AI in architecture and engineering from misconceptions to game-changing prospects (original) (raw)

Abstract

Artificial intelligence invades our lives and professions at an ever-increasing pace and intensity. Architecture, engineering, construction, and operation of the real estate have been joining the trend only timidly and belatedly. The paper overviews the basic concepts, methods, general background, and results of artificial intelligence in architecture to date, discusses the achievements and prospects, and concludes the perspectives on the deployment of machine learning in the field. The record of some of the most recent "famous achievements" in the field is set straight and challenged, the flawed idea of a (truly) creative potential of the technology is debunked. Its roots equidistributed both in a farsighted vision of the next workflow of both productive and creative architectural and engineering designing, and construction and real estate management on the one hand and state-of-the-art machine learning on the other, an ambitious though realistic blueprint for R&D of AI-fostered architectural creativity, building design, planning, and operation is tabled for discussion. The attention turns to open-source patterns platforms, generative patterns processing, generative pre-design, parametric evaluation and optimization, latest achievements in machine learning building on reinforcement learning, imitation-based learning, learning a behavior policy from demonstration, and selflearning paradigms zooming in on the design-development processes instead of only on their results. Leveraging the objectivity of assessments and streamlining workflows, artificial intelligence promises to unleash true architectural creativity and leverage the productivity and efficiency of the design, planning, and operation processes.

Figures (7)

Loading...

Loading Preview

Sorry, preview is currently unavailable. You can download the paper by clicking the button above.

References (121)

  1. AlphaGo. (2023a). Google DeepMind. https:// www. deepm ind. com/ resea rch/ highl ighted-resea rch/ alpha go. Accessed 21 Jan 2023
  2. AlphaGo. (2023b). Zero: Starting from scratch. Google DeepMind. https:// www. deepm ind. com/ blog/ alpha go-zero-start ing-from-scrat ch (accessed Feb. 1, 2023)
  3. Ayush, T. (2023). Understanding Reinforcement Learning from Human Feed- back (RLHF): Part 1. Weights & Biases.https:// wandb. ai/ ayush-thakur/ RLHF/ repor ts/ Under stand ing-Reinf orcem ent-Learn ing-from-Human- Feedb ack-RLHF-Part-1--Vmlld zoyOD k5MTIx. Accessed 3 Nov 2022
  4. Balestriero, R., Ibrahim, M., Sobal, V., Morcos, A., Shekhar, S., Goldstein, T., Bordes, F., Bardes, A., Mialon, G., Tian, Y., Schwarzschild, A., Gordon A., Geiping, W. J., Garrido, Q., Fernandez, P., Bar, A., Pirsiavash, H., LeCun, H., Goldblum, M. (2023). A Cookbook of Self-Supervised Learning. arXiv. Cornell University. arXiv:2304.12210. https:// doi. org/ 10. 48550/ arXiv. 2304. 12210. Accessed 29 Apr 2023
  5. Barker, N. (2023). Architects may become a thing of the past, says ChatGPT. Dezeen. https:// www. dezeen. com/ 2023/ 02/ 13/ chat-gpt-ai-archi tectu re/? utm_ medium= email & utm_ campa ign= Daily% 20Dez een& utmco ntent= Daily% 20Dez een+ CID_ 6238f 12347 0e57e ff112 7de07 aa0e2 b5& utm_ source= Dezeen% 20Mai l& utm_ term= Read% 20more (accessed Mar. 3, 2023)
