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ARCH7074 Architecture and Its DiscoursesFinal essay
Tian Sheng
3035378640
tsheng@connect.hku.hk
1. Transformation of Choi Hung, [2022]. AI-generated image by Tian Sheng, created using stable diffusion, a text-to-image generation software. Simulating the estate's appearance during the proposed redevelopment scheme.
Digital Sandstorm: Navigating the AI-Driven Shift from Inspiration to Algorithm in Architectural Design
Historical Context, Practical Applications, and Curatorship in AI-Driven Architectural Design
AI in Architecture: Understanding and Predicting Its Impact
To accurately predict the potential impacts of AI on the architectural industry and the design process, a precise and scientific definition of AI is essential. As professionals and scholars preparing to face the profound influence of AI, one must understand its origins and functionalities. Although AI initially gained attention through visually striking applications, it extends far beyond mere image generation. N. Leach describes AI as a "capable digital brain," embodying both the pinnacle of human wisdom and an aggregation of all human-produced data, forming a complex neural network.1 Understanding AI's nature allows us to speculate on its significant impact on the industry. This involves examining both the technological attributes of AI and the inherent characteristics of the architectural profession that make it susceptible to disruption by such advanced tools.
AI is fundamentally concerned with software programming and data processing. Since its inception at Dartmouth College in 1956, AI development has seen periods of fluctuation. However, the deep learning revolution of 2006 and subsequent advancements in GPU hardware, driven by the demands of computer graphic development, have marked significant milestones. Today, "deep learning" more aptly describes AI's current state, though for convenience, we use "AI" interchangeably in this context. The term "machine hallucination" denotes the complexities
1. Neil Leach, "Alien Intelligence: AI and the Future of Architecture and Urbanism," lecture, Melbourne School of Design, May 15, 2024, video, 58:00, https:// www.youtube.com/watch?v=TsYGmopvA88&list=PLY8MMydiCf2AsBjBTNZ2JYVtMAf7hCEan&index=18.
ARCH7074 Architecture and Its Discourses
of the underlying system. At this stage, generative neural networks have matured, enabling machines to "see" and communicate at the image level, thus challenging the "architect's eye".1 From a practical standpoint, it is essential to separate broad AI discussions and focus on its machine properties, emphasizing intrinsic logic, data learning, and human-machine interaction.2
The complexity of modern architecture promotes a look beyond elemental thinking and focusing on building performance.3 This perspective aligns with the increasing sophistication of technological advancements and reinvigorates the emphasis on the final building product and its performance. Architecture's interaction with technological applications is a long-standing discourse. This complexity also prompts reflection on the architectural profession's nature. Instead of solely addressing grand events, architecture can be viewed as a collection and assembly of objects, identifying the processes and mechanization behind them—the materiality of processes underpinning the modern world.5 This approach invites inquiry into the tools shaping our modes of living, emphasizing the importance of understanding the mechanisms behind our profession.
Observing the architectural design process directly may reveal intuitive, speculative, and experience-based aspects. However, examining the AI-assisted design process highlights significant simplification and transformation. The ease of software usage rapidly changes architectural office dynamics and user functions.2 Notions such as "invisible assistant," "fast uninterrupted workflow," "maximizing variety of solutions," and "more powerful iterative process" reflect the industry's vulnerability to AI and the potential for complementary integration.
1. Hao Zheng, "Architecture + AI: Where the Future Stands," seminar, The University of Hong Kong, April 17, 2024.
2. Neil Leach, Architecture in the Age of Artificial Intelligence: An Introduction to AI for Architects (New York: Bloomsbury Publishing USA, 2021), [114].
3. Stalder, Laurent. ‘An Elementary Proposition’. e-flux, November 2017. Accessed March 21, 2024.
5. Giedion, Siegfried, and Sigfried Giedion. Mechanization Takes Command: A Contribution to Anonymous History. New York: Northon, 1975 [1848].
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ARCH7074 Architecture and Its DiscoursesFinal essay
Tian Sheng 3035378640 tsheng@connect.hku.hk
Examining specific architectural practices illustrates AI's transformative potential. For instance, XKool's investment in deep learning focuses on large-scale urban design. Utilizing revolutionary neural networks, Xkool can incrementally identify and rationalize random processes, exemplifying human-machine collaborative design (Fig. 1).1 Conversely, another AIrelated technology company Spacemaker's use of various tools alongside AI aims to enhance platform control and user-friendliness. The recent acquisition by Autodesk raises questions about its future direction.2 These examples underscore data collection, compatibility, platform issues, and user experience challenges. One one hand, AI presents new avenues, prompting reevaluation of design outcomes and processes, challenging the notion of the "creative genius", on the other hand, traditional aesthetic sensibilities—the architect’s “eye”—remain vital in evaluating AI-generated outcomes.2
In terms of AI's application in manipulation of forms and materials, H. Zheng's work trained AI to replace traditional workflow of massing and form finding. Utilizing existing training sets, AI can observe, and distinguish materiality. It can even replicate styles of renowned architects from history, demonstrating a form of compatibility with traditional workflow (Fig. 2). AI simplifies workflows, allowing only a few steps to generate final renderings and introduces innovative design methods. This process revamps and re-examines our intrinsic design logic and data learning.
