:36Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1519862023-09-01T03:23:42Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Lippman, Andrew B.
Dunnell, Kevin F.
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
2023-08-30T15:56:47Z
2023-08-30T15:56:47Z
2023-06
2023-08-16T20:34:05.922Z
https://hdl.handle.net/1721.1/151986
0009-0002-4430-2564
This Master’s thesis investigates the potential of artificial intelligence (AI) models, particularly machine learning and natural language processing techniques, to facilitate brainstorming and ideation in the invention process. The thesis centers around the iterative development of “Latent Lab,” an interactive tool for exploring relationships among MIT Media Lab research projects. The work offers insights into AI systems as co-inventors by addressing the challenges of organizing, searching, and synthesizing content. Our method for interacting with the material is based on “exploration” rather than search. The primary objective was to create a human-AI co-invention system and evaluate its performance on the novelty of co-created ideas. However, the research underscored the importance of accurate data organization for meaningful data generation. Consequently, later versions of Latent Lab focused primarily on improving data organization and interactive exploration. The tool’s success was measured by its effectiveness in familiarizing users with research projects at the Media Lab, ultimately laying the foundation for the future development of human-AI co-invention systems.
S.M.
Massachusetts Institute of Technology
In Copyright - Educational Use Permitted
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https://rightsstatements.org/page/InC-EDU/1.0/
Latent Lab: Exploration Beyond Search and Synthesis
Thesis
application/pdf
Master
Master of Science in Media Arts and Sciences
Publication
unknown
unknown
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<Abstract>This Master’s thesis investigates the potential of artificial intelligence (AI) models, particularly machine learning and natural language processing techniques, to facilitate brainstorming and ideation in the invention process. The thesis centers around the iterative development of “Latent Lab,” an interactive tool for exploring relationships among MIT Media Lab research projects. The work offers insights into AI systems as co-inventors by addressing the challenges of organizing, searching, and synthesizing content. Our method for interacting with the material is based on “exploration” rather than search. The primary objective was to create a human-AI co-invention system and evaluate its performance on the novelty of co-created ideas. However, the research underscored the importance of accurate data organization for meaningful data generation. Consequently, later versions of Latent Lab focused primarily on improving data organization and interactive exploration. The tool’s success was measured by its effectiveness in familiarizing users with research projects at the Media Lab, ultimately laying the foundation for the future development of human-AI co-invention systems.</Abstract>
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