  6. Barreto, S. (2022). Differences between backpropagation and feedforward networks. Baeldung. https:// www. baeld ung. com/ cs/ neural-netwo rks- backp rop-vs-feedf orward (accessed Nov. 5, 2022)
  7. Bishop, C. M. (2020). Pattern recognition and machine learning. Springer. ISBN 0-387-31073-8
  8. Bolojan, D. (2022). Coop Himmelb(l)au,: Deep HIMMELB(L)AU. YouTube. https:// www. youtu be. com/ watch?v= 8G3jp GQMvWE (accessed Oct. 5, 2022)
  9. Burke, J. (2022). Successful generative AI examples woth noting. Tech Accelerator. https:// www. techt arget. com/ searc hente rpris eai/ tip/ Succe ssful-gener ative-AI-examp les-worth-noting (accessed Sept. 15, 2022)
  10. Campbell, M., Hoane, A. J., Jr., & Hsu, F. (2002). Deep Blue. Artificial Intelligence. Elsevier, 134(1-2), 57-83. https:// doi. org/ 10. 1016/ S0004-3702(01) 00129-1
  11. Chai, L., Wulff, J., & Isola, P. (2023). Using latent space regression to analyze and leverage compositionality in GANs. Github. https:// chail. github. io/ latent- compo sition/ (accessed Feb. 12, 2023)
  12. Chaillou, S. (2022a). Artificial intelligence and architecture from research to prac- tice. Birkhauser Verlag GmbH. ISBN 978-3-0356-2400-7, pp. 112-146
  13. Chaillou, S. (2022b). Artificial intelligence and architecture from research to prac- tice. Birkhauser Verlag GmbH. ISBN 978-3-0356-2400-7, pp. 80, 86-89
  14. Chaillou, S. (2022c). Artificial intelligence and architecture from research to prac- tice. Birkhauser Verlag GmbH. ISBN 978-3-0356-2400-7, p. 90
  15. Chaillou, S. (2022e). Artificial intelligence and architecture from research to practice (pp. 23-48). Birkhauser Verlag GmbH. ISBN 978-3-0356-2400-7. ibid. f, p. 98
  16. Chaillou, S. (2022f ). Artificial intelligence and architecture from research to prac- tice. Birkhauser Verlag GmbH. ISBN 978-3-0356-2400-7, pp. 83-86
  17. Chaillou, S. (2022g). Artificial intelligence and architecture from research to prac- tice. Birkhauser Verlag GmbH. ISBN 978-3-0356-2400-7, pp. 103-109
  18. Chaillou, S. (2022h). Artificial intelligence and architecture from research to prac- tice. Birkhauser Verlag GmbH. ISBN 978-3-0356-2400-7, pp. 80, 86-92
  19. Chaillou, S. (2023). ArchiGAN: a Generative Stack for Apartment Building Design. NVIDIA DEVELOPER Technical Blog. Jul. 17, 2019. https:// devel oper. nvidia. com/ blog/ archi gan-gener ative-stack-apart ment-build ing- design/. Accessed 29 Mar 2023
  20. Christian, B. (2020a). The Alignment Problem: Machine Learning and Human Values. 1st ed. W.W.Norton. ISBN 9780393635829, p. 206
  21. Christian, B. (2020b). The Alignment Problem: Machine Learning and Human Values. 1st ed. W.W.Norton. ISBN 9780393635829, p. 209
  22. Christian, B. (2020c). The Alignment Problem: Machine Learning and Human Values. 1st ed. W.W.Norton. ISBN 9780393635829, p. 222
  23. Christian, B. (2020d). The Alignment Problem: Machine Learning and Human Values. 1st ed. W.W.Norton. ISBN 9780393635829, pp. 240-243
  24. Christian, B. (2020e). The Alignment Problem: Machine Learning and Human Values. 1st ed. W.W.Norton. ISBN 9780393635829, pp. 28-64
  25. Christian, B. (2020f ). The Alignment Problem: Machine Learning and Human Values. 1st ed. W.W.Norton. ISBN 9780393635829, p. 126
