Malzeme Odaklı Yapay Zeka Destekli Bir Tasarım Süreci Önerisi: Doğal Malzemeden Yapay Zekâya

Abstract

Malzeme/yapay zeka etkileşiminin tasarım sürecinde kısıtlayıcı mı yoksa özgürleştirici mi bir rol oynadığının araştırılacağı bu proje, nesnelerin tasarlanma ve üretim süreçlerindeki malzeme ve form ilişkisinin deneyim yoluyla incelenmesine odaklanmaktadır. Bu deneyim ortamı, bir çalıştay kapsamında, malzemenin temel özelliklerinin tanımlanması, bu tanım çerçevesinde formun dijital olarak tasarlanması, tasarlanan formun söz konusu malzeme ile üretilmesi ve bu üretim sürecinde malzeme ve/veya forma ilişkin karşılaşılan sorunların gereken revizyonlar ile çözülmeye çalışması gibi çeşitli aşamalardan oluşmaktadır. Malzemelerin çevre dostu biyo-polimer baz kısmına tekstil, kestane kabuğu veya talaş eklenmesiyle oluşturulacak kişisel reçeteler, yapay zeka destekli Midjourney uygulamasında forma dönüştürülecektir. [Hakem incelemesi için çıkarıldı] [Hakem incelemesi için çıkarıldı] Fakültesi bünyesinde toplam 43 öğrencinin tasarladıkları malzemelerin belirleyecekleri duyusal, anlamsal, duygusal, performatif ve potansiyel özelliklerini yapay zeka girdisi olarak kullanacakları araştırmada, enformel bir ortamda yaparak öğrenme hedeflenmektedir. Dijital olarak üretilen formun, malzeme deneyleri ile fiziksel olarak üretilebilirliklerinin deneyimlenmesi, üretim sürecindeki zorluklar ve bunların nasıl çözümlendiği araştırmanın önemli aşamalarındandır. Öğrencilerin süreci çeşitli, yazılı ve görsel verilerle kaydedeceği çalıştayda, elde edilecek veriler sınıflandırılarak, çalışmada anahtar kelime olarak tariflenen ifadelerinin değişimleri analiz edilmiştir. Yenilenebilir biyo-polimer malzeme kullanarak güncel bir dijital tasarım yöntemi ile form oluşturmaya temellenen bu proje, bir yandan sürdürülebilirlik ve tasarımda yapay zeka kullanımı üzerine farkındalık oluştururken, bir yandan da deneyim yoluyla enformel öğrenme süreçlerini yeniden gündeme getirmektedir.

Keywords

• Malzeme deneyimi
• Biyo-polimer
• Yapay zeka
• Form oluşturma
• Yaparak öğrenme.

A Proposal for a Material-Focused AI-Supported Design Process: From Bio Material to Artificial Intelligence

Abstract

This project investigates whether the interaction between material and artificial intelligence plays a constraining or liberating role in the design process, while focusing on examining the relationship between material and form in the design and production processes of objects through experience. This experiential environment consists of several stages within the scope of a workshop, including defining the fundamental properties of the material, digitally designing the form based on this definition, producing the designed form with the specified material, and attempting to resolve any issues related to the material and/or form encountered during this production process through necessary revisions. Personal recipes, created by adding textile, chestnut shell, or sawdust to the environmentally friendly bio-polymer base of the materials, will be transformed into forms using the AI-supported Midjourney application. [Omitted for blind review] [Omitted for blind review] Faculty, a total of 43 students use the sensory, semantic, emotional, performative, and potential properties of the materials they design as AI inputs in the research, aiming to learn by doing in an informal setting. Experiencing the physical producibility of the digitally generated form through material experiments, the challenges in the production process, and how these challenges are resolved are significant stages of the research. In the workshop, where students record the process with various written and visual data, the obtained data is classified, and the changes in expressions defined as keywords in the study are analyzed. This project, based on creating form using a current digital design method with renewable bio-polymer material, not only raises awareness about sustainability and the use of artificial intelligence in design but also revisits informal learning processes through experience.

Keywords

• Material experience
• Bio-Polymer
• Artificial Intelligence
• Form creation
• Learning by doing.