Reflecting on the concept of mechanization, AI represents a mechanical form of intelligence, potentially surpassing human intelligence in certain areas.1 It shapes our understanding and
1. "Wanyu He, "AI and Architecture Convergence: Envisioning the Evolution of Design Processes," seminar, The University of Hong Kong, April 23, 2024.
2. Giedion, "Mechanization Takes Command", [page number].
3. Zheng, "Architecture + AI.
4. Leach, "Architecture in the Age of Artificial Intelligence", [114-131].
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ARCH7074 Architecture and Its DiscoursesFinal essay application of technology, prompting introspection of existing architectural mechanisms and their complementarity.2 As it is the case that when architecture is viewed as the collection of processes which defines the professional identity and industry direction, its design and the final building are naturally systems of our creativity.3
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ARCH7074 Architecture and Its DiscoursesFinal essay
Tian Sheng 3035378640
tsheng@connect.hku.hk
AI in Architecture: Human-AI Co-Creation
Collaboration between humans and AI in creative projects is becoming more widespread, building on the legacy of "Computer-Supported Cooperative Work and creativity support systems".1 The advent of Generative AI Models has accelerated this trend, establishing a new research domain known as "Human-AI Co-Creation". In this field, designers and AI work together on creative processes and jointly bear responsibility for the outcomes.1
Despite the availability of powerful Generative AI Models, effectively integrating them into interactive, user-friendly designs remains challenging. Their long-term impact on creative practices and regulations is still unclear. Studies show that Human-AI teams often fail to achieve synergy, sometimes performing worse than humans or AI alone. Ensuring safe use and understanding practical impacts requires further research. (Fig. 3)1
The degree to which humans can participate in the design process serves as a key measure of an architect's involvement, emphasizing the compatibility between human intelligence and AI. The focus is on collaboration and interconnection. "Human-in-the-Loop" (HITL) approaches incorporate human input to direct training or optimization algorithms. "Bayesian optimization", which optimizes complex designs with numerous adjustable parameters, has gained attention in human-computer interaction for three main reasons: First, it guides users through complex decision spaces. Second, it optimizes opaque functions that include subjective inputs such as human preferences. Lastly, it is sample-efficient. In practice, Bayesian optimization offers design
1. Lucio Davide Spano, *Human-AI Co-creation: Evaluating the Impact of Large-Scale Text-to-Image Generative Models on the Creative Process*.
2. Giedion, "Mechanization Takes Command", [page number].
3. Zheng, "Architecture + AI.
4. Leach, "Architecture in the Age of Artificial Intelligence", [114-131].
options to a judge (human), builds a proxy model from feedback, and then selects the next sampling point to find the optimal solution.1
If the level of designer participation is a vertical axis, and the breadth of design represents collective intent as a horizontal axis, a co-creation scenario can be proposed where humans primarily control the process while AI continuously optimizes results. This approach offers the opportunity to generalize architecture, utilizing AI to create a user-centric and process-oriented design method. However, evaluating the recommendations of AI systems remains difficult because these models often function as black boxes. For systems that provide advice, such as recommender systems or predictive classifiers, it is crucial that their advice can be explained in a way that humans can understand. This requires methods to automatically produce clear and comprehensible explanations for users interacting with these systems.1
By examining these perspectives and examples, we gain a deeper understanding of the complexities involved in integrating human and AI design. The evaluation process and how we exhaust the solution space can be studied in greater detail. From an architectural standpoint, the use of technology has been evaluated through a well-established discourse for decades. The Architecture Machine Group at MIT, since the mid-20th century, has explored topics such as human-machine interaction, cybernetic feedback loops, and creating immersive environments. Although limited by computational power, these explorations remain relevant today, presenting AI as a catalyst for advancing computational developments and industrialization in architecture. This technological discourse often accompanies updates in manufacturing techniques, establishing a separate evaluative framework, such as object theory and parametric systems. As a subset of architectural operations, the integration of architectural technology with AI design