  26. Christian, B. (2020g). The Alignment Problem: Machine Learning and Human Values. 1st ed. W.W.Norton. ISBN 9780393635829, pp. 241-243
  27. Christian, B. (2020h). The Alignment Problem: Machine Learning and Human Values. 1st ed. W.W.Norton. ISBN 9780393635829, p. 109
  28. Christian, B. (2020i). The Alignment Problem: Machine Learning and Human Values. 1st ed. W.W.Norton. ISBN 9780393635829, p. 114
  29. Christian, B. (2020j). The Alignment Problem: Machine Learning and Human Values. 1st ed. W.W.Norton. ISBN 9780393635829, p. 113
  30. Christian, B. (2020k). The Alignment Problem: Machine Learning and Human Values. 1st ed. W.W.Norton. ISBN 9780393635829, pp. 17-80
  31. Christian, B. (2020l). The Alignment Problem: Machine Learning and Human Values. 1st ed. W.W.Norton. ISBN 9780393635829, pp. 52-92
  32. Coop Himmelb(l)au, Meet DeepHimmelb(l)au. (2023). Our algorithm learns CHBL' s semantic characteristics to generate new interpretations and new worlds. Facebook Watch. https:// www. faceb ook. com/ watch/?v= 14733 00746 151435 (accessed Mar. 3, 2023)
  33. Cove.tool. (2022). Supercharge your building design capabilities. Cove.tool. https:// www. cove. tools/ (accessed Oct. 1, 2022)
  34. Creo: Design. (2022). The way it should be. ptc. https:// www. ptc. com/ en/ produ cts/ creo (accessed Oct. 2, 2022)
  35. Crunchbase. (2022). XKool Technolgy. Crunchbase. https:// www. crunc hbase. com/ organ izati on/ xkool-techn ology (accessed Dec. 4, 2022)
  36. Datamind. (2022). ARTIFICIAL INTELLIGENCE, MACHINE LEARNING, NEURAL NET- WORKS. Datamind. https:// www. datam ind. cz/ en/ servi ces/ Artifi cial_ intel ligen ce_ machi ne_ learn ing_ deep_ neural_ netwo rks? msclk id= 83db1 4a2aa 13173 a1045 708f7 8459a a3 (accessed Nov. 3, 2022)
  37. del Campo, M., Manninger, S. 2019 2022 Imaginary Plans. Proceedings of the 2019 ACADIA Conference -Ubiquity and Autonomy. https:// www. acade mia. edu/ 40735 096/ Imagi nary_ Plans (accessed Nov. 11, 2022)
  38. FinancesOnline. 2022 Cove.tool Reviev. FiancesOnline. https:// revie ws. finan ceson line. com/p/ cove-tool/ (accessed Oct. 4, 2022)
  39. Firth-Butterfield, K. (2022). These were the biggest AI developments in 2022. Now we must decide how to use them. World Economic Forum. https:// www.
  40. wefor um. org/ agenda/ 2023/ 01/ davos 23-bigge st-ai-devel opmen ts- how-to-use-them (accessed Feb. 19, 2023)
  41. Foster+Partners. (2022). Applied R+D. Foster+Partners. https:// www. foste randp artne rs. com/ people/ teams/ appli ed-rplusd (accessed Nov. 11, 2022)
  42. Fukushima, K. (1980). Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics. Springer-Verlag., 36(9), 193-202. https:// www. rctn. org/ bruno/ public/ papers/ Fukus hima1 980. pdf (accessed Dec. 2, 2022)
  43. Gershgorn, D. (2017). IT'S NOT ABOUT THE ALGORITHM THE data that trans- formed AI research-And possibly the world. Quartz. July 26, 2017. https:// qz. com/ 10349 72/ the-data-that-chang ed-the-direc tion-of-ai-resea rch- and-possi bly-the-world (accessed Nov. 22, 2022)
  44. Giraffe. (2022). The platform for utban analytics. Giraffe. https:// www. giraff e. build/ (accessed Oct. 2, 2022)
  45. Github: Let's build from here. Github. https:// github. com (accessed Mar. 24, 2023) Gitlab: Software. Faster. GitLab. https:// gitlab. com/ (accessed Mar. 24, 2023)
