skip to main content
10.1145/3527927.3535197acmconferencesArticle/Chapter ViewAbstractPublication Pagesc-n-cConference Proceedingsconference-collections
demonstration

The Idea Machine: LLM-based Expansion, Rewriting, Combination, and Suggestion of Ideas

Published: 20 June 2022 Publication History

Abstract

We introduce the Idea Machine, a creativity support tool that leverages large language models (LLMs) to empower people engaged in idea generation tasks. The tool includes a number of affordances that can be used to enable various levels of automation and intelligent support. Each idea entered into the system can be expanded, rewritten, or combined with other ideas or concepts. An idea suggestion mode can also be enabled to make the system proactively suggest ideas.

Supplementary Material

MP4 File (cc22-76.mp4)
Supplemental video

References

[1]
Salvatore Andolina, Khalil Klouche, Diogo Cabral, Tuukka Ruotsalo, and Giulio Jacucci. 2015. InspirationWall: Supporting Idea Generation Through Automatic Information Exploration. In Proceedings of the 2015 ACM SIGCHI Conference on Creativity and Cognition (Glasgow, United Kingdom) (C&C ’15). ACM, New York, NY, USA, 103–106. https://doi.org/10.1145/2757226.2757252
[2]
Salvatore Andolina, Hendrik Schneider, Joel Chan, Khalil Klouche, Giulio Jacucci, and Steven Dow. 2017. Crowdboard: Augmenting In-Person Idea Generation with Real-Time Crowds. In Proceedings of the 2017 ACM SIGCHI Conference on Creativity and Cognition (Singapore, Singapore) (C&C ’17). ACM, New York, NY, USA, 106–118. https://doi.org/10.1145/3059454.3059477
[3]
Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, 2021. On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258(2021).
[4]
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, 2020. Language models are few-shot learners. Advances in neural information processing systems 33 (2020), 1877–1901.
[5]
Lydia B Chilton, Ecenaz Jen Ozmen, Sam H Ross, and Vivian Liu. 2021. VisiFit: Structuring Iterative Improvement for Novice Designers. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (Yokohama, Japan) (CHI ’21). Association for Computing Machinery, New York, NY, USA, Article 574, 14 pages. https://doi.org/10.1145/3411764.3445089
[6]
Lydia B. Chilton, Savvas Petridis, and Maneesh Agrawala. 2019. VisiBlends: A Flexible Workflow for Visual Blends. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (Glasgow, Scotland Uk) (CHI ’19). Association for Computing Machinery, New York, NY, USA, 1–14. https://doi.org/10.1145/3290605.3300402
[7]
Jonas Frich, Lindsay MacDonald Vermeulen, Christian Remy, Michael Mose Biskjaer, and Peter Dalsgaard. 2019. Mapping the Landscape of Creativity Support Tools in HCI. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (Glasgow, Scotland Uk) (CHI ’19). Association for Computing Machinery, New York, NY, USA, 1–18. https://doi.org/10.1145/3290605.3300619
[8]
Katy Ilonka Gero and Lydia B. Chilton. 2019. Metaphoria: An Algorithmic Companion for Metaphor Creation. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (Glasgow, Scotland Uk) (CHI ’19). Association for Computing Machinery, New York, NY, USA, 1–12. https://doi.org/10.1145/3290605.3300526
[9]
Scarlett R. Herring, Chia-Chen Chang, Jesse Krantzler, and Brian P. Bailey. 2009. Getting Inspired! Understanding How and Why Examples Are Used in Creative Design Practice. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Boston, MA, USA) (CHI ’09). Association for Computing Machinery, New York, NY, USA, 87–96. https://doi.org/10.1145/1518701.1518717
[10]
Angel Hsing-Chi Hwang and Andrea Stevenson Won. 2021. IdeaBot: Investigating Social Facilitation in Human-Machine Team Creativity. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (Yokohama, Japan) (CHI ’21). Association for Computing Machinery, New York, NY, USA, Article 712, 16 pages. https://doi.org/10.1145/3411764.3445270
[11]
Mina Lee, Percy Liang, and Qian Yang. 2022. CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities. In CHI Conference on Human Factors in Computing Systems (New Orleans, LA, USA) (CHI ’22). Association for Computing Machinery, New York, NY, USA, Article 388, 19 pages. https://doi.org/10.1145/3491102.3502030
[12]
Mark A. Runco and Selcuk Acar. 2012. Divergent Thinking as an Indicator of Creative Potential. Creativity Research Journal 24, 1 (2012), 66–75. https://doi.org/10.1080/10400419.2012.652929 arXiv:https://doi.org/10.1080/10400419.2012.652929
[13]
Hanieh Shakeri, Carman Neustaedter, and Steve DiPaola. 2021. SAGA: Collaborative Storytelling with GPT-3. In Companion Publication of the 2021 Conference on Computer Supported Cooperative Work and Social Computing (Virtual Event, USA) (CSCW ’21). Association for Computing Machinery, New York, NY, USA, 163–166. https://doi.org/10.1145/3462204.3481771
[14]
Ben Shneiderman. 2007. Creativity support tools: Accelerating discovery and innovation. Commun. ACM 50, 12 (2007), 20–32.
[15]
Ben Shneiderman. 2022. Human-Centered AI. Oxford University Press. https://doi.org/10.1093/oso/9780192845290.001.0001
[16]
Pao Siangliulue, Joel Chan, Krzysztof Z. Gajos, and Steven P. Dow. 2015. Providing Timely Examples Improves the Quantity and Quality of Generated Ideas. In Proceedings of the 2015 ACM SIGCHI Conference on Creativity and Cognition (Glasgow, United Kingdom) (C&C ’15). Association for Computing Machinery, New York, NY, USA, 83–92. https://doi.org/10.1145/2757226.2757230
[17]
Hao-Chuan Wang, Dan Cosley, and Susan R Fussell. 2010. Idea expander: supporting group brainstorming with conversationally triggered visual thinking stimuli. In Proc. of CSCW’10. 103–106.
[18]
Steven M Smith Thomas B Ward and Ronald A Finke. 1995. The creative cognition approach. MIT press.
[19]
Tongshuang Wu, Michael Terry, and Carrie Jun Cai. 2022. AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts. In CHI Conference on Human Factors in Computing Systems (New Orleans, LA, USA) (CHI ’22). Association for Computing Machinery, New York, NY, USA, Article 385, 22 pages. https://doi.org/10.1145/3491102.3517582
[20]
Lixiu Yu and Jeffrey V. Nickerson. 2011. Cooks or Cobblers? Crowd Creativity through Combination. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Vancouver, BC, Canada) (CHI ’11). Association for Computing Machinery, New York, NY, USA, 1393–1402. https://doi.org/10.1145/1978942.1979147
[21]
Lixiu Yu and Jeffrey V. Nickerson. 2013. An Internet-Scale Idea Generation System. ACM Trans. Interact. Intell. Syst. 3, 1, Article 2 (apr 2013), 24 pages. https://doi.org/10.1145/2448116.2448118