References

1. Aaliya, B., Sunooj, K. V., & Lackner, M. (2021). Biopolymer composites: a review. International Journal of Biobased Plastics, 3(1):40-84. DOI:10.1080/24759651.2021.1881214.
2. Ahmad, S. F., Rahmat, M. K., Mubarik, M.S., Alam, M.M., & Hyder, S.I. (2021). Artificial intelligence and its role in education. Sustainability, 13(22), 12902. DOI:10.3390/su132212902.
3. Anantrasirichai, N., Bull, D. (2022). Artificial intelligence in the creative industries: a review. Artificial Intelligence Review, 55(589–656). DOI:10.1007/s10462-021-10039-7.
4. As, I., Siddharth, P., & Prithwish, B. (2018). Artificial intelligence in architecture: generating conceptual design via deep learning. International Journal of Architectural Computing, 16 (4): 306–27. DOI:10.1177/1478077118800982.
5. Baudrillard, J. (2003). Simülakrlar ve simülasyon. Doğu Batı Yayınları.
6. Berman, M. (2004). Katı olan herşey buharlaşıyor. İletişim Yayınları.
7. Boden, M.A. (1998). Creativity and artificial intelligence. Artificial Intelligence, 103( 347–356). DOI:10.1016/S0004-3702(98)00055-1.
8. Bölek, B., Tutal, O., & Özbaşaran, H. (2023). A systematic review on artificial intelligence applications in architecture. Journal of Design for Resilience in Architecture and Planning, 4: 91–104. DOI:10.47818/DRArch.2023.v4i1085.