  46. Gonfalonieri, A.: Inverse Reinforcement Learning Introduction and Main Issues. TDS. https:// towar dsdat ascie nce. com/ inver se-reinf orcem ent-learn ing- 6453b 7cdc9 0d (acces. Apr. 7, 2023)
  47. Google Scholar https:// schol ar. google. com/ citat ions? view_ op= view_ citat ion& hl= en& user= xezhJ cAAAA J& citat ion_ for_ view= xegzh JcAAA AJ: u5HHm VD_ uO8C (accessed Feb 28, 2023)
  48. Gowans, A., Ackerman, J. S., Scruton, R., Collins, P.: Architecture. Encyclopedia Britannica https:// www. brita nnica. com/ topic/ archi tectu re (accessed Jan. 15, 2023)
  49. Gozallo-Birzuela, R., & Garrido-Merchan, E. C. (2023). GPT is not all you need. A state of the art review of large generative AI models. arXiv. Cornell Univer- sity. https:// arxiv. org/ abs/ 2301. 04655 (accessed Jan. 29, 2023)
  50. Harari, Y. N., Musk, E., Wozniak, S., Bengio, Y., & Russel, S. (2023). Among other multiple authors: Pause Giant AI Experiments: An Open Letter. future of life. https:// futur eofli fe. org/ open-letter/ pause-giant-ai-exper iments/ (accessed Apr. 3, 2023)
  51. Harouk, Ch.: Spacemaker Proposes AI-Powered Generative Design to Create More Sustainable Spaces and Cities. Archdaily. https:// www. archd aily. com/ 952850/ space maker-propo ses-ai-power ed-gener ative-design-to- create-more-susta inabl espac es-and-cities (accessed Jan. 2, 2023)
  52. Heidegger, M. (2000). Voll Verdienst, doch dichterisch wohnet / Der Mensch auf dieser Erde. Heidegger und Hölderlin, herausgegeben von Peter Trawny. Vittorio Klostermann. ISBN 978-3-465-03084-3
  53. Heidegger, M. (2006). Sein und Zeit (pp. 146-216). Max Niemeier Verlag, Tubingen. ISBN 3-484-70153-6. In English being and time. Translated by Macquarrie, J., Robinson, E. 1st ed., Blackwell publishers, 1962. ISBN 0-631-19770-2 IBM: What is supervised learning? IBM. https:// www. ibm. com/ topics/ super vised-learn ing (accessed Oct. 30, 2022a)
  54. IBM: What is unsupervised learning? IBM. https:// www. ibm. com/ topics/ unsup ervis ed-learn ing (accessed Oct. 30, 2022b)
  55. IMAGENET. https:// www. image-net. org (accessed Nov. 5, 2022)
  56. Isola, P: Phillip Isola. MIT. http:// web. mit. edu/ phill ipi/ (accessed Feb. 23, 2023)
  57. Jagtap, R., 2022, Understanding the Makarov Decision Process (MDP). built in. https:// built in. com/ machi ne-learn ing/ markov-decis ion-proce ss (accessed Nov. 3, 2022)
  58. Kerr, J., Min Kim, Ch., Goldberg, K., Kanazawa, A., Tancik, M. LERF: Language Embedded Radi-ance Fields. arXiv. Cornell University. arXiv:2303.09553; arXiv:2303.09553v1. https:// doi. org/ 10. 48550/ arXiv. 2303. 09553. Accessed 29 Mar 2023
  59. Kilian, K.: Čína má odpověď na ChatGPT. A ne jednu, další chatbot ukazuje Alibaba. Živě. https:// www. zive. cz/ clanky/ cina-ma-odpov ed-na-chatgp- a-ne-jednu-dalsi-ai-chatb ot-ukazu jeali baba/ sc-3-a-221685/ defau lt. aspx (accessed Apr. 16, 2023)
  60. Krizhnevsky, A., Sutskever, I., & Hinton, G. (2012). ImageNet classification with deep convolutional neural networks. Computer Science, Communica- tions of the ACM, 3. https://doi.org/10.1145/3065386 https:// www. seman ticsc holar. org/ paper/ Image Net-class ifica tion-with-deep-convo lutio nal-Krizh evsky-Sutsk ever/ abd1c 34249 54321 71beb 7ca8f d9551 ef13c bd0ff (accessed Feb 23, 2023)