1. Spano, *Human-AI Co-creation*.
2. Mario Carpo, *Rise of the Machines: Mario Carpo on Robotic Construction* (Artforum 58, no. 7, 2020): 172-79, 235.
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ARCH7074 Architecture and Its DiscoursesFinal essay
Tian Sheng 3035378640
tsheng@connect.hku.hk
processes presents intriguing reflections, revealing multiple parallels and considerations that could be beneficial when harnessed.2
Prominent result of digital architecture design is becoming increasingly trans-scalar, with the tectonics of architecture becoming more intelligible. Discussions around ornamentation, while not conflicting with AI, help us rationally evaluate AI-assisted design outcomes. Image training and generation reintroduce stylistic emphasis, a topic not commonly discussed during the modernist era. Technological progress is inevitable, but architectural discourse and evaluation are not entirely dependent on it. Making sense of digital architecture remains a central concern for humanity.1
Technological advancements and process simplifications do not imply uniform evaluation within the industry. The end user's support will significantly influence the quality of Human-AI Co-Creation. Tangible Virtual Reality (VR), incorporating physical objects and surfaces into a virtual world, exemplifies this complexity. Tangible VR enhances real-world training, spatial cognition, and interactive experiences, requiring sophisticated integration of hardware, creation tools, and game engines.2 (Fig. 4)
In computational design, the emphasis on combinatory and modular features, such as robotic assembly and standardized modular chunks, highlights the evolving aesthetics within architecture. This shift underscores the need for architects to engage with AI, as those who ignore it risk obsolescence. It is imperative for architects to embrace AI's potential to revolutionize architecture, or they risk being left behind as the industry advances.3
1. Picon, Antoine. ‘Architecture and Digital Memory’. In Architecture and Digital Archives: Architecture in the Digital Ages: A Question of Memory, edited by David Peycer and Florence
2. Carpo, *Rise of the Machines*, 172.
3. Leach, "Architecture in the Age of Artificial Intelligence", [114-131].
ARCH7074 Architecture and Its DiscoursesFinal essay
Fig. 5. This workflow of training ai models directly from the parameters to improve structural performance. The original ways of taking parameters such as structural density subdivision, but now instead of roof force calculation directly predict its structural performance from parameters value raw data. And a successful training of this machine learning model can substitute the original very slow calculation process escaping the entire calculation process to directly predict Structural performance
ARCH7074 Architecture and Its DiscoursesFinal essay
Tian Sheng
3035378640
tsheng@connect.hku.hk
The Impact of AI on Architectural Practice
Building on the previous discussion about the digitization and discourse around digital architecture, the next phase involves the practical implementation within the profession. This encompasses issues such as the division of labor, hardware advancements like GPUs, and the development of deep learning. The most crucial aspect is the technological content used to deliver projects and how AI can redefine architectural practice.
One significant development is the optimization of entire solution spaces, which were previously tackled through brute-force parametric calculations. Deep learning and training models have revolutionized this process, significantly enhancing the speed and quality of data processing and structural optimization outcomes. For instance, by training AI models directly from parameters, architects can predict structural performance without exhaustive calculations, thereby reducing the time spent to find the best solution. This generative workflow is a direct result of AI's advent.1 (Fig. 5)
The concept of architects as technologists is increasingly prevalent. For example, the Xkool
AI Design Platform exemplifies this trend by creating software that collects and labels data, trains and fine-tunes models, and optimizes design generation speed and quality. This enables the deployment of deep learning models like CNN and GAN for commercial use, positioning architects as purveyors of technological products.2 (Fig. 6)
AI also promotes the idea of architects as consultants, where the design process is handed over
1. Zheng, "Architecture + AI."
2. Wanyu He and Xiaodi Yang, "Artificial Intelligence Design, from Research to Practice," in *Proceedings of the 2019 DigitalFUTURES* (Singapore: Springer Nature, 2020), 191.
to clients, making the design process more intuitive and accessible. This method, referred to as associative design, uses "quantifiable parameter systems to manage non-quantifiable parameter changes."1 By creating associative models grounded in correlative geometries, it defines and describes the interactive relationships between different logical entities. This approach enables clients to directly participate in the design process via software developed by architects. This shift towards client engagement transforms the value conversion process, improving trust and reducing the need for architects to provide more services for lower fees. As design generation becomes less labor-intensive, the profession becomes more industrialized, and architects take on the role of technological consultants.1
AI's increasing functionality and ease of use, along with expanding databases and improved accuracy, make it fluid for architects to assume different roles. This empowerment hints at a return to the ancient role of the master builder. By collecting data and training machine learning models to replicate traditional workflows, architects can rethink their processes, maintaining overall project control without needing exhaustive technological expertise.2
AI acts as a sub-designer, but human architects must train themselves to master AI rather than be mastered by it. This involves understanding AI's capabilities and integrating them into the design process, ensuring that architects retain their central role in creative and strategic decision-making.
