Kai Wang - AI for Innovation1.1r.pdf
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This document discusses using AI for innovation and idea generation. It summarizes several studies that compare human and AI performance on divergent thinking and business idea generation tasks. The studies found that while AI can generate useful ideas, humans tend to be more innovative. The document also discusses how AI can be used as innovation tools to explore problems and generate solutions, but that overreliance on AI risks deskilling humans. It emphasizes that human expertise is still needed to effectively use AI for creativity.
Kai Wang - AI for Innovation1.1r.pdf
- 1. Using Generative AI for Innovation: Research and Practice Kai Wang, PhD
- 2. About Me © AI Ager LLC 2 PHD IN TECHNOLOGY MANAGEMENT PROFESSOR OF MANAGEMENT FOR 8 YEARS FOUNDER OF AI AGER
- 3. 1. Idea Generation: Humans vs. AI © AI Ager LLC 3
- 4. Divergent Thinking • Humans (n = 256) vs. ChatGPT3.5, ChatGPT4, and Copy.Ai (based on GPT3) • Alternate uses task: rope, box, pencil, and candle • On average: AI’s responses were more creative • The best human ideas still matched or exceeded AI’s • Humans generated some low-creativity ideas, while AI did not • ChatGPT4 appeared to be the best chatbot © AI Ager LLC 4 (Koivisto & Grassini, 2023)
- 5. Business Ideas I • GPT-4 vs. Crowdsourcing • Sustainable, circular economy business idea • AI solutions had more environmental and financial value • Human solutions were more innovative © AI Ager LLC 5 (Boussioux et al., 2023)
- 6. Business Ideas II • ChatGPT-4 (temperature 0.7) vs. College Students • A new physical product for the college student market that would be likely to retail for less than $50 • AI ideas had higher purchase intent • High quality examples further increased AI performance • Human ideas were more novel © AI Ager LLC 6 (Girotra, et al., 2023)
- 7. 1. Idea Generation: Humans vs. AI © AI Ager LLC • For divergent thinking tasks, ChatGPT-4 is the best AI for generating creative ideas • For business problems, humans seem more innovative, yet AI can generate useful ideas. 7
- 8. 2. AI as Innovation Tools © AI Ager LLC 8
- 9. Innovation as Problem Solving © AI Ager LLC 9 Problem Space Solution Space Selected Problems Selected Solutions Problem Exploration Problem Selection Solution Exploration Solution Selection
- 10. Problem Exploration I • Zoom In - Parts: What are the parts of this problem? • Zoom Out - Types: What is this problem a part of? • Analogize: What are analogies for this problem? • Reflect - What is missing from the current problem statement? © AI Ager LLC 10 (Rick, et al., 2023)
- 11. Problem Exploration II • Text summarization • Sentiment analysis • Insight generation © AI Ager LLC 11 (Bouschery, et al., 2023)
- 12. Idea Generation I • Trisociation • Combining the concepts of chocolate, elephant, and butterfly • Function follows form © AI Ager LLC 12 (Eapen et al., 2023)
- 13. Idea Generation II • TRIZ • Principle of Segmentation suggests that an object or system can be improved by dividing it into independent parts or by making it easy to disassemble. • How might I use this principle for this problem? © AI Ager LLC 13 (Livotov, 2023)
- 14. 3. Risks of AI Overreliance © AI Ager LLC 14
- 15. © AI Ager LLC • It takes a lot of human expertise to use AI well • Creative people can better use AI for creative output • People are more willing to implement ideas they think of 15
- 16. • AI reduces the amount of practice • Lack of practice can lead to deskilling © AI Ager LLC 16 (Beane, 2022)
- 17. “creativity, …is … an act of intellectual or aesthetic persuasion” --Dean Simonton © AI Ager LLC 17 (Simonton, 1988)
- 18. Thank you © AI Ager LLC • www.aiager.com • Innovation training and consulting • Soft skills training, such as persuasion • All AI-infused • https://www.linkedin.com/in/kaiidea/ 18
- 19. References I • Beane, M. (2022). Today’s Robotic Surgery Turns Surgical Trainees into Spectators: Medical Training in the Robotics Age Leaves Tomorrow’s Surgeons Short on Skills. IEEE Spectrum, 59(8), 32–37. • Bouschery, S. G., Blazevic, V., & Piller, F. T. (2023). Augmenting human innovation teams with artificial intelligence: Exploring transformer-based language models. JOURNAL OF PRODUCT INNOVATION MANAGEMENT, 40(2), 139–153. • Boussioux, L., N Lane, J., Zhang, M., Jacimovic, V., & Lakhani, K. R. (2023). The Crowdless Future? How Generative AI Is Shaping the Future of Human Crowdsourcing. The Crowdless Future. • Eapen, T. T., Finkenstadt, D. J., Folk, J., & Venkataswamy, L. (2023). How Generative AI Can Augment Human Creativity. HARVARD BUSINESS REVIEW, 101(7–8), 55–64. • Girotra, K., Meincke, L., Terwiesch, C., & Ulrich, K. T. (2023). Ideas are dimes a dozen: Large language models for idea generation in innovation. Available at SSRN 4526071. • Koivisto, M., & Grassini, S. (2023). Best humans still outperform artificial intelligence in a creative divergent thinking task. Scientific Reports, 13(1), Article 1. © AI Ager LLC 19
- 20. References II • Livotov, P. (2023). ENHANCING ENGINEERING CREATIVITY WITH AUTOMATED FORMULATION OF ELEMENTARY SOLUTION PRINCIPLES. Proceedings of the Design Society, 3, 1645–1654. • Rick, S. R., Giacomelli, G., Wen, H., Laubacher, R. J., Taubenslag, N., Heyman, J. L., ... & Malone, T. W. (2023). Supermind Ideator: Exploring generative AI to support creative problem-solving. arXiv preprint arXiv:2311.01937. • Simonton, D. K. (1988). Age and outstanding achievement: What do we know after a century of research? Psychological Bulletin, 104(2), 251–267. • Sutton, R. I., & Hargadon, A. (1996). Brainstorming groups in context: Effectiveness in a product design firm. Administrative science quarterly, 685-718. © AI Ager LLC 20