  61. Kyle, R.: Spacemaker: Merging AI technology with Urban Planning and Design. HBS Digital Initiative. https:// d3. harva rd. edu/ platf orm-digit/ submi ssion/ space maker-mergi ng-ai-techn ology-with-urban-plann ing-and-design/ (accessed Nov. 11, 2022)
  62. Leach, N. (2018a). Design in the age of artificial intelligence. Landscape archi- tecture Frontiers. Higher Education Press, Peking University, 6(2), 115-118. ISSN 2096-336X
  63. Leach, N. (2018b). Design in the age of artificial intelligence. Landscape archi- tecture Frontiers. Higher Education Press, Peking University, 6(2), 115-118. ISSN 2096-336X. ibid. b, p. 118
  64. Leach, N. (2018c). Design in the age of artificial intelligence. Landscape archi- tecture Frontiers. Higher Education Press, Peking University, 6(2), 115-118. ISSN 2096-336X. ibid. c, pp. 113-114
  65. Leach, N. (2018d). Design in the age of artificial intelligence. Landscape archi- tecture Frontiers. Higher Education Press, Peking University, 6(2), 115-118. ISSN 2096-336X. ibid. d, p. 114
  66. Leach, N. (2018e). Design in the age of artificial intelligence. Landscape archi- tecture Frontiers. Higher Education Press, Peking University, 6(2), 115-118. ISSN 2096-336X. ibid. e, p. 117
  67. Leach, N. (2022a). Architecture in the age of artificial intelligence an introduc- tion to AI for architects (1st ed., p. 104). Bloomsbury Visual Arts. ISBN 978-1-3501-6551-9
  68. Leach, N. (2022b). Architecture in the age of artificial intelligence an introduction to AI for architects (1st ed., p. 104). Bloomsbury Visual Arts. ISBN 978-1- 3501-6551-9. ibid. b, pp. 26-64
  69. Leach, N. (2022c). Architecture in the age of artificial intelligence an introduc- tion to AI for architects (1st ed., p. 104). Bloomsbury Visual Arts. ISBN 978-1-3501-6551-9. ibid. c, pp. 62-74
  70. Leach, N. (2022d). Architecture in the age of artificial intelligence an introduction to AI for architects (1st ed., p. 104). Bloomsbury Visual Arts. ISBN 978-1- 3501-6551-9. ibid. d, p. 109
  71. Leach, N. (2022e). Architecture in the age of artificial intelligence an introduction to AI for architects (1st ed., p. 104). Bloomsbury Visual Arts. ISBN 978-1- 3501-6551-9. ibid. e, p. 106
  72. Liu, Z., Feng, Y., Black, M. J., Nowrouzezahrai, D., Paull, L., Liu, W.: MeshDiffusion: Score-based Generative 3D Mesh Modelling. arXiv. Cornell University. arXiv:2303.08133; arXiv:2303.08133v2; https:// doi. org/ 10. 48550/ arXiv. 2303. 08133 (accessed Apr. 21, 2023)
  73. Martin, B.: Investors didn't think much of Baidu's ChatGPT -competitor debut, wiping $3 billion off the tech giant's value -Fortune. Inferse. https:// www. infer se. com/ 484983/ inves tors-didnt-think-much-of-baidus-chatg pt-compe titor-debut-wiping-3-billi on-off-the-tech-giants-value-fortu ne/ (acces-sed Apr. 16, 2023)
  74. McCulloch, W., Pitts, W. (1943). A Logical Calculus of Ideas Immanent in Nerv- ous Activity. Bulletin of Mathematical Biophysics, 5(4), 115-133. https:// doi. org/ 10. 1007/ BF024 78259
  75. Mittal, A. Neural Radiance Fields: Past, Present, and Future. arXiv. Cornell University. arXiv:2304.10050. https:// doi. org/ 10. 48550/ arXiv. 2304. 10050. Accessed 21 Apr 2023
