Theoretical Article
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What if AI Apprentices Outperform Their Human Counterparts?

Year 2020, Volume: 1 Issue: 3, 153 - 166, 30.09.2020

Abstract

The focus of this study is to draw a vision of how architects have been augmented by computers in the last decades and how computer aided architecture may now evolve into artificial intelligence aided architecture where architecture’s knowledge base is handed over to an AI. The vision is depicted in correspondence with the ages of human evolution. Implicit knowledge of architecture is therefore explored in connection with the hierarchi of data/information/knowledge and wisdom. Therefore the conceptual levels of AI as the narrow AI, general AI and superintelligent AI are introduced to the reader in the context of defining the current scene and the possible future of AI applications. The narrow AI applications are independently being worked on at several different domains. This work introduces a hypothetical architect AI that learns all the knowledge of architecture during the knowledge age and later links itself to AGI in the wisdom age. An emphasis is put on occupant centric approach that architects should take on if they would want to train their future apprentices for the best and customized space creation practices. Whithin this context the output of AI produced designs are discussed in terms of whether they may still be considered “design”.

References

  • Aish, R (2003). Extensible Computational Design Tools for exploratory Architecture, in B Kolarevic(ed) Architecture in the Diigtal Age, Spon Press, New YORK
  • Belém, C., Santos, L., & Leitão, A. (2019). On the Impact of Machine Learning. CAAD Futures 19
  • Carrara, G., Kalay, Y.E., Novembri, G. (1994). Knowledge-based computational support for architectural design, Automation in Construction, 3 (2–3) , pp. 123-142
  • Cross, N. (1984). Developments in Design Methodology, John Wiley and Sons, Chickester, UK
  • Cudzik, J., & Radziszewski, K. (2018). “Artificial Intelligence Aided Architectural Design”. p: 77-84, AI For Design And Built Environment - Volume 1 - eCAADe 36
  • Dreyfus, H. L. (1972). What Computers Can’t Do: A Critique of Artificial Reason. Harper and Row.
  • Dreyfus, H. L. (1992). What Computers Still Can’t Do: A Critique of Artificial Reason. MIT Press.
  • Domingos, P. (2015). The Master Algorithm: How The Quest For The Ultimate Learning Machine Will Remake Our World. Basic Books.
  • Eastman, C.; Fisher, D, Lafue G., Lividini, J., Douglas, S., Yessios, C. (1974). An Outline of the Building Description System Research (Report No. 50) Carnegie-Mellon Univ., Pittsburgh, PA. Inst. of Physical Planning.. 23 pp. (ED113833)
  • Eastman, C., Teicholz, P., Sacks, R., & Liston, K. (2008). BIM Handbook: A Guide to Building Information Modeling for Owners, Managers, Designers, Engineers and Contractors.
  • Fjelland, R. (2020). Why general artificial intelligence will not be realized. Humanities& Social Sciences Communications 7, 10.
  • Girasa R. (2020) AI as a Disruptive Technology. In: Artificial Intelligence as a Disruptive Technology. Palgrave Macmillan, Cham
  • Goertzel, B. (2020). Grounding Occam's Razor in a Formal Theory of Simplicity. ArXiv, abs/2004.05269.
  • Goodfellow, I. (2017) NIPS 2016 Tutorial: Generative Adversarial Networks arXiv:1701.00160v4
  • Huang, W ., Zheng, H .(2018) Architectural Drawings Recognition and Generation through Machine Learning. ACADIA Computational Infidelities
  • Isola, P., Zhu, JY., Zhou, T., Efros, A. A. (2018) Image-to-Image Translation with Conditional Adversarial Networks, ArXiv.
  • Kurzweil, R (2006) The coming merger of biological and non biological intelligence. In Proceedings of the 2006 ACM/IEEE conference on Supercomputing (SC '06). Association for Computing Machinery, New York, NY, USA, 195–es.
  • Lee, S., Maisonneuve, N., Crandall, D.J., Efros, A.A., & Sivic, J. (2015). Linking Past to Present: Discovering Style in Two Centuries of Architecture. 2015 IEEE International Conference on Computational Photography (ICCP), 1-10.
  • Leach, N. (2018) Design in The Age of Artificial Intelligence. Landscape Architecture Frontiers / papers. Volume 6 / Issue 2 / APRIL 2018 pg: 9-19
  • Lee, S., Maisonneuve, N., Crandall, D.,Efros, A., Sivic, J. (2015). Linking Past to Present: Discovering Style in Two Centuries of Architecture.
  • Mathias, M., Martinovic, A., Weissenberg, J., Haegler, S., & Gool, L.V. (2012). Automatic Architectural Style Recognition. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 171-176.
  • McCarthy, J., Minsky, M., Shannon, C. E., Rochester, N., & Dartmouth College. (1955). A proposal for the Dartmouth Summer Research Project on Artificial Intelligence.
  • Mitchell, W (1990). The Logic of Architecture: Design Computation and Cognition MIT Press, Cambridge;
  • Mitchell, WJ, (2005). Constructing Complexity, B. Martens and A. Brown (eds.), Computer Aided Architectural Design Futures 2005, 41-50. Springer. Printed in the Netherlands.
  • Oxman, R. (2006). Theory and design in the first digital age. Design Studies, 27, 229-265.
  • Penrose, R. (1989). The Emperor’s New Mind. Concerning computers, minds and the Law of Physics. ….
  • Shalunts G., Haxhimusa Y., Sablatnig R. (2012) Architectural Style Classification of Domes. In: Bebis G. et al. (eds) Advances in Visual Computing. ISVC 2012. Lecture Notes in Computer Science, vol 7432. Springer, Berlin, Heidelberg.
  • Vallor S. (2017). “AI and the Automation of Wisdom”, Ed.: Powers T., Philosophy and Computing. Philosophical Studies Series, vol 128. Springer, Cham., pg: 161-178
  • Williams, K. and Ostwald, M.J. (eds.) (2015) Architecture and Mathematics from Antiquity to the Future, Springer International Publishing Switzerland
  • Yoshimura, Y., Cai, B.Y., Wang, Z., & Ratti, C. (2018). Deep Learning Architect: Classification for Architectural Design through the Eye of Artificial Intelligence. ArXiv, abs/1812.01714.
  • Zeppelzauer, M., Despotovic, M., Sakeena, M., Koch, D., & Döller, M. (2018). Automatic Prediction of Building Age from Photographs.

