A Discussion on The Creativity of Artificial Intelligence

Abstract

Yapay Zekâ’nın Yaratıcılığı Üzerine Bir Tartışma

Abstract

Yapay Zeka (AI), insan zekasını taklit eden doğası nedeniyle son on yılda en büyüleyici ve tartışılan teknolojidir. Yapay zeka, basitçe insan benzeri duyuya (algılamaya), analize veya anlamaya ve yanıt vermeye sahip makineler inşa etme çalışması anlamına gelmektedir. Yapay zeka sistemleri, aldığı komutlarla belirli bir tür işi yapma kapasitesine sahiptir. Yapay zekanın yolculuğu 1950'li yıllarda başlamış olsa da, üç nedenden dolayı son yıllarda popüler hale gelmiş ve kullanılmaya başlanmıştır. Birincisi, büyük verinin mevcudiyeti; e-ticaret, sosyal ağlar ve işletmeler tarafından üretilen devasa miktarda veri, ikincisi makine öğrenimi algoritmalarının geliştirilmesi ve daha güvenilir olması, üçüncüsü ise bulut ve yüksek performanslı bilgisayar sistemlerinin ucuzlamasıdır. Yapay zeka her yeni günde bir çok alanda işlevsel bir halde kullanılmaktadır. Müzik çalmaktan satranç oynamaya, resim yapmaktan sürücüsüz arabalara, teoremleri kanıtlamaktan şiir yazmaya kadar genelden özele kadar çok çeşitli ürünler geliştirmek için kullanılmaya başlanmıştır. Ayrıca yapay zeka, otomobil, ulaşım, lojistik, sağlık, hisse senedi ticareti, robotik, finans, eğitim gibi alanlarda yaygın olarak kullanılmaktadır. Bu araştırmada henüz insan bilincine ve yaratıcı düşünceye sahip olamayan yapay zekanın plastik sanatlarda gelecekte bir sanatçı mı olacağı? yoksa sanatçı için sadece bir araç olarak mı kalacağı irdelenmeye çalışılmıştır. Ayrıca yapay zekanın makine öğrenimi ve derin öğrenmeyle ilişkisinin tanımlanması yapılarak, yapay zekanın yaratıcılık boyutunun ele alınmasının yanı sıra günümüz sanat dünyasındaki avantajlarına ve zorluklarına ilişkin örnekler üzerinden bir değerlendirme yapılması amaçlanmıştır.

Keywords

• Yapay Zeka
• Sanat
• Yaratıcılık

References

1. Aggarwal, A., Mittal, M., & Battineni, G. (2021). Generative adversarial network: An overview of theory and applications. International Journal of Information Management Data Insights, 1(1), 1-9. https://doi.org/10.1016/j.jjimei.2020. 100004
2. Aslan, E. (2019). Yapay zekâ resimleri ve sanatın başkalaşan mecrası üzerine. Atatürk Üniversitesi Güzel Sanatlar Enstitüsü Dergisi. 42, 231-242. https://doi.org/10.32547/ataunigsed. 516382
3. Bailey, J. (2019, February 6). AI artist Robbie Barrat and painter Ronan Barrot collaborate on “infinite skulls”. Artnome. https://tinyurl.com/2j95xd7m
4. Barreau, P. (2018, April). How AI could compose a personalized soundtrack to your life [Video]. Ted Conferences. https://www.ted.com/talks/pierre_barreau_how_ai_could_compose_a_personalized_soundtrack_to_your_life?language=en
5. Baryannis, G., Validi, S., Dani, S., & Antoniou, G. (2019). Supply chain risk management and artificial intelligence: State of the art and future research directions. International Journal of Production Research. 57(7), 2179–2202. https://doi.org/ 10.1080/00207543.2018.1530476
6. Bayraklı, D. (2023, Haziran 26). Yapay zekâ ve sanatın geleceği. Edebifikir. https://edebifikir.com/fikir/yapay-zeka-ve-sanatin-gelecegi.html
7. Boden, M. A. (2004). The creative mind: Myths and mechanisms. Psychology Press.
8. Bose, B. K. (2017). Artificial intelligence techniques in smart grid and renewable energy systems-some example applications, Proceedings of the IEEE, 105(11), 2262–2273. https://doi.org/ 10.1109/JPROC.2017.2756596