  76. Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., Riedmiller, M.: Plaing Atari with Deep Reinforcement Learning. Papers with Code. paperswithcode.com/paper/ playing-atari-with-deep- reinforcement (accessed Nov. 3, 2022) MVRDV: NEW EXPERIMENTAL TECHNOLOGIES. MVRDV. https:// www. mvrdv. com/ themes/ 15/ next. (accessed Feb. 2, 2023)
  77. Nguyen, A., Karampatziakis, N., Chen, W.: Meet in the Middle: A New Pre- training Paradigm. arXiv. Cornell University. https:// arxiv. org/ abs/ 2303. 07295; arXiv:2303.07295v1; https:// doi. org/ 10. 48550/ arXiv. 2303. 07295 (accessed Mar. 29, 2023)
  78. Pandey, P.: TCAV: Interpretability Beyon Feature Attribution. Towards Data Science. https://towards datascience.com/tcav-interpretability-beyond- feature-attribution-79b4d3610b4d (accessed Nov. 30, 2022) Papers with Code: Imitation Learning. Papers with Code. https:// paper swith code. com/ task/ imita tion-learn ing (accessed Apr. 12, 2023)
  79. Patternforge: What is PatternForge? PatternForge. https:// patte rnfor ge. org (accessed Mar. 24, 2023)
  80. Pichai, S., 2023 An important next step on your AI journey. Google The Key- word. https:// blog-google/ techn ology/ ai/ bard-google-ai-search-updat es/ (accessed Apr. 2, 2023)
  81. Pix2Pix. (2023). Machine Learning 4 Artists Github. https:// ml4a. github. io/ guides/ Pix2P ix/ (accessed Jan. 6, 2023)
  82. PTC. (2022). What' s in Creo9. ptc. https:// www. ptc. com/ en/ blogs/ cad/ whats-in- creo9 (accessed Oct. 4, 2022)
  83. Qi, Ch., Cun, X., Zhang, Y., Lei, Ch., Wang, X., Shan, Y., Chen, Q.: FateZero: Fusing Attentions for Zero-shot Text-based Video Editing. arXiv.
  84. Cornell University. arXiv:2303.09535; arXiv:2303.09535v2; https://doi. org/10.48550/arXiv.2303.09535 (accessed Apr. 18, 2023)
  85. Quintana, M., Schiavon, S., Wai Tham, K., & Miller, C. (2020). Balancing thermal comfort datasets: We GAN, but should we? Buildsys ´20: Proceedings of the 7th ACM International Conference on Systems for Energy-Eficient Buildings, Cities, and Transportation (pp. 120-129). https:// doi. org/ 10. 1145/ 34083 08. 34276 12
  86. Rijmenam Van, M. (2023). What is generative AI, and how will it disrupt society? The Digital Speaker. https:// www. thedi gital speak er. com/ what-is-gener ative-ai-how-disru pt-socie ty/ (accessed Feb. 24, 2023)
  87. Ross, S., Gordon, G., Bagnell, D. (2011). A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning. Proceedings of the Fourteenth International Conference on Artificial Intelligence and Sta- tistics. Proceedings of Machine Learning Research. vol. 15. pp. 627-635. https:// proce edings. mlr. press/ v15/ ross1 1a. html. Accessed 22 Jan 2023
  88. Salehi, H., & Burgueňo, R. (2023). Emerging artificial intelligence methods in struc- tural engineering. ScienceDirect. https:// www. scien cedir ect. com/ scien ce/ artic le/ abs/ pii/ S0141 02961 73355 26 (accessed Feb. 7, 2023)
  89. Schmidt, M. (2022). 3 AI predictions for 2023 and beyond, according to an AI expert. World Economic Forum. https:// www. wefor um. org/ agenda/ 2023/ 01/ key-ai-predi ctions-for-2023-and-beyon d/? utm_ source= sfmc& utm_ medium= email & utm_ campa ign= 27946 72_ Weekl yAgen da3Fe bruar y2023 Georg Signi ng& utm_ term= & email Type= Agenda% 20Wee kly (accessed Sept. 19, 2022)
  90. SCIA ENGINEER. (2023). Scia. https:// www. scia. net/ en/ scia-engin eer (accessed Feb. 7, 2023)