What if AI Apprentices Outperform Their Human Counterparts?

Year 2020, Volume: 1 Issue: 3, 153 - 166, 30.09.2020

Abstract

Bu çalışmanın odak noktası, mimarinin bilgi tabanı bir yapay zekaya teslim edildiğinde bilgisayar destekli mimarinin artık yapay zeka destekli mimariye (AIAA) nasıl dönüşebileceğine dair bir vizyon çizmektir. Bu nedenle böyle bir mimarinin vizyonu, insanın evriminin çağlarına uygun olarak tasvir edilmiştir. Bu nedenle, mimarlığın örtük bilgisi, veri / bilgi / bilgi birikimi ve bilgelik hiyerarşisi ile bağlantılı olarak incelenmektedir. Bu nedenle, AI uygulamalarının mevcut durumunu ve olası geleceğini tanımlama bağlamında, Yapay Dar Zekâ (YDZ)I, Yapay Genel Zekâ (YGZ) ve Yapay Süper Zekâ (YSZ)olarak kavramsal seviyeleri okuyucuya tanıtılmaktadır. Dar yapay zeka uygulamaları, farklı alanlarda birbirinden bağımsız olarak çalışılmaktadır. Bu çalışma, bilgi çağında mimarlığın tüm bilgisini öğrenen ve daha sonra kendisini bilgelik çağında YGZ'ye bağlayan varsayımsal bir mimar yapay zekayı okuyucuya sunulmaktadır. Mimarların, gelecekteki çıraklarını en iyi ve özelleştirilmiş mekanları yaratma uygulamaları için eğitmek istiyorlarsa üstlenmeleri gereken, kullanıcı odaklı yaklaşıma vurgu yapılmaktadır. Bu bağlamda, yapay zeka ile üretilen çıktıların, hala “tasarım” olarak kabul edilip edilemeyeği tartışılmaktadır.