9. Campo, M., Leach, N., (2022). Machine hallucinations: architecture and artificial intelligence. John Wiley & Sons.
10. Chapman, J. (2014). Meaningful stuff: Toward longer lasting products. In E. Karana, O. Pedgley, & V. Rognoli (Eds.), Materials experience: Fundamentals of materials and design (pp.135-143), Oxford, UK: Butterworth-Heinemann.
11. Christian, S. J. (2020). Natural fibre-reinforced noncementitious composites (biocomposites). In K.A. Harries, B. Sharma (Eds.), Nonconventional and vernacular construction materials (pp.169-187), Woodhead Publishing.
12. Cui, H., Wang, C., Maan, H., Pang, K., Luo, F., & Wang, B. (2023). Towards building a foundation model for singlecell multi-omics using generative ai., Nature Method, 21: 1470–1480. DOI:10.1038/s41592-024-02201-0.
13. Colton, S., & Wiggins, G. A. (2012). Computational creativity: The final frontier? In ECAI 2012 - 20th European Conference on Artificial Intelligence, 27-31 August 2012, Montpellier, France - Including Prestigious Applications of Artificial Intelligence (PAIS-2012) System Demonstration (pp. 21-26). (Frontiers in Artificial Intelligence and Applications; Vol. 242). IOS Press. https://doi.org/10.3233/978-1-61499-098-7-21
14. Csikszentmihalyi, M. (1997). Human behavior and the science center. In P. G. Heltne & L. A. Marquardt (Eds.), Science learning in the in- formal setting. Proceedings of the Symposium of the Chicago Academy of Sciences (pp. 79- 87). Chicago: University of Chicago Press.
15. Dalla-Torre, H., Gonzalez, L., Mendoza Revilla, J., Lopez Carranza, N., Henryk Grywaczewski, A., Oteri, F., Dallago, C., Trop, E., Sirelkhatim, H., Richard, G., Skwark, M., Beguir, K., Lopez, M., & Pierrot, T. (2023). The nucleotide transformer: Building and evaluating robust foundation models for human genomics. bioRxiv, DOI:10.1101/2023.01.11.523679.
16. Dartnall, T. (1994). Artificial intelligence and creativity: An interdisciplinary approach. Springer. Netherlands. Dave, B., Buda, A., Nurminen, A., Främling, K. (2018). A framework for integrating BIM and IoT through open standards. Automation in Construction, 95(35-45). DOI:10.1016/j.autcon.2018.07.022.
17. Fischer, G., Nakakoji, K. (1994). Amplifying designers' creativity with domain-oriented design environments. In: Dartnall, T. (Ed.), Artificial Intelligence and Creativity. An Interdisciplinary Approach, pp. 343–364. Springer. Graham M., D. (2015). CoDesign with data [Doctoral dissertation, City University of London]. Open access City UK.
18. Guilford, J.P. (1975). Varieties of creative giftedness, their measurement and development. Gifted Child Quarterly, 19 (107–121). https://psycnet.apa.org/record/1975-31796-001.
19. Hertzmann, A. (2022). Give this AI a few words of description and it produces a stunning image – but is it art?. The Conversation. Retrieved December 28, 2023. https://theconversation.com/give-this-ai-a-few-words-of-description-and-it-produces-a-stunning-image-but-is-it-art-184363.
20. Hoff, D. J. (2001). Progress lacking on US students’ grasp of science. Education Week, 21, 1 & 14. Howes, P. D., Wongsriruksa, S., Laughlin, Z., Witchel, H. J., & Miodownik, M. (2014). The perception of materials through oral sensation. Plos One, 9(8): e105035. DOI:10.1371/journal.pone.0105035.
21. Hsieh, H. F., & Shannon, S. E. (2005). Three approaches to qualitative content analysis. Qualitative Health Research, 15(9), 1277–1288.
22. Hsu, C., Verkuil, R., Liu, J., Lin, Z., Hie, B., Sercu, T., Lerer, A., & Rives, A. (2022). Learning inverse folding from millions of predicted structures. In Chaudhuri, K., Jegelka, S., Song, L., Szepesvari, C., Niu, G., and Sabato, S. (Eds.), Proceedings of the 39th International Conference on MachineLearning,162,pp.8946–8970. https://proceedings.mlr.press/ v162/hsu22a.html.
23. Itten, J. (1975). Design and form: The basic course at the Bauhaus and later. New York, NY: John Wiley & Sons. Jablonka, K. M., Ai, Q., Al-Feghali, A., Badhwar, S., Bocarsly, J. D., Bran, A. M., Bringuier, S., Brinson, L. C., Choudhary, K., Circi, D. (2023). 14 examples of how llms can transform materials science and chemistry: a reflection on a large language model hackathon. Digital Discovery, 2(5):1233–1250. DOI:10.1039/D3DD00113J.
24. Ji, H., Han, I., & Ko, Y. (2022). A systematic review of conversational AI in language education: Focusing on the collaboration with human teachers. Journal of Research on Technology in Education, 55(2):1-16. Doi:10.1080/15391523.2022.2142873.
25. Johnson, A. (2023). ChatGPT in schools: Here’s where it’s banned—and how it could potentially help students. Forbes. https://www.forbes.com/sites/ariannajohnson/2023/01/18/chatgpt-in-schools-heres-where-its-banned-and-how-it-could-potenti ally-help-students/.
26. Karana, E., Hekkert, P., & Kandachar, P. (2008). Materials experience: Descriptive categories in material appraisals. In Proceedings of the Conference on Tools and Methods in Competitive Engineering (pp. 399-412). Delft, the Netherlands: Delft University of Technology.
27. Karana, E. (2009). Meanings of materials (Doctoral dissertation). Delft University of Technology, Delft, the Netherlands.