  91. Shaban, P. (2023). MS architekti. https:// www. wemak espac es. archi/ cs/ lide (accessed Jan. 23, 2023)
  92. Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., Chen, Y., Lillicrap, T., Fan, H., Sifre, L., Driessche, G. V. D., Graepel, T., & Hassabis, D. (2017). Mastering the game of Go without human knowledge (PDF). Nature, 550(7676), 354-359. https:// doi. 10. 1038/ natur e24270. Bibcode:2017Natur.550.354S. ISSN 0028-0836. PMID 29052630. S2CID 205261034. closed access
  93. Sourek, M. (2014). From functional areas towards metropolitans structure: Public space in sustainable development context. Advanced Engineering Forum. Trans Tech Publications, 12, 176-180. ISBN 978-3-03835-323-2
  94. Sourek, M. (2022). Virtual twins of architecture: The singularity of the profession and the field. International Journal of Architecture and Planning, 2(2), 1-26. https:// doi. org/ 10. 51483/ IJARP.2. 2. 2022.1-26
  95. Spacemaker. (2022). Early-stage planning, reimagined. Autodesk. https:// www. autod esk. com/ produ cts/ space maker/ overv iew (accessed Oct. 1, 2022)
  96. StackExchange. (2022). What are the fundamental differences between VAE and GAN for image generation? StackExchange. https:// ai. stack excha nge. com/ quest ions/ 25601/ what-are-the-funda mental-diffe rences-betwe en-vae-and-gan-for-image-gener ation (accessed Sept. 15, 2022)
  97. Steinfeld, K. (2023a). Berkeley College of Environmental Design. https:// ced. berke ley. edu/ wpcon tent/ uploa ds/ 2022/ 02/ pix2p ixgri d01. jpg. Accessed 6 Jan 2023
  98. Steinfeld, K. (2023b). Sketch2Pix. CDRF. http:// blah. kstei nfe. com/ 200625/ cdrf_ 2020. html. Accessed 6 Jan 2023
  99. Steinfeld, K. (2023c). Steinfeld, K. Berkeley College of Environmental Design. https:// ced. berke ley. edu/ people/ kyle-stein feld. Accessed 6 Jan 2023
  100. Stewart, M. (2022). GANs vs, autoencoders: Comparison of deep generative models. Towards Data Science. https:// towar dsdat ascie nce. com/ gans- vs-autoe ncode rs-compa rison-of-deep-gener ative-models-985cf 15936 ea (accessed Nov. 5, 2022)
  101. Storm, J. (2023). This week alone, more than 200 new AI tools were released. LinkedIn. https:// www. linke din. com/ posts/ joerg storm_ drsto rm-techn ology-autom otive-activ ity-70453 22482-66003 6609-CBeT? utm_ source= share & utm_ medium= member_ andro id (accessed Apr. 2, 2023)
  102. Stylemania.it. Před Chat GPT: zde jsou průkopníci, kteří připravili půdu pro umělou inteligenci. Style Mania https:// www. msn. com/ cs-cz/ zpravy/ other/ před- chat-gpt-zde-jsou-průkop-níci-kteří-připrav ili-půdu-pro-umělou-intel igenci/ ss-AA18T 6Hs? ocid= msedg dhp& pc= U531& cvid= b90b0 42fad 6c4ca da697 39b51 d8854 51& ei= 59# image= 11 (accessed Mar. 26, 2023)
  103. TechCrunch. (2022). Spacemaker: AI software for urban development is acquired by Autodesk for 240M. TechCrunch. https:// techc runch. com/ 2020/ 11/ 17/ space maker-ai-softw are-for-urban-devel opment-is-acqui red-by-autod esk-for-240mEC (accessed Oct. 2, 2022)
  104. Tian, R.: Suggestive Site Planning with Conditional GAN and Urban Data. Yuan, P.F., Yao, J., Yan, C., Wang, X., Leach, N. (eds), Proceedings of the 2020 DigitalFUTURES. CDRF 2020. Springer, Singapore. doi: 10.1007/978-981- 33-4400-610. https:// link. sprin ger. com/ chapt er/ 10. 1007/ 978-981-33- 4400-6_ 10 (accessed Nov. 11, 2022)