Cited By

View all
  • (2024)Enhancing user experience in large language models through human-centered design: Integrating theoretical insights with an experimental study to meet diverse software learning needs with a single document knowledge baseComputing and Artificial Intelligence10.59400/cai.v2i1.5352:1(535)Online publication date: 19-Apr-2024
  • (2024)Making ChatGPT Work For MeSSRN Electronic Journal10.2139/ssrn.4700354Online publication date: 2024
  • (2024)Proposal of User Interface Based on Heavy User Usage Analysis in LLM ServiceArchives of Design Research10.15187/adr.2024.08.37.4.28737:4(287-313)Online publication date: 31-Aug-2024
  • Show More Cited By

Recommendations

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences
C&C '22: Proceedings of the 14th Conference on Creativity and Cognition
June 2022
710 pages
ISBN:9781450393270
DOI:10.1145/3527927
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 June 2022

Check for updates

Author Tags

  1. Creativity
  2. brainstorming
  3. human-centered AI
  4. idea generation
  5. large language models

Qualifiers

  • Demonstration
  • Research
  • Refereed limited

Funding Sources

  • The Italian Ministry of Education, University and Research (MIUR)

Conference

C&C '22
Sponsor:
C&C '22: Creativity and Cognition
June 20 - 23, 2022
Venice, Italy

Acceptance Rates

Overall Acceptance Rate 108 of 371 submissions, 29%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)574
  • Downloads (Last 6 weeks)43
Reflects downloads up to 24 Oct 2024

Other Metrics

Citations

Cited By

View all
  • (2024)Enhancing user experience in large language models through human-centered design: Integrating theoretical insights with an experimental study to meet diverse software learning needs with a single document knowledge baseComputing and Artificial Intelligence10.59400/cai.v2i1.5352:1(535)Online publication date: 19-Apr-2024
  • (2024)Making ChatGPT Work For MeSSRN Electronic Journal10.2139/ssrn.4700354Online publication date: 2024
  • (2024)Proposal of User Interface Based on Heavy User Usage Analysis in LLM ServiceArchives of Design Research10.15187/adr.2024.08.37.4.28737:4(287-313)Online publication date: 31-Aug-2024
  • (2024)The Silicon Ceiling: Auditing GPT’s Race and Gender Biases in HiringProceedings of the 4th ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization10.1145/3689904.3694699(1-18)Online publication date: 29-Oct-2024
  • (2024)Serendipity Wall: A Discussion Support System Using Real-time Speech Recognition and Large Language ModelProceedings of the Augmented Humans International Conference 202410.1145/3652920.3652931(237-247)Online publication date: 4-Apr-2024
  • (2024)Jamplate: Exploring LLM-Enhanced Templates for Idea ReflectionProceedings of the 29th International Conference on Intelligent User Interfaces10.1145/3640543.3645196(907-921)Online publication date: 18-Mar-2024
  • (2024)Challenges and Opportunities of LLM-Based Synthetic Personae and Data in HCIExtended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613905.3636293(1-5)Online publication date: 11-May-2024
  • (2024)From Paper to Card: Transforming Design Implications with Generative AIProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642266(1-15)Online publication date: 11-May-2024
  • (2024)AI-Assisted Causal Pathway Diagram for Human-Centered DesignProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642179(1-19)Online publication date: 11-May-2024
  • (2024)BLIP: Facilitating the Exploration of Undesirable Consequences of Digital TechnologiesProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642054(1-18)Online publication date: 11-May-2024
  • Show More Cited By

View Options

Get Access

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media