28. Karana, E., Barati, B., Rognoli, V., & Zeeuw Van Der Laan, A. (2015). Material driven design (MDD): A method to design for material experiences. International Journal of Design, 9(2), 35-54.
29. Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., & Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. DOI:10.1016/J.LINDIF.2023.102274.
30. King, R., Churchill, E.F., Tan, C. (2017). Designing with data: improving the user experience with A/B testing. O'Reilly Media.
31. Krstić, L., Aleksić, V., & Krstić, M. (2022). Artificial intelligence in education: A review. 9th International Scientific Conference Technics and Informatics in Education. Doi:10.46793/TIE22.223K.
32. Leach, N. (2018). Design in the age of artificial intelligence. Landscape Architecture Frontiers, 6 (2): 8–20. Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., Smetanin, N., Verkuil, R., Kabeli, O., Shmueli, Y. (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, 379(6637): 1123–1130.
33. London, K., & Ostwald, M. (2004). Linda Groat and David Wang: Architectural Research Methods, New York: John Wiley and Sons, 2002. Nexus Network Journal, 6(1), 51-53. DOI:10.1007/s00004-004-0006-7.
34. Miller, A. I. (2012). Insights of Genius: Imagery and creativity in science and art. Springer Science & Business Media.
35. Miller, A.I. (2019). The artist in the machine. the world of AI-powered creativity. The MIT Press.
36. Miret, S. & Anoop Krishnan, N. M. (2024). Are LLMs Ready for Real-World Materials Discovery?, arxiv. DOI:10.48550/arXiv.2402.05200.
37. Nast, C. (2023). Where the AI art boom came from—and where it’s going. WIRED. https://www.wired.com/gallery/ where-the-ai-art-boom-came-from-and-where-its-going/
38. Owen, K. (1996). Digital creativity. Calouste Gulbenkian Foundation, London.
39. Pacheco-Torgal, F., Ivanov, V., Karak, N., & Jonkers, H. (2016). Biopolymers and biotech admixtures for eco-efficient construction materials. Woodhead Publishing.
40. Pallasmaa, J. (2007). The Eyes of the Skin: Architecture and Senses, Great Britain: John Wiley & Sons Ltd. Palmiero, M., Piccardi, L., Nori, R., Palermo, L., Salvi, C., & Guariglia, C. (2019). Editorial: creativity: education and rehabilitation. Front. Psychol. 10 (1500). Doi:10.3389/fpsyg.2019.01500.
41. Ploennigs, J., Berger, M. (2022). AI art in architecture. Arxiv. https://doi. org/10.48550/arXiv.2212.09399.
42. Poincaré, H. (1913). The Foundations of science. Science, New York.
43. Poincaré, H. (2001). Bilim ve varsayım. İstanbul: MEB Yayınları.
44. Rios-Campos, C., Cánova, E.S., Zaquinaula, I.R., Zaquinaula, H.E., Vargas, D.J., Peña, W.S., Idrogo, C.E., & Arteaga, R.M. (2023). Artificial intelligence and education. South Florida Journal of Development. 4(2):641-655. Doi:10.46932/sfjdv4n2-001.
45. Rognoli, V. (2010). A broad survey on expressive-sensorial characterization of materials for design education. METU Journal of The Faculty of Architecture, 27(2), 287-300.DOI:10.4305/METU.JFA.2010.2.16.
46. Saadi, J., & Yang, M., (2023). Observations on the implications of generative design tools on design process and designer behaviour. Proceedings of the Design Society, 3, 2805-2814. DOI:10.1017/pds.2023.281.
47. Sadiku, M.N., Ashaolu, T.J., Ajayi-Majebi, A.J., & Musa, S.M. (2021). Artificial intelligence in education. International Journal Of Scientific Advances. 2(1). Doi:10.51542/ijscia.v2i1.2.
48. Schreier, M. (2012). Qualitative content analysis in practice. London: Sage Publications.
49. Simonton, D.K. (1999). Origins of genius: Darwinian perspectives on creativity. Oxford University Press.
50. Stringer, E. T. (2008). Action research in education. Upper Saddle River, NJ: Pearson Prentice Hall.
51. Tong, H., Türel, A., Şenkal, H., Yagci Ergun, S. F., Güzelci, O. Z., & Alaçam, S. (2023), Can AI Function As A New Mode of Sketching. International Journal of Emerging Technologies in Learning (IJET), 18(18), 234–248. DOI:10.3991/ijet.v18i18.42603.
52. UNESCO-UIS (UNESCO Institute for Statistics) (2012). International Standard Classification of Education ISCED 2011. Canada.
53. URL-1 https://bilimgenc.tubitak.gov.tr/makale/doga-dostu-biyoplastik-yapalim Erişim Tarihi: 06.03.2024. Van Kesteren, I. (2008). Selecting materials in product design (Doctoral dissertation). Delft University of Technology, Delft, The Netherlands.
54. Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., Le, Q. V., Zhou, D., et al. Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems, 35: 24824–24837. Doi:10.48550/arXiv.2201.11903.
55. Wertheimer, M. (2020). Productive thinking. Springer. Doi:10. 1007/978-3-030-36063-4. Wiberg, M. (2014). Methodology for materiality: Interaction design research through a material lens. Personal and Ubiquitous Computing, 18(3), 625-636.
56. Wick, R. K. (2000). Teaching at the Bauhaus. Stuttgart, Germany: Hatje Cantz. Xu, M., Yuan, X., Miret, S., &Tang, J. (2023). Protst: Multimodality learning of protein sequences and biomedical texts. arXiv. Doi:2301.12040, 2023.
57. Zhang, C., Zhang, M., & Kweon, I. S. (2023). Text-toimage diffusion model in generative ai: A survey. arXiv. Doi:2303.07909, 2023.
58. Zuo, H. (2010). The selection of materials to match human sensory adaptation and aesthetic expectation in industrial design. METU Journal of the Faculty of Architecture, 27(2), 301-319. Doi:10.4305/METU.JFA.2010.2.17.