  105. Tribby3D. (2023). Structural Engineering Software to Learn in 2023: Top 10 Best. Tribby3d. https:// tribb y3d. com/ blog/ struc tural-engin eering-softw are/ (accessed Jan. 30, 2023)
  106. Turing, A. M. (1937). On Computable Numbers, with an Application to the Entscheidungsproblem. Proceedings of the London Mathematical Society, 42(1), 230-265. https:// doi. org/ 10. 1112/ plms/ s2-42.1. 230
  107. Urban, P. (2023). Amazon má továrnu na AI a chatboty. Na umělé inteligenci u něj pracuje víc lidí než u Microsoftu nebo Googlu. Connect! https:// conne ct. zive. cz/ clanky/ amazon-bedro ck-titan-gener ativni-ai/ sc-320-a-221729/ defau lt. aspx (accessed Apr. 16, 2023)
  108. van den Burgh, S. (2022). MVRDV next. AECtech. https:// www. youtu be. com/ watch?v= ZIza0 cM5wBM (accessed Feb. 3, 2023)
  109. Vavra, D. (2023). Jak chytra je umela inteligence? PROTI VETRU s Danem Vavrou. https://youtube/vYQ33kP7BdYhttps://youtu.be/vYQ33kP7BdY (accessed Feb. 27, 2023)
  110. Vesely, D. (2004). Architecture in the age of divided representation: The ques- tion of creativity in the shadow of production (1st ed.). MIT Press. ISBN 0-262-22067-9
  111. Wearrecho. (2023). Embrace new technology and stay ahead of the game: Wear- recho will take you to the top. https:// wearr echo. space (accessed Apr. 3, 2023)
  112. Weng, Z., Yang, X., Li, A., Wu, Z., & Jiang, Y. (2023). Transforming CLIP to an Open-vocabulary Video Model via Interpolated Weight Optimization. arXiv. arXiv:2302.00624; https://doi.org/10.48550/arXiv.2302.00624 (accessed Apr. 18, 2023)
  113. Wikipedia, a. (2022). Reinforcement learning. Wikipedia. https:// en. wikip edia. org/ wiki/ Reinf orcem ent_ learn ing (accessed Oct. 30, 2022)
  114. Wikipedia, b. (2022). Convolutional neural network. Wikipedia. https:// en. wikip edia. org/ wiki/ Convo lutio nal_ neural_ netwo rk (accessed Nov. 5, 2022)
  115. Wikipedia, c. (2022). Recursive neural network. Wikipedia. https:// en. wikip edia. org/ wiki/ Recur sive_ neural_ netwo rk (accessed Nov. 7, 2022)
  116. Wikipedia, d. (2022). Recurrent neural network. Wikipedia. https:// en. wikip edia. org/ wiki/ Recur rent_ neural_ netwo rk (accessed Nov. 7, 2022)
  117. Wikipedia, e. (2022). Variational autoencoder. Wikipedia. https:// en. wikip edia. org/ wiki/ Varia tional_ autoe ncoder (accessed Sept. 15, 2022)
  118. XKool. https:// www. xkool. ai (accessed Dec. 4, 2022)
  119. Zaha Hadid Studio. (2023). Antic Style Architectur. Bing.com Videos. https:// www. bing. com/ videos/ search? q= zaha+ ai& docid= 60800 83055 23768 389& mid= 399E3 EA3BE EECAC 53B89 399E3 EA3BE EECAC 53B89 & view= detai l& FORM= VIRE (accessed Feb. 13, 2023)
  120. Zheng, Y., Jiang, Ch., Mao, J., Han, J., Ye, Ch., Huang, Q., Yeung, D., Yang, Z., Liang, X., Xu, H.. 2023: CLIP2: Cantrastive Language-Image-Point Prtraining from Real-World Point Cloud Data. arXiv. Cornell Univer- sity. arXiv:2303.12417; arXiv:2303.12417v2; https://doi.org/10.48550/ arXiv.2303.12417 (accessed Apr. 18, 2023)
  121. Zorloni, L. (2023). Il Garante della privaci blocca ChatGPT in Italia. Wired. https:// www. wired. it/ artic le/ chatg pt-blocco-italia-garan te-priva cy/ (accessed Apr. 2, 2023)