References

  • Aish, R (2003). Extensible Computational Design Tools for exploratory Architecture, in B Kolarevic(ed) Architecture in the Diigtal Age, Spon Press, New YORK
  • Belém, C., Santos, L., & Leitão, A. (2019). On the Impact of Machine Learning. CAAD Futures 19
  • Carrara, G., Kalay, Y.E., Novembri, G. (1994). Knowledge-based computational support for architectural design, Automation in Construction, 3 (2–3) , pp. 123-142
  • Cross, N. (1984). Developments in Design Methodology, John Wiley and Sons, Chickester, UK
  • Cudzik, J., & Radziszewski, K. (2018). “Artificial Intelligence Aided Architectural Design”. p: 77-84, AI For Design And Built Environment - Volume 1 - eCAADe 36
  • Dreyfus, H. L. (1972). What Computers Can’t Do: A Critique of Artificial Reason. Harper and Row.
  • Dreyfus, H. L. (1992). What Computers Still Can’t Do: A Critique of Artificial Reason. MIT Press.
  • Domingos, P. (2015). The Master Algorithm: How The Quest For The Ultimate Learning Machine Will Remake Our World. Basic Books.
  • Eastman, C.; Fisher, D, Lafue G., Lividini, J., Douglas, S., Yessios, C. (1974). An Outline of the Building Description System Research (Report No. 50) Carnegie-Mellon Univ., Pittsburgh, PA. Inst. of Physical Planning.. 23 pp. (ED113833)
  • Eastman, C., Teicholz, P., Sacks, R., & Liston, K. (2008). BIM Handbook: A Guide to Building Information Modeling for Owners, Managers, Designers, Engineers and Contractors.
  • Fjelland, R. (2020). Why general artificial intelligence will not be realized. Humanities& Social Sciences Communications 7, 10.
  • Girasa R. (2020) AI as a Disruptive Technology. In: Artificial Intelligence as a Disruptive Technology. Palgrave Macmillan, Cham
  • Goertzel, B. (2020). Grounding Occam's Razor in a Formal Theory of Simplicity. ArXiv, abs/2004.05269.
  • Goodfellow, I. (2017) NIPS 2016 Tutorial: Generative Adversarial Networks arXiv:1701.00160v4
  • Huang, W ., Zheng, H .(2018) Architectural Drawings Recognition and Generation through Machine Learning. ACADIA Computational Infidelities
  • Isola, P., Zhu, JY., Zhou, T., Efros, A. A. (2018) Image-to-Image Translation with Conditional Adversarial Networks, ArXiv.
  • Kurzweil, R (2006) The coming merger of biological and non biological intelligence. In Proceedings of the 2006 ACM/IEEE conference on Supercomputing (SC '06). Association for Computing Machinery, New York, NY, USA, 195–es.
  • Lee, S., Maisonneuve, N., Crandall, D.J., Efros, A.A., & Sivic, J. (2015). Linking Past to Present: Discovering Style in Two Centuries of Architecture. 2015 IEEE International Conference on Computational Photography (ICCP), 1-10.
  • Leach, N. (2018) Design in The Age of Artificial Intelligence. Landscape Architecture Frontiers / papers. Volume 6 / Issue 2 / APRIL 2018 pg: 9-19
  • Lee, S., Maisonneuve, N., Crandall, D.,Efros, A., Sivic, J. (2015). Linking Past to Present: Discovering Style in Two Centuries of Architecture.
  • Mathias, M., Martinovic, A., Weissenberg, J., Haegler, S., & Gool, L.V. (2012). Automatic Architectural Style Recognition. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 171-176.
  • McCarthy, J., Minsky, M., Shannon, C. E., Rochester, N., & Dartmouth College. (1955). A proposal for the Dartmouth Summer Research Project on Artificial Intelligence.
  • Mitchell, W (1990). The Logic of Architecture: Design Computation and Cognition MIT Press, Cambridge;
  • Mitchell, WJ, (2005). Constructing Complexity, B. Martens and A. Brown (eds.), Computer Aided Architectural Design Futures 2005, 41-50. Springer. Printed in the Netherlands.
  • Oxman, R. (2006). Theory and design in the first digital age. Design Studies, 27, 229-265.
  • Penrose, R. (1989). The Emperor’s New Mind. Concerning computers, minds and the Law of Physics. ….
  • Shalunts G., Haxhimusa Y., Sablatnig R. (2012) Architectural Style Classification of Domes. In: Bebis G. et al. (eds) Advances in Visual Computing. ISVC 2012. Lecture Notes in Computer Science, vol 7432. Springer, Berlin, Heidelberg.
  • Vallor S. (2017). “AI and the Automation of Wisdom”, Ed.: Powers T., Philosophy and Computing. Philosophical Studies Series, vol 128. Springer, Cham., pg: 161-178
  • Williams, K. and Ostwald, M.J. (eds.) (2015) Architecture and Mathematics from Antiquity to the Future, Springer International Publishing Switzerland
  • Yoshimura, Y., Cai, B.Y., Wang, Z., & Ratti, C. (2018). Deep Learning Architect: Classification for Architectural Design through the Eye of Artificial Intelligence. ArXiv, abs/1812.01714.
  • Zeppelzauer, M., Despotovic, M., Sakeena, M., Koch, D., & Döller, M. (2018). Automatic Prediction of Building Age from Photographs.
There are 31 citations in total.

Details

Primary Language English
Subjects Architecture
Journal Section Research Articles
Authors

Lale Basarir

Publication Date September 30, 2020
Published in Issue Year 2020 Volume: 1 Issue: 3

Cite

APA Basarir, L. (2020). What if AI Apprentices Outperform Their Human Counterparts?. Journal of Computational Design, 1(3), 153-166.

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