9. Brynjolfsson, E., & McAfee, A. (2017). The business of artificial intelligence. Harvard Business Review.
10. Çakıcıoğlu İlhan, F. (2021). Üretken sanat eserlerinde otonom sistemlerin kullanımı. Ulakbilge, 64(2021 Eylül), 1203–1210. https://doi.org/10.7816/ulakbilge-09-64-07
11. Castells, M. (2005). Enformasyon çağı: Ekonomi, toplum ve kültür, Cilt1: Ağ toplumunun yükselişi. (E. Kılıç, Çev.). İstanbul Bilgi Üniversitesi Yayınları.
12. CBC Radio. (2019, April 5). Artist shares credit with AI 'collaborator'. https://www.cbc.ca/radio/spark/spark-433-1.5081993/artist-shares-credit-with-ai-collaborator-1.5081999
13. Cetinic, E., & She, J. (2021). Understanding and creating art with AI: Review and outlook. arXiv, 1-17. https://doi.org/10.48550/ arXiv.2102.09109
14. Chen, X. W., & Lin, X. (2014). Big data deep learning: Challenges and perspectives. IEEE Access, 2, 514–525. https://doi.org/ 10.1109/ACCESS.2014.2325029
15. Cheng, M. (2022). The creativity of artificial ıntelligence in art. Proceedings of The 2021 Summit of the International Society for the Study of Information, USA, 81(1), 110-114. https://doi.org/ 10.3390/proceedings2022081110
16. Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions. In proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1251-1258). IEEE. https://doi.org/10.1109/CVPR.2017.195
17. Cohen, H. (2010). Driving the creative machine. Crossroads Lecture Series. Orcas Center.
18. Cohen, H. (2022, 14 October-19 November). The aaron retrospective [Exhibition]. Gazlli Art House, London, England. https://gazelliarthouse.com/exhibitions/159-the-aaron-retros pective-harold-cohen/
19. Collins, G. S., & Moons, K. G. M. (2019). Reporting of artificial intelligence prediction models. The Lancet, 393(10181), 1577-1579. https://doi.org/10.1016/S0140-6736(19)30037-6
20. Dase, R. K., & Pawar, D. D. (2010). Application of artificial neural network for stock market predictions: A review of literature. International Journal of Machine Intelligence, 2(2), 14-17. https://doi.org/10.9735/0975-2927.2.2.14-17
21. Duan, L., & Xu, L. (2012). Business ıntelligence for enterprise systems: A survey. IEEE Transactions on Industrial Informatics. 8(3) 679–687. https://doi.org/10.1109/TII.2012. 2188804
22. Duan, L., Xu, L., Liu, Y., & Lee, J. (2009). Cluster-based outlier detection. Annals of Opererations Research, 168(1) 151–168. https://doi.org/10.1007/s10479-008-0371-9
23. Foucault, M. (1992). What is an author? In J. L. Marsh, & J. D. Caputo (Eds.), Modernity and its discontents (1st ed., pp. 299-314). Fordham University Press.
24. Galanter, P. (2001). Foundations of generative art systems-A hybrid survey and studio class for graduate students [Conference presentation]. Generative Art: Proceedings of the 4th International Conference, Milan, Italy. https://papers. cumincad.org/data/works/att/ga0125.content.pdf
25. Huang, B., Huan, Y., Xu, L., Zheng, L., & Zou, Z. (2019). Automated trading systems statistical and machine learning methods and hardware implementation: A survey, Enterprise Information Systems, 13(1), 132–144. https://doi.org/10.1080/17517575. 2018.1493145
26. Kurt, D. E. (2018). Artistic creativity in artificial ıntelligence (Publication No. 5ae1a7fabbba3) [Master dissertation, Radboud University]. Faculteit der Letteren.
27. Liu, M. Y., & Tuzel, O. (2016). Coupled generative adversarial networks. Computer Science-Computer Vision and Pattern Recognition, 1-32. https://doi.org/10.48550/arXiv.1606.07536
28. Lu, Y. (2019). Artificial intelligence: A survey on evolution, models, applications and future trends. Journal of Management Analytics, 6(1), 1–29. https://doi.org/10.1080/23270012.2019. 1570365
29. Lu, Y., & Xu, L. D. (2018). Internet of things (IoT) cybersecurity research: A review of current research topics. IEEE Internet Things Journal. 6(2), 2103–2115. https://doi.org/10.1109/ JIOT.2018.2869847
30. Makkar A., Garg, S., Kumar, N., Hossain, M. S., Ghoneim, A., & Alrashoud, M. (2020). An efficient spam detection technique for Iot devices using machine learning. IEEE Transactions on Industrial Informatics, 17(2), 903–912. https://doi.org/ 10.1109/TII.2020.2968927