With a more nuanced understanding of technology, the binary view that technology is inherently good or bad is outdated. The focus should be on understanding and harnessing the
1. Wanyu He and Xiaodi Yang, "Artificial Intelligence Design, from Research to Practice," in *Proceedings of the 2019 DigitalFUTURES* (Singapore: Springer Nature, 2020), 191.
2. Phillip G. Bernstein, "Three Strategies for New Value Propositions of Design Practice," in *The Architect as Worker* (London: Bloomsbury Academic, 2015), 210.
ARCH7074 Architecture and Its DiscoursesFinal essay
Tian Sheng 3035378640
tsheng@connect.hku.hk
technological and practical benefits of new innovations, particularly AI. The development of AI is a confluence of historical events, opportunities, and technological advancements. Its uncertainty and creativity, combined with its disruptive capabilities, make AI a transformative force in architecture. A "digital sandstorm," the problem of ignorance and avoidance while developments continue unnoticed.1
In considering how architecture is technological, it's clear that the entire process of building has become technologically driven. As William Brahma notes, "The process of building is now wholly technological, as is the society in which buildings are conceived, financed, and evaluated."2 This requires reevaluating practical and ethical aspects of change and evolution within architecture. The acceptance and widespread use of new technologies are shaped by social, cultural, and psychological influences, not just by scientifically measurable factors like efficiency and reliability. To truly understand the evolving nature of technology in architecture, one must delve into the historical conditions and processes of technological realization.2
The advent of AI is a natural progression in technological development. Despite its elusive nature, AI represents the vast possibilities within neural networks and Gaussian point clouds.
Architecture is entering an era of high computational power, where the entire solution space is visible, not just the immediate equation. To harness this digital sandstorm, a deep understanding of design processes, intrinsic logic, and data learning is essential. Familiarity with machine intelligence operation is crucial. This aligns with Vitruvius's original definition of the architect as a master builder and represents the future of the profession.
1. Leach, "Architecture in the Age of Artificial Intelligence", [114-131].
2. Reyner Banham, "A Home is Not a House," in *The Architect as Worker* (New York: Penguin Books, 1965), 70.
Bernstein, Philip G. "Three Strategies for New Value Propositions of Design Practice." In The Architect as Worker, 209-218. Berkeley, CA: University of California Press, 2015.
Carpool, Maria. "Rise of the Machines: Mario Carpo on Robotic Construction." Artforum 58, no. 7 (2020): 172-79, 235.
Forty, Adrian. Objects of Desire: Design and Society, 1750-1980. London: Thames and Hudson, 1986. Giedion, Siegfried, and Sigfried Giedion. Mechanization Takes Command: A Contribution to Anonymous History. New York: Norton, 1975 [1848].
He, Wanyu. "Artificial Intelligence Design: From Research to Practice." Accessed May 16, 2024. https:// www.youtube.com/watch?v=-hgixsSSJ3I&list=PLY8MMydiCf2AsBjBTNZ2JYVtMAf7hCEan& index=14.
He, Wanyu, and Xiaodi Yang. "Artificial Intelligence Design, from Research to Practice." In *Proceedings of the 2019 DigitalFUTURES*. Singapore: Springer Nature, 2020.
Leach, Neil. Architecture in the Age of Artificial Intelligence: An Introduction to AI for Architects. Bloomsbury Publishing USA, 2021..
Leach, Neil. "Alien Intelligence." Accessed May 16, 2024. https://www.youtube.com/watch?v=TsYGmo pvA88&list=PLY8MMydiCf2AsBjBTNZ2JYVtMAf7hCEan.
Reyner Banham, "A Home is Not a House," in *The Architect as Worker* (New York: Penguin Books, 1965), 70, fig. 1.
Spano, Lucio Davide. "Human-AI Co-creation: Evaluating the Impact of Large-Scale Text-to-Image Generative Models on the Creative Process." Accessed May 16, 2024.
Zheng, Hao. "Shell Optimization." Accessed May 16, 2024. https://www.youtube.com/ watch?v=8lzBDcoR-SA&list=PLY8MMydiCf2AsBjBTNZ2JYVtMAf7hCEan&index=15.
Zheng, Hao, Vahid Moosavi, and Masoud Akbarzadeh. "Machine Learning Assisted Evaluations in Structural Design and Construction." Automation in Construction 119 (2020): 103346.