31. McCorduck, P. (1991). Meta-art, artificial Intelligence and the work of Harold Cohen. W. H. Freeman & Co.
32. Mondal, B. (2020). Artificial intelligence: State of the art. In V. Balas, R. Kumar, & R. Srivastava (Eds.), Recent trends and advances in artificial intelligence and internet of things (pp. 389-425). Springer. https://doi.org/10.1007/978-3-030-32644-9_32
33. Moura, L. (2018). Robot art: An interview with Leonel Moura. Arts, 7(3), 28-32. https://doi.org/10.3390/arts7030028
34. Najafabadi, M. M., Villanustre, F., Khoshgoftaar T. M., Seliya, N., Wald, R., & Muharemagic, E. (2015). Deep learning applications and challenges in big data analytics. Journal of Big Data, 2(1), 1–21. https://doi.org/10.1186/s40537-014-0007-7
35. Nilsson, N. J. (2014). Principles of artificial intelligence. Morgan Kaufmann.
36. Paksın, B. (2020). Görsel sanatlarda yapay zekâ ve yaratıcılık ilişkisi (Tez No. 635352) [Yüksek lisans tezi, Dokuz Eylül Üniversitesi]. YÖK Tez Merkezi.
37. Rajkomar, A., Oren, E., Chen, K., Dai, A. M., Hajaj, N., Hardt, M., Liu, P. J., Liu, X., Marcus, J., Sun, M., & Sundberg, P. (2018). Scalable and accurate deep learning with electronic health records. NPJ Digital Medicine, 1(1), 1–10. https://doi.org/ 10.1038/s41746-018-0029-1
38. Regan, M. (2018). Generative adversarial networks & the art market (Publication No. 13809393) [Doctoral dissertation, State University of New York]. ProQuest Dissertations & Theses Global.
39. Sawyer, R. K. (2014). Explaining creativity: The science of human ınnovation. Oxford University Press.
40. Seyf, T. (2023, Nisan 27). Yapay zekâ ve bilinmeyen dünyası. Independent Türkçe. https://www.indyturk.com/node/627486 /d%C3%BCnyadan-sesler/yapay-zeka-ve-bilinmeyen-d%C3%BC nyas%C4%B1
41. Shi, Z., Huang, Y., He, Q., Xu, L., Liu, S., Qin, L., Jia, Z., Li, J., Huang, H., & Zhao, L. (2007). MSMiner-a developing platform for OLAP. Decision Support Systems, 42(4), 2016–2028. https://doi.org/10.1016/j.dss.2004.11.006
42. Vincent, J. (2018, October 23). How three French students used borrowed code to put the first ai portrait in Christie’s. The Verge. https://www.theverge.com/2018/10/23/18013190/ai-art-portrait-auction-christies-belamy-obvious-robbie-barrat-gans
43. Xu, L. (2013). Introduction: Systems science in industrial sectors. Systems Research and Behavioral Science, 30(3), 211–213. https://doi.org/10.1002/sres.2186
44. Xu, L., Tan, W., Zhen, H., & Shen, W. (2008). An approach to enterprise process dynamic modeling supporting enterprise process evolution. Information Systems Frontiers, 10(5), 611–624. https://doi.org/10.1007/s10796-008-9114-3
45. Yang, B., Li, L. X., Ji, H., & Xu, J. (2001). An early warning system for loan risk assessment using artificial neural networks. Knowledge-Based Systems, 14(5–6), 303–306. https://doi.org/ 10.1016/S0950-7051(01)00110-1
46. Zhang, C. (2019). Research on the fluctuation and factors of China TFP of IT Industry. Journal of Industrial Integration and Management, 04(04). https://doi.org/10.1142/S24248622195 00131
47. Zhang, C. M., & Chu, H. N. (2020). Preprocessing method of structured big data in human resource archives database. In 2020 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI) (pp. 379–384). IEEE. https://doi.org/10.1109/IAAI51705.2020.9332880
48. Zhang, C., & Chen, Y. (2020). A review of research relevant to the emerging ındustry trends: Industry 4.0, IoT, Blockchain, and Business Analytics. Journal of Industrial Integration and Management, 01(05), 165-180. https://doi.org/10.1142/S2424 862219500192
49. Zhang, C., & Fu, W. (2021). Optimal model for patrols of UAVs in power grid under time constraints. International Journal of Performability Engineering, 17(1), 103–113. https://doi.org/ 10.23940/ijpe.21.01.p10.103113
50. Zhang, C., Patras, P., & Haddadi, H. (2019). Deep learning in mobile and wireless networking: A survey. IEEE Communications Surveys & Tutorials, 21(3), 2224–2287. https://doi.org/ 10.1109/COMST.2019.2904897
51. Zhang, L., Wang, S., & Liu, B. (2017). Deep learning for sentiment analysis: A survey. WIREs Data Mining and Knowledge Discovery, 8(4), 1-25. https://doi.org/10.1002/widm.1253